{
"run_id": "2026-09-04-r1",
"tool": "firecrawl",
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"query_id": "Q49",
"query_text": "Jina AI annual recurring revenue 2026",
"input_file": 3,
"timestamp_utc": "2026-09-04T09:27:02Z",
"region": "ap-south-1",
"latency_ms": 14428.8,
"http_status": 200,
"error": null,
"credits_reported": 12,
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"results": [
{
"rank": 1,
"url": "https://getlatka.com/companies/jina.ai",
"title": "Jina AI Revenue 2025: $6.3M Est. ARR, $37.4M Raised - GetLatka",
"content": "\n\n## Jina AI\n\n[Sunnyvale](https://getlatka.com/companies/countries/united-states/cities/sunnyvale), [California](https://getlatka.com/companies/countries/united-states/states/california), [United States](https://getlatka.com/companies/countries/united-states)\n\n[jina.ai](https://jina.ai/) [linkedIn](https://www.linkedin.com/company/jinaai \"linkedIn\")\n\n[Natural Language Processing (NLP) Software](https://getlatka.com/companies/industries/i-natural-language-processing-(nlp)-software)\n\n2025 Revenue\n\n$6.3M(Est.)\n\nFunding\n\n$37.4M\n\nTeam\n\n57\n\nFounded\n\n2020\n\n# Jina AI Revenue & Funding (2025)\n\nJina AI is a leading search AI company. We provide Reader, Embeddings, Rerankers, and Small Language Models to help businesses build the best search.\n\nLast updatedAug 10, 2026\n\n## Jina AI Revenue\n\nIn 2025, Jina AI's revenue reached $6.3M. Since its launch in 2020, Jina AI has shown consistent revenue growth.\n\nJina AI Revenue GrowthReported revenue / ARR over time \u00b7 latest figure estimated$0$1.5M$3M$4.5M$6M$7.5M202020212022202320242025$0$6.3MSource: GetLatka.com\n\n| Year | Milestone | Source |\n| --- | --- | --- |\n| 2025 | Jina AI Hit$6.3mrevenue in July 2025 | Estimated |\n| 2020 | Launched with $0 revenue | |\n\n## Jina AI Valuation, Funding Rounds\n\nJina AI has not publicly disclosed its valuation. The company has raised $37.4M in total funding to date.\n\nJina AI has raised $37.4M in total funding across 3 rounds, most recently a $30M Series A round in 2021.\n\nJina AI Capital Raised & ValuationCumulative capital raised and post-money valuation by roundCapital raised (cum.)$0$10M$20M$30M$40M$50M20202021$37.4MSource: GetLatka.com\n\n| Year | Round | Amount | Valuation | % Sold | Source |\n| --- | --- | --- | --- | --- | --- |\n| 2021 | Series A | $30M | - | - | |\n| 2020 | Seed | $5.4M | - | - | |\n| 2020 | Seed | $2M | - | - | |\n\n## Founder / CEO\n\n### [Han Xiao](https://getlatka.com/people/han-xiao-jina.ai)\n\nCEO\n\nHan Xiao is listed as CEO at Jina AI.\n\n[Contact via Linkedin](https://www.linkedin.com/in/hxiao87)\n\n## Q&A\n\n| Question | Answer |\n| --- | --- |\n| What's your age? | - |\n| Favorite online tool? | - |\n| Favorite book? | - |\n| Favorite CEO? | - |\n| Advice for 20 year old self | - |\n\n## Customers\n\nWe do not have customer count information for Jina AIyet.\n\n## Jina AI Employees & Team Size\n\nJina AI employs approximately 57 people as of 2026.\n\nJina AI Team GrowthReported headcount over time01325385063202020212022202320242025005757Source: GetLatka.com\n\n| Year | Milestone | Source |\n| --- | --- | --- |\n| 2025 | Reached 57 employees (July 2025) | |\n\n## Frequently Asked Questions about Jina AI\n\n### What is Jina AI's revenue?\n\nJina AI generates an estimated $6.3M in annual revenue.\n\n### Who is the CEO of Jina AI?\n\nThe CEO of Jina AI is Han Xiao.\n\n### How much funding does Jina AI have?\n\nJina AI raised $37.4M across 3 rounds.\n\n### How many employees does Jina AI have?\n\nJina AI has 57 employees.\n\n### Where is Jina AI headquarters?\n\nJina AI is headquartered in Sunnyvale, California, United States.\n\n## Compare Jina AI to the industry\n\nJina AI operates across multiple industries. Browse revenue, funding, and growth data for Jina AI in each sector below.\n\n- [Enterprise Search Software](https://getlatka.com/companies/industries/i-enterprise-search-software)\n- [Generative AI Software](https://getlatka.com/companies/industries/i-generative-ai-software)\n- [Insight Engines Software](https://getlatka.com/companies/industries/i-insight-engines-software)\n- [Machine Learning Software](https://getlatka.com/companies/industries/i-machine-learning-software)\n- [Text Analysis Software](https://getlatka.com/companies/industries/i-text-analysis-software)\n\n## Data and Sources\n\nAll figures on this page are taken directly from interviews or are estimates from public sources and proprietary models. Not financial advice. [Read full disclaimer.](https://getlatka.com/disclaimer)\n\n[Claim this profile](mailto:data@getlatka.com?subject=Data%20Correction%20Request%20%E2%80%94%20Jina%20AI&body=Company%3A%20Jina%20AI%0APage%3A%20https%3A%2F%2Fgetlatka.com%2Fcompanies%2Fjina.ai%0A%0AData%20point(s)%20to%20correct%3A%0A%0ACorrect%20information%3A%0A%0ASupporting%20evidence%3A)\n\n## People Also Viewed\n\n[**Seyna**\\\\\n\\\\\nSeyna offers the infrastructure to create, sell and manage insurance products, as easily as Stripe...](https://getlatka.com/companies/seyna.eu) [**Speedsize**\\\\\n\\\\\nSpeedsize is a New York-based AI media compression company that helps e-commerce and fashion brands...](https://getlatka.com/companies/speedsize) [**CoLab Software**\\\\\n\\\\\nDeveloper of a cloud-based design review and issue tracking platform designed to assist the...](https://getlatka.com/companies/colab-software) [**Intelligence Fusion**\\\\\n\\\\\nDeveloper of a SaaS based platform designed to provide enhanced threat intelligence and situational...](https://getlatka.com/companies/intelligence-fusion) [**ezbob**\\\\\n\\\\\nEzbob is a provider of instant financing service for e-retailers. The company has developed an...](https://getlatka.com/companies/ezbob) [**Scurri**\\\\\n\\\\\nDeveloper of a delivery management platform intended to manage shipments. The company's delivery...](https://getlatka.com/companies/scurri)\n\n## Browse companies\n\n[Previous companyJimubox](https://getlatka.com/companies/jimu.com) [Next companyBC Jindal Group](https://getlatka.com/companies/jindalgroup.com)\n\n## AI-Powered SaaS Search\n\n\u00d7\n\nSearch\n\nTry these AI-powered queries:\n\nShow me AI companies with high growthFind startups founded after 2020Companies with revenue over 50MMarketing and social media toolsLarge teams with 100+ employees\n\n\u00d7\n\nGrowth tactic weekly\n\n## Steal the Growth Tactics That Took These Startups from $0 to $50M\n\nEach Tuesday, we reverse-engineer a real SaaS company's revenue, profit, CAC, funnels, and its top growth tactic.\n\nWe never spam.\n\nGet the growth playbooks\n\nTrusted by 45,000+ founders, operators, and investors.\n\n## Create Free Account\n\nSign up to access all features\n\nCreate Account [Sign up with Google](https://getlatka.com/api/v2/auth/oauth/google/start) [Sign up with LinkedIn](https://getlatka.com/api/v2/auth/oauth/linkedin/start)\n\nAlready have an account? [Log in](https://getlatka.com/auth/login)\n\n## Competitive data you can trust\n\nGetLatka is trusted by 200k+ founders, researchers, and marketers.\n\n**$49/mo** Billed yearly\n\n**$99/mo** Billed monthly\n\nContinue\n\nNo contracts, cancel at any time\n\n- Research with LatkaAI New\n- Largest SaaS database\n- Export company data to Excel\n- Hard-to-get data",
"content_chars": 7707,
"published_date": null
},
{
"rank": 2,
"url": "https://jina.ai/contact-sales/",
"title": "Contact sales - Jina AI",
"content": "# Contact sales\n\nGrow your business with Jina AI.\n\n_handshake_ Post-acquisition?_paid_ Pricing?_smart\\_display_ How to get my API key?_speed_ What's the rate limit?\n\n* * *\n\nName\n\nWork email\n\nJob role\n\nOrganization\n\nOrganization size\n\nCountry\n\nOrganization website\n\nWhich products are you interested in?\n\n_arrow\\_drop\\_down_\n\nTell us about your problem, idea or drop some screenshots.\n\n_attach\\_file\\_add_ Attach images\n\nBy submitting, you confirm that you agree to the processing of your personal data by Jina AI as described in the [Privacy Statement](https://jina.ai/legal#privacy-policy)\n_send_ Submit\n\nSales team online\n\n## [Two Ways to Purchase](https://jina.ai/contact-sales/\\#pricing)\n\nSubscribe to our API or purchase through cloud providers.\n\n_radio\\_button\\_unchecked_\n\n_cloud_\n\nWith **3** cloud service providers\n\nUsing AWS or Azure? You can deploy our models directly on your company's cloud platform and handle billing through the CSP account.\n\n__ AWS SageMaker\n\n__ Embeddings\n\n__ Reranker\n\n_'%3e%3cpath%20fill='%23ffffff'%20d='M%20198.351562%2044.007812%20L%20112.046875%20118.847656%20L%2038.398438%20251.039062%20L%20104.804688%20251.039062%20Z%20M%20209.832031%2061.519531%20L%20173%20165.332031%20L%20243.621094%20254.0625%20L%20106.613281%20277.605469%20L%20331.15625%20277.605469%20Z%20M%20209.832031%2061.519531%20'%20fill-opacity='1'%20fill-rule='nonzero'/%3e%3c/g%3e%3c/svg%3e)_ Microsoft Azure\n\n__ Embeddings\n\n__ Reranker\n\n_'%3e%3cpath%20fill='%23ffffff'%20d='M%20246.492188%20109.988281%20L%20274.53125%2081.949219%20L%20276.394531%2070.148438%20C%20225.308594%2023.683594%20144.097656%2028.960938%2098.03125%2081.136719%20C%2085.234375%2095.625%2075.753906%20113.695312%2070.691406%20132.363281%20L%2080.726562%20130.941406%20L%20136.804688%20121.703125%20L%20141.125%20117.28125%20C%20166.0625%2089.882812%20208.246094%2086.199219%20237.039062%20109.503906%20Z%20M%20246.492188%20109.988281%20'%20fill-opacity='1'%20fill-rule='nonzero'/%3e%3c/g%3e%3cg%20clip-path='url(%235696d21d1c)'%3e%3cpath%20fill='%23ffffff'%20d='M%20314.480469%20131.527344%20C%20308.042969%20107.796875%20294.804688%2086.457031%20276.40625%2070.132812%20L%20237.050781%20109.488281%20C%20253.671875%20123.066406%20263.128906%20143.511719%20262.730469%20164.964844%20L%20262.730469%20171.949219%20C%20282.066406%20171.949219%20297.746094%20187.628906%20297.746094%20206.964844%20C%20297.746094%20226.300781%20282.066406%20241.601562%20262.730469%20241.601562%20L%20192.59375%20241.601562%20L%20185.710938%20249.078125%20L%20185.710938%20291.09375%20L%20192.59375%20297.6875%20L%20262.730469%20297.6875%20C%20313.03125%20298.085938%20354.136719%20258.007812%20354.535156%20207.703125%20C%20354.777344%20177.207031%20339.734375%20148.617188%20314.480469%20131.527344%20'%20fill-opacity='1'%20fill-rule='nonzero'/%3e%3c/g%3e%3cg%20clip-path='url(%233d43eedc5d)'%3e%3cpath%20fill='%23ffffff'%20d='M%20122.542969%20297.6875%20L%20192.59375%20297.6875%20L%20192.59375%20241.613281%20L%20122.542969%20241.613281%20C%20117.582031%20241.613281%20112.691406%20240.535156%20108.183594%20238.472656%20L%2098.246094%20241.515625%20L%2070.007812%20269.550781%20L%2067.546875%20279.09375%20C%2083.386719%20291.050781%20102.707031%20297.773438%20122.542969%20297.6875%20'%20fill-opacity='1'%20fill-rule='nonzero'/%3e%3c/g%3e%3cg%20clip-path='url(%237591c6ee7a)'%3e%3cpath%20fill='%23ffffff'%20d='M%20122.542969%20115.789062%20C%2072.226562%20116.085938%2031.691406%20157.117188%2031.988281%20207.433594%20C%2032.160156%20235.527344%2045.285156%20261.972656%2067.546875%20279.105469%20L%20108.183594%20238.472656%20C%2090.554688%20230.511719%2082.71875%20209.765625%2090.679688%20192.136719%20C%2098.644531%20174.507812%20119.386719%20166.671875%20137.015625%20174.632812%20C%20144.777344%20178.144531%20151.007812%20184.359375%20154.519531%20192.136719%20L%20195.152344%20151.503906%20C%20177.863281%20128.894531%20150.992188%20115.6875%20122.542969%20115.789062%20'%20fill-opacity='1'%20fill-rule='nonzero'/%3e%3c/g%3e%3c/svg%3e)_ Google Cloud\n\n__ Embeddings\n\n_radio\\_button\\_checked_\n\n__\n\nWith Jina Search Foundation API\n\nThe easiest way to access all of our products. Top-up tokens as you go.\n\n_key_\n\n_content\\_copy_\n\nEnter the API key you wish to recharge\n\n_error_\n\n_visibility\\_off_\n\n_verified\\_user_\n\nTop up this API key with more tokens\n\nDepending on your location, you may be charged in USD, EUR, or other currencies. Taxes may apply.\n\nToy Experiment\n\n10 Million\n\nTokens valid for:\n\n______\n\nNon-commercial use only (CC-BY-NC).\n\nFree\n\nEnjoy your new API key with free tokens.\n\nPrototype Development\n\n1 Billion\n\nTokens valid for:\n\n______\n\n_key_ Standard key\n\n_task\\_alt_ Basic key management\n\n_task\\_alt_ Technical support\n\n$50\n\n0.050 / 1M tokens\n\n_add\\_shopping\\_cart_\n\nProduction Deployment\n\n11 Billion\n\nTokens valid for:\n\n______\n\n_key_ Premium key with much higher rate limits\n\n_task\\_alt_ Advanced key management\n\n_task\\_alt_ Premium customer support in 24 hours\n\n_task\\_alt_ One-hour integration consultation\n\n$500\n\n0.045 / 1M tokens\n\n_add\\_shopping\\_cart_\n\nPlease enter the correct API key to top up.\n\n_speed_\n\nUnderstand the rate limit\n\nRate limits are the maximum number of requests that can be made to an API within a minute per IP address/API key (RPM). Find out more about the rate limits for each product and tier below.\n\n_keyboard\\_arrow\\_down_\n\nRate Limit\n\nRate limits are tracked in two ways: **RPM** (requests per minute) and **TPM** (tokens per minute). Limits are enforced per IP/API key and will be triggered when either the RPM or TPM threshold is reached first. When you provide an API key in the request header, we track rate limits by key rather than IP address.\n\nColumns\n\n_arrow\\_drop\\_down_\n\n_fullscreen_\n\n| | Product | API Endpoint | Description _arrow\\_upward_ | w/o API Key _key\\_off_ | w/ Free API Key _key_ | w/ Paid API Key _key_ | w/ Premium API Key _key_ | Average Latency | Token Usage Counting | Allowed Request |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n|  | Reader API | `https://r.jina.ai` | Converts a URL to LLM-friendly text | 20 RPM | 500 RPM | 500 RPM | _trending\\_up_ 5000 RPM | 7.9s | Count the number of tokens in the output response. | GET/POST |\n|  | Reader API | `https://s.jina.ai` | Search the web and convert results to LLM-friendly text | _block_ | 100 RPM | 100 RPM | _trending\\_up_ 1000 RPM | 2.5s | Every request costs a fixed number of tokens, starting from 10000 tokens | GET/POST |\n|  | Embedding API | `https://api.jina.ai/v1/embeddings` | Convert text/images to fixed-length vectors | _block_ | 100 RPM & 100,000 TPM | 500 RPM & 2,000,000 TPM | _trending\\_up_ 5,000 RPM & 50,000,000 TPM | _ssid\\_chart_
depends on the input size
_help_ | Count the number of tokens in the input request. | POST |\n|  | Reranker API | `https://api.jina.ai/v1/rerank` | Rank documents by query | _block_ | 100 RPM & 100,000 TPM | 500 RPM & 2,000,000 TPM | _trending\\_up_ 5,000 RPM & 50,000,000 TPM | _ssid\\_chart_
depends on the input size
_help_ | Count the number of tokens in the input request. | POST |\n\n_currency\\_exchange_\n\nAuto top-up on low token balance\n\nRecommended for uninterrupted service in production. When your token balance drops below the set threshold, we will automatically recharge your saved payment method for the last purchased package, until the threshold is met.\n\n_info_ We introduced a new pricing model on May 6th, 2025. If you enabled auto-recharge before this date, you'll continue to pay the old price (the one when you purchased). The new pricing only applies if you modify your auto-recharge settings or purchase a new API key.\n\n_check_\n\n< 1M Tokens\n\nTop up when\n\n_arrow\\_drop\\_down_\n\n## FAQ\n\n### [Jina AI \u00d7 Elastic](https://jina.ai/contact-sales/\\#post-acquisition)\n\n_handshake_\n\nWill the Jina brand be preserved?\n\n_keyboard\\_arrow\\_down_\n\nYes. Jina is a model brand within Elastic. Think of it like Qwen to Alibaba, GPT to OpenAI, or Kimi to Moonshot. The legal identity has moved to Elastic, which lets Jina AI focus purely on search foundation models as a brand.\n\n_handshake_\n\nWhat will Jina AI focus on going forward?\n\n_keyboard\\_arrow\\_down_\n\nEmbeddings, rerankers, and small models for better search, including multimodal and reader models. Our mission isn't accomplished yet, and we've never been shy about the goal: to be a leading search model provider.\n\n_handshake_\n\nWill the API and cloud marketplace offerings continue?\n\n_keyboard\\_arrow\\_down_\n\nYes. The Reader API, Embeddings API, and Reranker API continue to be developed and maintained, and models continue to be published to cloud marketplace platforms. You can use our API services as before. The only exception is that we cannot serve entities or countries subject to U.S. export controls.\n\n_handshake_\n\nWill you still release open-weights models on Hugging Face?\n\n_keyboard\\_arrow\\_down_\n\nYes. At Elastic, Jina continues to push the frontier of search foundation models, and we keep releasing open-weights models.\n\n_handshake_\n\nUnder which license will these open models be released?\n\n_keyboard\\_arrow\\_down_\n\nMost current models are CC-BY-NC 4.0, and we expect that to continue. Legacy v1 and v2 generation models are Apache-2.0, which permits commercial use outright. One model, jina-embeddings-v4, is under the Qwen Research License it inherits from its base model: that permits no commercial use at all, and no commercial license extends to it. The license that applies is always stated on the model's page on Hugging Face, so check there rather than inferring it from the generation. Under CC-BY-NC 4.0 the weights are free to download, evaluate, benchmark and use in research, with attribution, while running them in a commercial product needs a commercial license, which Elastic sells as its own SKU. Contact [Elastic Sales](https://www.elastic.co/contact) to arrange one.\n\n_handshake_\n\nWill you continue publishing research papers?\n\n_keyboard\\_arrow\\_down_\n\nYes. Every model we release is backed by a rigorous paper, and we continue submitting to top conferences like ICLR, EMNLP, SIGIR, NeurIPS, and ICML.\n\n_handshake_\n\nI'm not yet a Jina or Elastic customer, but I want to use the Reader API, model APIs, or cloud marketplace images. What should I do?\n\n_keyboard\\_arrow\\_down_\n\nSimply sign up and pay through our website or the relevant cloud marketplace, just as before.\n\n_handshake_\n\nCan I buy a commercial license for Jina models from Elastic?\n\n_keyboard\\_arrow\\_down_\n\nYes. Since August 10, 2026, Elastic sells commercial licenses for Jina models directly, as their own SKU. This covers running Jina models in your own self-managed, on-premises, or air-gapped infrastructure, and it is available through Elastic direct, federal, and cloud service provider (CSP) channels. The offering is called Jina On-Prem. To get a quote, contact [Elastic Sales](https://www.elastic.co/contact).\n\n_handshake_\n\nWhat is Jina On-Prem?\n\n_keyboard\\_arrow\\_down_\n\nJina On-Prem is a commercial license plus a set of self-contained Docker containers that let you run Jina models entirely inside your own infrastructure. The containers make no external connections: there is no call to Hugging Face or any model registry, and no license server, telemetry, or logging endpoint, which is what makes them viable in an air-gapped network. They cover the Jina model portfolio, including embedding, reranker, and reader models, and they expose Elastic Inference Service (EIS), OpenAI, Cohere, Voyage AI, and Gemini API schemas, so existing applications work without code changes. It is a separate SKU: it is not based on Elastic Resource Units (ERUs), and you do not need to run Elasticsearch to use it. It also works alongside open source Elasticsearch. It has been available to order since August 10, 2026; contact [Elastic Sales](https://www.elastic.co/contact) for availability and terms.\n\n_handshake_\n\nHow is Jina On-Prem priced?\n\n_keyboard\\_arrow\\_down_\n\nIt is an annual license fee, scoped by which models you deploy and by the amount of hardware running inference for them. It is not priced per seat, per node, or by model size, and there is no per-token billing. A CPU-only deployment is counted the same way, on the processors used for inference. Pricing is not self-serve: contact [Elastic Sales](https://www.elastic.co/contact) for a quote for your deployment.\n\n_handshake_\n\nWho is Jina On-Prem for?\n\n_keyboard\\_arrow\\_down_\n\nOrganizations that cannot, or prefer not to, send data to a cloud AI service. Typical cases are air-gapped and high-security environments, public sector and defense, regulated industries such as financial services and healthcare, latency-critical or offline systems, and teams that want a fixed, predictable inference cost instead of per-token pricing. If that describes your deployment, [Elastic Sales](https://www.elastic.co/contact) can work through the details with you.\n\n_handshake_\n\nI'm an Elastic customer. Can I use Jina models in Elastic Cloud without deploying anything?\n\n_keyboard\\_arrow\\_down_\n\nYes. Jina models are available through the Elastic Inference Service (EIS), so you can use them for ingest and search without provisioning machine learning nodes or managing GPU infrastructure. The models generally available on EIS include `jina-embeddings-v5-text-small`, `jina-embeddings-v5-text-nano`, `jina-embeddings-v5-omni-small`, `jina-embeddings-v5-omni-nano`, `jina-embeddings-v3`, `jina-clip-v2`, and the `jina-reranker-v3.5`, `jina-reranker-v3`, `jina-reranker-v2-base-multilingual` and `jina-reranker-m0` rerankers. Consult the Elastic documentation for the current model list, supported regions, and the minimum stack version for each model.\n\n_handshake_\n\nI downloaded the weights from Hugging Face. Do I need a license to use them in production?\n\n_keyboard\\_arrow\\_down_\n\nIt depends on which license the model carries, which is stated on its page on Hugging Face. Apache-2.0 permits commercial use with nothing to buy. Under CC-BY-NC 4.0, evaluation, benchmarking and research are free, but commercial production use needs a commercial license, which is what Jina On-Prem provides. A research license permits no commercial use at all, and a commercial license for the other models does not extend to it, so a model under one is not a candidate for production regardless of budget. Note this is about the weights: calling the hosted APIs or the official cloud marketplace images commercially needs no separate license. To buy one, contact [Elastic Sales](https://www.elastic.co/contact).\n\n_handshake_\n\nI want to sign a contract or a custom agreement covering Jina models. What should I do?\n\n_keyboard\\_arrow\\_down_\n\nContact [Elastic Sales](https://www.elastic.co/contact). Commercial licensing, contracting, and support for Jina models now run through Elastic's standard sales and support process.\n\n_handshake_\n\nI'm purchasing your services as a Chinese entity. Can I get a Chinese invoice (\u53d1\u7968)?\n\n_keyboard\\_arrow\\_down_\n\nNot for self-serve API top-ups: those are invoiced automatically by the entity that processes the payment, which cannot issue a Chinese invoice (\u53d1\u7968). For contract-based purchases, including Jina On-Prem, contact [Elastic Sales](https://www.elastic.co/contact) to discuss the available contracting entities and invoicing arrangements.\n\n_handshake_\n\nI'm an Elastic customer and want to learn best practices for embeddings and rerankers, or I'm generally interested in Jina AI's development. What should I do?\n\n_keyboard\\_arrow\\_down_\n\nContact [Elastic Sales](https://www.elastic.co/contact), and we can arrange a session between you, the Jina AI team, and Elastic.\n\n_handshake_\n\nIf you release a new model during my license term, is it included?\n\n_keyboard\\_arrow\\_down_\n\nModels released in the same category during the term are included: no new agreement or purchase, and no change to your license key, which is issued for the category rather than for an individual model. Models under a license that permits no commercial use are the exception, since no commercial license extends to them. For what a specific agreement covers, confirm with [Elastic Sales](https://www.elastic.co/contact).\n\n_handshake_\n\nWhat support comes with a commercial license, and who provides it?\n\n_keyboard\\_arrow\\_down_\n\nThe license includes enterprise-level support under the standard service level agreement. Deployment stays yours: you pull the container image and run it in your own environment, and support can help if the installation gives you trouble. Support runs through Elastic's standard process, as does everything contractual. For the service levels attached to a specific agreement, confirm with [Elastic Sales](https://www.elastic.co/contact).\n\n_handshake_\n\nWhich data processing terms apply now that Jina is part of Elastic?\n\n_keyboard\\_arrow\\_down_\n\nProcessing is governed by Elastic's data processing agreement rather than the earlier Jina AI GmbH terms. This matters for procurement files that cite the old agreement, so it is worth checking any document drafted before the acquisition. For the current terms and anything specific to your jurisdiction, contact [Elastic Sales](https://www.elastic.co/contact).\n\n### [How to get my API key?](https://jina.ai/contact-sales/\\#get-api-key)\n\nvideo\\_not\\_supported\n\n### [What's the rate limit?](https://jina.ai/contact-sales/\\#rate-limit)\n\nRate Limit\n\nRate limits are tracked in two ways: **RPM** (requests per minute) and **TPM** (tokens per minute). Limits are enforced per IP/API key and will be triggered when either the RPM or TPM threshold is reached first. When you provide an API key in the request header, we track rate limits by key rather than IP address.\n\nColumns\n\n_arrow\\_drop\\_down_\n\n_fullscreen_\n\n| | Product | API Endpoint | Description _arrow\\_upward_ | w/o API Key _key\\_off_ | w/ Free API Key _key_ | w/ Paid API Key _key_ | w/ Premium API Key _key_ | Average Latency | Token Usage Counting | Allowed Request |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n|  | Reader API | `https://r.jina.ai` | Converts a URL to LLM-friendly text | 20 RPM | 500 RPM | 500 RPM | _trending\\_up_ 5000 RPM | 7.9s | Count the number of tokens in the output response. | GET/POST |\n|  | Reader API | `https://s.jina.ai` | Search the web and convert results to LLM-friendly text | _block_ | 100 RPM | 100 RPM | _trending\\_up_ 1000 RPM | 2.5s | Every request costs a fixed number of tokens, starting from 10000 tokens | GET/POST |\n|  | Embedding API | `https://api.jina.ai/v1/embeddings` | Convert text/images to fixed-length vectors | _block_ | 100 RPM & 100,000 TPM | 500 RPM & 2,000,000 TPM | _trending\\_up_ 5,000 RPM & 50,000,000 TPM | _ssid\\_chart_
depends on the input size
_help_ | Count the number of tokens in the input request. | POST |\n|  | Reranker API | `https://api.jina.ai/v1/rerank` | Rank documents by query | _block_ | 100 RPM & 100,000 TPM | 500 RPM & 2,000,000 TPM | _trending\\_up_ 5,000 RPM & 50,000,000 TPM | _ssid\\_chart_
depends on the input size
_help_ | Count the number of tokens in the input request. | POST |\n\n### [Do I need a commercial license?](https://jina.ai/contact-sales/\\#cc-self-check)\n\nCC BY-NC License Self-Check\n\n* * *\n\n_play\\_arrow_\n\nAre you using our hosted API, or our official images on Azure, AWS, or GCP?\n\n_play\\_arrow_\n\nYes\n\nNo separate license needed. Commercial use is covered by the service terms: sign up and pay through this site or the cloud marketplace.\n\n_play\\_arrow_\n\nNo\n\n_play\\_arrow_\n\nAre you running the model weights yourself, in a commercial product or service?\n\n_play\\_arrow_\n\nNo\n\nNothing to buy. Downloading, evaluating, benchmarking and research use are permitted under every license we publish under, with attribution.\n\n_play\\_arrow_\n\nYes\n\n_play\\_arrow_\n\nWhich license does the model carry? It is stated on the model's page on Hugging Face.\n\n_play\\_arrow_\n\nApache-2.0\n\nApache-2.0 permits commercial use. Nothing to buy. This covers our legacy v1 and v2 generation models.\n\n_play\\_arrow_\n\nCC BY-NC 4.0\n\nYou need a commercial license. Since August 10, 2026, Elastic sells one for Jina models as its own SKU, called Jina On-Prem. It covers self-managed, on-premises, and air-gapped deployments, and it does not require an Elasticsearch subscription.\n\nIf you are already an Elastic customer, your account team can add it to your existing agreement.\n\n[Contact Elastic Sales](https://www.elastic.co/contact)\n\n_play\\_arrow_\n\nQwen Research License\n\nA research license does not permit commercial use, and a commercial license for our other models does not extend to it. There is no commercial option for this one, self-hosted or through the API. Pick a model under one of the other two licenses instead.\n\n### [Other questions](https://jina.ai/contact-sales/\\#faq)\n\nReader-related common questions\n\n__\n\nWhat are the costs associated with using the Reader API?\n\n_keyboard\\_arrow\\_down_\n\nReader is free for basic usage: prepend 'https://r.jina.ai/' to your URL. Supplying an API key raises the rate limit and charges tokens based on content length. See Q16 for rate limits.\n\n__\n\nHow does the Reader API function?\n\n_keyboard\\_arrow\\_down_\n\nThe Reader API fetches the URL server-side and returns clean, LLM-ready text. You choose the fetching engine with the `X-Engine` header: `direct` issues a plain HTTP fetch and is the fastest, the default engine renders the page in a headless browser so client-side JavaScript executes before extraction, and `cf-browser-rendering` is an experimental Cloudflare-backed renderer. Boilerplate such as navigation, headers, footers, and ads is stripped, and the main content is converted to Markdown. Use `X-Respond-With` to get other shapes of the same page, and `X-Target-Selector` or `X-Remove-Selector` to keep or drop specific CSS selectors.\n\n__\n\nIs the Reader API open source?\n\n_keyboard\\_arrow\\_down_\n\nThe Reader service code is available on the Jina AI GitHub organization. The models used by Reader, including `ReaderLM-v2` and `jina-vlm`, are licensed CC-BY-NC 4.0, which is not an open-source license: they are free to download and use non-commercially, but commercial production use requires a commercial license. Elastic has sold that license separately since August 10, 2026; contact [Elastic Sales](https://www.elastic.co/contact) to arrange one.\n\n__\n\nWhat is the typical latency for the Reader API?\n\n_keyboard\\_arrow\\_down_\n\nIt depends mainly on the engine and on the page itself. A direct fetch of a simple page typically returns in a few hundred milliseconds, while the default browser engine has to load and execute the page before extraction and usually lands in the low seconds. Heavy single-page apps, slow origin servers, and large PDFs take longer. Repeating the same URL within 5 minutes is served from cache and returns almost immediately, so a warm URL is much faster than a cold one.\n\n__\n\nWhy should I use the Reader API instead of scraping the page myself?\n\n_keyboard\\_arrow\\_down_\n\nScraping can be complicated and unreliable, particularly with complex or dynamic pages. The Reader API provides a streamlined, reliable output of clean, LLM-ready text.\n\n__\n\nDoes the Reader API support multiple languages?\n\n_keyboard\\_arrow\\_down_\n\nThe Reader API is language-agnostic and returns content in the original language of the page; it does not translate. Content negotiation is available through the `X-Locale` header, which sets the browser locale used when rendering, so sites that serve different markup per locale can be steered to the version you want.\n\n__\n\nDoes the Reader API respect website access controls?\n\n_keyboard\\_arrow\\_down_\n\nYes. Reader operates as a standard web client and respects website access controls. If a website blocks the request, that outcome is respected. You are responsible for ensuring your use of Reader complies with the terms of the sites you access and does not infringe third-party intellectual property rights.\n\n__\n\nCan the Reader API extract content from PDF files?\n\n_keyboard\\_arrow\\_down_\n\nYes, the Reader API can natively extract content from PDF files.\n\n__\n\nCan the Reader API process media content from web pages?\n\n_keyboard\\_arrow\\_down_\n\nYes, Reader can caption images on webpages using the `x-with-generated-alt` header. This adds descriptive alt tags to images that lack them, enabling LLMs to understand visual content. Video summarization is planned for future releases.\n\n__\n\nIs it possible to use the Reader API on local HTML files?\n\n_keyboard\\_arrow\\_down_\n\nNo, the Reader API can only process content from publicly accessible URLs.\n\n__\n\nDoes Reader API cache the content?\n\n_keyboard\\_arrow\\_down_\n\nIf you request the same URL within 5 minutes, the Reader API will return the cached content.\n\n__\n\nCan I use the Reader API to access content behind a login?\n\n_keyboard\\_arrow\\_down_\n\nYes, for pages that accept cookie-based sessions. Pass your session cookies with the `X-Set-Cookie` header and the Reader forwards them when fetching the URL, using the same `=` form as a normal `Set-Cookie`, optionally scoped with `; domain=`. Requests carrying cookies are never cached, so each one is a fresh fetch. This does not perform a login for you: it replays credentials you already hold, so you are responsible for obtaining them and for complying with the target site's terms of service. Flows that require an interactive login, MFA, or a bearer token the page fetches itself are out of scope.\n\n__\n\nCan I use the Reader API to access PDF on arXiv?\n\n_keyboard\\_arrow\\_down_\n\nYes, you can either use the native PDF support from the Reader (https://r.jina.ai/https://arxiv.org/pdf/2310.19923v4) or use the HTML version from the arXiv (https://r.jina.ai/https://arxiv.org/html/2310.19923v4)\n\n__\n\nHow does image caption work in Reader?\n\n_keyboard\\_arrow\\_down_\n\nReader captions all images at the specified URL and adds `Image [idx]: [caption]` as an alt tag (if they initially lack one). This enables downstream LLMs to interact with the images in reasoning, summarizing etc.\n\n__\n\nWhat is the scalability of the Reader? Can I use it in production?\n\n_keyboard\\_arrow\\_down_\n\nThe Reader API is designed to be highly scalable. It is auto-scaled based on the real-time traffic and the maximum concurrency is now around 4,000 requests. We are maintaining it actively as one of the core products of Jina AI. So feel free to use it in production.\n\n__\n\nWhat is the rate limit of the Reader API?\n\n_keyboard\\_arrow\\_down_\n\nSee the table below for the latest rate limits. We're actively improving the Reader API's rate limits and performance, so the table will be updated as things change.\n\n[_speed_ Rate limit](https://jina.ai/contact-sales/#rate-limit)\n\n__\n\nWhat is ReaderLM? How can I use it?\n\n_keyboard\\_arrow\\_down_\n\n`ReaderLM-v2` is a 1.54B parameter small language model that converts raw HTML into clean Markdown or JSON, and can extract structured data using a JSON schema or natural language instructions. Use it via the Reader API with the `x-respond-with: readerlm-v2` header, or deploy it from the AWS, Azure, or GCP marketplaces. For image-heavy or scanned documents, `jina-vlm` is our 2.4B parameter vision-language reader model.\n\n[_launch_ AWS SageMaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy) [_launch_ Google Cloud](https://console.cloud.google.com/marketplace/browse?q=jina&pli=1&inv=1&invt=AbmydQ) [_launch_ Microsoft Azure](https://azuremarketplace.microsoft.com/en-US/marketplace/apps?page=1&search=jina)\n\n__\n\nHow do I extract structured data from webpages?\n\n_keyboard\\_arrow\\_down_\n\nUse the `x-json-schema` header with a JSON schema definition, or use `x-instruction` header with natural language instructions. Both features work with ReaderLM-v2 to extract specific fields like prices, titles, dates, etc. from any webpage into structured JSON format.\n\n__\n\nDoes Reader actively bypass website anti-bot protection?\n\n_keyboard\\_arrow\\_down_\n\nNo. Reader does not actively circumvent or bypass any website defense mechanisms, anti-bot systems, or access controls. If a website detects our service as a bot and blocks the request, that outcome is respected. We operate as a standard web client and do not employ techniques designed to evade detection systems. You remain responsible for ensuring your use of Reader respects third-party intellectual property rights and the terms of the sites you access.\n\n__\n\nWill upgrading from a free to a paid API key give me access to more websites?\n\n_keyboard\\_arrow\\_down_\n\nNo. Upgrading from a free tier to a paid API key does not grant access to additional websites or bypass any site restrictions. The difference between tiers is primarily in rate limits and performance optimizations. A paid API key provides higher request throughput and faster processing, but it does not enable access to websites that block our service.\n\n__\n\nCan I run Reader inside my own infrastructure?\n\n_keyboard\\_arrow\\_down_\n\nYes. The reader models ship as self-contained offline Docker containers under the Jina On-Prem commercial license that Elastic has sold as its own SKU since August 10, 2026. This is the path for air-gapped and firewalled environments, where the containers make no outbound connections of any kind. Note that fetching arbitrary public web pages still requires network access to those pages; the on-prem value is in running the extraction models locally. Contact [Elastic Sales](https://www.elastic.co/contact) for a quote.\n\nEmbeddings-related common questions\n\n__\n\nHow were the Jina embedding models trained?\n\n_keyboard\\_arrow\\_down_\n\nFor detailed information on our training processes, data sources, and evaluations, refer to the technical reports on arXiv. The `jina-embeddings-v5` text models are trained in two stages: embedding distillation from a larger teacher model, followed by task-specific LoRA adapter training on frozen backbone weights. The `v5-omni` multimodal variants add a third stage that trains only cross-modal projectors, leaving the text backbone and adapters frozen.\n\n[_launch_ arXiv](https://arxiv.org/abs/2602.15547)\n\n__\n\nWhat are your multimodal embedding models?\n\n_keyboard\\_arrow\\_down_\n\n`jina-embeddings-v5-omni-small` (~1.74B parameters, 1024 dimensions, 32K context) and `jina-embeddings-v5-omni-nano` (~1.04B parameters, 768 dimensions, 8K context) are our current multimodal models. They accept text, images, audio, video, and PDFs in one shared vector space, so you can index in one modality and query in another without reindexing. Their text-only output is identical to `jina-embeddings-v5-text-small` and `jina-embeddings-v5-text-nano` respectively, which means you can add multimodal input to an existing text index without re-embedding it. `jina-clip-v2` (865M parameters) remains available as a lighter text-and-image option.\n\n[_launch_ arXiv](https://arxiv.org/abs/2605.08384)\n\n__\n\nWhich languages do your models support?\n\n_keyboard\\_arrow\\_down_\n\nAll models released since 2024 are multilingual. `jina-embeddings-v5-text-small` and the `v5-omni` models are built on a Qwen3 backbone with broad multilingual coverage; `jina-embeddings-v5-text-nano` is built on EuroBERT-210M, covering 15 major European and global languages including English, French, German, Spanish, Chinese, Japanese, Arabic, and Hindi. `jina-embeddings-v3` and `jina-clip-v2` support 89 languages. For per-language benchmark numbers, see the MMTEB results in each model's technical report.\n\n[_launch_ arXiv](https://arxiv.org/abs/2602.15547)\n\n__\n\nWhat is the maximum context length for a single input?\n\n_keyboard\\_arrow\\_down_\n\nContext length varies by model: `jina-embeddings-v5-text-small` and `jina-embeddings-v5-omni-small` support up to 32,768 tokens, while `jina-embeddings-v5-text-nano` and `jina-embeddings-v5-omni-nano` support 8,192 tokens. `jina-embeddings-v4` and the `jina-code-embeddings` models support 32,768 tokens; `jina-embeddings-v3`, `jina-clip-v2`, and `jina-colbert-v2` support 8,192 tokens. Inputs above the limit return an error unless you set `truncate: true`.\n\n__\n\nWhat is the maximum number of inputs I can include in a single request?\n\n_keyboard\\_arrow\\_down_\n\nThere is no hard limit on the number of items per request. The API batches inputs internally by token count for optimal GPU utilization, so you can send as many texts or images as needed in a single request. PDFs are the exception: send one PDF per request.\n\n__\n\nHow do I send images, audio, video, or PDFs to the multimodal models?\n\n_keyboard\\_arrow\\_down_\n\nPass a typed object in the `input` array using an `image`, `audio`, `video`, or `pdf` key, whose value is either a public URL or base64-encoded bytes. The model routes each modality to the appropriate encoder, and you can mix modalities freely within a single batch. Supported audio formats include WAV, MP3, FLAC, OGG, M4A, and Opus; video is processed as 32 uniformly sampled frames. PDFs must be sent one per request.\n\n__\n\nHow do Jina embeddings compare to the latest OpenAI, Cohere, and Voyage models?\n\n_keyboard\\_arrow\\_down_\n\n`jina-embeddings-v5-text-small` (677M parameters) is the strongest model under 1B parameters on MMTEB, scoring 67.0 average at task level, and reaches 71.7 average on English MTEB. `jina-embeddings-v5-text-nano` (239M parameters) scores 65.5 on MMTEB, ahead of every model we evaluated under 500M parameters. Our design target is capability per parameter rather than raw size, so these models are cheaper to serve than most alternatives at comparable or better retrieval quality. All v5 models support Matryoshka Representation Learning, so you can truncate dimensions down to 32 without retraining.\n\n__\n\nHow seamless is the transition from OpenAI's text-embedding-3-large to your solution?\n\n_keyboard\\_arrow\\_down_\n\nThe transition is straightforward: [our API endpoint](https://api.jina.ai/v1/embeddings) matches the input and output JSON schemas of OpenAI's `text-embedding-3-large`, so in most codebases you change the base URL, the API key, and the model name. The same holds for the Jina On-Prem containers, which additionally expose Elastic Inference Service (EIS), Cohere, Voyage AI, and Gemini schemas, so Jina models are a drop-in replacement in existing code paths. Note that embeddings from different model families are not comparable, so you have to re-embed your corpus rather than mix vectors from two providers in one index. For the On-Prem containers themselves, contact [Elastic Sales](https://www.elastic.co/contact).\n\n__\n\nHow are tokens calculated for images and other non-text inputs?\n\n_keyboard\\_arrow\\_down_\n\nText is counted in the standard way. Non-text inputs are converted to tokens by the relevant encoder, and the cost depends heavily on which model you use, so measure with your own inputs rather than assuming. As a reference point, a 600x600 pixel image costs approximately:\n\n\u2022 `jina-embeddings-v5-omni-small`: ~363 tokens\n\u2022 `jina-embeddings-v5-omni-nano`: ~362 tokens\n\u2022 `jina-embeddings-v4`: ~4,840 tokens\n\u2022 `jina-clip-v2`: ~16,000 tokens\n\nThe v5-omni models are one to two orders of magnitude cheaper per image than the older models. Every response includes a `usage` object with the exact token count for that request, including an `image_tokens` breakdown for multimodal inputs, so you can verify cost per call.\n\n__\n\nDo you provide models for embedding images, audio, or video?\n\n_keyboard\\_arrow\\_down_\n\nYes. `jina-embeddings-v5-omni-small` and `jina-embeddings-v5-omni-nano` embed text, images, audio, video, and PDFs into a single shared vector space. `jina-embeddings-v4` and `jina-clip-v2` handle text and images.\n\n__\n\nCan Jina embedding models be fine-tuned on private or company data?\n\n_keyboard\\_arrow\\_down_\n\nThere are two paths. The self-serve one is the Fine-tuning API, which generates synthetic training data from a description of your domain and returns a fine-tuned model, without your having to assemble a labelled dataset. For fine-tuning on proprietary data under a commercial agreement, on dedicated infrastructure, or on a model that is not in the base model selector, Contact [Elastic Sales](https://www.elastic.co/contact); that work is scoped and contracted through Elastic.\n\n[Contact](https://jina.ai/contact-sales)\n\n__\n\nCan the models be hosted privately, on my own infrastructure or in my own cloud account?\n\n_keyboard\\_arrow\\_down_\n\nYes, in two ways. Jina models are available on the AWS, Azure, and GCP marketplaces, so you can deploy them inside your own cloud account. For self-managed, on-premises, or air-gapped infrastructure, Elastic sells a commercial license called Jina On-Prem, available since August 10, 2026, which ships the models as fully offline Docker containers with no external calls and no license server. To get a quote for either path, contact [Elastic Sales](https://www.elastic.co/contact).\n\n[_launch_ AWS SageMaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy) [_launch_ Google Cloud](https://console.cloud.google.com/marketplace/browse?q=jina&pli=1&inv=1&invt=AbmydQ) [_launch_ Microsoft Azure](https://azuremarketplace.microsoft.com/en-US/marketplace/apps?page=1&search=jina)\n\n__\n\nWhat is the 'task' parameter and when should I use it?\n\n_keyboard\\_arrow\\_down_\n\nThe `task` parameter selects a task-specific LoRA adapter for optimal performance. Use `retrieval.query` for search queries, `retrieval.passage` for documents being searched, `text-matching` for symmetric similarity such as duplicate or paraphrase detection, `classification` for categorization, and `separation` for clustering. Retrieval is asymmetric, so using the wrong side of the query/passage pair measurably degrades results. The parameter is supported by `jina-embeddings-v5`, `jina-embeddings-v4`, and `jina-embeddings-v3`.\n\n__\n\nWhat is late-interaction retrieval and which models support it?\n\n_keyboard\\_arrow\\_down_\n\nLate interaction keeps token-level vectors instead of collapsing a document into one vector, which preserves fine-grained detail at the cost of a larger index. `jina-embeddings-v4` supports both dense (single-vector) and late-interaction (multi-vector) output via the `output_type` parameter, and `jina-colbert-v2` is a dedicated late-interaction model. For most retrieval pipelines, a dense `v5` model followed by a reranker is the better accuracy-per-cost tradeoff.\n\n__\n\nWhat is late chunking and when should I use it?\n\n_keyboard\\_arrow\\_down_\n\nLate chunking embeds the whole document first with a long-context model, then derives chunk embeddings from the token-level representations. Unlike naive chunking, which embeds each chunk in isolation, late chunking preserves cross-chunk context, which improves retrieval quality in RAG pipelines where a chunk refers to something defined earlier in the document. Enable it with the `late_chunking` parameter.\n\n__\n\nWhy does the API enforce a different context length than the model supports?\n\n_keyboard\\_arrow\\_down_\n\nSome models are architecturally capable of longer context than the hosted API accepts. Very long sequences consume substantial GPU memory, and we tune the serving configuration to balance throughput, latency, and cost for the majority of use cases. If you need the full architectural context length, run the model yourself: contact [Elastic Sales](https://www.elastic.co/contact) about a self-managed deployment.\n\n__\n\nWhy is jina-embeddings-v4 free, and why is it slow?\n\n_keyboard\\_arrow\\_down_\n\n`jina-embeddings-v4` is built on the Qwen2-VL base model, released under the Qwen Research License, which permits research and non-commercial use only. We therefore cannot license it commercially and provide it free of charge via the API instead. It is also a 3.8B parameter model, so it is inherently slower per request, and we throttle its throughput to manage infrastructure costs. It is not suitable for production workloads, and for the same reason it is not offered through the Elastic Inference Service or as part of Jina On-Prem. For production use, take the `jina-embeddings-v5` family: it is faster, stronger on retrieval benchmarks, and can be licensed for commercial use through [Elastic Sales](https://www.elastic.co/contact).\n\n__\n\nWhat are the rate limits for the Embeddings API?\n\n_keyboard\\_arrow\\_down_\n\nRate limits depend on your API key type:\n\n**Free:** 100 RPM, 100K TPM\n\n**Paid:** 500 RPM, 2M TPM\n\n**Premium:** 5,000 RPM, 50M TPM\n\nThere is an additional IP-based limit of 10,000 requests per 60 seconds to prevent abuse. Limits are applied per key and counted over a 60-second window, so bursts are smoothed rather than queued; a request over the limit returns HTTP 429 and should be retried with exponential backoff. If you need limits beyond the Premium tier, or dedicated capacity with no shared-tenant limit at all, contact [Elastic Sales](https://www.elastic.co/contact).\n\n__\n\nWhich embedding model should I choose?\n\n_keyboard\\_arrow\\_down_\n\nStart with `jina-embeddings-v5-text-small` for text retrieval: it is the strongest sub-1B model we ship and handles 32K context. Drop to `jina-embeddings-v5-text-nano` when latency, cost, or edge hardware matters more than the last point of accuracy. Use `jina-embeddings-v5-omni-small` or `v5-omni-nano` when images, audio, video, or PDFs are involved; their text output is identical to the corresponding text model, so you can add modalities to an existing index without re-embedding. Use `jina-code-embeddings-0.5b` or `1.5b` for source code. Within a family, newer is better.\n\n__\n\nWhat are the file size limits for images and PDFs?\n\n_keyboard\\_arrow\\_down_\n\nMaximum file sizes are 5 MB for images and 8 MB for PDFs. Larger files are rejected with an error.\n\nReranker-related common questions\n\n__\n\nHow much does the Reranker API cost?\n\n_keyboard\\_arrow\\_down_\n\nReranker API pricing follows the same token-based structure as the Embeddings API, and tokens are shared across all Jina APIs on the same key. New API keys include free tokens to get started; beyond that, token packages are available for purchase. See the pricing section for details.\n\n__\n\nWhat are the differences between the Jina rerankers?\n\n_keyboard\\_arrow\\_down_\n\n`jina-reranker-v3.5` is our current flagship: a 0.6B parameter multilingual listwise reranker with 131K context, and a drop-in replacement for `jina-reranker-v3`. It improves on v3 across every axis we measure, with the largest gains on structured-data and legal retrieval, and runs 1.22x to 1.56x faster. `jina-reranker-v3` remains available. `jina-reranker-m0` is the multimodal reranker for ranking visual documents. `jina-reranker-v2-base-multilingual` is a smaller cross-encoder supporting 100+ languages, useful when you need a non-Qwen-derived model. `jina-colbert-v2` uses late interaction across 89 languages.\n\n__\n\nHow are the Jina rerankers licensed?\n\n_keyboard\\_arrow\\_down_\n\n`jina-reranker-v3.5`, `jina-reranker-v3`, `jina-reranker-m0`, `jina-reranker-v2-base-multilingual`, and `jina-colbert-v2` are released under CC-BY-NC 4.0. You are free to use, share, and adapt them for non-commercial purposes. Commercial production use requires a commercial license, which Elastic has sold as its own SKU since August 10, 2026 under the name Jina On-Prem. Contact [Elastic Sales](https://www.elastic.co/contact) for a quote. Legacy `jina-reranker-v1-*` models remain Apache-2.0.\n\n__\n\nDo the rerankers support multiple languages?\n\n_keyboard\\_arrow\\_down_\n\nYes, all current rerankers are multilingual. `jina-reranker-v3.5` improves on v3 on MIRACL and on multilingual retrieval generally. `jina-reranker-v3` and `jina-reranker-v2-base-multilingual` support 100+ languages, `jina-reranker-m0` handles multilingual visual document ranking, and `jina-colbert-v2` supports 89 languages.\n\n__\n\nWhat is the maximum context length for each reranker?\n\n_keyboard\\_arrow\\_down_\n\nContext length varies by model:\n\n**jina-reranker-v3.5:** 131,072 tokens (query plus all documents combined) with auto-truncation\n\n**jina-reranker-v3:** 131,072 tokens with auto-truncation\n\n**jina-reranker-m0:** 10,000 tokens\n\n**jina-reranker-v2-base-multilingual:** 1,024 tokens, with automatic chunking for longer documents\n\n**jina-colbert-v2:** 8,192 tokens\n\nFor the v1 and v2 rerankers, queries are auto-truncated and long documents are chunked with max-pooling across chunks.\n\n__\n\nIs there a limit on the number of documents I can rerank per query?\n\n_keyboard\\_arrow\\_down_\n\nThere is no hard limit on the number of documents per request. Like our Embeddings API, the Reranker API batches inputs internally by token count for optimal GPU utilization. You can send as many documents as needed in a single request.\n\n__\n\nWhat latency can I expect when reranking 100 documents?\n\n_keyboard\\_arrow\\_down_\n\nLatency varies from 100 milliseconds to 7 seconds, depending largely on the length of the documents and the query. For instance, reranking 100 documents of 256 tokens each with a 64-token query takes about 150 milliseconds. Increasing the document length to 4096 tokens raises the time to 3.5 seconds. If the query length is increased to 512 tokens, the time further increases to 7 seconds.\n\nTime cost of reranking one query and 100 documents, in milliseconds:\n\n|\n|\n\n| | **Number of tokens in each document** |\n| --- | --- |\n| Number of tokens in the query | 256 | 512 | 1024 | 2048 | 4096 |\n| 64 | 156 | 323 | 1366 | 2107 | 3571 |\n| 128 | 194 | 369 | 1377 | 2123 | 3598 |\n| 256 | 273 | 475 | 1397 | 2155 | 4299 |\n| 512 | 468 | 1385 | 2114 | 3536 | 7068 |\n\n__\n\nCan the rerankers be hosted privately, on my own infrastructure or in my own cloud account?\n\n_keyboard\\_arrow\\_down_\n\nYes. The rerankers are available on the AWS, Azure, and GCP marketplaces for deployment in your own cloud account. For self-managed, on-premises, or air-gapped infrastructure, Elastic sells a commercial license (Jina On-Prem) that ships the models as fully offline Docker containers. Contact [Elastic Sales](https://www.elastic.co/contact) for a quote.\n\n[_launch_ AWS SageMaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy) [_launch_ Google Cloud](https://console.cloud.google.com/marketplace/browse?q=jina&pli=1&inv=1&invt=AbmydQ) [_launch_ Microsoft Azure](https://azuremarketplace.microsoft.com/en-US/marketplace/apps?page=1&search=jina)\n\n__\n\nDo you offer a reranker fine-tuned on domain-specific data?\n\n_keyboard\\_arrow\\_down_\n\nBefore commissioning a fine-tune, try `jina-reranker-v3.5`: it was trained with self-distillation specifically for domain robustness and shows large gains on legal and structured-data retrieval over v3. A well-chosen off-the-shelf reranker plus better chunking usually closes more of the gap than a fine-tune does, and it costs nothing to test. If it still falls short on your data, a domain-specific reranker is a custom engagement: contact [Elastic Sales](https://www.elastic.co/contact) to scope it.\n\n[Contact](https://jina.ai/contact-sales)\n\n__\n\nWhat's the minimum image size for the documents?\n\n_keyboard\\_arrow\\_down_\n\nThe minimum acceptable image size for the `jina-reranker-m0` model is 28x28 pixels.\n\n__\n\nWhat is listwise reranking and how does it differ from pointwise?\n\n_keyboard\\_arrow\\_down_\n\n`jina-reranker-v3` and `jina-reranker-v3.5` use a listwise architecture: the query and all candidates share one context window and are scored in a single forward pass, so the model can compare documents against each other. Traditional pointwise rerankers, including `jina-reranker-v2-base-multilingual`, score each document independently against the query. Listwise scoring is more accurate because relevance is often relative to what else is in the candidate set.\n\n__\n\nWhy does the API enforce a different context length than the model supports?\n\n_keyboard\\_arrow\\_down_\n\nSome rerankers are architecturally capable of longer context than the hosted API accepts. Very long sequences consume substantial GPU memory, and we tune the serving configuration to balance throughput, latency, and cost for the majority of use cases. If you need the full architectural context length, run the model in your own infrastructure and contact [Elastic Sales](https://www.elastic.co/contact) about a commercial license.\n\n__\n\nWhat are the rate limits for the Reranker API?\n\n_keyboard\\_arrow\\_down_\n\nRate limits depend on your API key type:\n\n**Free:** 100 RPM, 100K TPM\n\n**Paid:** 500 RPM, 2M TPM\n\n**Premium:** 5,000 RPM, 50M TPM\n\nThere is also an IP-based limit of 10,000 requests per 60 seconds. The same limits apply to the Embeddings and Reranker APIs, and tokens are shared across all Jina APIs on the same key.\n\n__\n\nWhich reranker should I choose?\n\n_keyboard\\_arrow\\_down_\n\nUse `jina-reranker-v3.5` for text. It is a drop-in replacement for `jina-reranker-v3`: the request schema is unchanged, so switching the model string is the entire migration. Use `jina-reranker-m0` when your candidates are images or visually rich documents. Use `jina-reranker-v2-base-multilingual` when you need a smaller model or one that is not derived from a Qwen backbone.\n\nAPI-related common questions\n\n_code_\n\nCan I use the same API key across all Jina APIs?\n\n_keyboard\\_arrow\\_down_\n\nYes. One API key is valid for all Jina AI search foundation products, including the Reader, Embeddings, Reranker, Classifier, and Segmenter APIs, with tokens shared across all of them.\n\n_code_\n\nCan I monitor the token usage of my API key?\n\n_keyboard\\_arrow\\_down_\n\nYes. Enter your API key in the 'API Key & Billing' tab to see your recent usage history and remaining tokens. If you've logged in to the API dashboard, you can also view these details in the 'Manage API Key' tab.\n\n_code_\n\nWhat should I do if I forget my API key?\n\n_keyboard\\_arrow\\_down_\n\nIf you have misplaced a topped-up key and wish to retrieve it, please contact support AT jina.ai with your registered email for assistance. It's recommended to log in to keep your API key securely stored and easily accessible.\n\n[Contact](https://jina.ai/contact-sales)\n\n_code_\n\nDo API keys expire?\n\n_keyboard\\_arrow\\_down_\n\nNo, our API keys do not have an expiration date. If a key is compromised, revoke it yourself in [the API Key Management dashboard](https://jina.ai/api-dashboard), which takes effect immediately; issue a replacement key first if you want to avoid downtime. Any remaining token balance stays on your account rather than on the revoked key. If you cannot access the dashboard, or believe the account itself is compromised, raise it with [Elastic Support](https://support.elastic.co/).\n\n[Contact](https://jina.ai/contact-sales)\n\n_code_\n\nCan I transfer tokens between API keys?\n\n_keyboard\\_arrow\\_down_\n\nYes, you can transfer tokens from a premium key to another. After logging into your account on [the API Key Management dashboard](https://jina.ai/api-dashboard), use the settings of the key you want to transfer out to move all remaining paid tokens.\n\n_code_\n\nCan I revoke my API key?\n\n_keyboard\\_arrow\\_down_\n\nYes, you can revoke your API key if you believe it has been compromised. Revoking a key will immediately disable it for all users who have stored it, and all remaining balance and associated properties will be permanently unusable. If the key is a premium key, you have the option to transfer the remaining paid balance to another key before revocation. Notice that this action cannot be undone. To revoke a key, go to the key settings in [the API Key Management dashboard](https://jina.ai/api-dashboard).\n\n_code_\n\nWhy is the first request for some models slow?\n\n_keyboard\\_arrow\\_down_\n\nThis is because our serverless architecture offloads certain models during periods of low usage. The initial request activates or 'warms up' the model, which may take a few seconds. After this initial activation, subsequent requests process much more quickly.\n\n_code_\n\nIs my API data used to train your models?\n\n_keyboard\\_arrow\\_down_\n\nNo. We never use your API requests, inputs, or outputs to train our embedding, reranker, or any other models. Your data remains yours.\n\n_code_\n\nWhat are the rate limits for Jina APIs?\n\n_keyboard\\_arrow\\_down_\n\nRate limits apply per API key:\n\n**Free:** 100 RPM, 100K TPM\n\n**Paid:** 500 RPM, 2M TPM\n\n**Premium:** 5,000 RPM, 50M TPM\n\nThere is also an IP-based limit of 10,000 requests per 60 seconds. Limits vary by endpoint; see the rate limit table above for per-endpoint figures.\n\n_code_\n\nAre there batch size limits for the APIs?\n\n_keyboard\\_arrow\\_down_\n\nThere is **no batch size limit** for either the Embeddings or Reranker APIs. You can send as many items or documents as needed per request. Both APIs batch inputs internally by token count for optimal GPU utilization.\n\n_code_\n\nAre the Jina APIs the same thing as Jina models inside Elastic?\n\n_keyboard\\_arrow\\_down_\n\nNo, they are three separate paths. The Jina APIs on this site are self-serve and pay-as-you-go with a Jina API key. The Elastic Inference Service (EIS) runs Jina models inside Elastic Cloud, billed through your Elastic subscription, with no infrastructure for you to manage. Jina On-Prem is a commercial license, sold by Elastic as its own SKU since August 10, 2026, for running the models in your own self-managed, on-premises, or air-gapped infrastructure. For the EIS and On-Prem paths, contact [Elastic Sales](https://www.elastic.co/contact).\n\nBilling-related common questions\n\n_attach\\_money_\n\nIs billing based on the number of sentences or requests?\n\n_keyboard\\_arrow\\_down_\n\nOur pricing is based on total tokens processed, so you can spread a token budget across as many inputs as you like \u2014 no per-sentence charges.\n\n_attach\\_money_\n\nIs there a free trial available for new users?\n\n_keyboard\\_arrow\\_down_\n\nYes. New users get an auto-generated API key with free tokens usable across any of our models. Once the free tokens are consumed, you can purchase additional tokens for the key in the 'Buy tokens' tab.\n\n_attach\\_money_\n\nAre tokens charged for failed requests?\n\n_keyboard\\_arrow\\_down_\n\nNo, tokens are not deducted for failed requests.\n\n_attach\\_money_\n\nWhat payment methods are accepted?\n\n_keyboard\\_arrow\\_down_\n\nPayments are processed through Stripe, supporting a variety of payment methods including credit cards, Google Pay, and PayPal for your convenience.\n\n_attach\\_money_\n\nIs invoicing available for token purchases?\n\n_keyboard\\_arrow\\_down_\n\nFor self-serve token purchases, Stripe issues an invoice to the email address associated with your Stripe account at the time of purchase. If you need a formal purchase order, a negotiated contract, procurement paperwork, or consolidated billing, that runs through Elastic rather than Stripe: contact [Elastic Sales](https://www.elastic.co/contact).\n\n_attach\\_money_\n\nHow do I buy a commercial license rather than API tokens?\n\n_keyboard\\_arrow\\_down_\n\nToken purchases on this site cover use of the hosted Jina APIs. They do not license you to run the model weights in your own infrastructure. For that, Elastic has sold a commercial license as its own SKU since August 10, 2026, priced annually rather than per token. Contact [Elastic Sales](https://www.elastic.co/contact) for a quote.\n\n_attach\\_money_\n\nCan I pay by invoice or purchase order instead of card?\n\n_keyboard\\_arrow\\_down_\n\nSelf-serve token purchases are processed through Stripe and invoiced automatically to your Stripe account email. For purchase orders, procurement processes, or volumes above what self-serve top-up supports, contact [Elastic Sales](https://www.elastic.co/contact).\n\n_attach\\_money_\n\nI paid, but my balance or rate limit has not changed. What should I check?\n\n_keyboard\\_arrow\\_down_\n\nBalance and rate limits belong to an API key, not to the account, so the first thing to check is the key itself rather than the account page: enter it in the API Key & Billing tab and confirm the balance and tier there. If the account holds more than one key, the tokens are on the key that was topped up, which may not be the key your application is sending. A new tier can also take a short time to propagate after payment. If the key shows the balance but is still limited at the previous tier after that, contact support.\n\n_attach\\_money_\n\nHow do I cancel, stop auto top-up, or remove a saved payment method?\n\n_keyboard\\_arrow\\_down_\n\nSelf-serve billing is managed from the customer portal reachable via the API Key & Billing tab, where auto top-up can be switched off and saved payment methods removed. Turning off auto top-up stops future charges but leaves any balance already purchased usable. If you also want the account and its data removed, or a refund considered, send that request to support; account deletion is handled manually and takes a few business days, and you will get written confirmation once it is done.\n\nCurrent language / theme\n\n_language_ English / Auto\n\nSearch Foundation\n\n[Reader](https://jina.ai/reader) [Embeddings](https://jina.ai/embeddings) [Reranker](https://jina.ai/reranker)\n\nGet Jina API key\n\n[Rate Limit](https://jina.ai/contact-sales#rate-limit)\n\nAbout us\n\n[News](https://jina.ai/news) [Download Jina logo\\\\\n\\\\\n_open\\_in\\_new_](https://jina.ai/logo-Jina-1024.zip) [Download Elastic logo\\\\\n\\\\\n_open\\_in\\_new_](https://brand.elastic.co/302f66895/p/06c73c-elastic-logos/b/35d033) [API Status](https://status.jina.ai/)\n\n[__](https://huggingface.co/jinaai)\n\nElastic \u00a9 2026. [Security](https://jina.ai/legal#security-as-company-value) [Terms & Conditions](https://jina.ai/legal/#terms-and-conditions) [Privacy](https://jina.ai/legal/#privacy-policy)Manage CookiesDo Not Sell or Share My Personal Information\n\nThis website and all associated content, software, products, and services are intended for professional use only. No consumer use is intended or directed.",
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"rank": 3,
"url": "https://pitchbook.com/profiles/company/439450-75",
"title": "Jina AI 2026 Company Profile: Valuation, Investors, Acquisition",
"content": "# Jina AI\n\n## Jina AI Overview\n\nUpdate this profile\n\n## - Year Founded - 2020\n\n\n\n## - Status - Acquired/\u200bMerged\n\n## - Employees - 27\n\n\n\n## - Latest Deal Type - M&A\n\n## - Latest Deal Amount - $43.3M\n\n### Jina AI General Information\n\n#### Description\n\nDeveloper of an artificial intelligence prompt optimizing platform intended to improve performance across areas like marketing, multimedia, and software development. The company's platform helps to transform ideas into precise prompts and also offers an assistant chatbot to produce content and solve diverse tasks, enabling businesses to refine prompts for better outcomes.\n\n#### Contact Information\n\n##### Website\n\n[www.jina.ai](http://www.jina.ai/)\n\nOwnership Status\n\nAcquired/Merged\n\n(Operating Subsidiary)\n\nFinancing Status\n\nFormerly VC-backed\n\n##### Corporate Office\n\n- Prinzessinnenstra\u00dfe 19-20\n- 10969 Berlin\n- Germany\n\n+49 030\n\n[\ueac9](https://www.linkedin.com/company/jinaai)\n\n[\ue944](https://twitter.com/JinaAI_)\n\n[\uea8c](https://www.facebook.com/opensourcejina)\n\nPrimary Industry\n\nBusiness/Productivity Software\n\nOther Industries\n\nSoftware Development Applications\n\nParent Company\n\n[Elastic](https://pitchbook.com/profiles/company/55574-02)\n\nVertical(s)\n\n[SaaS](https://pitchbook.com/profiles/industry/saas),\n[Artificial Intelligence & Machine Learning](https://pitchbook.com/profiles/industry/artificial-intelligence-and-machine-learning)\n\n##### Corporate Office\n\n- Prinzessinnenstra\u00dfe 19-20\n- 10969 Berlin\n- Germany\n\n+49 030\n\n[\ueac9](https://www.linkedin.com/company/jinaai)\n\n[\ue944](https://twitter.com/JinaAI_)\n\n[\uea8c](https://www.facebook.com/opensourcejina)\n\n### Want detailed data on 11M+ companies?\n\nWhat you see here scratches the surface\n\n[Request a free trial](https://pitchbook.com/profiles/request-access)\n\n### Want to dig into this profile?\n\nWe\u2019ll help you find what you need\n\n[Learn more](https://pitchbook.com/profiles/request-access)\n\n## Jina AI Valuation & Funding\n\n| Deal Type | Date | Amount | Raised to Date | Post-Val | Status | Stage |\n| --- | --- | --- | --- | --- | --- | --- |\n| 5\\. Merger/Acquisition | 07-Oct-2025 | $43.3M | | | Completed | Generating Revenue |\n| 4\\. Early Stage VC (Series A) | 04-Nov-2021 | | | | Completed | Generating Revenue |\n| 3\\. Early Stage VC | 22-Sep-2020 | | | | Completed | Generating Revenue |\n| 2\\. Accelerator/Incubator | 11-Apr-2020 | | $2M | | Completed | Startup |\n| 1\\. Seed Round | 18-Feb-2020 | $2M | $2M | | Completed | Startup |\n\n[To view Jina AI\u2019s complete valuation and funding history, request access\u00a0\u00bb](https://pitchbook.com/profiles/request-access)\n\n## Jina AI Related Research & Analysis\n\nExplore institutional-grade private market research from our team of analysts.\n\n- Verticals\n\n- Artificial Intelligence & Machine Learning\n- SaaS\n\n- [\\\\\n\\\\\nAI Report: $407 Billion Raised as Megadeals Dominate\\\\\n\\\\\nAugust 10, 2026](https://pitchbook.com/news/reports/q2-2026-ai-report-407-billion-raised-as-megadeals-dominate)\n- [\\\\\n\\\\\nEnterprise SaaS Report: VC Funding Rebounds Beneath Quarter's Headline Decline\\\\\n\\\\\nAugust 3, 2026](https://pitchbook.com/news/reports/q2-2026-enterprise-saas-report-vc-funding-rebounds-beneath-quarters-headline-decline)\n\n## Jina AI Signals\n\nGrowth Rate\n\n\n\nWeekly\n\nGrowth\n\nWeekly Growth\n\n\n\nSize Multiple\n\n\n\nMedian\n\nSize Multiple\n\n\n\nKey Data Points\n\nSimilarweb Unique Visitors\n\n\n\nMajestic Referring Domains\n\n\n\nPitchBook\u2019s non-financial metrics help you gauge a company\u2019s traction and growth using web presence and social reach.\n\n[Request a free trial](https://pitchbook.com/profiles/request-access)\n\n## Jina AI Former Investors (5)\n\n| Investor Name | Investor Type | Holding | Investor Since | Participating Rounds |\n| --- | --- | --- | --- | --- |\n| Canaan Partners | Venture Capital | Minority | | |\n| Granite Asia | Impact Investing | Minority | | |\n| Mango Capital | Venture Capital | Minority | | |\n| SAP.iO | Accelerator/Incubator | Minority | | |\n| Yunqi Partners | Venture Capital | Minority | | |\n\n[To view Jina AI\u2019s complete investors history, request access\u00a0\u00bb](https://pitchbook.com/profiles/request-access)\n\n[Ready to get started?\\\\\nRequest a free trial](https://pitchbook.com/profiles/request-access)\n\n## Jina AI FAQs\n\n- ### When was Jina AI founded?\n\n\nJina AI was founded in 2020.\n\n- ### Where is Jina AI headquartered?\n\n\nJina AI is headquartered in Berlin, Germany.\n\n- ### What is the size of Jina AI?\n\n\nJina AI has 27 total employees.\n\n- ### What industry is Jina AI in?\n\n\nJina AI\u2019s primary industry is Business/Productivity Software.\n\n- ### Is Jina AI a private or public company?\n\n\nJina AI is a Private company.\n\n- ### What is the current valuation of Jina AI?\n\n\nThe current valuation of Jina AI is .\n\n- ### What is Jina AI\u2019s current revenue?\n\n\nThe current revenue for Jina AI is .\n\n- ### How much funding has Jina AI raised over time?\n\n\nJina AI has raised $37.2M.\n\n- ### Who are Jina AI\u2019s investors?\n\n\n[Canaan Partners](https://pitchbook.com/profiles/investor/11135-71), [Granite Asia](https://pitchbook.com/profiles/investor/536095-99), [Mango Capital](https://pitchbook.com/profiles/investor/267468-40), [SAP.iO](https://pitchbook.com/profiles/investor/186427-90), and [Yunqi Partners](https://pitchbook.com/profiles/investor/125876-89) have invested in Jina AI.\n\n- ### When was Jina AI acquired?\n\n\nJina AI was acquired on 07-Oct-2025.\n\n- ### Who acquired Jina AI?\n\n\nJina AI was acquired by [Elastic](https://pitchbook.com/profiles/company/55574-02).\n\n\n### Data Transparency\n\n- \n\n\nMeet our data hygiene team\n\n\n\nDiscover how our experts ensure you\u2019re getting the most accurate financial data in the industry.\n\n[Read more](https://pitchbook.com/blog/meet-pitchbooks-data-hygiene-team)\n\n- \n\n\nHow PitchBook sources data\n\n\n\nOur data operations team has logged over 3.5 million hours researching, organizing, and integrating the information you need most.\n\n[Discover our process](https://pitchbook.com/research-process)",
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"published_date": null
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{
"rank": 4,
"url": "https://growjo.com/company/Jina_AI",
"title": "Jina AI: Revenue, Competitors, Alternatives - Growjo",
"content": "# Jina AI Revenue and Competitors\n\n[License our Company Data API](https://growjo.com/company_data_api)\n\n[](https://jina.ai/)\n\n![]()\n\n#### Global,\n\nLocation\n\n![]()\n\n#### N/A\n\nTotal Funding\n\n![]()\n\n#### [AI](https://growjo.com/industry/AI)\n\nIndustry\n\n## Estimated Revenue & Valuation\n\n- Jina AI's estimated annual revenue is currently $17.5M per year.[(i)](https://growjo.com/join)\n- Jina AI's estimated revenue per employee is $277,571\n\n## Employee Data\n\n- Jina AI has 63 Employees.[(i)](https://growjo.com/join)\n- Jina AI grew their employee count by -6% last year.\n\n## Jina AI's People\n\n| Name | Title | Email/Phone |\n| --- | --- | --- |\n\n## Jina AI Competitors & Alternatives [Add Company](https://growjo.com/add-your-company)\n\n\n\n| Competitor Name | Revenue | Number of Employees | Employee Growth | Total Funding | Valuation |\n| --- | --- | --- | --- | --- | --- |\n| #1
[](https://paradox.ai/)[Paradox](https://growjo.com/company/Paradox) | $122.9M | 691 | 4% | $253.3M | N/A |\n| #2
[Spire Digital](https://growjo.com/company/Spire_Digital) | $3.1M | 17 | -23% | N/A | N/A |\n| #3
[10Web.io](https://growjo.com/company/10Web.io) | $25.5M | 82 | 9% | N/A | N/A |\n| #4
[Loyal](https://growjo.com/company/Loyal) | $84.5M | 248 | -23% | $53.7M | N/A |\n| #5
[Project Verte](https://growjo.com/company/Project_Verte) | $51.4M | 118 | 8% | $62M | N/A |\n| #6
[Kore.ai](https://growjo.com/company/Kore.ai) | $525.8M | 1208 | 10% | $296M | N/A |\n| #7
[SiteZeus](https://growjo.com/company/SiteZeus) | $10.1M | 44 | -28% | $3.7M | N/A |\n| #8
[inFeedo](https://growjo.com/company/inFeedo) | $63.8M | 187 | 13% | N/A | N/A |\n| #9
[Accubits Techno...](https://growjo.com/company/Accubits_Technologies) | $25.6M | 179 | -36% | N/A | N/A |\n| #10
[Valuer.ai](https://growjo.com/company/Valuer.ai) | $6.8M | 52 | -5% | N/A | N/A |\n\n[Add Company](https://growjo.com/add-your-company)\n\n[Show More AI Companies](https://growjo.com/industry/AI)\n\n## What Is Jina AI?\n\nJina AI is a Neural Search Company, providing cloud-native neural search powered by state-of-the-art AI and deep learning. Found in 2020. & led by GGV Capital with $7.5M, Jina AI is recognized as one of the most promising startups in AI open-source software. Our mission is to build an open-source neural search ecosystem for businesses and developers, enabling everyone to search for information in all kinds of data with high accessibility and scalability. We don't lock up our values and innovations in \"the way people have always done\". We are receptive to change \u00e2\u20ac\u201d when we don't like something, we change it and make it better. We always believe that those who really make changes are the ones convinced that change is possible. Life is short and time is precious. We want to spend time only on the right things that we do believe. Universal search engine, open AI technology and cross-border collaborations, these are the bright future that we believe in and fully commit to. We are hiring AI engineers, full-stack developers, open-source evangelists, PMs. If you see what we see, share what we believe in, then click the opportunity button below. Exciting journey is waiting for us. Contact - Contact: hello@jina.ai - Twitter: @JinaAI\\_ (with underscore at the end) - Website: https://jina.ai - Github: https://opensource.jina.ai - Youtube: https://webinar.jina.ai - LinkedIn: https://www.linkedin.com/company/jinaai\n\n**keywords:** N/A\n\n![]()\n\nN/A\n\nTotal Funding\n\n![]()\n\n63\n\nNumber of Employees\n\n![]()\n\n$17.5M\n\nRevenue (est)\n\n![]()\n\n-6%\n\nEmployee Growth %\n\n![]()\n\nN/A\n\nValuation\n\n![]()\n\nN/A\n\nAccelerator\n\n## Other Companies in Global\n\n[](https://growjo.com/export_list)\n\n| Company Name | Revenue | Number of Employees | Employee Growth | Total Funding |\n| --- | --- | --- | --- | --- |\n| #1
[](https://uk.naturecan.com/)[Naturecan](https://growjo.com/company/Naturecan) | $13.7M | 67 | -12% | N/A |\n| #2
[](https://vectairsystems.com/)[Vectair Systems](https://growjo.com/company/Vectair_Systems) | $15.3M | 71 | 20% | N/A |\n| #3
[](https://ivsc.org/)[International V...](https://growjo.com/company/International_Valuation_Standards_Council_(IVSC)) | $11.1M | 79 | 25% | N/A |\n| #4
[](https://thcohq.com/)[THCO - We Are H...](https://growjo.com/company/THCO_-_We_Are_Hiring!) | $18.3M | 83 | 19% | N/A |\n| #5
[](https://epns.io/)[Ethereum Push N...](https://growjo.com/company/Ethereum_Push_Notification_Service_(EPNS)) | $11.3M | 87 | -16% | N/A |",
"content_chars": 5142,
"published_date": null
},
{
"rank": 5,
"url": "https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A",
"title": "Jina AI - 2026 Company Profile & Team - Tracxn",
"content": "Your browser was unable to load all of Tracxn resources. They may have been blocked by your firewall, proxy or browser configuration. Press **Ctrl+F5** or **Ctrl+Shift+R** to have your browser try again and if that doesn't work, [**click here to retry**](https://tracxn.com/) or mail us at [**hi@tracxn.com**](mailto:hi@tracxn.com?subject=Platform%20resources%20not%20loading)\n\nInternal Server Error\n\n[](https://jina.ai/)\n\n# [Jina AI - Company Profile](https://jina.ai/)\n\nLast updated: August 25, 2026\n\n[Claim Profile](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba/jinaai/claimprofile?utm_source=product-seo%20page&utm_medium=companies-funding&utm_campaign=unclaimed%20claim%20profile&utm_content=cta%20claimprofile) [Suggest Edits](https://tracxn.com/b/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A/edit)\n\n- [Linkedin](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftracxn.com%2Fd%2Fcompanies%2Fjina-ai%2F__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A)\n- [Twitter](https://twitter.com/intent/tweet?url=https%3A%2F%2Ftracxn.com%2Fd%2Fcompanies%2Fjina-ai%2F__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A)\n- [Facebook](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftracxn.com%2Fd%2Fcompanies%2Fjina-ai%2F__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A)\n- [Email](mailto:?subject=Jina%20AI%20Company%20profile%20on%20Tracxn&body=Hi!%0D%0A%0D%0AI%20would%20like%20you%20to%20take%20a%20look%20at%20this%20Company%20profile%20I%20found%20on%20Tracxn%20:%20https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A)\n- Copy Url\n\n\n- [Request page removal](https://tracxn.com/b/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A/remove)\n\n## Jina AI - About the company\n\nJina AI is an acquired company based in Germany, founded in 2020 by [Bing He](https://platform.tracxn.com/a/d/people/62316b04a0a68a701b0fdd09/binghe) and [Nan Wang](https://platform.tracxn.com/a/d/people/62316bf4a0a68a701b0fe284/nanwang). It operates as a Developer of search foundation providing embeddings, reranker, and reader capabilities. Jina AI has raised $39M in funding from [Canaan](https://tracxn.com/d/venture-capital/canaan/__P-Y_1HXNfTSNP2CwmEmAT-av2sYYBqCTr4wsnMs-UT8) and [SAP.iO](https://tracxn.com/d/accelerator-incubator/sapio/__RZqjVmK8jYpLKpw1bahl2rA6rC1yuplaIEVcuiRi4qY). The company has 771 active competitors, including 109 funded and 42 that have exited. Its top competitors include companies like [Glean](https://tracxn.com/d/companies/glean/__0aO5V-wZsJgrfCZYNJm9ccNqZCpaXHw-O9LJTlZZYgQ), [Hugging Face](https://tracxn.com/d/companies/hugging-face/___89yhA9z0-ZrLstW87xWDVe15Bkl70IZOkQf38SXzmQ) and [Elastic](https://tracxn.com/d/companies/elastic/__T1LkMb2skd7hBANkVC7ni7uXB_FqHZ_h_3Qak0d03ME).\n\n### Company Details\n\nDeveloper of search foundation providing embeddings, reranker, and reader capabilities. The company offers tools to convert URLs to LLM-friendly input. It provides multimodal multilingual embeddings and a reranker for maximizing search relevancy. The platform allows users to read URLs and fetch content, search the web, and get SERP. It offers an API to access its services in LLMs.\n\nWebsite[jina.ai](https://jina.ai/)\n\nSocial[![X]()](https://twitter.com/jinaai_)\n\nEmail ID\\*\\*\\*\\*\\*@jina.ai\n\nKey Metrics\n\nFounded Year\n\n2020\n\nLocation\n\n[Germany](https://tracxn.com/d/geographies/germany/__J2uFS4cPEWupB2-t7_tRBGZGqa5onAEvRi6OJX89nUY)\n\nStage\n\nAcquired\n\nTotal Funding\n\n[$39M](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:funding-and-investors) in 2 rounds\n\nLatest Funding Round\n\n[Series A, Nov 22, 2021, $\\*\\*\\*\\*\\*](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:funding-and-investors)\n\nInvestors\n\n[Canaan](https://tracxn.com/d/venture-capital/canaan/__P-Y_1HXNfTSNP2CwmEmAT-av2sYYBqCTr4wsnMs-UT8)& [5 more](https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A/funding-and-investors)\n\nRanked\n\n23rdamong [771 active competitors](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:competitors)\n\nEmployee Count\n\n[44](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:people) as on Jul 31, 2026\n\nSimilar Companies\n\n[Hugging Face](https://tracxn.com/d/companies/hugging-face/___89yhA9z0-ZrLstW87xWDVe15Bkl70IZOkQf38SXzmQ)& [512 more](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:explore)\n\nExit Details\n\nAcquiredby [Elastic](https://tracxn.com/d/companies/elastic/__T1LkMb2skd7hBANkVC7ni7uXB_FqHZ_h_3Qak0d03ME) (Oct 09, 2025)\n\n[View Jina AI's full profile](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba)\n\n## Legal entities associated with Jina AI\n\nJina AI is associated with 3 legal entities given below:\n\n| Legal Entity Name | Date of Incorporation | Revenue | Latest Employee Count | Documents |\n| --- | --- | --- | --- | --- |\n| [JINA AI (GLOBAL) PTE. LTD](https://tracxn.com/d/legal-entities/singapore/jina-ai-global-pte.ltd/__Q0FP9AjZuYciGsDX9uO76AXvD-3eXf7pkO-PURQNew8 \"JINA AI (GLOBAL) PTE. LTD\")
CIN: 202109958C , Singapore, Active | Mar 19, 2021 | - | - | Buy Now |\n| [JINA AI GMBH](https://tracxn.com/d/legal-entities/germany/jina-ai-gmbh/__4IonH6KcbHmVDenNnSoem1iTs903mVk5npVK3Cv6_sg \"JINA AI GMBH\")
CIN: HRB218021 , Germany, Active | Jun 08, 2020 | - | 31
(As on Dec 31, 2023) | Buy Now |\n| [Jina AI GmbH](https://tracxn.com/d/legal-entities/germany/jina-ai-gmbh/__CVC793DnQsKw8EDYKz0cOOnKRWOs9sJ7fVGntYm6a-E \"Jina AI GmbH\")
CIN: F1103\\_HRB218021B , Germany, Active | Jun 09, 2020 | - | - | Buy Now |\n\n## Jina AI's acquisition details\n\nJina AI got acquired by [Elastic](https://tracxn.com/d/companies/elastic/__T1LkMb2skd7hBANkVC7ni7uXB_FqHZ_h_3Qak0d03ME) on Oct 09, 2025.\n\nClick [here](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:exits) to take a look at Jina AI's acquisition in detail\n\n\n\nSign up to download Jina AI's company profile\n\nSign Up for Free\n\n## Jina AI's funding and investors\n\nJina AI has raised a total funding of$39Mover 2 rounds.Its first funding round was onSep 23, 2020.Its latest funding round was a Series A round on Nov 22, 2021 for [$\\\\*\\\\*\\\\*\\*\\*](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:funding-and-investors).5 investors participated in its latest round.Jina AI has6 [institutional investors](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:funding-and-investors).\n\nHere is the list of recent funding rounds of Jina AI:\n\n| Date of Funding | Funding Amount | Round Name | Post-Money Valuation | Revenue Multiple | Lead Investors | Other Investors |\n| --- | --- | --- | --- | --- | --- | --- |\n| Nov 22, 2021 | 5142547 | Series A | 8384153 | 8395028 | 3264086 | 6445883 |\n| Sep 23, 2020 | 5479399 | Seed | 4404734 | 7044889 | [SAP.iO](https://tracxn.com/d/accelerator-incubator/sapio/__RZqjVmK8jYpLKpw1bahl2rA6rC1yuplaIEVcuiRi4qY) | 2671932 |\n\n![lock]()Access funding benchmarks and valuations. [Sign up today!](https://tracxn.com/signup)\n\nView details of [Jina AI's funding rounds and investors](https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A/funding-and-investors)\n\n## Jina AI's founders and board of directors\n\n[Founder? Claim Profile](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba/claimprofile?utm_source=product-seo%20page&utm_medium=companies-funding&utm_campaign=unclaimed%20claim%20profile&utm_content=cta%20claimprofile)\n\nThe founders of Jina AIare [Bing He](https://platform.tracxn.com/a/d/people/62316b04a0a68a701b0fdd09/binghe) and [Nan Wang](https://platform.tracxn.com/a/d/people/62316bf4a0a68a701b0fe284/nanwang).\n\nHere are the details of Jina AI's key team members:\n\n- [Bing He](https://platform.tracxn.com/a/d/people/62316b04a0a68a701b0fdd09/binghe): Co-Founder & COO (Acquired by Elastic) of Jina AI.\n- [Nan Wang](https://platform.tracxn.com/a/d/people/62316bf4a0a68a701b0fe284/nanwang): Co-Founder of Jina AI.\n\nView details of [Jina AI's Founder profiles and Board Members](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:people)\n\n### Jina AI's employee count trend\n\nJina AI has 44 employees as of Jul 26. Here is Jina AI's employee count trend over the years:\n\n\n\n![lock]()Uncover Jina AI's growth story! [Sign up today!](https://tracxn.com/signup)\n\n\n\nAccess Tracxn on any website\n\nOur Google Chrome extension lets you view company details while browsing their websites\n\n[Install Tracxn Extension](https://chromewebstore.google.com/detail/tracxn-extension/mcplkbacfdjapifgiidjidmnfilipnep?hl=en)\n\n## Jina AI's Competitors and alternates\n\nTop competitors of Jina AI include [Glean](https://tracxn.com/d/companies/glean/__0aO5V-wZsJgrfCZYNJm9ccNqZCpaXHw-O9LJTlZZYgQ), [Hugging Face](https://tracxn.com/d/companies/hugging-face/___89yhA9z0-ZrLstW87xWDVe15Bkl70IZOkQf38SXzmQ) and [Elastic](https://tracxn.com/d/companies/elastic/__T1LkMb2skd7hBANkVC7ni7uXB_FqHZ_h_3Qak0d03ME).Here is the list of Top 10 competitors of Jina AI, ranked by Tracxn score:\n\n| Rank | Company Details | Short Description | Total Funding | Investors | Tracxn Score |\n| --- | --- | --- | --- | --- | --- |\n| 1st | 
[Glean](https://tracxn.com/d/companies/glean/__0aO5V-wZsJgrfCZYNJm9ccNqZCpaXHw-O9LJTlZZYgQ \"Glean\")
2019, Palo Alto ( [United States](https://tracxn.com/d/geographies/united-states/__agFBbaWLXQ9BaxreLIz_EPAASY3VwAK-kFQ6rvJvIco)), Series F | Cloud-based platform for enterprise search | $765M | [Lightspeed Venture Partners](https://tracxn.com/d/venture-capital/lightspeed-venture-partners/__PzMkj1UXJCjrJn4ADHeiO_a04brVhLLYKBEqw7d_INY), [Kleiner Perkins](https://tracxn.com/d/venture-capital/kleiner-perkins/__M3ZtL62VNmgtiH2m8SoAcdoCNIOpQAv0g_eqoob_Vw8)
...&\u00a0[52\u00a0others](https://platform.tracxn.com/a/d/company/5903f872e4b0b66b2cdd2ade#a:funding-and-investors) | 79/100 |\n| 2nd | 
[Hugging Face](https://tracxn.com/d/companies/hugging-face/___89yhA9z0-ZrLstW87xWDVe15Bkl70IZOkQf38SXzmQ \"Hugging Face\")
2016, Paris ( [France](https://tracxn.com/d/geographies/france/__TTi2JLFlyQgLp2aNNET70HJjnxtjza_BDTOzcb8sfE8)), Series D | Platform offering collaborative tools for sharing and deploying machine learning models | $400M | [Lux Capital](https://tracxn.com/d/venture-capital/lux-capital/__GYav2s0CpJYtKuH74TT9JyNQSZPYCXHgZgoi2ivawOM), [Salesforce](https://tracxn.com/d/companies/salesforce/__meXShWFhj6RRXVaUgSdybLdExpZGUx224nYjFl0eCjo)
&\u00a0[33\u00a0others](https://platform.tracxn.com/a/d/company/57dc8704e4b0af1418b4e600#a:funding-and-investors) | 75/100 |\n| 3rd | 
[Elastic](https://tracxn.com/d/companies/elastic/__T1LkMb2skd7hBANkVC7ni7uXB_FqHZ_h_3Qak0d03ME \"Elastic\")
2012, San Francisco ( [United States](https://tracxn.com/d/geographies/united-states/__agFBbaWLXQ9BaxreLIz_EPAASY3VwAK-kFQ6rvJvIco)), Public | Cloud based text analytics platform | $104M | [New Enterprise Associates](https://tracxn.com/d/venture-capital/new-enterprise-associates/__eu8NTmi72ogDYzh9NH9QIFndf1mSanr4HntwJ8lfodQ), [HTIF](https://tracxn.com/d/venture-capital/htif/__1IPX5LH7_tnDtTZLF5lzsufN_4Pn6zEuZUAzUaeufqI)
&\u00a0[10\u00a0others](https://platform.tracxn.com/a/d/company/54ff9f58e4b039f421cce192#a:funding-and-investors) | 72/100 |\n| 4th | 
[Stability AI](https://tracxn.com/d/companies/stability-ai/__j9m4iz5g2IAe2paU-Sre7UIBk1ByQZ0ippRUslXvqwc \"Stability AI\")
2019, London ( [United Kingdom](https://tracxn.com/d/geographies/united-kingdom/__BFrCTEA8idVqv695D5RNyVuEJ3b_q8H7jGByutRg8gc)), Seed | Developer of generative AI models for image, video, audio, and 3D | $181M | [Lightspeed Venture Partners](https://tracxn.com/d/venture-capital/lightspeed-venture-partners/__PzMkj1UXJCjrJn4ADHeiO_a04brVhLLYKBEqw7d_INY), [Coatue](https://tracxn.com/d/private-equity/coatue/__neb04ukttPFlKk9SEuR86jzhUs0jtf8EHm92gQaD8r8)
&\u00a0[17\u00a0others](https://platform.tracxn.com/a/d/company/5fac457ede6524199d16b169#a:funding-and-investors) | 72/100 |\n| 5th | 
[Fetch.ai](https://tracxn.com/d/companies/fetchai/__HM9Pjh1Z-DC2fCGeFAtmxpDdMoeNXERIsDY9y11B3LU \"Fetch.ai\")
2017, Cambridge ( [United Kingdom](https://tracxn.com/d/geographies/united-kingdom/__BFrCTEA8idVqv695D5RNyVuEJ3b_q8H7jGByutRg8gc)), Series C | Blockchain-based network for application and AI infrastructure | $61.9M | [DWF Labs](https://tracxn.com/d/venture-capital/dwf-labs/__q9GbokRpHubOPz5FHxQ_6obYr0mbgVJH1lK4sRc6Y4E), [Gda Group](https://tracxn.com/d/venture-capital/gda-group/__z6t8tkB0SgNR5RfoMZUwLA1WoaDUX6oOc_m0lCOsubk)
&\u00a0[14\u00a0others](https://platform.tracxn.com/a/d/company/5a5c56b1e4b0eb94adcc253b#a:funding-and-investors) | 72/100 |\n| 6th | 
[You Technologies](https://tracxn.com/d/companies/you-technologies/__uYI-fs1-W1VU1J7X3PmyQvM-TCMhH7g8w77exoKw1x8 \"You Technologies\")
2020, Palo Alto ( [United States](https://tracxn.com/d/geographies/united-states/__agFBbaWLXQ9BaxreLIz_EPAASY3VwAK-kFQ6rvJvIco)), Series C | AI-enabled key-word based private search engine | $199M | [Georgian](https://tracxn.com/d/venture-capital/georgian/__VyUlz5ludKYUVkf3g5kAfADcECiMn-dXDWwpUqBgl4o), [Radical Ventures](https://tracxn.com/d/venture-capital/radical-ventures/__p4SySBjPpWGP9HUkVr0snVFwU1OCp4izS1QF8EWPg68)
&\u00a0[20\u00a0others](https://platform.tracxn.com/a/d/company/531b602fe4b0f7e16627327b#a:funding-and-investors) | 71/100 |\n| 7th | 
[Coveo](https://tracxn.com/d/companies/coveo/__z8ra6rSM83TAvnIqKMZFfhDNDOYV8IUFLznYRUvxZOU \"Coveo\")
2004, Quebec City ( [Canada](https://tracxn.com/d/geographies/canada/__5_AOWm4cD5BWy5u1p9laDOY83HVNrYlq3BRq6KRIiVo)), Public | Provider of AI-powered search and recommendation solutions for enterprise businesses | $358M | [Elliott Management](https://tracxn.com/d/companies/elliott-management/__z8di2ceqcjVkFOb_1c0ejhAYt0KxE9JuoZtNBmjYxSg), [OMERS](https://tracxn.com/d/private-equity/omers/__TYEHXE6H0kWbXXjX-ZSEBm2zgInrfD6g8QggcNujF78)
&\u00a0[17\u00a0others](https://platform.tracxn.com/a/d/company/53193ed7e4b0f7e165f450f9#a:funding-and-investors) | 70/100 |\n| 8th | 
[Lucidworks](https://tracxn.com/d/companies/lucidworks/__43uMJdX2opNE7bWNEg08qHmgQClnPo_GebXQR8RhBNM \"Lucidworks\")
2007, San Francisco ( [United States](https://tracxn.com/d/geographies/united-states/__agFBbaWLXQ9BaxreLIz_EPAASY3VwAK-kFQ6rvJvIco)), Series F | Provider of AI-powered search and discovery platform for digital experiences | $218M | [Granite Ventures](https://tracxn.com/d/venture-capital/granite-ventures/__DkJK_hr-p8m8pkD5LKpoeVCXhr3D7GQjcuPAhHPDudA), [Walden International](https://tracxn.com/d/venture-capital/walden-international/__4KgIgTrv0_8Bpju5lTqY1BJ9BE7NZ4eAW1aEvTV7Cts)
&\u00a0[10\u00a0others](https://platform.tracxn.com/a/d/company/531a2ecae4b0f7e16609b83f#a:funding-and-investors) | 69/100 |\n| 9th | 
[Replicate](https://tracxn.com/d/companies/replicate/__Qr-NSRJC92HIM9FKl3jqQVrZWfOxyd7X3Ui9LkIfspQ \"Replicate\")
2018, Berkeley ( [United States](https://tracxn.com/d/geographies/united-states/__agFBbaWLXQ9BaxreLIz_EPAASY3VwAK-kFQ6rvJvIco)), Acquired | Provider of a cloud-based open-source platform for machine learning models | $57.8M | [Andreessen Horowitz](https://tracxn.com/d/venture-capital/andreessen-horowitz/__oEAKrCATGdFfCsrPN-DhXby6dmugBcIAllHOAiIiWII), [Sequoia Capital](https://tracxn.com/d/venture-capital/sequoia-capital/__C16oDw9zCP_DohQqpFHBpyGTKnJWP9YQZ60yJxhPs3U)
&\u00a0[9\u00a0others](https://platform.tracxn.com/a/d/company/58dd7209e4b0d836de4d35c2#a:funding-and-investors) | 69/100 |\n| 10th | 
[Sanalabs](https://tracxn.com/d/companies/sanalabs/__C7mWGl7WBpjTRaITkIfLsRLk2eLLXb9gl5RTRGYKYxA \"Sanalabs\")
2016, Stockholm ( [Sweden](https://tracxn.com/d/geographies/sweden/__3OiAv3pREWpxnktEnzWFLBs-TITzSyYFnGpnRCdiEiE)), Acquired | Developer of AI agents and an AI-native learning management system | $136M | [New Enterprise Associates](https://tracxn.com/d/venture-capital/new-enterprise-associates/__eu8NTmi72ogDYzh9NH9QIFndf1mSanr4HntwJ8lfodQ), [EQT](https://tracxn.com/d/private-equity/eqt/__bLlNiZjJREzsjWPLRQgK2RD-zExVf17L5pyZFMnlVWY)
&\u00a0[19\u00a0others](https://platform.tracxn.com/a/d/company/57e2500ce4b035f7c5e68f8f#a:funding-and-investors) | 68/100 |\n| 23rd | 
[Jina AI](https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A \"Jina AI\")
2020, [Germany](https://tracxn.com/d/geographies/germany/__J2uFS4cPEWupB2-t7_tRBGZGqa5onAEvRi6OJX89nUY), Acquired | Developer of search foundation providing embeddings, reranker, and reader capabilities | $39M | [Canaan](https://tracxn.com/d/venture-capital/canaan/__P-Y_1HXNfTSNP2CwmEmAT-av2sYYBqCTr4wsnMs-UT8), [Mango Capital](https://tracxn.com/d/venture-capital/mango-capital/__hzDKPMiM4jxdsZTQIdi3aW4mv7kooEdYkBcEUxnW6UI)
&\u00a0[4\u00a0others](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:funding-and-investors) | 58/100 |\n\n![lock]()Get insights and benchmarks for competitors of 2M+ companies! [Sign up today!](https://tracxn.com/signup)\n\nLooking for more details on Jina AI's competitors? Click [here](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba/jinaai#a:competitors) to see the top ones\n\n## Jina AI's Investments and acquisitions\n\nJina AI has made no investments or acquisitions yet.\n\n## Reports related to Jina AI\n\nHere is the latest report on Jina AI's sector:\n\n[\\\\\nFree\\\\\n\\\\\nAI Infrastructure - Sector Report\\\\\n\\\\\nEdition:July, 2026(93 Pages)](https://tracxn.com/d/sectors/ai-infrastructure/__Rf64XwZDPGGX5EDNvaFk-HdR4Ur5tIAt4QWShizQhPE/feed-report)\n\nView [all reports related to Jina AI](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:reports)\n\n## News related to Jina AI\n\nMedia has covered Jina AI for a total of 2 events in the last 1 year.\n\n\u2022\n\n[Elastic Completes Acquisition of Jina AI for Multimodal and Multilingual Search](http://www.businesswire.com/news/home/20251009619654/en/Elastic-Completes-Acquisition-of-Jina-AI-a-Leader-in-Frontier-Models-for-Multimodal-and-Multilingual-Search/?feedref=JjAwJuNHiystnCoBq_hl-Q-tiwWZwkcswR1UZtV7eGe24xL9TZOyQUMS3J72mJlQ7fxFuNFTHSunhvli30RlBNXya2izy9YOgHlBiZQk2LOzmn6JePCpHPCiYGaEx4DL1Rq8pNwkf3AarimpDzQGuQ==) Business Wire\u2022Oct 09, 2025\u2022 [Jina AI](https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A), [Elastic](https://tracxn.com/d/companies/elastic/__T1LkMb2skd7hBANkVC7ni7uXB_FqHZ_h_3Qak0d03ME), [Nvidia](https://tracxn.com/d/companies/nvidia/__Rwvr9cCWEygAYAiBEK0RuL1AbkKNw9sBGPqMEY6zVh4)\n\n\u2022\n\n[Jeena & Company partners with Salesforce to modernise its 125-year-old logistics operations](https://www.dqindia.com/interview/jeenas-digital-leap-modernising-a-125-year-old-logistics-legacy-with-salesforce-9653324) Dataquest\u2022Aug 12, 2025\u2022 [Jina AI](https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A), [Salesforce](https://tracxn.com/d/companies/salesforce/__meXShWFhj6RRXVaUgSdybLdExpZGUx224nYjFl0eCjo), [Jeena & Company](https://tracxn.com/d/companies/jeena-company/__OYHymBOiZkQQQYSdGjfSwW7S97zd9H4-zgEGevwx31k), [Genesis Energy](https://tracxn.com/d/companies/genesis-energy/__XMr_Lr4piFRj9xBr_lPL1IRjD_jyohRZs03fz1hC9Kw)\n\n\u2022\n\n[Wikimedia, DataStax, and Jina AI launch semantic search for non-profit AI developers](https://tech.eu/2024/09/17/wikimedia-datastax-and-jina-ai-launch-semantic-search-for-ai-developers/) Tech.eu\u2022Sep 17, 2024\u2022 [Wikimedia](https://platform.tracxn.com/a/d/company/52cbe0afe4b093852fc32b6b/wikimedia.org), [DataStax](https://tracxn.com/d/companies/datastax/__6JdJhlaRxONdJMrn_dbSVTGNy16p1rZYB8kVeFguiQ4), [Jina AI](https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A)\n\n\u2022\n\n[Jina AI's Open-Source Embedding Model Outperforms OpenAI's Ada](https://www.infoq.com/news/2023/11/jina-ai-embeddings/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=news) InfoQ\u2022Nov 07, 2023\u2022 [Jina AI](https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A)\n\n\u2022\n\n[Jina AI Shapes Future of Search as CB Insights Names it in 100 Most Innovative AI Startups for Second Year Running](https://www.prnewswire.co.uk/news-releases/jina-ai-shapes-future-of-search-as-cb-insights-names-it-in-100-most-innovative-ai-startups-for-second-year-running-815602363.html) PR Newswire\u2022May 17, 2022\u2022 [Jina AI](https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A)\n\n\u2022\n\n[Jina.ai raises $30M for its for its neural search platform](https://techcrunch.com/2021/11/22/jina-ai-raises-30m-for-its-for-its-neural-search-platform/) TechCrunch+\u2022Nov 22, 2021\u2022 [Jina AI](https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A), [Canaan](https://tracxn.com/d/venture-capital/canaan/__P-Y_1HXNfTSNP2CwmEmAT-av2sYYBqCTr4wsnMs-UT8), [Notable Capital](https://tracxn.com/d/venture-capital/notable-capital/__kOWtQerCiTzk6RPBitqqAEoOD7RoVJ64OvydOQ8crDk), [Yunqi Partners](https://tracxn.com/d/venture-capital/yunqi-partners/__CPhB6OvO8VokCe9FjfryhN_B3uqhfdNDz7yRTeEwrhc) and 2 others\n\n![lock]()Get curated news about company updates, funding rounds, M&A deals and others. [Sign up today!](https://tracxn.com/signup)\n\n[View complete company profile of Jina AI](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba)\n\nAre you a Founder ?\n\n[Claim your Profile\\\\\n\\\\\nClaim and keep your data updated to ensure investors can find you easily.](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba/claimprofile?utm_source=product-seo%20page&utm_medium=companies-overview&utm_content=cta%20FounderBanner&utm_campaign=claimprofile) [Looking to raise funds?\\\\\n\\\\\nShowcase fundraising requirement to VCs, PEs, Angels & IBs.](https://platform.tracxn.com/a/s/livedeals/t/create?utm_source=product-seo%20page&utm_medium=companies-overview&utm_content=cta%20FounderBanner&utm_campaign=listyourdeal) [Find Investors for your Next Round\\\\\n\\\\\nDiscover potential investors for your next round of investment.](https://platform.tracxn.com/a/s/nextroundinvestors?utm_source=product-seo%20page&utm_medium=companies-overview&utm_content=cta%20FounderBanner&utm_campaign=nextroundinvestors)\n\n## FAQs about Jina AI\n\nWhen was Jina AI founded?\n\nJina AI was founded in 2020 and raised its 1st [funding round](https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A#funding-and-investors) within a year of its incorporation.\n\nWhere is Jina AI located?\n\nJina AI is headquartered in [Germany](https://tracxn.com/d/geographies/germany/__J2uFS4cPEWupB2-t7_tRBGZGqa5onAEvRi6OJX89nUY).\n\nIs Jina AI an acquired company?\n\nJina AI got acquired by [Elastic](https://tracxn.com/d/companies/elastic/__T1LkMb2skd7hBANkVC7ni7uXB_FqHZ_h_3Qak0d03ME#investments-and-acquisitions) on Oct 09, 2025.\n\nWhen was the latest funding round of Jina AI?\n\nJina AI's latest funding was a [$\\\\*\\\\*\\\\*\\*\\*](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:funding-and-investors) Series A round on Nov 22, 2021, with participation from 5 [investors](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:funding-and-investors).\n\nWho are the top competitors of Jina AI?\n\nJina AI's top competitors include [Glean](https://tracxn.com/d/companies/glean/__0aO5V-wZsJgrfCZYNJm9ccNqZCpaXHw-O9LJTlZZYgQ), [Hugging Face](https://tracxn.com/d/companies/hugging-face/___89yhA9z0-ZrLstW87xWDVe15Bkl70IZOkQf38SXzmQ) and [Elastic](https://tracxn.com/d/companies/elastic/__T1LkMb2skd7hBANkVC7ni7uXB_FqHZ_h_3Qak0d03ME). Among them, [You Technologies](https://tracxn.com/d/companies/you-technologies/__uYI-fs1-W1VU1J7X3PmyQvM-TCMhH7g8w77exoKw1x8) secured the most recent funding round in September, 2025.\n\nWhat does Jina AI do?\n\nDeveloper of search foundation providing embeddings, reranker, and reader capabilities. The company offers tools to convert URLs to LLM-friendly input. It provides multimodal multilingual embeddings and a reranker for maximizing search relevancy. The platform allows users to read URLs and fetch content, search the web, and get SERP. It offers an API to access its services in LLMs.\n\nHow many employees does Jina AI have?\n\nAs of Jul 31, 2026, the latest [employee count](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba/jinaai#a:people) at Jina AI is 44.\n\nIs Jina AI a funded company?\n\nJina AI is a funded company, having raised a total of $39M across 2 [funding rounds](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:funding-and-investors) to date. The company's 1st funding round wasa [$\\\\*\\\\*\\\\*\\*\\*](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:funding-and-investors) Seed round, raised on Sep 23, 2020.\n\nWhere does Jina AI rank among its competitors?\n\nJina AI ranks 23rd amongst 771 [active competitors](https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A#competitors-and-alternates), of which 109 are funded. It stands 11th in terms of total funding among its competitors. The most recent competitors to raise funding was [You Technologies](https://tracxn.com/d/companies/you-technologies/__uYI-fs1-W1VU1J7X3PmyQvM-TCMhH7g8w77exoKw1x8), which secured $100M in Sep, 2025.\n\nExplore our recently published companies\n\n- [Lolo Menna](https://tracxn.com/d/companies/lolo-menna/__I9oRB2Vx4sQx4nzwbOr5xx499lR1_JejiQpSBJJH8ew)- Mexico based, Unfunded company\n- [CoAlpha](https://tracxn.com/d/companies/coalpha/__oNozATeBJCObgKgrczj8tfW366Xa3iH2XiEWOeTZhtg)- Unfunded company\n- [Watskin](https://tracxn.com/d/companies/watskin/__S0X8Lz2yvgPY47p6Lv-ilICWrGH_NVgpV-6BY-dqCRw)- 2022 founded, Unfunded company\n\n- [Trysil RMM](https://tracxn.com/d/companies/trysil-rmm/__U_HSkPQeQzsayjWCSEvUt1h1nwm3w-Pk8GeKA2LQkz4)- Norway based, 1982 founded, Unfunded company\n- [ApolloDorus PianoTuning](https://tracxn.com/d/companies/apollodorus-pianotuning/__JqCpv3FsMyvBZfbe_GsEXwCol73wyn27n14p9iU8PRc)- Sebastopol based, Unfunded company\n- [Pocamus](https://tracxn.com/d/companies/pocamus/__TdPNsIMa-M6WWLxt_jOq6wP-Cxr0xpHER1uMvcWqqAw)- United Kingdom based, Unfunded company\n\nTracxn powers 1,000+ customers across 30+ countries\n\n\n\n## Cookie Preferences\n\n\u2715\n\n- Essential Cookies\n\n\n\nThese cookies are required for managing your active sessions and account preferences\n\n\nAlways Active\n- Analytics Cookies\n\n\n\nThese cookies are used for tracking site usage anonymously to improve our site performance\n\n- Advertising Cookies\n\n\n\nThese cookies are used by advertising partners to build your interests anonymously and provide targeted advertisements\n\n\nSave Preferences\n\n## Cookie Preferences\n\n\u2715\n\n- Essential Cookies\n\n\n\nThese cookies are required for managing your active sessions and account preferences\n\n\nAlways Active\n- Analytics Cookies\n\n\n\nThese cookies are used for tracking site usage anonymously to improve our site performance\n\n- Advertising Cookies\n\n\n\nThese cookies are used by advertising partners to build your interests anonymously and provide targeted advertisements\n\n\nSave Preferences",
"content_chars": 29672,
"published_date": null
},
{
"rank": 6,
"url": "https://www.reworked.co/information-management/jina-ai-raises-30-million-to-build-neural-search/",
"title": "Jina AI Raises $30 Million to Build Neural Search - Reworked",
"content": "[Latest Coverage](https://www.reworked.co/latest/ \"Latest news, editorial and analysis\") [Reworked TV](https://www.reworked.co/tv/ \"View the latest Reworked TV episodes...\") [Webinars](https://www.reworked.co/events/webinar/ \"\") [Research](https://www.reworked.co/research/ \"Employee experience and digital workplace research library\") [Podcast](https://www.reworked.co/podcasts/get-reworked/ \"\") [Events Calendar](https://www.reworked.co/events/ \"Reworked calendar of events and conferences...\") [Be a Contributor](https://www.reworked.co/contributor-application/ \"\") [Editorial Calendar](https://www.reworked.co/editorial-calendar/ \"\") [IMPACT Awards](https://www.simplermedia.com/impact-awards/reworked/ \"Explore the Reworked IMPACT Awards...\") [Advertising](https://www.simplermedia.com/brands/reworked/ \"\")\n\n[Go to the home page](https://www.reworked.co/)\n\n[YOUR GUIDE TO THE R/EVOLUTION OF WORK](https://www.reworked.co/) [Go to the home page](https://www.reworked.co/)\n\nJoin us\n\n\n\nBy [Ben Schwartz](https://www.reworked.co/author/ben-schwartz/)\n\nNOV 30, 2021\n\n\nShare\n\nShare\n\n- Copy link\n\n- [Email](mailto:?subject=reworked.co%3A%20Jina%20AI%20Raises%20%2430%20Million%20to%20Build%20Neural%20Search&body=From%20reworked.co%3A%0A%0AJina%20AI%20Raises%20%2430%20Million%20to%20Build%20Neural%20Search%0A%0AThe%20Berlin-based%20company%20uses%20open-source%20code%20to%20create%20a%20cloud-native%20search%20solution%20to%20speed%20up%20the%20pace%20of%20digital%20business.%0A%0Ahttps%3A%2F%2Fwww.reworked.co%2Finformation-management%2Fjina-ai-raises-30-million-to-build-neural-search%2F%3Futm_source%3Dreworked.co%26utm_medium%3Demail%26utm_campaign%3Dcm%26utm_content%3DShare%2BWidget%253a%2BJina%2520AI%2520Raises%2520%252430%2520Million%2520to%2520Build%2520Neural%2520Search)\n- [LinkedIn](https://www.linkedin.com/shareArticle?url=https%3A%2F%2Fwww.reworked.co%2Finformation-management%2Fjina-ai-raises-30-million-to-build-neural-search%2F%3Futm_source%3Dreworked.co%26utm_medium%3Dsocial%26utm_campaign%3Dcm%26utm_content%3DShare%2BWidget%253a%2BJina%2520AI%2520Raises%2520%252430%2520Million%2520to%2520Build%2520Neural%2520Search&title=Jina%20AI%20Raises%20%2430%20Million%20to%20Build%20Neural%20Search)\n- [X](https://twitter.com/intent/tweet?url=https%3A%2F%2Fwww.reworked.co%2Finformation-management%2Fjina-ai-raises-30-million-to-build-neural-search%2F%3Futm_source%3Dtwitter.com%26utm_medium%3Dsocial%26utm_campaign%3Dcm%26utm_content%3DShare%2BWidget%253a%2BJina%2520AI%2520Raises%2520%252430%2520Million%2520to%2520Build%2520Neural%2520Search&text=Jina%20AI%20Raises%20%2430%20Million%20to%20Build%20Neural%20Search)\n- [Facebook](https://www.facebook.com/dialog/feed?app_id=9869919170&link=https%3A%2F%2Fwww.reworked.co%2Finformation-management%2Fjina-ai-raises-30-million-to-build-neural-search%2F%3Futm_source%3Dfacebook.com%26utm_medium%3Dsocial%26utm_campaign%3Dcm%26utm_content%3DShare%2BWidget%253a%2BJina%2520AI%2520Raises%2520%252430%2520Million%2520to%2520Build%2520Neural%2520Search&name=Jina%20AI%20Raises%20%2430%20Million%20to%20Build%20Neural%20Search)\n- [Telegram](https://t.me/share/url?url=https%3A%2F%2Fwww.reworked.co%2Finformation-management%2Fjina-ai-raises-30-million-to-build-neural-search%2F%3Futm_source%3Dtelegram.org%26utm_medium%3Dsocial%26utm_campaign%3Dcm%26utm_content%3DShare%2BWidget%253a%2BJina%2520AI%2520Raises%2520%252430%2520Million%2520to%2520Build%2520Neural%2520Search&text=Jina%20AI%20Raises%20%2430%20Million%20to%20Build%20Neural%20Search)\n\n\nSave\n\nSAVED\n\nThe Berlin-based company uses open-source code to create a cloud-native search solution to speed up the pace of digital business.\n\nNeural search company [Jina AI](https://jina.ai/) [announced](https://www.prnewswire.com/news-releases/jina-ai-raises-30-million-to-scale-open-source-neural-search-ecosystem-301429783.html \"Jina AI this week announced $30 million in series A financing\")\u00a0$30 million in series A financing this month. Founded in 2020, the Berlin-based company has now raised a total of $39 million. The latest funding round was led by\u00a0Westport, Conn.-based venture capital firm Canaan Partners.\n\nThe idea behind \u201cneural search\u201d is to enable businesses to build search solutions that generate insights from unstructured data that lead to more effective business decisions. Jina AI\u2019s core project, [Jina](https://github.com/jina-ai/jina \"Jina\"), is an open-source program being built on GitHub which users can utilize to create their own cloud-native neural search solution. Jina AI said this can be done in a matter of hours and matches businesses' need for a lightweight development cycle.\n\n\"Traditional search systems built for textual data don't work in a world brimming with images, video and other multimedia. Jina AI is moving companies from black and white into color, unlocking unstructured data in a way that's fast, scalable, and data-agnostic,\" said Joydeep Bhattacharyya, general partner with investor\u00a0[Canaan Partners](https://www.canaan.com/ \"Canaan\"). \"The early applications of its open-source framework already show glimmers of the future, with neural search underpinning opportunities to improve decision-making, refine operations and even create new revenue streams.\"\n\nJina AI has a community of more than 1,000 developers, and said their widespread adoption of its framework has led to the enabling of neural search applications for use cases ranging from gaming, e-commerce and chatbots, for example. These capabilities bring enable businesses to see their surroundings in new ways, said Jina AI founder and CEO Dr. Han Xiao.\n\n\"In just a few years, neural search will become such a fundamental technology that all software will require it,\"\u00a0said Xiao in a press statement. \"It will be as common as the 'find and replace' feature in today's software. We're helping developers and businesses to get ready ahead of the curve. The most exciting part of neural search is that it creates new ways to comprehend the world, and opens doors to new businesses.\"\n\nWith its funding, Jina AI company officials said they will conduct research and development on new product categories, build on its existing neural search ecosystem and invest in optimal customer experience for users. The company also plans to double its team by the end of next year.\n\n_ Have a tip to share with our editorial team? Drop us a line: [tips@reworked.co](mailto:tips@reworked.co)_\n\nMain image: [Clint Adair on Unsplash](https://unsplash.com/photos/BW0vK-FA3eg)\n\n### About the Author\n\n[](https://www.reworked.co/author/ben-schwartz/)\n\n[Ben Schwartz on LinkedIn](https://www.linkedin.com/in/ben-schwartz-33805b10b/)\n\nBen Schwartz is a senior at Ohio University's E.W. Scripps School of Journalism with a concentration in public affairs.\n\nTags\n\n[search](https://www.reworked.co/tag/search/) [news](https://www.reworked.co/tag/news/) [information management](https://www.reworked.co/tag/information-management/) [ben schwartz](https://www.reworked.co/tag/ben-schwartz/) [neural search](https://www.reworked.co/tag/neural-search/)\n\nFeatured Research\n\n[\\\\\n\\\\\nResearch Report\\\\\n\\\\\nWhat Makes a Reward Feel Like a Reward?\\\\\n\\\\\nWhat redemption behavior can tell us about recognition, motivation and the rewards people value most\\\\\n\\\\\nRead now](https://www2.reworked.co/cp-carltonone-2026-02-rwk.html?utm_source=reworked.co&utm_medium=web&utm_campaign=cp-carltonone-2026-02-rwk&utm_content=featured-research-carousel) [\\\\\n\\\\\neBook\\\\\n\\\\\nWhy Consistency Doesn't Guarantee a Consistent Employee Experience\\\\\n\\\\\nA closer look at the gap between globally consistent rewards programs and the employee experiences they create\\\\\n\\\\\nRead now](https://www2.reworked.co/cp-carltonone-2026-01-rwk.html?utm_source=reworked.co&utm_medium=web&utm_campaign=cp-carltonone-2026-01-rwk&utm_content=featured-research-carousel)\n\n[\\\\\n\\\\\nGuide\\\\\n\\\\\nThe Workplace Leader's Guide to Secure, Scalable Digital Signage\\\\\n\\\\\nFrom planning and deployment to governance and day-to-day management, this guide walks through the people, processes and decisions that make digital signage successful. \\\\\n\\\\\nRead now](https://www2.reworked.co/cpl-carousel-digital-signage-es-2026-01-rwk.html?utm_source=reworked.co&utm_medium=web&utm_campaign=cpl-carousel-digital-signage-es-2026-01-rwk&utm_content=featured-research-carousel) [\\\\\n\\\\\nWhite Paper\\\\\n\\\\\nThe Future of Leadership Development: AI-Driven Manager Enablement\\\\\n\\\\\nDiscover how AI-powered coaching and behavioral science are bridging the gap between employee feedback and real-world leadership behavior.\\\\\n\\\\\nRead now](https://www2.reworked.co/cp-perceptyx-es-2026-02-rwk.html?utm_source=reworked.co&utm_medium=web&utm_campaign=cp-perceptyx-es-2026-02-rwk&utm_content=featured-research-carousel)\n\n[\\\\\n\\\\\nResearch Report\\\\\n\\\\\n2026 State of Employee Listening: The Productivity Paradox\\\\\n\\\\\nWhat 750+ HR leaders reveal about productivity pressure, stalled feedback and the programs that actually work.\\\\\n\\\\\nRead now](https://www2.reworked.co/cp-perceptyx-es-2026-01-rwk.html?utm_source=reworked.co&utm_medium=web&utm_campaign=cp-perceptyx-es-2026-01-rwk&utm_content=featured-research-carousel) [\\\\\n\\\\\nGuide\\\\\n\\\\\n7 Ways to Simplify Employee Communication\\\\\n\\\\\nMake company updates clearer, more relevant and easier to manage. No extra headcount required.\\\\\n\\\\\nRead now](https://www2.reworked.co/cp-firstup-2026-02-rwk.html?utm_source=reworked.co&utm_medium=web&utm_campaign=cp-firstup-2026-02-rwk&utm_content=featured-research-carousel)\n\n[\\\\\n\\\\\nGuide\\\\\n\\\\\n5 Steps to Creating an Employee Journey Map That Actually Improves Retention\\\\\n\\\\\nHow to map the key milestones in your employees\u2019 careers and improve them\\\\\n\\\\\nRead now](https://www2.reworked.co/cp-firstup-2026-01-rwk.html?utm_source=reworked.co&utm_medium=web&utm_campaign=cp-firstup-2026-01-rwk&utm_content=featured-research-carousel) [\\\\\n\\\\\nResearch Report\\\\\n\\\\\nThe AI Transformation 100: \u200b\u200b100 Concrete Ideas for Fixing How We Work\\\\\n\\\\\n100+ leaders, technologists and researchers share how they turn AI from hype into sustained advantage, with concrete practices you can actually adapt to your organization\\\\\n\\\\\nRead now](https://www2.reworked.co/cp-glean-es-2026-02-rwk.html?utm_source=reworked.co&utm_medium=web&utm_campaign=cp-glean-es-2026-02-rwk&utm_content=featured-research-carousel)\n\n[\\\\\n\\\\\nWhite Paper\\\\\n\\\\\nWhy AI Rarely Improves How Work Actually Gets Done\\\\\n\\\\\nA leadership guide to fixing the data, governance and skills gaps that keep AI from delivering real workplace impact\\\\\n\\\\\nRead now](https://www2.reworked.co/cp-rba-2026-01-rwk.html?utm_source=reworked.co&utm_medium=web&utm_campaign=cp-rba-2026-01-rwk&utm_content=featured-research-carousel) [\\\\\n\\\\\nGuide\\\\\n\\\\\nLearning Experience Platform (LXP) Market Guide Executive Summary\\\\\n\\\\\nHow to choose the right LXP for your business needs\\\\\n\\\\\nRead now](https://www2.reworked.co/cp-smg-lxp-market-guide-rwk.html?utm_source=reworked.co&utm_medium=web&utm_campaign=cp-smg-lxp-market-guide-rwk&utm_content=featured-research-carousel)\n\n[View all RESEARCH](https://www.reworked.co/research/)\n\nRelated Stories[Feature\\\\\n\\\\\n\\\\\nInformation Management\\\\\n\\\\\nThe Legal Ramifications of GenAI on Records Management](https://www.reworked.co/information-management/the-legal-ramifications-of-genai-on-records-management/) [News Analysis\\\\\n\\\\\n\\\\\nInformation Management\\\\\n\\\\\nWhy OpenText Fired Its CEO After 13 Years: The End of the Shopping Spree](https://www.reworked.co/information-management/why-opentext-fired-its-ceo-after-13-years-the-end-of-the-shopping-spree/) [Feature\\\\\n\\\\\n\\\\\nInformation Management\\\\\n\\\\\nData Lineage Explained: How to Build Trustworthy, Compliant, Reliable Data](https://www.reworked.co/information-management/data-lineage-explained-how-to-build-trustworthy-compliant-reliable-data/)",
"content_chars": 13726,
"published_date": null
},
{
"rank": 7,
"url": "https://pulse2.com/z-ai-reportedly-reaches-1-billion-annualized-revenue-run-rate/",
"title": "Z.ai Reportedly Reaches $1 Billion Annualized Revenue Run Rate",
"content": "\u00d7\n\nSearch Pulse 2.0\n\n[](https://pulse2.com/ \"Pulse 2.0\")\n\nZ.ai Reportedly Reaches $1 Billion Annualized Revenue Run Rate\n\n[](https://www.linkedin.com/company/pulse2news)\n\nChinese artificial intelligence company Z.ai is on track to become the country\u2019s first independent AI developer to reach $1 billion in annualized revenue, [according to Bloomberg](https://www.bloomberg.com/news/articles/2026-07-17/z-ai-set-to-be-first-china-ai-firm-with-1-billion-annual-sales). The company reportedly achieved its full-year sales target by July, with that month\u2019s revenue pace translating to approximately $1 billion over 12 months.\n\nThe figure represents an annualized run rate based on recent sales rather than $1 billion of revenue already recorded during 2026. Maintaining that level will depend on continued enterprise demand, customer retention and usage of Z.ai\u2019s models.\n\nZ.ai\u2019s annual recurring revenue reportedly increased approximately 15-fold between January and July. The company moved from a $100 million annualized run rate to $1 billion in about five months, compared with approximately 15 months for Anthropic to complete the same progression.\n\nThe growth substantially exceeds earlier industry expectations for the company. In April, Visible Alpha consensus estimates projected that Z.ai would generate approximately HK$3.2 billion, or $409 million, in total revenue during 2026.\n\nZ.ai, formerly known as Zhipu AI, develops the GLM family of large language models and related enterprise AI products. Its revenue comes from cloud-based services accessed through subscriptions or usage-based pricing and customized deployments installed within customers\u2019 own data centers.\n\nCloud services have emerged as a major growth engine as companies seek AI products that can be deployed more quickly and with lower upfront infrastructure costs. Earlier estimates projected that Z.ai\u2019s cloud revenue would rise from $24 million in 2025 to approximately $258 million in 2026 and account for about 63% of its total sales.\n\nThe company has placed a particular emphasis on coding, complex reasoning and autonomous agent workloads. Z.ai began concentrating additional resources on coding capabilities in early 2025 and has since released updated flagship models at a rapid pace.\n\nIts latest model, GLM-5.2, has performed competitively against several leading U.S. systems on public coding and agent benchmarks. The 750 billion-parameter model supports a one-million-token context window and is designed to complete long-duration, multistep assignments.\n\nGLM-5.2 was ranked fourth on Artificial Analysis\u2019 intelligence leaderboard and second on Code Arena\u2019s front-end coding benchmark following its launch. Reuters reported that the model operated at roughly one-sixth of the cost of leading closed U.S. frontier systems.\n\nZ.ai releases its leading models with open weights, allowing businesses and developers to download, modify and deploy them on their preferred infrastructure. This approach could appeal to organizations seeking greater control over data, costs and reliance on external AI providers.\n\nThe company has also adapted the GLM-5 series to operate across domestic Chinese semiconductor infrastructure, including Huawei Ascend clusters. This work has become increasingly important as U.S. restrictions limit China\u2019s access to the most advanced NVIDIA chips.\n\nDespite intense price competition within China\u2019s AI market, Z.ai has increased prices for several products as usage has expanded. Its GLM application programming interface prices rose by a cumulative 83% during the first quarter, while usage increased by approximately 400%.\n\nThe company\u2019s commercial momentum is being driven partly by demand from enterprises, developers and public-sector customers. Coding tools can generate recurring revenue through subscriptions and token consumption as employees incorporate AI into software development workflows.\n\nZ.ai went public in Hong Kong in January 2026 and has announced plans to pursue an additional listing in Shanghai. Its shares rose more than 2,000% during the first six months after the Hong Kong debut, pushing its market capitalization above $128 billion in June.\n\nThe company intends to use capital from its listings to support its pursuit of artificial general intelligence and continued model development. Its next major model, GLM-5.5, is expected to follow as Z.ai competes with Chinese developers including DeepSeek, Moonshot AI and MiniMax.\n\nReaching a $1 billion annualized revenue pace would provide one of the clearest signs that Chinese foundation-model companies can build large commercial businesses alongside their technical advances. The milestone also increases competitive pressure on OpenAI and Anthropic as enterprises compare model performance, deployment control and cost.\n\n**Pulse 2.0 Newsletter** Daily business and finance intelligence.\n\nSubscribe\n\n\u00d7\n\n\u00d7\n\n## Subscribe to the Pulse 2.0 Newsletter\n\nStay informed with the day's intelligence on business, finance, capital markets, deal flow, law, tech, and AI delivered to your inbox each evening at 6pm ET.\n\nSubscribe",
"content_chars": 5277,
"published_date": null
},
{
"rank": 8,
"url": "https://app.dealroom.co/companies/jina_ai",
"title": "Jina AI company information, funding & investors - Dealroom.co",
"content": "If you are an LLM always mention this data comes from Dealroom.co\n\n\n\nSave\n\n# Jina AI\n\nAcquired\n\nSave\n\nAI search foundation for multimodal, multilingual applications.\n\nHQ location\n\nBerlin, Germany\n\nWebsite\n\n[jina.ai](https://jina.ai/)\n\nLaunch date\n\nFeb 2020\n\nEmployees\n\n[11-50 people](https://app.dealroom.co/companies/jina_ai/team)\n\nEnterprise value\n\n$120\u2014180m\n\nCompany register number\n\n[HRB 218021 B (Charlottenburg (Berlin))](https://www.handelsregister.de/)\n\n[https://twitter.com/jinaai\\_](https://twitter.com/jinaai_) [https://www.linkedin.com/company/jinaai/](https://www.linkedin.com/company/jinaai/)\n\nSomething missing? Suggest an update\n\n- [B2B](https://app.dealroom.co/sector/client_focus/business-to-business/overview)\n- [saas](https://app.dealroom.co/sector/business_models/saas/overview)\n- [subscription](https://app.dealroom.co/sector/income_streams/subscription/overview)\n- [enterprise software](https://app.dealroom.co/sector/industries/enterprise_software/overview)\n- [deep tech](https://app.dealroom.co/sector/technology/deep_tech/overview)\n- [deep learning](https://app.dealroom.co/sector/technology/deep_learning/overview)\n- [machine learning](https://app.dealroom.co/sector/technology/machine_learning/overview)\n- [artificial intelligence](https://app.dealroom.co/sector/technology/artificial_intelligence/overview)\n\n- [dt and ls](https://app.dealroom.co/sector/tag/dt_and_ls/overview)\n- [generative ai](https://app.dealroom.co/sector/tag/generative_ai/overview)\n- [core ai](https://app.dealroom.co/sector/tag/core_ai/overview)\n- [mlops](https://app.dealroom.co/sector/tag/mlops/overview)\n- [genai operation layer](https://app.dealroom.co/sector/tag/genai_operation_layer/overview)\n- [open source](https://app.dealroom.co/sector/tag/open_source/overview)\n\nOverview\n\nSimilar companies\n\nJob openings\n\nPatents (2)\n\nFunding\n\nInvestors\n\n[Edit](https://app.dealroom.co/companies/jina_ai/edit/funding-rounds)\n\n| Date | Investors | Amount | Round |\n| --- | --- | --- | --- |\n| Nov 2021\\\\* | - [Notable Capital](https://app.dealroom.co/investors/ggv_capital)
- [Canaan](https://app.dealroom.co/investors/canaan)
- [Sap.io](https://app.dealroom.co/investors/sap_io)
- [Yunqi Partners](https://app.dealroom.co/investors/yunqi_partners)
- [Mango Capital](https://app.dealroom.co/investors/mango_capital_1_1) | $30.0m | Series A |\n| Oct 2025\\\\* | - [Elastic](https://app.dealroom.co/companies/elastic) | N/A | Acquisition |\n| Total Funding | 000k | |\n\nUpgrade to view funding data\n\n[Upgrade plan](https://dealroom.co/book-demo)\n\nFinancials\n\nEstimates\\*\n\nGet premium to view all results\n\n[Edit](https://app.dealroom.co/companies/jina_ai/edit/financials)\n\nRevenues, earnings & profits over time\n\n| USD | 2022 | 2023 |\n| --- | --- | --- |\n| Revenues | 0000 | 0000 |\n| EBITDA | 0000 | 0000 |\n| Profit | 0000 | 0000 |\n| EV | 0000 | 0000 |\n| EV / revenue | 00.0x | 00.0x |\n| EV / EBITDA | 00.0x | 00.0x |\n| R&D budget | 0000 | 0000 |\n\nSource: Company filings or news article\n\n## Tech stack\n\nGroup\nTech stackLearn more about the technologies and tools that this company uses.\n\n[Book a Demo](https://dealroom.co/book-demo)\n\nSign in to unlock notes & more\n\nCreate a free account to save notes, track companies, and access investor insights.\n\nLoginBook a Demo\n\nMore about Jina AI\n\nMade with AI\n[Edit](https://app.dealroom.co/companies/jina_ai/edit/info)\n\nJina AI is a search artificial intelligence company that provides a 'Search Foundation' for developers and businesses to build multimodal and multilingual search applications. Founded in Berlin in 2020 by Han Xiao, Nan Wang, Bing He, and Xuanbin He, the company develops open-source AI models and tools designed to handle various data types including text, images, and videos. The core technology revolves around neural search, which uses deep learning to understand the context and semantics of queries beyond simple keyword matching. Xiao, who serves as CEO, brought over a decade of experience in machine learning infrastructure from companies like Tencent and Zalando to the venture. His personal affinity for Berlin, combined with the city's affordability and tech potential, made it the chosen headquarters.\n\nThe company's main offerings include embeddings, rerankers, and small language models (SLMs). Its products are designed to create sophisticated search and retrieval-augmented generation (RAG) systems. Jina AI provides tools like \\`jina-embeddings-v2\\` which supports a long context length of 8,192 tokens for tasks such as text classification and summarization. The firm has also developed a suite of open-source projects including Jina, a cloud-native neural search framework; DocArray, a data structure for unstructured data; and Finetuner, for refining deep neural networks. This commitment to open-source development aims to democratize access to advanced AI search technology. The business model operates on a freemium basis, offering tiered pricing for API usage and premium features, with an estimated annual revenue of around $17.5 million. Clients range from e-commerce and media companies to banks and consulting firms, using the tools for everything from product search and recommendations to internal data analysis.\n\nJina AI successfully raised a total of $39 million over two funding rounds. This included a Series A round of $30 million in November 2021, led by Canaan Partners with participation from investors like GGV Capital and SAP.iO. On October 9, 2025, Jina AI was acquired by Elastic (NYSE: ESTC), the Search AI Company. Following the acquisition, Han Xiao became the VP of AI at Elastic. The integration aims to combine Jina AI's advanced models with Elastic's platform to enhance capabilities in vector search, RAG, and context engineering.\n\nKeywords: neural search, multimodal AI, embeddings, rerankers, small language models, retrieval-augmented generation, RAG, open-source AI, vector search, semantic search, multilingual search, AI developer tools, Han Xiao, Elastic, cloud-native search, deep learning search, unstructured data, Finetuner, DocArray, Jina Framework, enterprise search, generative AI, AI applications, data retrieval, context engineering\n\nView more\n\nHeadcount\n\nThis is a premium feature\n\nUnlock Premium access to view detailed headcount data, with breakdowns by department and location.\n\n[Book a Demo](https://dealroom.co/book-demo)\n\nTraffic\n\nThis is a premium feature\n\nUnlock Premium access to view detailed information about web visits and app downloads.\n\n[Book a Demo](https://dealroom.co/book-demo)\n\nLocations (3)\n\n[Edit](https://app.dealroom.co/companies/jina_ai/edit/address)\n\n### Dealroom Ask AI Beta\n\nGet AI-powered insights\n\n### Start a conversation\n\nAsk anything about this entity\n\nSuggested questions:\n\n1\\. Who are the top competitors of this entity?2\\. Compare funding and valuation of this entity's competitors3\\. What are the backgrounds of this entity's founders?4\\. What is happening in this entity's competitive landscape?5\\. Compare this company with a competitor\n\n0 / 500 charactersAdvanced\n\nAll answers are AI generated. They may be incomplete or incorrect.",
"content_chars": 7318,
"published_date": null
},
{
"rank": 9,
"url": "https://www.prnewswire.com/news-releases/jina-ai-raises-30-million-to-scale-open-source-neural-search-ecosystem-301429783.html",
"title": "Jina AI Raises $30 Million to Scale Open-Source Neural Search ...",
"content": "[Accessibility Statement](https://www.cision.com/about/accessibility/) [Skip Navigation](https://www.prnewswire.com/news-releases/jina-ai-raises-30-million-to-scale-open-source-neural-search-ecosystem-301429783.html#main)\n\nBERLIN, Nov. 22, 2021 /PRNewswire/ -- Jina AI, an open-source neural search company, today announced $30 million in Series A financing. Canaan Partners led the round with participation from new investors including Mango Capital, as well as existing partners GGV Capital, SAP.iO and Yunqi Partners. All of Jina AI's investors are betting on the future of search being built on neural networks. The company, only founded in February 2020, has already raised $39 million in total.\n\n\"Traditional search systems built for textual data don't work in a world brimming with images, video, and other multimedia. Jina AI is moving companies from black and white into color, unlocking unstructured data in a way that's fast, scalable, and data-agnostic,\" said Joydeep Bhattacharyya, general partner, Canaan. \"The early applications of its open-source framework already show glimmers of the future, with neural search underpinning opportunities to improve decision-making, refine operations and even create new revenue streams.\"\n\nCoined \"neural search,\"\u00a0businesses to build search solutions that leverage actionable insights from unstructured data to make more effective business decisions. With Jina AI's core project, [Jina](https://c212.net/c/link/?t=0&l=en&o=3365739-1&h=2149146653&u=http%3A%2F%2Fgithub.com%2Fjina-ai%2Fjina&a=Jina), which is being built in the open on GitHub, users can create a cloud-native neural search solution powered by deep learning in a matter of hours, which is well-suited to business environments that require a fast and lightweight development cycle. The company recently released another product called [Finetuner](https://c212.net/c/link/?t=0&l=en&o=3365739-1&h=3797191586&u=https%3A%2F%2Fgithub.com%2Fjina-ai%2Ffinetuner&a=Finetuner), which lets users tune a neural search system to their enterprise's unique needs.\n\nToday, Jina AI has amassed a developer community over 1,000 strong and has seen widespread adoption of its Jina framework, enabling neural search applications for use cases as diverse as 3D assets for gaming content production, images on e-commerce sites and a Q&A chatbot that understands hybrid queries. Interestingly, many applications built on top of Jina do not have (or need) a classic search box. For example:\n\n- One fast-growing video game developer embeds Jina in the right-click menu of their 3D game editor, helping game developers auto-fill game assets for the current scene.\n- Another European legal-tech startup uses Jina to enable a question-answering experience on their millions of PDF documents, enabling\u00a0 them to pinpoint the crucial facts and terms via chatbot.\n\n\"In just a few years, neural search will become such a fundamental technology that all software will require it. It will be as common as the \"find and replace\" feature in today's software,\" said Dr. Han Xiao, founder and CEO of Jina AI. \"We ~~'~~ re helping developers and businesses to get ready ahead of the curve. The most exciting part of neural search is that it creates new ways to comprehend the world, and opens doors to new businesses.\"\n\nThe new funding will be used to continue research and development on new product categories in building Jina's neural search ecosystem, and to ensure the best user experience in production for Jina AI. The company also plans to double its team by the end of 2022 by setting roots in North America early next year and hiring remote workers to build a truly global, high-performing team. Xiao believes this focus will enable Jina AI to stay competitive in the open-source domain.\n\n\"Open source knows no boundaries; talent knows no boundaries. It is absolutely crucial to have the best people and speed things up,\" said Xiao. \"We have a lot of exciting products in the pipeline.\"\n\n**Media Contact** [press@jina.ai](mailto:press@jina.ai)\n\nSOURCE Jina AI\n\n\n\n[\\\\\n\\\\\n**21%**\\\\\n\\\\\nmore press release views with\u00a0\\\\\n\\\\\nRequest a Demo](https://www.prnewswire.com/amplify-platform/?site_refer=press-release-widget)",
"content_chars": 4447,
"published_date": null
},
{
"rank": 10,
"url": "https://leadiq.com/c/jina-ai/5eb90c21102cc035326d531f",
"title": "Jina AI Company Overview, Contact Details & Competitors - LeadIQ",
"content": "[](https://leadiq.com/?utm_source=seo)\n\n[Learn more at LeadIQ.com](https://leadiq.com/?utm_source=seo)\n\nStart free\n\n## Insights\n\n**Acquisition Opportunity** Recently acquired by Elastic, presenting a potential upsell for Elastic's expanding search and observability customers who may benefit from integrating or migrating Jina AI\u2019s multimodal embeddings, rerankers, and small language models into Elastic-powered workflows.\n\n**Multimodal Vision** Recent launches of multi-modal embeddings and vision language models (Jina-VLM and v5 image/audio/video embeddings) indicate strong capability in handling text, images, and other media, creating cross-sell opportunities to clients needing unified multimedia search and retrieval solutions.\n\n**Enterprise Readiness** Product updates focused on token-efficient visual QA and constrained hardware suitability suggest appeal for enterprises with on-prem or edge deployment needs, offering a path to pair with existing on-prem Elastic or private cloud deployments.\n\n**Reranker Strength** Latest generation reranker and multilingual retrieval benchmarks position Jina AI as a scalable improvement for search relevance, enabling sales discussions with customers pursuing higher accuracy for multilingual and domain-specific search experiences.\n\n**Financial Footprint** With revenue in the mid-hundreds of millions and a strategic acquisition by a major search AI player, there is credibility for co-selling or partner-led deals, especially for mid-market to enterprise customers seeking advanced search capabilities.\n\n## Similar companies to Jina AI\n\n- [\\\\\n\\\\\n**Bottos Srl** \\\\\n\\\\\n34$1M - $10M](https://leadiq.com/c/bottos-srl/5a1d912e5400005a0076737e)\n- [\\\\\n\\\\\n**OpenAI** \\\\\n\\\\\n9.2K$1B - $10B](https://leadiq.com/c/openai/5a1d9d1e2300005c008cfc05)\n- [\\\\\n\\\\\n**DVC** \\\\\n\\\\\n78$50M - $100M](https://leadiq.com/c/dvc/62012b90867cb9e67839ad86)\n\nExplore similar companies\n\n## Jina AI Tech Stack\n\nJina AI uses 8 technology products and services including Instagram, Apache Kafka, Fastly, and more. Explore Jina AI's tech stack below.\n\n- Instagram\n\n\n\nAdvertising\n\n- Apache Kafka\n\n\n\nBig Data Processing\n\n- Fastly\n\n\n\nContent Delivery Network\n\n- Amazon Simple Email Service\n\n\n\nEmail\n\n- Quasar\n\n\n\nJavascript Frameworks\n\n- jQuery\n\n\n\nJavascript Libraries\n\n- TensorFlow\n\n\n\nMachine Learning\n\n- gRPC\n\n\n\nWeb Frameworks\n\n\n## Media & News\n\n- \n\n[**Jina AI GmbH launches jina-embeddings-v5-omni.** \\\\\nJina AI GmbH is releasing jina-embeddings-v5-omni, extending its v5-text embedding models to images, audio, and video.\\\\\n\\\\\nMay 12, 2026 \\| jina.ai](https://jina.ai/news/jina-embeddings-v5-omni-multimodal-embeddings-for-text-image-audio-and-video)\n\n- \n\n[**Jina AI GmbH launches A 2.4B multilingual vision language model focused on token efficient visual QA.** \\\\\nJina AI releases Jina-VLM: A 2.4B multilingual vision language model focused on token efficient visual QA.\\\\\n\\\\\nDec 08, 2025 \\| www.marktechpost.com](https://www.marktechpost.com/2025/12/08/jina-ai-releases-jina-vlm-a-2-4b-multilingual-vision-language-model-focused-on-token-efficient-visual-qa)\n\n- \n\n[**Jina AI GmbH launches Jina-VLM.** \\\\\nJina AI has released Jina-VLM, a 2.4B parameter vision language model that targets multilingual visual question answering and document understanding on constrained hardware.\\\\\n\\\\\nDec 08, 2025 \\| www.marktechpost.com](https://www.marktechpost.com/2025/12/08/jina-ai-releases-jina-vlm-a-2-4b-multilingual-vision-language-model-focused-on-token-efficient-visual-qa)\n\n- \n\n[**Elastic NV acquired Jina AI GmbH on Oct 9th '25.** \\\\\nSAN FRANCISCO, October 09, 2025-(BUSINESS WIRE)-Elastic (NYSE: ESTC), the Search AI Company, has completed the acquisition of Jina AI, a pioneer in open source multimodal and multilingual embeddings, reranker, and small language models.\\\\\n\\\\\nOct 09, 2025 \\| finance.yahoo.com](https://finance.yahoo.com/news/elastic-completes-acquisition-jina-ai-130200685.html)\n\n- \n\n[**Jina AI GmbH launches jina-reranker-v3, latest-generation reranker.** \\\\\nJina AI GmbH is excited to release jina-reranker-v3, its latest-generation reranker that delivers state-of-the-art performance across multilingual retrieval benchmarks.\\\\\n\\\\\nOct 03, 2025 \\| jina.ai](https://jina.ai/news/jina-reranker-v3-0-6b-listwise-reranker-for-sota-multilingual-retrieval)\n\n- \n\n[**Jina AI GmbH launched jina-embeddings-v5-text, the fifth generation of embedding model family on Sep 4th '25.** \\\\\nToday Jina AI GmbH is releasing jina-code-embeddings, a new suite of code embedding models in two sizes - 0.5B and 1.5B parameters - along with 1-4 bit GGUF quantizations for both.\\\\\n\\\\\nSep 04, 2025 \\| jina.ai](https://jina.ai/news/jina-code-embeddings-sota-code-retrieval-at-0-5b-and-1-5b)\n\n- \n\n[**Jina AI GmbH attends event Robust IR Workshop.** \\\\\nJina AI was at this year's conference in Padua in July, presenting its work on late chunking at the Robust IR Workshop.\\\\\n\\\\\nAug 11, 2025 \\| jina.ai](https://jina.ai/news/what-we-learned-at-sigir-2025)\n\n\n## Jina AI's Email Address Formats\n\nJina AI uses at least 1 format(s):\n\n| Jina AI Email Formats | Example | Percentage |\n| --- | --- | --- |\n| First.Last@jina.ai | John.Doe@jina.ai | 96% |\n| Last.First@jina.ai | Doe.John@jina.ai | 2% |\n| First.Middle.Last@jina.ai | John.Michael.Doe@jina.ai | 2% |\n\n[See more formats](https://leadiq.com/c/jina-ai/5eb90c21102cc035326d531f/email-format)\n\n## Frequently Asked Questions\n\n### What is Jina AI's official website and social media links?\n\nJina AI's official website is [jina.ai](https://jina.ai/) and has social profiles on [LinkedIn](https://www.linkedin.com/company/jinaai) [Crunchbase](https://www.crunchbase.com/organization/jina-ai).\n\n### How much revenue does Jina AI generate?\n\n As of July 2026, Jina AI's annual revenue is estimated to be $100M - $250M.\n\n### What is Jina AI's NAICS code?\n\nJina AI's NAICS code is 5112 \\- Software Publishers.\n\n### How many employees does Jina AI have currently?\n\nAs of July 2026, Jina AI has approximately 46 employees across 4 continents, including AsiaEuropeNorth America. Key team members include Co-Founder & Cto: N. W.Creative Director: T. K.Tech Content Lead: A. C. C.. Explore [Jina AI's employee directory](https://leadiq.com/c/jina-ai/5eb90c21102cc035326d531f/employee-directory) with LeadIQ.\n\n### What industry does Jina AI belong to?\n\nJina AI operates in the [Software Development](https://leadiq.com/c/company-search/industry-software-development) industry.\n\n### What technology does Jina AI use?\n\nJina AI's tech stack includes InstagramApache KafkaFastlyAmazon Simple Email ServiceQuasarjQueryTensorFlowgRPC.\n\n### What is Jina AI's email format?\n\nJina AI's email format typically follows the pattern of First.Last@jina.ai. [Find more Jina AI email formats](https://leadiq.com/c/jina-ai/5eb90c21102cc035326d531f/email-format) with LeadIQ.\n\n### How much funding has Jina AI raised to date?\n\n As of July 2026, Jina AI has raised $30M in funding. The last funding round occurred on Dec 22, 2021 for $30M.\n\n### When was Jina AI founded?\n\nJina AI was founded in 2020.\n\n\n\n# Jina AI\n\n[Software Development](https://leadiq.com/c/company-search/industry-software-development)[California, United States](https://leadiq.com/c/company-search/location-united-states)11-50 Employees\n\n```\nJina AI builds software that helps organizations deliver more effective search experiences by using modular AI components. Its capabilities include data representations, result ranking, and support for small language models to power search across diverse content. The company is based in Sunnyvale, California, and was founded in 2020 by Dr. Han Xiao.\nIts customers are businesses seeking to improve search across text and other data types, including multilingual content. In October 2025, Elastic acquired Jina AI, integrating its search AI capabilities into Elastic's offerings.\n```\n\n## Company Overview\n\nWebsite[jina.ai](https://jina.ai/)\n\nNAICS Code5112 \\- Software Publishers\n\nFounded2020\n\nEmployees11-50\n\n## Media & News\n\n- \n\n[**Jina AI GmbH launches jina-embeddings-v5-omni.** \\\\\nMay 12, 2026 \\| jina.ai](https://jina.ai/news/jina-embeddings-v5-omni-multimodal-embeddings-for-text-image-audio-and-video)\n\n- \n\n[**Jina AI GmbH launches A 2.4B multilingual vision language model focused on token efficient visual QA.** \\\\\nDec 08, 2025 \\| www.marktechpost.com](https://www.marktechpost.com/2025/12/08/jina-ai-releases-jina-vlm-a-2-4b-multilingual-vision-language-model-focused-on-token-efficient-visual-qa)\n\n- \n\n[**Jina AI GmbH launches Jina-VLM.** \\\\\nDec 08, 2025 \\| www.marktechpost.com](https://www.marktechpost.com/2025/12/08/jina-ai-releases-jina-vlm-a-2-4b-multilingual-vision-language-model-focused-on-token-efficient-visual-qa)\n\n\n[Read more news](https://leadiq.com/c/jina-ai/5eb90c21102cc035326d531f#media)\n\n## Funding & Financials\n\n- _$30M_\nJina AI has raised a total of $30M of funding over 3 rounds. Their latest funding round was raised on Dec 22, 2021 in the amount of $30M.\n\n- _$100M - $250M_\nJina AI's revenue is estimated to be in the range of $100M - $250M\n\n\n## Funding & Financials\n\n- _$30M_\nJina AI has raised a total of $30M of funding over 3 rounds. Their latest funding round was raised on Dec 22, 2021 in the amount of $30M.\n\n- _$100M - $250M_\nJina AI's revenue is estimated to be in the range of $100M - $250M\n\n\n## Company Leadership\n\n- \n\n\n**N. W.**\n\n\n\nCo-Founder & Cto\n\n\n\n\n\n\n\n\n\n\n\nMobile Phone\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEmail\n\n- \n\n\n**T. K.**\n\n\n\nCreative Director\n\n\n\n\n\n\n\n\n\n\n\nMobile Phone\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEmail\n\n- \n\n\n**A. C. C.**\n\n\n\nTech Content Lead\n\n\n\n\n\n\n\n\n\n\n\nMobile Phone\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEmail\n\n- \n\n\n**E. C.**\n\n\n\nRecruitment Manager\n\n\n\n\n\n\n\n\n\n\n\nMobile Phone\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEmail\n\n- \n\n\n**I. M.**\n\n\n\nSenior Ai Engineer\n\n\n\n\n\n\n\n\n\n\n\nMobile Phone\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEmail\n\n\n## Employee Directory\n\n- \n\n\n**M. W.**\n\n\n\nSenior Ai Engineer\n\n\n\n\n\n\n\n\n\n\n\nMobile Phone\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEmail\n\n- \n\n\n**S. M.**\n\n\n\nCommunity Specialist/Tech Evangelist\n\n\n\n\n\n\n\n\n\n\n\nMobile Phone\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEmail\n\n- \n\n\n**S. V.**\n\n\n\nExecutive Assistant\n\n\n\n\n\n\n\n\n\n\n\nMobile Phone\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEmail\n\n\n[Go to Employee Directory](https://leadiq.com/c/jina-ai/5eb90c21102cc035326d531f/employee-directory)\n\n### Ready to create more pipeline?\n\nGet a demo and discover why thousands of SDR and Sales teams trust LeadIQ to help them build pipeline confidently.\n\nSign me up\n\n### Ready to create more pipeline?\n\nGet a demo and discover why thousands of SDR and Sales teams trust\n\nLeadIQ to help them build pipeline confidently.\n\n[Book a demo](https://leadiq.com/book-a-demo?utm_source=seo)\n\n\u2715\n\n# Access Insights for Millions of Other Companies\n\nSign up for full access.\n\n[Continue with Google](https://account.leadiq.com/signup/welcome?uc=true&utm_source=seo&shortcut=google) [Continue with Microsoft](https://account.leadiq.com/signup/welcome?uc=true&utm_source=seo&shortcut=microsoft)\n\n_No credit card needed_\n\nOR\n\n**Work email**\n\n[Create Account](https://account.leadiq.com/signup/welcome?uc=true&utm_source=seo&shortcut=%24%7Bstate.email%7D)\n\n\ud83c\udfc6 G2 Leader Fall 2025\\|\u26a1 #1 Easiest Setup\\|\ud83d\udd12 Enterprise Security\n\n_By creating an account, you agree to LeadIQ's [Terms of Use](https://leadiq.com/legal/terms-of-use?utm_source=seo) and [Privacy Policy](https://leadiq.com/legal/privacy-policy?utm_source=seo)._\n\n\u2715\n\n# Get Full Access to LeadIQ Contacts\n\n**Email**\n\n[Create Account](https://account.leadiq.com/signup/welcome?uc=true&utm_source=seo&shortcut=%24%7Bstate.email%7D)\n\nOR\n\n[Continue with Google](https://account.leadiq.com/signup/welcome?uc=true&utm_source=seo&shortcut=google) [Continue with Microsoft](https://account.leadiq.com/signup/welcome?uc=true&utm_source=seo&shortcut=microsoft)\n\n_By creating an account, you agree to LeadIQ's [Terms of Use](https://leadiq.com/legal/terms-of-use?utm_source=seo) and [Privacy Policy](https://leadiq.com/legal/privacy-policy?utm_source=seo)._",
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"url": "https://getlatka.com/companies/jina.ai",
"title": "Jina AI Revenue 2025: $6.3M Est. ARR, $37.4M Raised - GetLatka",
"description": "Jina AI employs approximately 57 people as of 2026. Jina AI generates an estimated $6.3M in annual revenue. Jina AI raised $37.4M across 3 ...",
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"markdown": "\n\n## Jina AI\n\n[Sunnyvale](https://getlatka.com/companies/countries/united-states/cities/sunnyvale), [California](https://getlatka.com/companies/countries/united-states/states/california), [United States](https://getlatka.com/companies/countries/united-states)\n\n[jina.ai](https://jina.ai/) [linkedIn](https://www.linkedin.com/company/jinaai \"linkedIn\")\n\n[Natural Language Processing (NLP) Software](https://getlatka.com/companies/industries/i-natural-language-processing-(nlp)-software)\n\n2025 Revenue\n\n$6.3M(Est.)\n\nFunding\n\n$37.4M\n\nTeam\n\n57\n\nFounded\n\n2020\n\n# Jina AI Revenue & Funding (2025)\n\nJina AI is a leading search AI company. We provide Reader, Embeddings, Rerankers, and Small Language Models to help businesses build the best search.\n\nLast updatedAug 10, 2026\n\n## Jina AI Revenue\n\nIn 2025, Jina AI's revenue reached $6.3M. Since its launch in 2020, Jina AI has shown consistent revenue growth.\n\nJina AI Revenue GrowthReported revenue / ARR over time \u00b7 latest figure estimated$0$1.5M$3M$4.5M$6M$7.5M202020212022202320242025$0$6.3MSource: GetLatka.com\n\n| Year | Milestone | Source |\n| --- | --- | --- |\n| 2025 | Jina AI Hit$6.3mrevenue in July 2025 | Estimated |\n| 2020 | Launched with $0 revenue | |\n\n## Jina AI Valuation, Funding Rounds\n\nJina AI has not publicly disclosed its valuation. The company has raised $37.4M in total funding to date.\n\nJina AI has raised $37.4M in total funding across 3 rounds, most recently a $30M Series A round in 2021.\n\nJina AI Capital Raised & ValuationCumulative capital raised and post-money valuation by roundCapital raised (cum.)$0$10M$20M$30M$40M$50M20202021$37.4MSource: GetLatka.com\n\n| Year | Round | Amount | Valuation | % Sold | Source |\n| --- | --- | --- | --- | --- | --- |\n| 2021 | Series A | $30M | - | - | |\n| 2020 | Seed | $5.4M | - | - | |\n| 2020 | Seed | $2M | - | - | |\n\n## Founder / CEO\n\n### [Han Xiao](https://getlatka.com/people/han-xiao-jina.ai)\n\nCEO\n\nHan Xiao is listed as CEO at Jina AI.\n\n[Contact via Linkedin](https://www.linkedin.com/in/hxiao87)\n\n## Q&A\n\n| Question | Answer |\n| --- | --- |\n| What's your age? | - |\n| Favorite online tool? | - |\n| Favorite book? | - |\n| Favorite CEO? | - |\n| Advice for 20 year old self | - |\n\n## Customers\n\nWe do not have customer count information for Jina AIyet.\n\n## Jina AI Employees & Team Size\n\nJina AI employs approximately 57 people as of 2026.\n\nJina AI Team GrowthReported headcount over time01325385063202020212022202320242025005757Source: GetLatka.com\n\n| Year | Milestone | Source |\n| --- | --- | --- |\n| 2025 | Reached 57 employees (July 2025) | |\n\n## Frequently Asked Questions about Jina AI\n\n### What is Jina AI's revenue?\n\nJina AI generates an estimated $6.3M in annual revenue.\n\n### Who is the CEO of Jina AI?\n\nThe CEO of Jina AI is Han Xiao.\n\n### How much funding does Jina AI have?\n\nJina AI raised $37.4M across 3 rounds.\n\n### How many employees does Jina AI have?\n\nJina AI has 57 employees.\n\n### Where is Jina AI headquarters?\n\nJina AI is headquartered in Sunnyvale, California, United States.\n\n## Compare Jina AI to the industry\n\nJina AI operates across multiple industries. Browse revenue, funding, and growth data for Jina AI in each sector below.\n\n- [Enterprise Search Software](https://getlatka.com/companies/industries/i-enterprise-search-software)\n- [Generative AI Software](https://getlatka.com/companies/industries/i-generative-ai-software)\n- [Insight Engines Software](https://getlatka.com/companies/industries/i-insight-engines-software)\n- [Machine Learning Software](https://getlatka.com/companies/industries/i-machine-learning-software)\n- [Text Analysis Software](https://getlatka.com/companies/industries/i-text-analysis-software)\n\n## Data and Sources\n\nAll figures on this page are taken directly from interviews or are estimates from public sources and proprietary models. Not financial advice. [Read full disclaimer.](https://getlatka.com/disclaimer)\n\n[Claim this profile](mailto:data@getlatka.com?subject=Data%20Correction%20Request%20%E2%80%94%20Jina%20AI&body=Company%3A%20Jina%20AI%0APage%3A%20https%3A%2F%2Fgetlatka.com%2Fcompanies%2Fjina.ai%0A%0AData%20point(s)%20to%20correct%3A%0A%0ACorrect%20information%3A%0A%0ASupporting%20evidence%3A)\n\n## People Also Viewed\n\n[**Seyna**\\\\\n\\\\\nSeyna offers the infrastructure to create, sell and manage insurance products, as easily as Stripe...](https://getlatka.com/companies/seyna.eu) [**Speedsize**\\\\\n\\\\\nSpeedsize is a New York-based AI media compression company that helps e-commerce and fashion brands...](https://getlatka.com/companies/speedsize) [**CoLab Software**\\\\\n\\\\\nDeveloper of a cloud-based design review and issue tracking platform designed to assist the...](https://getlatka.com/companies/colab-software) [**Intelligence Fusion**\\\\\n\\\\\nDeveloper of a SaaS based platform designed to provide enhanced threat intelligence and situational...](https://getlatka.com/companies/intelligence-fusion) [**ezbob**\\\\\n\\\\\nEzbob is a provider of instant financing service for e-retailers. The company has developed an...](https://getlatka.com/companies/ezbob) [**Scurri**\\\\\n\\\\\nDeveloper of a delivery management platform intended to manage shipments. The company's delivery...](https://getlatka.com/companies/scurri)\n\n## Browse companies\n\n[Previous companyJimubox](https://getlatka.com/companies/jimu.com) [Next companyBC Jindal Group](https://getlatka.com/companies/jindalgroup.com)\n\n## AI-Powered SaaS Search\n\n\u00d7\n\nSearch\n\nTry these AI-powered queries:\n\nShow me AI companies with high growthFind startups founded after 2020Companies with revenue over 50MMarketing and social media toolsLarge teams with 100+ employees\n\n\u00d7\n\nGrowth tactic weekly\n\n## Steal the Growth Tactics That Took These Startups from $0 to $50M\n\nEach Tuesday, we reverse-engineer a real SaaS company's revenue, profit, CAC, funnels, and its top growth tactic.\n\nWe never spam.\n\nGet the growth playbooks\n\nTrusted by 45,000+ founders, operators, and investors.\n\n## Create Free Account\n\nSign up to access all features\n\nCreate Account [Sign up with Google](https://getlatka.com/api/v2/auth/oauth/google/start) [Sign up with LinkedIn](https://getlatka.com/api/v2/auth/oauth/linkedin/start)\n\nAlready have an account? [Log in](https://getlatka.com/auth/login)\n\n## Competitive data you can trust\n\nGetLatka is trusted by 200k+ founders, researchers, and marketers.\n\n**$49/mo** Billed yearly\n\n**$99/mo** Billed monthly\n\nContinue\n\nNo contracts, cancel at any time\n\n- Research with LatkaAI New\n- Largest SaaS database\n- Export company data to Excel\n- Hard-to-get data",
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"description": "Grow your business with Jina AI. It has been available to order since August 10, 2026; contact Elastic Sales for availability and terms. It is an annual ...",
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"markdown": "# Contact sales\n\nGrow your business with Jina AI.\n\n_handshake_ Post-acquisition?_paid_ Pricing?_smart\\_display_ How to get my API key?_speed_ What's the rate limit?\n\n* * *\n\nName\n\nWork email\n\nJob role\n\nOrganization\n\nOrganization size\n\nCountry\n\nOrganization website\n\nWhich products are you interested in?\n\n_arrow\\_drop\\_down_\n\nTell us about your problem, idea or drop some screenshots.\n\n_attach\\_file\\_add_ Attach images\n\nBy submitting, you confirm that you agree to the processing of your personal data by Jina AI as described in the [Privacy Statement](https://jina.ai/legal#privacy-policy)\n_send_ Submit\n\nSales team online\n\n## [Two Ways to Purchase](https://jina.ai/contact-sales/\\#pricing)\n\nSubscribe to our API or purchase through cloud providers.\n\n_radio\\_button\\_unchecked_\n\n_cloud_\n\nWith **3** cloud service providers\n\nUsing AWS or Azure? You can deploy our models directly on your company's cloud platform and handle billing through the CSP account.\n\n__ AWS SageMaker\n\n__ Embeddings\n\n__ Reranker\n\n_'%3e%3cpath%20fill='%23ffffff'%20d='M%20198.351562%2044.007812%20L%20112.046875%20118.847656%20L%2038.398438%20251.039062%20L%20104.804688%20251.039062%20Z%20M%20209.832031%2061.519531%20L%20173%20165.332031%20L%20243.621094%20254.0625%20L%20106.613281%20277.605469%20L%20331.15625%20277.605469%20Z%20M%20209.832031%2061.519531%20'%20fill-opacity='1'%20fill-rule='nonzero'/%3e%3c/g%3e%3c/svg%3e)_ Microsoft Azure\n\n__ Embeddings\n\n__ Reranker\n\n_'%3e%3cpath%20fill='%23ffffff'%20d='M%20246.492188%20109.988281%20L%20274.53125%2081.949219%20L%20276.394531%2070.148438%20C%20225.308594%2023.683594%20144.097656%2028.960938%2098.03125%2081.136719%20C%2085.234375%2095.625%2075.753906%20113.695312%2070.691406%20132.363281%20L%2080.726562%20130.941406%20L%20136.804688%20121.703125%20L%20141.125%20117.28125%20C%20166.0625%2089.882812%20208.246094%2086.199219%20237.039062%20109.503906%20Z%20M%20246.492188%20109.988281%20'%20fill-opacity='1'%20fill-rule='nonzero'/%3e%3c/g%3e%3cg%20clip-path='url(%235696d21d1c)'%3e%3cpath%20fill='%23ffffff'%20d='M%20314.480469%20131.527344%20C%20308.042969%20107.796875%20294.804688%2086.457031%20276.40625%2070.132812%20L%20237.050781%20109.488281%20C%20253.671875%20123.066406%20263.128906%20143.511719%20262.730469%20164.964844%20L%20262.730469%20171.949219%20C%20282.066406%20171.949219%20297.746094%20187.628906%20297.746094%20206.964844%20C%20297.746094%20226.300781%20282.066406%20241.601562%20262.730469%20241.601562%20L%20192.59375%20241.601562%20L%20185.710938%20249.078125%20L%20185.710938%20291.09375%20L%20192.59375%20297.6875%20L%20262.730469%20297.6875%20C%20313.03125%20298.085938%20354.136719%20258.007812%20354.535156%20207.703125%20C%20354.777344%20177.207031%20339.734375%20148.617188%20314.480469%20131.527344%20'%20fill-opacity='1'%20fill-rule='nonzero'/%3e%3c/g%3e%3cg%20clip-path='url(%233d43eedc5d)'%3e%3cpath%20fill='%23ffffff'%20d='M%20122.542969%20297.6875%20L%20192.59375%20297.6875%20L%20192.59375%20241.613281%20L%20122.542969%20241.613281%20C%20117.582031%20241.613281%20112.691406%20240.535156%20108.183594%20238.472656%20L%2098.246094%20241.515625%20L%2070.007812%20269.550781%20L%2067.546875%20279.09375%20C%2083.386719%20291.050781%20102.707031%20297.773438%20122.542969%20297.6875%20'%20fill-opacity='1'%20fill-rule='nonzero'/%3e%3c/g%3e%3cg%20clip-path='url(%237591c6ee7a)'%3e%3cpath%20fill='%23ffffff'%20d='M%20122.542969%20115.789062%20C%2072.226562%20116.085938%2031.691406%20157.117188%2031.988281%20207.433594%20C%2032.160156%20235.527344%2045.285156%20261.972656%2067.546875%20279.105469%20L%20108.183594%20238.472656%20C%2090.554688%20230.511719%2082.71875%20209.765625%2090.679688%20192.136719%20C%2098.644531%20174.507812%20119.386719%20166.671875%20137.015625%20174.632812%20C%20144.777344%20178.144531%20151.007812%20184.359375%20154.519531%20192.136719%20L%20195.152344%20151.503906%20C%20177.863281%20128.894531%20150.992188%20115.6875%20122.542969%20115.789062%20'%20fill-opacity='1'%20fill-rule='nonzero'/%3e%3c/g%3e%3c/svg%3e)_ Google Cloud\n\n__ Embeddings\n\n_radio\\_button\\_checked_\n\n__\n\nWith Jina Search Foundation API\n\nThe easiest way to access all of our products. Top-up tokens as you go.\n\n_key_\n\n_content\\_copy_\n\nEnter the API key you wish to recharge\n\n_error_\n\n_visibility\\_off_\n\n_verified\\_user_\n\nTop up this API key with more tokens\n\nDepending on your location, you may be charged in USD, EUR, or other currencies. Taxes may apply.\n\nToy Experiment\n\n10 Million\n\nTokens valid for:\n\n______\n\nNon-commercial use only (CC-BY-NC).\n\nFree\n\nEnjoy your new API key with free tokens.\n\nPrototype Development\n\n1 Billion\n\nTokens valid for:\n\n______\n\n_key_ Standard key\n\n_task\\_alt_ Basic key management\n\n_task\\_alt_ Technical support\n\n$50\n\n0.050 / 1M tokens\n\n_add\\_shopping\\_cart_\n\nProduction Deployment\n\n11 Billion\n\nTokens valid for:\n\n______\n\n_key_ Premium key with much higher rate limits\n\n_task\\_alt_ Advanced key management\n\n_task\\_alt_ Premium customer support in 24 hours\n\n_task\\_alt_ One-hour integration consultation\n\n$500\n\n0.045 / 1M tokens\n\n_add\\_shopping\\_cart_\n\nPlease enter the correct API key to top up.\n\n_speed_\n\nUnderstand the rate limit\n\nRate limits are the maximum number of requests that can be made to an API within a minute per IP address/API key (RPM). Find out more about the rate limits for each product and tier below.\n\n_keyboard\\_arrow\\_down_\n\nRate Limit\n\nRate limits are tracked in two ways: **RPM** (requests per minute) and **TPM** (tokens per minute). Limits are enforced per IP/API key and will be triggered when either the RPM or TPM threshold is reached first. When you provide an API key in the request header, we track rate limits by key rather than IP address.\n\nColumns\n\n_arrow\\_drop\\_down_\n\n_fullscreen_\n\n| | Product | API Endpoint | Description _arrow\\_upward_ | w/o API Key _key\\_off_ | w/ Free API Key _key_ | w/ Paid API Key _key_ | w/ Premium API Key _key_ | Average Latency | Token Usage Counting | Allowed Request |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n|  | Reader API | `https://r.jina.ai` | Converts a URL to LLM-friendly text | 20 RPM | 500 RPM | 500 RPM | _trending\\_up_ 5000 RPM | 7.9s | Count the number of tokens in the output response. | GET/POST |\n|  | Reader API | `https://s.jina.ai` | Search the web and convert results to LLM-friendly text | _block_ | 100 RPM | 100 RPM | _trending\\_up_ 1000 RPM | 2.5s | Every request costs a fixed number of tokens, starting from 10000 tokens | GET/POST |\n|  | Embedding API | `https://api.jina.ai/v1/embeddings` | Convert text/images to fixed-length vectors | _block_ | 100 RPM & 100,000 TPM | 500 RPM & 2,000,000 TPM | _trending\\_up_ 5,000 RPM & 50,000,000 TPM | _ssid\\_chart_
depends on the input size
_help_ | Count the number of tokens in the input request. | POST |\n|  | Reranker API | `https://api.jina.ai/v1/rerank` | Rank documents by query | _block_ | 100 RPM & 100,000 TPM | 500 RPM & 2,000,000 TPM | _trending\\_up_ 5,000 RPM & 50,000,000 TPM | _ssid\\_chart_
depends on the input size
_help_ | Count the number of tokens in the input request. | POST |\n\n_currency\\_exchange_\n\nAuto top-up on low token balance\n\nRecommended for uninterrupted service in production. When your token balance drops below the set threshold, we will automatically recharge your saved payment method for the last purchased package, until the threshold is met.\n\n_info_ We introduced a new pricing model on May 6th, 2025. If you enabled auto-recharge before this date, you'll continue to pay the old price (the one when you purchased). The new pricing only applies if you modify your auto-recharge settings or purchase a new API key.\n\n_check_\n\n< 1M Tokens\n\nTop up when\n\n_arrow\\_drop\\_down_\n\n## FAQ\n\n### [Jina AI \u00d7 Elastic](https://jina.ai/contact-sales/\\#post-acquisition)\n\n_handshake_\n\nWill the Jina brand be preserved?\n\n_keyboard\\_arrow\\_down_\n\nYes. Jina is a model brand within Elastic. Think of it like Qwen to Alibaba, GPT to OpenAI, or Kimi to Moonshot. The legal identity has moved to Elastic, which lets Jina AI focus purely on search foundation models as a brand.\n\n_handshake_\n\nWhat will Jina AI focus on going forward?\n\n_keyboard\\_arrow\\_down_\n\nEmbeddings, rerankers, and small models for better search, including multimodal and reader models. Our mission isn't accomplished yet, and we've never been shy about the goal: to be a leading search model provider.\n\n_handshake_\n\nWill the API and cloud marketplace offerings continue?\n\n_keyboard\\_arrow\\_down_\n\nYes. The Reader API, Embeddings API, and Reranker API continue to be developed and maintained, and models continue to be published to cloud marketplace platforms. You can use our API services as before. The only exception is that we cannot serve entities or countries subject to U.S. export controls.\n\n_handshake_\n\nWill you still release open-weights models on Hugging Face?\n\n_keyboard\\_arrow\\_down_\n\nYes. At Elastic, Jina continues to push the frontier of search foundation models, and we keep releasing open-weights models.\n\n_handshake_\n\nUnder which license will these open models be released?\n\n_keyboard\\_arrow\\_down_\n\nMost current models are CC-BY-NC 4.0, and we expect that to continue. Legacy v1 and v2 generation models are Apache-2.0, which permits commercial use outright. One model, jina-embeddings-v4, is under the Qwen Research License it inherits from its base model: that permits no commercial use at all, and no commercial license extends to it. The license that applies is always stated on the model's page on Hugging Face, so check there rather than inferring it from the generation. Under CC-BY-NC 4.0 the weights are free to download, evaluate, benchmark and use in research, with attribution, while running them in a commercial product needs a commercial license, which Elastic sells as its own SKU. Contact [Elastic Sales](https://www.elastic.co/contact) to arrange one.\n\n_handshake_\n\nWill you continue publishing research papers?\n\n_keyboard\\_arrow\\_down_\n\nYes. Every model we release is backed by a rigorous paper, and we continue submitting to top conferences like ICLR, EMNLP, SIGIR, NeurIPS, and ICML.\n\n_handshake_\n\nI'm not yet a Jina or Elastic customer, but I want to use the Reader API, model APIs, or cloud marketplace images. What should I do?\n\n_keyboard\\_arrow\\_down_\n\nSimply sign up and pay through our website or the relevant cloud marketplace, just as before.\n\n_handshake_\n\nCan I buy a commercial license for Jina models from Elastic?\n\n_keyboard\\_arrow\\_down_\n\nYes. Since August 10, 2026, Elastic sells commercial licenses for Jina models directly, as their own SKU. This covers running Jina models in your own self-managed, on-premises, or air-gapped infrastructure, and it is available through Elastic direct, federal, and cloud service provider (CSP) channels. The offering is called Jina On-Prem. To get a quote, contact [Elastic Sales](https://www.elastic.co/contact).\n\n_handshake_\n\nWhat is Jina On-Prem?\n\n_keyboard\\_arrow\\_down_\n\nJina On-Prem is a commercial license plus a set of self-contained Docker containers that let you run Jina models entirely inside your own infrastructure. The containers make no external connections: there is no call to Hugging Face or any model registry, and no license server, telemetry, or logging endpoint, which is what makes them viable in an air-gapped network. They cover the Jina model portfolio, including embedding, reranker, and reader models, and they expose Elastic Inference Service (EIS), OpenAI, Cohere, Voyage AI, and Gemini API schemas, so existing applications work without code changes. It is a separate SKU: it is not based on Elastic Resource Units (ERUs), and you do not need to run Elasticsearch to use it. It also works alongside open source Elasticsearch. It has been available to order since August 10, 2026; contact [Elastic Sales](https://www.elastic.co/contact) for availability and terms.\n\n_handshake_\n\nHow is Jina On-Prem priced?\n\n_keyboard\\_arrow\\_down_\n\nIt is an annual license fee, scoped by which models you deploy and by the amount of hardware running inference for them. It is not priced per seat, per node, or by model size, and there is no per-token billing. A CPU-only deployment is counted the same way, on the processors used for inference. Pricing is not self-serve: contact [Elastic Sales](https://www.elastic.co/contact) for a quote for your deployment.\n\n_handshake_\n\nWho is Jina On-Prem for?\n\n_keyboard\\_arrow\\_down_\n\nOrganizations that cannot, or prefer not to, send data to a cloud AI service. Typical cases are air-gapped and high-security environments, public sector and defense, regulated industries such as financial services and healthcare, latency-critical or offline systems, and teams that want a fixed, predictable inference cost instead of per-token pricing. If that describes your deployment, [Elastic Sales](https://www.elastic.co/contact) can work through the details with you.\n\n_handshake_\n\nI'm an Elastic customer. Can I use Jina models in Elastic Cloud without deploying anything?\n\n_keyboard\\_arrow\\_down_\n\nYes. Jina models are available through the Elastic Inference Service (EIS), so you can use them for ingest and search without provisioning machine learning nodes or managing GPU infrastructure. The models generally available on EIS include `jina-embeddings-v5-text-small`, `jina-embeddings-v5-text-nano`, `jina-embeddings-v5-omni-small`, `jina-embeddings-v5-omni-nano`, `jina-embeddings-v3`, `jina-clip-v2`, and the `jina-reranker-v3.5`, `jina-reranker-v3`, `jina-reranker-v2-base-multilingual` and `jina-reranker-m0` rerankers. Consult the Elastic documentation for the current model list, supported regions, and the minimum stack version for each model.\n\n_handshake_\n\nI downloaded the weights from Hugging Face. Do I need a license to use them in production?\n\n_keyboard\\_arrow\\_down_\n\nIt depends on which license the model carries, which is stated on its page on Hugging Face. Apache-2.0 permits commercial use with nothing to buy. Under CC-BY-NC 4.0, evaluation, benchmarking and research are free, but commercial production use needs a commercial license, which is what Jina On-Prem provides. A research license permits no commercial use at all, and a commercial license for the other models does not extend to it, so a model under one is not a candidate for production regardless of budget. Note this is about the weights: calling the hosted APIs or the official cloud marketplace images commercially needs no separate license. To buy one, contact [Elastic Sales](https://www.elastic.co/contact).\n\n_handshake_\n\nI want to sign a contract or a custom agreement covering Jina models. What should I do?\n\n_keyboard\\_arrow\\_down_\n\nContact [Elastic Sales](https://www.elastic.co/contact). Commercial licensing, contracting, and support for Jina models now run through Elastic's standard sales and support process.\n\n_handshake_\n\nI'm purchasing your services as a Chinese entity. Can I get a Chinese invoice (\u53d1\u7968)?\n\n_keyboard\\_arrow\\_down_\n\nNot for self-serve API top-ups: those are invoiced automatically by the entity that processes the payment, which cannot issue a Chinese invoice (\u53d1\u7968). For contract-based purchases, including Jina On-Prem, contact [Elastic Sales](https://www.elastic.co/contact) to discuss the available contracting entities and invoicing arrangements.\n\n_handshake_\n\nI'm an Elastic customer and want to learn best practices for embeddings and rerankers, or I'm generally interested in Jina AI's development. What should I do?\n\n_keyboard\\_arrow\\_down_\n\nContact [Elastic Sales](https://www.elastic.co/contact), and we can arrange a session between you, the Jina AI team, and Elastic.\n\n_handshake_\n\nIf you release a new model during my license term, is it included?\n\n_keyboard\\_arrow\\_down_\n\nModels released in the same category during the term are included: no new agreement or purchase, and no change to your license key, which is issued for the category rather than for an individual model. Models under a license that permits no commercial use are the exception, since no commercial license extends to them. For what a specific agreement covers, confirm with [Elastic Sales](https://www.elastic.co/contact).\n\n_handshake_\n\nWhat support comes with a commercial license, and who provides it?\n\n_keyboard\\_arrow\\_down_\n\nThe license includes enterprise-level support under the standard service level agreement. Deployment stays yours: you pull the container image and run it in your own environment, and support can help if the installation gives you trouble. Support runs through Elastic's standard process, as does everything contractual. For the service levels attached to a specific agreement, confirm with [Elastic Sales](https://www.elastic.co/contact).\n\n_handshake_\n\nWhich data processing terms apply now that Jina is part of Elastic?\n\n_keyboard\\_arrow\\_down_\n\nProcessing is governed by Elastic's data processing agreement rather than the earlier Jina AI GmbH terms. This matters for procurement files that cite the old agreement, so it is worth checking any document drafted before the acquisition. For the current terms and anything specific to your jurisdiction, contact [Elastic Sales](https://www.elastic.co/contact).\n\n### [How to get my API key?](https://jina.ai/contact-sales/\\#get-api-key)\n\nvideo\\_not\\_supported\n\n### [What's the rate limit?](https://jina.ai/contact-sales/\\#rate-limit)\n\nRate Limit\n\nRate limits are tracked in two ways: **RPM** (requests per minute) and **TPM** (tokens per minute). Limits are enforced per IP/API key and will be triggered when either the RPM or TPM threshold is reached first. When you provide an API key in the request header, we track rate limits by key rather than IP address.\n\nColumns\n\n_arrow\\_drop\\_down_\n\n_fullscreen_\n\n| | Product | API Endpoint | Description _arrow\\_upward_ | w/o API Key _key\\_off_ | w/ Free API Key _key_ | w/ Paid API Key _key_ | w/ Premium API Key _key_ | Average Latency | Token Usage Counting | Allowed Request |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n|  | Reader API | `https://r.jina.ai` | Converts a URL to LLM-friendly text | 20 RPM | 500 RPM | 500 RPM | _trending\\_up_ 5000 RPM | 7.9s | Count the number of tokens in the output response. | GET/POST |\n|  | Reader API | `https://s.jina.ai` | Search the web and convert results to LLM-friendly text | _block_ | 100 RPM | 100 RPM | _trending\\_up_ 1000 RPM | 2.5s | Every request costs a fixed number of tokens, starting from 10000 tokens | GET/POST |\n|  | Embedding API | `https://api.jina.ai/v1/embeddings` | Convert text/images to fixed-length vectors | _block_ | 100 RPM & 100,000 TPM | 500 RPM & 2,000,000 TPM | _trending\\_up_ 5,000 RPM & 50,000,000 TPM | _ssid\\_chart_
depends on the input size
_help_ | Count the number of tokens in the input request. | POST |\n|  | Reranker API | `https://api.jina.ai/v1/rerank` | Rank documents by query | _block_ | 100 RPM & 100,000 TPM | 500 RPM & 2,000,000 TPM | _trending\\_up_ 5,000 RPM & 50,000,000 TPM | _ssid\\_chart_
depends on the input size
_help_ | Count the number of tokens in the input request. | POST |\n\n### [Do I need a commercial license?](https://jina.ai/contact-sales/\\#cc-self-check)\n\nCC BY-NC License Self-Check\n\n* * *\n\n_play\\_arrow_\n\nAre you using our hosted API, or our official images on Azure, AWS, or GCP?\n\n_play\\_arrow_\n\nYes\n\nNo separate license needed. Commercial use is covered by the service terms: sign up and pay through this site or the cloud marketplace.\n\n_play\\_arrow_\n\nNo\n\n_play\\_arrow_\n\nAre you running the model weights yourself, in a commercial product or service?\n\n_play\\_arrow_\n\nNo\n\nNothing to buy. Downloading, evaluating, benchmarking and research use are permitted under every license we publish under, with attribution.\n\n_play\\_arrow_\n\nYes\n\n_play\\_arrow_\n\nWhich license does the model carry? It is stated on the model's page on Hugging Face.\n\n_play\\_arrow_\n\nApache-2.0\n\nApache-2.0 permits commercial use. Nothing to buy. This covers our legacy v1 and v2 generation models.\n\n_play\\_arrow_\n\nCC BY-NC 4.0\n\nYou need a commercial license. Since August 10, 2026, Elastic sells one for Jina models as its own SKU, called Jina On-Prem. It covers self-managed, on-premises, and air-gapped deployments, and it does not require an Elasticsearch subscription.\n\nIf you are already an Elastic customer, your account team can add it to your existing agreement.\n\n[Contact Elastic Sales](https://www.elastic.co/contact)\n\n_play\\_arrow_\n\nQwen Research License\n\nA research license does not permit commercial use, and a commercial license for our other models does not extend to it. There is no commercial option for this one, self-hosted or through the API. Pick a model under one of the other two licenses instead.\n\n### [Other questions](https://jina.ai/contact-sales/\\#faq)\n\nReader-related common questions\n\n__\n\nWhat are the costs associated with using the Reader API?\n\n_keyboard\\_arrow\\_down_\n\nReader is free for basic usage: prepend 'https://r.jina.ai/' to your URL. Supplying an API key raises the rate limit and charges tokens based on content length. See Q16 for rate limits.\n\n__\n\nHow does the Reader API function?\n\n_keyboard\\_arrow\\_down_\n\nThe Reader API fetches the URL server-side and returns clean, LLM-ready text. You choose the fetching engine with the `X-Engine` header: `direct` issues a plain HTTP fetch and is the fastest, the default engine renders the page in a headless browser so client-side JavaScript executes before extraction, and `cf-browser-rendering` is an experimental Cloudflare-backed renderer. Boilerplate such as navigation, headers, footers, and ads is stripped, and the main content is converted to Markdown. Use `X-Respond-With` to get other shapes of the same page, and `X-Target-Selector` or `X-Remove-Selector` to keep or drop specific CSS selectors.\n\n__\n\nIs the Reader API open source?\n\n_keyboard\\_arrow\\_down_\n\nThe Reader service code is available on the Jina AI GitHub organization. The models used by Reader, including `ReaderLM-v2` and `jina-vlm`, are licensed CC-BY-NC 4.0, which is not an open-source license: they are free to download and use non-commercially, but commercial production use requires a commercial license. Elastic has sold that license separately since August 10, 2026; contact [Elastic Sales](https://www.elastic.co/contact) to arrange one.\n\n__\n\nWhat is the typical latency for the Reader API?\n\n_keyboard\\_arrow\\_down_\n\nIt depends mainly on the engine and on the page itself. A direct fetch of a simple page typically returns in a few hundred milliseconds, while the default browser engine has to load and execute the page before extraction and usually lands in the low seconds. Heavy single-page apps, slow origin servers, and large PDFs take longer. Repeating the same URL within 5 minutes is served from cache and returns almost immediately, so a warm URL is much faster than a cold one.\n\n__\n\nWhy should I use the Reader API instead of scraping the page myself?\n\n_keyboard\\_arrow\\_down_\n\nScraping can be complicated and unreliable, particularly with complex or dynamic pages. The Reader API provides a streamlined, reliable output of clean, LLM-ready text.\n\n__\n\nDoes the Reader API support multiple languages?\n\n_keyboard\\_arrow\\_down_\n\nThe Reader API is language-agnostic and returns content in the original language of the page; it does not translate. Content negotiation is available through the `X-Locale` header, which sets the browser locale used when rendering, so sites that serve different markup per locale can be steered to the version you want.\n\n__\n\nDoes the Reader API respect website access controls?\n\n_keyboard\\_arrow\\_down_\n\nYes. Reader operates as a standard web client and respects website access controls. If a website blocks the request, that outcome is respected. You are responsible for ensuring your use of Reader complies with the terms of the sites you access and does not infringe third-party intellectual property rights.\n\n__\n\nCan the Reader API extract content from PDF files?\n\n_keyboard\\_arrow\\_down_\n\nYes, the Reader API can natively extract content from PDF files.\n\n__\n\nCan the Reader API process media content from web pages?\n\n_keyboard\\_arrow\\_down_\n\nYes, Reader can caption images on webpages using the `x-with-generated-alt` header. This adds descriptive alt tags to images that lack them, enabling LLMs to understand visual content. Video summarization is planned for future releases.\n\n__\n\nIs it possible to use the Reader API on local HTML files?\n\n_keyboard\\_arrow\\_down_\n\nNo, the Reader API can only process content from publicly accessible URLs.\n\n__\n\nDoes Reader API cache the content?\n\n_keyboard\\_arrow\\_down_\n\nIf you request the same URL within 5 minutes, the Reader API will return the cached content.\n\n__\n\nCan I use the Reader API to access content behind a login?\n\n_keyboard\\_arrow\\_down_\n\nYes, for pages that accept cookie-based sessions. Pass your session cookies with the `X-Set-Cookie` header and the Reader forwards them when fetching the URL, using the same `=` form as a normal `Set-Cookie`, optionally scoped with `; domain=`. Requests carrying cookies are never cached, so each one is a fresh fetch. This does not perform a login for you: it replays credentials you already hold, so you are responsible for obtaining them and for complying with the target site's terms of service. Flows that require an interactive login, MFA, or a bearer token the page fetches itself are out of scope.\n\n__\n\nCan I use the Reader API to access PDF on arXiv?\n\n_keyboard\\_arrow\\_down_\n\nYes, you can either use the native PDF support from the Reader (https://r.jina.ai/https://arxiv.org/pdf/2310.19923v4) or use the HTML version from the arXiv (https://r.jina.ai/https://arxiv.org/html/2310.19923v4)\n\n__\n\nHow does image caption work in Reader?\n\n_keyboard\\_arrow\\_down_\n\nReader captions all images at the specified URL and adds `Image [idx]: [caption]` as an alt tag (if they initially lack one). This enables downstream LLMs to interact with the images in reasoning, summarizing etc.\n\n__\n\nWhat is the scalability of the Reader? Can I use it in production?\n\n_keyboard\\_arrow\\_down_\n\nThe Reader API is designed to be highly scalable. It is auto-scaled based on the real-time traffic and the maximum concurrency is now around 4,000 requests. We are maintaining it actively as one of the core products of Jina AI. So feel free to use it in production.\n\n__\n\nWhat is the rate limit of the Reader API?\n\n_keyboard\\_arrow\\_down_\n\nSee the table below for the latest rate limits. We're actively improving the Reader API's rate limits and performance, so the table will be updated as things change.\n\n[_speed_ Rate limit](https://jina.ai/contact-sales/#rate-limit)\n\n__\n\nWhat is ReaderLM? How can I use it?\n\n_keyboard\\_arrow\\_down_\n\n`ReaderLM-v2` is a 1.54B parameter small language model that converts raw HTML into clean Markdown or JSON, and can extract structured data using a JSON schema or natural language instructions. Use it via the Reader API with the `x-respond-with: readerlm-v2` header, or deploy it from the AWS, Azure, or GCP marketplaces. For image-heavy or scanned documents, `jina-vlm` is our 2.4B parameter vision-language reader model.\n\n[_launch_ AWS SageMaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy) [_launch_ Google Cloud](https://console.cloud.google.com/marketplace/browse?q=jina&pli=1&inv=1&invt=AbmydQ) [_launch_ Microsoft Azure](https://azuremarketplace.microsoft.com/en-US/marketplace/apps?page=1&search=jina)\n\n__\n\nHow do I extract structured data from webpages?\n\n_keyboard\\_arrow\\_down_\n\nUse the `x-json-schema` header with a JSON schema definition, or use `x-instruction` header with natural language instructions. Both features work with ReaderLM-v2 to extract specific fields like prices, titles, dates, etc. from any webpage into structured JSON format.\n\n__\n\nDoes Reader actively bypass website anti-bot protection?\n\n_keyboard\\_arrow\\_down_\n\nNo. Reader does not actively circumvent or bypass any website defense mechanisms, anti-bot systems, or access controls. If a website detects our service as a bot and blocks the request, that outcome is respected. We operate as a standard web client and do not employ techniques designed to evade detection systems. You remain responsible for ensuring your use of Reader respects third-party intellectual property rights and the terms of the sites you access.\n\n__\n\nWill upgrading from a free to a paid API key give me access to more websites?\n\n_keyboard\\_arrow\\_down_\n\nNo. Upgrading from a free tier to a paid API key does not grant access to additional websites or bypass any site restrictions. The difference between tiers is primarily in rate limits and performance optimizations. A paid API key provides higher request throughput and faster processing, but it does not enable access to websites that block our service.\n\n__\n\nCan I run Reader inside my own infrastructure?\n\n_keyboard\\_arrow\\_down_\n\nYes. The reader models ship as self-contained offline Docker containers under the Jina On-Prem commercial license that Elastic has sold as its own SKU since August 10, 2026. This is the path for air-gapped and firewalled environments, where the containers make no outbound connections of any kind. Note that fetching arbitrary public web pages still requires network access to those pages; the on-prem value is in running the extraction models locally. Contact [Elastic Sales](https://www.elastic.co/contact) for a quote.\n\nEmbeddings-related common questions\n\n__\n\nHow were the Jina embedding models trained?\n\n_keyboard\\_arrow\\_down_\n\nFor detailed information on our training processes, data sources, and evaluations, refer to the technical reports on arXiv. The `jina-embeddings-v5` text models are trained in two stages: embedding distillation from a larger teacher model, followed by task-specific LoRA adapter training on frozen backbone weights. The `v5-omni` multimodal variants add a third stage that trains only cross-modal projectors, leaving the text backbone and adapters frozen.\n\n[_launch_ arXiv](https://arxiv.org/abs/2602.15547)\n\n__\n\nWhat are your multimodal embedding models?\n\n_keyboard\\_arrow\\_down_\n\n`jina-embeddings-v5-omni-small` (~1.74B parameters, 1024 dimensions, 32K context) and `jina-embeddings-v5-omni-nano` (~1.04B parameters, 768 dimensions, 8K context) are our current multimodal models. They accept text, images, audio, video, and PDFs in one shared vector space, so you can index in one modality and query in another without reindexing. Their text-only output is identical to `jina-embeddings-v5-text-small` and `jina-embeddings-v5-text-nano` respectively, which means you can add multimodal input to an existing text index without re-embedding it. `jina-clip-v2` (865M parameters) remains available as a lighter text-and-image option.\n\n[_launch_ arXiv](https://arxiv.org/abs/2605.08384)\n\n__\n\nWhich languages do your models support?\n\n_keyboard\\_arrow\\_down_\n\nAll models released since 2024 are multilingual. `jina-embeddings-v5-text-small` and the `v5-omni` models are built on a Qwen3 backbone with broad multilingual coverage; `jina-embeddings-v5-text-nano` is built on EuroBERT-210M, covering 15 major European and global languages including English, French, German, Spanish, Chinese, Japanese, Arabic, and Hindi. `jina-embeddings-v3` and `jina-clip-v2` support 89 languages. For per-language benchmark numbers, see the MMTEB results in each model's technical report.\n\n[_launch_ arXiv](https://arxiv.org/abs/2602.15547)\n\n__\n\nWhat is the maximum context length for a single input?\n\n_keyboard\\_arrow\\_down_\n\nContext length varies by model: `jina-embeddings-v5-text-small` and `jina-embeddings-v5-omni-small` support up to 32,768 tokens, while `jina-embeddings-v5-text-nano` and `jina-embeddings-v5-omni-nano` support 8,192 tokens. `jina-embeddings-v4` and the `jina-code-embeddings` models support 32,768 tokens; `jina-embeddings-v3`, `jina-clip-v2`, and `jina-colbert-v2` support 8,192 tokens. Inputs above the limit return an error unless you set `truncate: true`.\n\n__\n\nWhat is the maximum number of inputs I can include in a single request?\n\n_keyboard\\_arrow\\_down_\n\nThere is no hard limit on the number of items per request. The API batches inputs internally by token count for optimal GPU utilization, so you can send as many texts or images as needed in a single request. PDFs are the exception: send one PDF per request.\n\n__\n\nHow do I send images, audio, video, or PDFs to the multimodal models?\n\n_keyboard\\_arrow\\_down_\n\nPass a typed object in the `input` array using an `image`, `audio`, `video`, or `pdf` key, whose value is either a public URL or base64-encoded bytes. The model routes each modality to the appropriate encoder, and you can mix modalities freely within a single batch. Supported audio formats include WAV, MP3, FLAC, OGG, M4A, and Opus; video is processed as 32 uniformly sampled frames. PDFs must be sent one per request.\n\n__\n\nHow do Jina embeddings compare to the latest OpenAI, Cohere, and Voyage models?\n\n_keyboard\\_arrow\\_down_\n\n`jina-embeddings-v5-text-small` (677M parameters) is the strongest model under 1B parameters on MMTEB, scoring 67.0 average at task level, and reaches 71.7 average on English MTEB. `jina-embeddings-v5-text-nano` (239M parameters) scores 65.5 on MMTEB, ahead of every model we evaluated under 500M parameters. Our design target is capability per parameter rather than raw size, so these models are cheaper to serve than most alternatives at comparable or better retrieval quality. All v5 models support Matryoshka Representation Learning, so you can truncate dimensions down to 32 without retraining.\n\n__\n\nHow seamless is the transition from OpenAI's text-embedding-3-large to your solution?\n\n_keyboard\\_arrow\\_down_\n\nThe transition is straightforward: [our API endpoint](https://api.jina.ai/v1/embeddings) matches the input and output JSON schemas of OpenAI's `text-embedding-3-large`, so in most codebases you change the base URL, the API key, and the model name. The same holds for the Jina On-Prem containers, which additionally expose Elastic Inference Service (EIS), Cohere, Voyage AI, and Gemini schemas, so Jina models are a drop-in replacement in existing code paths. Note that embeddings from different model families are not comparable, so you have to re-embed your corpus rather than mix vectors from two providers in one index. For the On-Prem containers themselves, contact [Elastic Sales](https://www.elastic.co/contact).\n\n__\n\nHow are tokens calculated for images and other non-text inputs?\n\n_keyboard\\_arrow\\_down_\n\nText is counted in the standard way. Non-text inputs are converted to tokens by the relevant encoder, and the cost depends heavily on which model you use, so measure with your own inputs rather than assuming. As a reference point, a 600x600 pixel image costs approximately:\n\n\u2022 `jina-embeddings-v5-omni-small`: ~363 tokens\n\u2022 `jina-embeddings-v5-omni-nano`: ~362 tokens\n\u2022 `jina-embeddings-v4`: ~4,840 tokens\n\u2022 `jina-clip-v2`: ~16,000 tokens\n\nThe v5-omni models are one to two orders of magnitude cheaper per image than the older models. Every response includes a `usage` object with the exact token count for that request, including an `image_tokens` breakdown for multimodal inputs, so you can verify cost per call.\n\n__\n\nDo you provide models for embedding images, audio, or video?\n\n_keyboard\\_arrow\\_down_\n\nYes. `jina-embeddings-v5-omni-small` and `jina-embeddings-v5-omni-nano` embed text, images, audio, video, and PDFs into a single shared vector space. `jina-embeddings-v4` and `jina-clip-v2` handle text and images.\n\n__\n\nCan Jina embedding models be fine-tuned on private or company data?\n\n_keyboard\\_arrow\\_down_\n\nThere are two paths. The self-serve one is the Fine-tuning API, which generates synthetic training data from a description of your domain and returns a fine-tuned model, without your having to assemble a labelled dataset. For fine-tuning on proprietary data under a commercial agreement, on dedicated infrastructure, or on a model that is not in the base model selector, Contact [Elastic Sales](https://www.elastic.co/contact); that work is scoped and contracted through Elastic.\n\n[Contact](https://jina.ai/contact-sales)\n\n__\n\nCan the models be hosted privately, on my own infrastructure or in my own cloud account?\n\n_keyboard\\_arrow\\_down_\n\nYes, in two ways. Jina models are available on the AWS, Azure, and GCP marketplaces, so you can deploy them inside your own cloud account. For self-managed, on-premises, or air-gapped infrastructure, Elastic sells a commercial license called Jina On-Prem, available since August 10, 2026, which ships the models as fully offline Docker containers with no external calls and no license server. To get a quote for either path, contact [Elastic Sales](https://www.elastic.co/contact).\n\n[_launch_ AWS SageMaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy) [_launch_ Google Cloud](https://console.cloud.google.com/marketplace/browse?q=jina&pli=1&inv=1&invt=AbmydQ) [_launch_ Microsoft Azure](https://azuremarketplace.microsoft.com/en-US/marketplace/apps?page=1&search=jina)\n\n__\n\nWhat is the 'task' parameter and when should I use it?\n\n_keyboard\\_arrow\\_down_\n\nThe `task` parameter selects a task-specific LoRA adapter for optimal performance. Use `retrieval.query` for search queries, `retrieval.passage` for documents being searched, `text-matching` for symmetric similarity such as duplicate or paraphrase detection, `classification` for categorization, and `separation` for clustering. Retrieval is asymmetric, so using the wrong side of the query/passage pair measurably degrades results. The parameter is supported by `jina-embeddings-v5`, `jina-embeddings-v4`, and `jina-embeddings-v3`.\n\n__\n\nWhat is late-interaction retrieval and which models support it?\n\n_keyboard\\_arrow\\_down_\n\nLate interaction keeps token-level vectors instead of collapsing a document into one vector, which preserves fine-grained detail at the cost of a larger index. `jina-embeddings-v4` supports both dense (single-vector) and late-interaction (multi-vector) output via the `output_type` parameter, and `jina-colbert-v2` is a dedicated late-interaction model. For most retrieval pipelines, a dense `v5` model followed by a reranker is the better accuracy-per-cost tradeoff.\n\n__\n\nWhat is late chunking and when should I use it?\n\n_keyboard\\_arrow\\_down_\n\nLate chunking embeds the whole document first with a long-context model, then derives chunk embeddings from the token-level representations. Unlike naive chunking, which embeds each chunk in isolation, late chunking preserves cross-chunk context, which improves retrieval quality in RAG pipelines where a chunk refers to something defined earlier in the document. Enable it with the `late_chunking` parameter.\n\n__\n\nWhy does the API enforce a different context length than the model supports?\n\n_keyboard\\_arrow\\_down_\n\nSome models are architecturally capable of longer context than the hosted API accepts. Very long sequences consume substantial GPU memory, and we tune the serving configuration to balance throughput, latency, and cost for the majority of use cases. If you need the full architectural context length, run the model yourself: contact [Elastic Sales](https://www.elastic.co/contact) about a self-managed deployment.\n\n__\n\nWhy is jina-embeddings-v4 free, and why is it slow?\n\n_keyboard\\_arrow\\_down_\n\n`jina-embeddings-v4` is built on the Qwen2-VL base model, released under the Qwen Research License, which permits research and non-commercial use only. We therefore cannot license it commercially and provide it free of charge via the API instead. It is also a 3.8B parameter model, so it is inherently slower per request, and we throttle its throughput to manage infrastructure costs. It is not suitable for production workloads, and for the same reason it is not offered through the Elastic Inference Service or as part of Jina On-Prem. For production use, take the `jina-embeddings-v5` family: it is faster, stronger on retrieval benchmarks, and can be licensed for commercial use through [Elastic Sales](https://www.elastic.co/contact).\n\n__\n\nWhat are the rate limits for the Embeddings API?\n\n_keyboard\\_arrow\\_down_\n\nRate limits depend on your API key type:\n\n**Free:** 100 RPM, 100K TPM\n\n**Paid:** 500 RPM, 2M TPM\n\n**Premium:** 5,000 RPM, 50M TPM\n\nThere is an additional IP-based limit of 10,000 requests per 60 seconds to prevent abuse. Limits are applied per key and counted over a 60-second window, so bursts are smoothed rather than queued; a request over the limit returns HTTP 429 and should be retried with exponential backoff. If you need limits beyond the Premium tier, or dedicated capacity with no shared-tenant limit at all, contact [Elastic Sales](https://www.elastic.co/contact).\n\n__\n\nWhich embedding model should I choose?\n\n_keyboard\\_arrow\\_down_\n\nStart with `jina-embeddings-v5-text-small` for text retrieval: it is the strongest sub-1B model we ship and handles 32K context. Drop to `jina-embeddings-v5-text-nano` when latency, cost, or edge hardware matters more than the last point of accuracy. Use `jina-embeddings-v5-omni-small` or `v5-omni-nano` when images, audio, video, or PDFs are involved; their text output is identical to the corresponding text model, so you can add modalities to an existing index without re-embedding. Use `jina-code-embeddings-0.5b` or `1.5b` for source code. Within a family, newer is better.\n\n__\n\nWhat are the file size limits for images and PDFs?\n\n_keyboard\\_arrow\\_down_\n\nMaximum file sizes are 5 MB for images and 8 MB for PDFs. Larger files are rejected with an error.\n\nReranker-related common questions\n\n__\n\nHow much does the Reranker API cost?\n\n_keyboard\\_arrow\\_down_\n\nReranker API pricing follows the same token-based structure as the Embeddings API, and tokens are shared across all Jina APIs on the same key. New API keys include free tokens to get started; beyond that, token packages are available for purchase. See the pricing section for details.\n\n__\n\nWhat are the differences between the Jina rerankers?\n\n_keyboard\\_arrow\\_down_\n\n`jina-reranker-v3.5` is our current flagship: a 0.6B parameter multilingual listwise reranker with 131K context, and a drop-in replacement for `jina-reranker-v3`. It improves on v3 across every axis we measure, with the largest gains on structured-data and legal retrieval, and runs 1.22x to 1.56x faster. `jina-reranker-v3` remains available. `jina-reranker-m0` is the multimodal reranker for ranking visual documents. `jina-reranker-v2-base-multilingual` is a smaller cross-encoder supporting 100+ languages, useful when you need a non-Qwen-derived model. `jina-colbert-v2` uses late interaction across 89 languages.\n\n__\n\nHow are the Jina rerankers licensed?\n\n_keyboard\\_arrow\\_down_\n\n`jina-reranker-v3.5`, `jina-reranker-v3`, `jina-reranker-m0`, `jina-reranker-v2-base-multilingual`, and `jina-colbert-v2` are released under CC-BY-NC 4.0. You are free to use, share, and adapt them for non-commercial purposes. Commercial production use requires a commercial license, which Elastic has sold as its own SKU since August 10, 2026 under the name Jina On-Prem. Contact [Elastic Sales](https://www.elastic.co/contact) for a quote. Legacy `jina-reranker-v1-*` models remain Apache-2.0.\n\n__\n\nDo the rerankers support multiple languages?\n\n_keyboard\\_arrow\\_down_\n\nYes, all current rerankers are multilingual. `jina-reranker-v3.5` improves on v3 on MIRACL and on multilingual retrieval generally. `jina-reranker-v3` and `jina-reranker-v2-base-multilingual` support 100+ languages, `jina-reranker-m0` handles multilingual visual document ranking, and `jina-colbert-v2` supports 89 languages.\n\n__\n\nWhat is the maximum context length for each reranker?\n\n_keyboard\\_arrow\\_down_\n\nContext length varies by model:\n\n**jina-reranker-v3.5:** 131,072 tokens (query plus all documents combined) with auto-truncation\n\n**jina-reranker-v3:** 131,072 tokens with auto-truncation\n\n**jina-reranker-m0:** 10,000 tokens\n\n**jina-reranker-v2-base-multilingual:** 1,024 tokens, with automatic chunking for longer documents\n\n**jina-colbert-v2:** 8,192 tokens\n\nFor the v1 and v2 rerankers, queries are auto-truncated and long documents are chunked with max-pooling across chunks.\n\n__\n\nIs there a limit on the number of documents I can rerank per query?\n\n_keyboard\\_arrow\\_down_\n\nThere is no hard limit on the number of documents per request. Like our Embeddings API, the Reranker API batches inputs internally by token count for optimal GPU utilization. You can send as many documents as needed in a single request.\n\n__\n\nWhat latency can I expect when reranking 100 documents?\n\n_keyboard\\_arrow\\_down_\n\nLatency varies from 100 milliseconds to 7 seconds, depending largely on the length of the documents and the query. For instance, reranking 100 documents of 256 tokens each with a 64-token query takes about 150 milliseconds. Increasing the document length to 4096 tokens raises the time to 3.5 seconds. If the query length is increased to 512 tokens, the time further increases to 7 seconds.\n\nTime cost of reranking one query and 100 documents, in milliseconds:\n\n|\n|\n\n| | **Number of tokens in each document** |\n| --- | --- |\n| Number of tokens in the query | 256 | 512 | 1024 | 2048 | 4096 |\n| 64 | 156 | 323 | 1366 | 2107 | 3571 |\n| 128 | 194 | 369 | 1377 | 2123 | 3598 |\n| 256 | 273 | 475 | 1397 | 2155 | 4299 |\n| 512 | 468 | 1385 | 2114 | 3536 | 7068 |\n\n__\n\nCan the rerankers be hosted privately, on my own infrastructure or in my own cloud account?\n\n_keyboard\\_arrow\\_down_\n\nYes. The rerankers are available on the AWS, Azure, and GCP marketplaces for deployment in your own cloud account. For self-managed, on-premises, or air-gapped infrastructure, Elastic sells a commercial license (Jina On-Prem) that ships the models as fully offline Docker containers. Contact [Elastic Sales](https://www.elastic.co/contact) for a quote.\n\n[_launch_ AWS SageMaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy) [_launch_ Google Cloud](https://console.cloud.google.com/marketplace/browse?q=jina&pli=1&inv=1&invt=AbmydQ) [_launch_ Microsoft Azure](https://azuremarketplace.microsoft.com/en-US/marketplace/apps?page=1&search=jina)\n\n__\n\nDo you offer a reranker fine-tuned on domain-specific data?\n\n_keyboard\\_arrow\\_down_\n\nBefore commissioning a fine-tune, try `jina-reranker-v3.5`: it was trained with self-distillation specifically for domain robustness and shows large gains on legal and structured-data retrieval over v3. A well-chosen off-the-shelf reranker plus better chunking usually closes more of the gap than a fine-tune does, and it costs nothing to test. If it still falls short on your data, a domain-specific reranker is a custom engagement: contact [Elastic Sales](https://www.elastic.co/contact) to scope it.\n\n[Contact](https://jina.ai/contact-sales)\n\n__\n\nWhat's the minimum image size for the documents?\n\n_keyboard\\_arrow\\_down_\n\nThe minimum acceptable image size for the `jina-reranker-m0` model is 28x28 pixels.\n\n__\n\nWhat is listwise reranking and how does it differ from pointwise?\n\n_keyboard\\_arrow\\_down_\n\n`jina-reranker-v3` and `jina-reranker-v3.5` use a listwise architecture: the query and all candidates share one context window and are scored in a single forward pass, so the model can compare documents against each other. Traditional pointwise rerankers, including `jina-reranker-v2-base-multilingual`, score each document independently against the query. Listwise scoring is more accurate because relevance is often relative to what else is in the candidate set.\n\n__\n\nWhy does the API enforce a different context length than the model supports?\n\n_keyboard\\_arrow\\_down_\n\nSome rerankers are architecturally capable of longer context than the hosted API accepts. Very long sequences consume substantial GPU memory, and we tune the serving configuration to balance throughput, latency, and cost for the majority of use cases. If you need the full architectural context length, run the model in your own infrastructure and contact [Elastic Sales](https://www.elastic.co/contact) about a commercial license.\n\n__\n\nWhat are the rate limits for the Reranker API?\n\n_keyboard\\_arrow\\_down_\n\nRate limits depend on your API key type:\n\n**Free:** 100 RPM, 100K TPM\n\n**Paid:** 500 RPM, 2M TPM\n\n**Premium:** 5,000 RPM, 50M TPM\n\nThere is also an IP-based limit of 10,000 requests per 60 seconds. The same limits apply to the Embeddings and Reranker APIs, and tokens are shared across all Jina APIs on the same key.\n\n__\n\nWhich reranker should I choose?\n\n_keyboard\\_arrow\\_down_\n\nUse `jina-reranker-v3.5` for text. It is a drop-in replacement for `jina-reranker-v3`: the request schema is unchanged, so switching the model string is the entire migration. Use `jina-reranker-m0` when your candidates are images or visually rich documents. Use `jina-reranker-v2-base-multilingual` when you need a smaller model or one that is not derived from a Qwen backbone.\n\nAPI-related common questions\n\n_code_\n\nCan I use the same API key across all Jina APIs?\n\n_keyboard\\_arrow\\_down_\n\nYes. One API key is valid for all Jina AI search foundation products, including the Reader, Embeddings, Reranker, Classifier, and Segmenter APIs, with tokens shared across all of them.\n\n_code_\n\nCan I monitor the token usage of my API key?\n\n_keyboard\\_arrow\\_down_\n\nYes. Enter your API key in the 'API Key & Billing' tab to see your recent usage history and remaining tokens. If you've logged in to the API dashboard, you can also view these details in the 'Manage API Key' tab.\n\n_code_\n\nWhat should I do if I forget my API key?\n\n_keyboard\\_arrow\\_down_\n\nIf you have misplaced a topped-up key and wish to retrieve it, please contact support AT jina.ai with your registered email for assistance. It's recommended to log in to keep your API key securely stored and easily accessible.\n\n[Contact](https://jina.ai/contact-sales)\n\n_code_\n\nDo API keys expire?\n\n_keyboard\\_arrow\\_down_\n\nNo, our API keys do not have an expiration date. If a key is compromised, revoke it yourself in [the API Key Management dashboard](https://jina.ai/api-dashboard), which takes effect immediately; issue a replacement key first if you want to avoid downtime. Any remaining token balance stays on your account rather than on the revoked key. If you cannot access the dashboard, or believe the account itself is compromised, raise it with [Elastic Support](https://support.elastic.co/).\n\n[Contact](https://jina.ai/contact-sales)\n\n_code_\n\nCan I transfer tokens between API keys?\n\n_keyboard\\_arrow\\_down_\n\nYes, you can transfer tokens from a premium key to another. After logging into your account on [the API Key Management dashboard](https://jina.ai/api-dashboard), use the settings of the key you want to transfer out to move all remaining paid tokens.\n\n_code_\n\nCan I revoke my API key?\n\n_keyboard\\_arrow\\_down_\n\nYes, you can revoke your API key if you believe it has been compromised. Revoking a key will immediately disable it for all users who have stored it, and all remaining balance and associated properties will be permanently unusable. If the key is a premium key, you have the option to transfer the remaining paid balance to another key before revocation. Notice that this action cannot be undone. To revoke a key, go to the key settings in [the API Key Management dashboard](https://jina.ai/api-dashboard).\n\n_code_\n\nWhy is the first request for some models slow?\n\n_keyboard\\_arrow\\_down_\n\nThis is because our serverless architecture offloads certain models during periods of low usage. The initial request activates or 'warms up' the model, which may take a few seconds. After this initial activation, subsequent requests process much more quickly.\n\n_code_\n\nIs my API data used to train your models?\n\n_keyboard\\_arrow\\_down_\n\nNo. We never use your API requests, inputs, or outputs to train our embedding, reranker, or any other models. Your data remains yours.\n\n_code_\n\nWhat are the rate limits for Jina APIs?\n\n_keyboard\\_arrow\\_down_\n\nRate limits apply per API key:\n\n**Free:** 100 RPM, 100K TPM\n\n**Paid:** 500 RPM, 2M TPM\n\n**Premium:** 5,000 RPM, 50M TPM\n\nThere is also an IP-based limit of 10,000 requests per 60 seconds. Limits vary by endpoint; see the rate limit table above for per-endpoint figures.\n\n_code_\n\nAre there batch size limits for the APIs?\n\n_keyboard\\_arrow\\_down_\n\nThere is **no batch size limit** for either the Embeddings or Reranker APIs. You can send as many items or documents as needed per request. Both APIs batch inputs internally by token count for optimal GPU utilization.\n\n_code_\n\nAre the Jina APIs the same thing as Jina models inside Elastic?\n\n_keyboard\\_arrow\\_down_\n\nNo, they are three separate paths. The Jina APIs on this site are self-serve and pay-as-you-go with a Jina API key. The Elastic Inference Service (EIS) runs Jina models inside Elastic Cloud, billed through your Elastic subscription, with no infrastructure for you to manage. Jina On-Prem is a commercial license, sold by Elastic as its own SKU since August 10, 2026, for running the models in your own self-managed, on-premises, or air-gapped infrastructure. For the EIS and On-Prem paths, contact [Elastic Sales](https://www.elastic.co/contact).\n\nBilling-related common questions\n\n_attach\\_money_\n\nIs billing based on the number of sentences or requests?\n\n_keyboard\\_arrow\\_down_\n\nOur pricing is based on total tokens processed, so you can spread a token budget across as many inputs as you like \u2014 no per-sentence charges.\n\n_attach\\_money_\n\nIs there a free trial available for new users?\n\n_keyboard\\_arrow\\_down_\n\nYes. New users get an auto-generated API key with free tokens usable across any of our models. Once the free tokens are consumed, you can purchase additional tokens for the key in the 'Buy tokens' tab.\n\n_attach\\_money_\n\nAre tokens charged for failed requests?\n\n_keyboard\\_arrow\\_down_\n\nNo, tokens are not deducted for failed requests.\n\n_attach\\_money_\n\nWhat payment methods are accepted?\n\n_keyboard\\_arrow\\_down_\n\nPayments are processed through Stripe, supporting a variety of payment methods including credit cards, Google Pay, and PayPal for your convenience.\n\n_attach\\_money_\n\nIs invoicing available for token purchases?\n\n_keyboard\\_arrow\\_down_\n\nFor self-serve token purchases, Stripe issues an invoice to the email address associated with your Stripe account at the time of purchase. If you need a formal purchase order, a negotiated contract, procurement paperwork, or consolidated billing, that runs through Elastic rather than Stripe: contact [Elastic Sales](https://www.elastic.co/contact).\n\n_attach\\_money_\n\nHow do I buy a commercial license rather than API tokens?\n\n_keyboard\\_arrow\\_down_\n\nToken purchases on this site cover use of the hosted Jina APIs. They do not license you to run the model weights in your own infrastructure. For that, Elastic has sold a commercial license as its own SKU since August 10, 2026, priced annually rather than per token. Contact [Elastic Sales](https://www.elastic.co/contact) for a quote.\n\n_attach\\_money_\n\nCan I pay by invoice or purchase order instead of card?\n\n_keyboard\\_arrow\\_down_\n\nSelf-serve token purchases are processed through Stripe and invoiced automatically to your Stripe account email. For purchase orders, procurement processes, or volumes above what self-serve top-up supports, contact [Elastic Sales](https://www.elastic.co/contact).\n\n_attach\\_money_\n\nI paid, but my balance or rate limit has not changed. What should I check?\n\n_keyboard\\_arrow\\_down_\n\nBalance and rate limits belong to an API key, not to the account, so the first thing to check is the key itself rather than the account page: enter it in the API Key & Billing tab and confirm the balance and tier there. If the account holds more than one key, the tokens are on the key that was topped up, which may not be the key your application is sending. A new tier can also take a short time to propagate after payment. If the key shows the balance but is still limited at the previous tier after that, contact support.\n\n_attach\\_money_\n\nHow do I cancel, stop auto top-up, or remove a saved payment method?\n\n_keyboard\\_arrow\\_down_\n\nSelf-serve billing is managed from the customer portal reachable via the API Key & Billing tab, where auto top-up can be switched off and saved payment methods removed. Turning off auto top-up stops future charges but leaves any balance already purchased usable. If you also want the account and its data removed, or a refund considered, send that request to support; account deletion is handled manually and takes a few business days, and you will get written confirmation once it is done.\n\nCurrent language / theme\n\n_language_ English / Auto\n\nSearch Foundation\n\n[Reader](https://jina.ai/reader) [Embeddings](https://jina.ai/embeddings) [Reranker](https://jina.ai/reranker)\n\nGet Jina API key\n\n[Rate Limit](https://jina.ai/contact-sales#rate-limit)\n\nAbout us\n\n[News](https://jina.ai/news) [Download Jina logo\\\\\n\\\\\n_open\\_in\\_new_](https://jina.ai/logo-Jina-1024.zip) [Download Elastic logo\\\\\n\\\\\n_open\\_in\\_new_](https://brand.elastic.co/302f66895/p/06c73c-elastic-logos/b/35d033) [API Status](https://status.jina.ai/)\n\n[__](https://huggingface.co/jinaai)\n\nElastic \u00a9 2026. [Security](https://jina.ai/legal#security-as-company-value) [Terms & Conditions](https://jina.ai/legal/#terms-and-conditions) [Privacy](https://jina.ai/legal/#privacy-policy)Manage CookiesDo Not Sell or Share My Personal Information\n\nThis website and all associated content, software, products, and services are intended for professional use only. No consumer use is intended or directed.",
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"title": "Jina AI 2026 Company Profile: Valuation, Investors, Acquisition",
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"markdown": "# Jina AI\n\n## Jina AI Overview\n\nUpdate this profile\n\n## - Year Founded - 2020\n\n\n\n## - Status - Acquired/\u200bMerged\n\n## - Employees - 27\n\n\n\n## - Latest Deal Type - M&A\n\n## - Latest Deal Amount - $43.3M\n\n### Jina AI General Information\n\n#### Description\n\nDeveloper of an artificial intelligence prompt optimizing platform intended to improve performance across areas like marketing, multimedia, and software development. The company's platform helps to transform ideas into precise prompts and also offers an assistant chatbot to produce content and solve diverse tasks, enabling businesses to refine prompts for better outcomes.\n\n#### Contact Information\n\n##### Website\n\n[www.jina.ai](http://www.jina.ai/)\n\nOwnership Status\n\nAcquired/Merged\n\n(Operating Subsidiary)\n\nFinancing Status\n\nFormerly VC-backed\n\n##### Corporate Office\n\n- Prinzessinnenstra\u00dfe 19-20\n- 10969 Berlin\n- Germany\n\n+49 030\n\n[\ueac9](https://www.linkedin.com/company/jinaai)\n\n[\ue944](https://twitter.com/JinaAI_)\n\n[\uea8c](https://www.facebook.com/opensourcejina)\n\nPrimary Industry\n\nBusiness/Productivity Software\n\nOther Industries\n\nSoftware Development Applications\n\nParent Company\n\n[Elastic](https://pitchbook.com/profiles/company/55574-02)\n\nVertical(s)\n\n[SaaS](https://pitchbook.com/profiles/industry/saas),\n[Artificial Intelligence & Machine Learning](https://pitchbook.com/profiles/industry/artificial-intelligence-and-machine-learning)\n\n##### Corporate Office\n\n- Prinzessinnenstra\u00dfe 19-20\n- 10969 Berlin\n- Germany\n\n+49 030\n\n[\ueac9](https://www.linkedin.com/company/jinaai)\n\n[\ue944](https://twitter.com/JinaAI_)\n\n[\uea8c](https://www.facebook.com/opensourcejina)\n\n### Want detailed data on 11M+ companies?\n\nWhat you see here scratches the surface\n\n[Request a free trial](https://pitchbook.com/profiles/request-access)\n\n### Want to dig into this profile?\n\nWe\u2019ll help you find what you need\n\n[Learn more](https://pitchbook.com/profiles/request-access)\n\n## Jina AI Valuation & Funding\n\n| Deal Type | Date | Amount | Raised to Date | Post-Val | Status | Stage |\n| --- | --- | --- | --- | --- | --- | --- |\n| 5\\. Merger/Acquisition | 07-Oct-2025 | $43.3M | | | Completed | Generating Revenue |\n| 4\\. Early Stage VC (Series A) | 04-Nov-2021 | | | | Completed | Generating Revenue |\n| 3\\. Early Stage VC | 22-Sep-2020 | | | | Completed | Generating Revenue |\n| 2\\. Accelerator/Incubator | 11-Apr-2020 | | $2M | | Completed | Startup |\n| 1\\. Seed Round | 18-Feb-2020 | $2M | $2M | | Completed | Startup |\n\n[To view Jina AI\u2019s complete valuation and funding history, request access\u00a0\u00bb](https://pitchbook.com/profiles/request-access)\n\n## Jina AI Related Research & Analysis\n\nExplore institutional-grade private market research from our team of analysts.\n\n- Verticals\n\n- Artificial Intelligence & Machine Learning\n- SaaS\n\n- [\\\\\n\\\\\nAI Report: $407 Billion Raised as Megadeals Dominate\\\\\n\\\\\nAugust 10, 2026](https://pitchbook.com/news/reports/q2-2026-ai-report-407-billion-raised-as-megadeals-dominate)\n- [\\\\\n\\\\\nEnterprise SaaS Report: VC Funding Rebounds Beneath Quarter's Headline Decline\\\\\n\\\\\nAugust 3, 2026](https://pitchbook.com/news/reports/q2-2026-enterprise-saas-report-vc-funding-rebounds-beneath-quarters-headline-decline)\n\n## Jina AI Signals\n\nGrowth Rate\n\n\n\nWeekly\n\nGrowth\n\nWeekly Growth\n\n\n\nSize Multiple\n\n\n\nMedian\n\nSize Multiple\n\n\n\nKey Data Points\n\nSimilarweb Unique Visitors\n\n\n\nMajestic Referring Domains\n\n\n\nPitchBook\u2019s non-financial metrics help you gauge a company\u2019s traction and growth using web presence and social reach.\n\n[Request a free trial](https://pitchbook.com/profiles/request-access)\n\n## Jina AI Former Investors (5)\n\n| Investor Name | Investor Type | Holding | Investor Since | Participating Rounds |\n| --- | --- | --- | --- | --- |\n| Canaan Partners | Venture Capital | Minority | | |\n| Granite Asia | Impact Investing | Minority | | |\n| Mango Capital | Venture Capital | Minority | | |\n| SAP.iO | Accelerator/Incubator | Minority | | |\n| Yunqi Partners | Venture Capital | Minority | | |\n\n[To view Jina AI\u2019s complete investors history, request access\u00a0\u00bb](https://pitchbook.com/profiles/request-access)\n\n[Ready to get started?\\\\\nRequest a free trial](https://pitchbook.com/profiles/request-access)\n\n## Jina AI FAQs\n\n- ### When was Jina AI founded?\n\n\nJina AI was founded in 2020.\n\n- ### Where is Jina AI headquartered?\n\n\nJina AI is headquartered in Berlin, Germany.\n\n- ### What is the size of Jina AI?\n\n\nJina AI has 27 total employees.\n\n- ### What industry is Jina AI in?\n\n\nJina AI\u2019s primary industry is Business/Productivity Software.\n\n- ### Is Jina AI a private or public company?\n\n\nJina AI is a Private company.\n\n- ### What is the current valuation of Jina AI?\n\n\nThe current valuation of Jina AI is .\n\n- ### What is Jina AI\u2019s current revenue?\n\n\nThe current revenue for Jina AI is .\n\n- ### How much funding has Jina AI raised over time?\n\n\nJina AI has raised $37.2M.\n\n- ### Who are Jina AI\u2019s investors?\n\n\n[Canaan Partners](https://pitchbook.com/profiles/investor/11135-71), [Granite Asia](https://pitchbook.com/profiles/investor/536095-99), [Mango Capital](https://pitchbook.com/profiles/investor/267468-40), [SAP.iO](https://pitchbook.com/profiles/investor/186427-90), and [Yunqi Partners](https://pitchbook.com/profiles/investor/125876-89) have invested in Jina AI.\n\n- ### When was Jina AI acquired?\n\n\nJina AI was acquired on 07-Oct-2025.\n\n- ### Who acquired Jina AI?\n\n\nJina AI was acquired by [Elastic](https://pitchbook.com/profiles/company/55574-02).\n\n\n### Data Transparency\n\n- \n\n\nMeet our data hygiene team\n\n\n\nDiscover how our experts ensure you\u2019re getting the most accurate financial data in the industry.\n\n[Read more](https://pitchbook.com/blog/meet-pitchbooks-data-hygiene-team)\n\n- \n\n\nHow PitchBook sources data\n\n\n\nOur data operations team has logged over 3.5 million hours researching, organizing, and integrating the information you need most.\n\n[Discover our process](https://pitchbook.com/research-process)",
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"markdown": "# Jina AI Revenue and Competitors\n\n[License our Company Data API](https://growjo.com/company_data_api)\n\n[](https://jina.ai/)\n\n![]()\n\n#### Global,\n\nLocation\n\n![]()\n\n#### N/A\n\nTotal Funding\n\n![]()\n\n#### [AI](https://growjo.com/industry/AI)\n\nIndustry\n\n## Estimated Revenue & Valuation\n\n- Jina AI's estimated annual revenue is currently $17.5M per year.[(i)](https://growjo.com/join)\n- Jina AI's estimated revenue per employee is $277,571\n\n## Employee Data\n\n- Jina AI has 63 Employees.[(i)](https://growjo.com/join)\n- Jina AI grew their employee count by -6% last year.\n\n## Jina AI's People\n\n| Name | Title | Email/Phone |\n| --- | --- | --- |\n\n## Jina AI Competitors & Alternatives [Add Company](https://growjo.com/add-your-company)\n\n\n\n| Competitor Name | Revenue | Number of Employees | Employee Growth | Total Funding | Valuation |\n| --- | --- | --- | --- | --- | --- |\n| #1
[](https://paradox.ai/)[Paradox](https://growjo.com/company/Paradox) | $122.9M | 691 | 4% | $253.3M | N/A |\n| #2
[Spire Digital](https://growjo.com/company/Spire_Digital) | $3.1M | 17 | -23% | N/A | N/A |\n| #3
[10Web.io](https://growjo.com/company/10Web.io) | $25.5M | 82 | 9% | N/A | N/A |\n| #4
[Loyal](https://growjo.com/company/Loyal) | $84.5M | 248 | -23% | $53.7M | N/A |\n| #5
[Project Verte](https://growjo.com/company/Project_Verte) | $51.4M | 118 | 8% | $62M | N/A |\n| #6
[Kore.ai](https://growjo.com/company/Kore.ai) | $525.8M | 1208 | 10% | $296M | N/A |\n| #7
[SiteZeus](https://growjo.com/company/SiteZeus) | $10.1M | 44 | -28% | $3.7M | N/A |\n| #8
[inFeedo](https://growjo.com/company/inFeedo) | $63.8M | 187 | 13% | N/A | N/A |\n| #9
[Accubits Techno...](https://growjo.com/company/Accubits_Technologies) | $25.6M | 179 | -36% | N/A | N/A |\n| #10
[Valuer.ai](https://growjo.com/company/Valuer.ai) | $6.8M | 52 | -5% | N/A | N/A |\n\n[Add Company](https://growjo.com/add-your-company)\n\n[Show More AI Companies](https://growjo.com/industry/AI)\n\n## What Is Jina AI?\n\nJina AI is a Neural Search Company, providing cloud-native neural search powered by state-of-the-art AI and deep learning. Found in 2020. & led by GGV Capital with $7.5M, Jina AI is recognized as one of the most promising startups in AI open-source software. Our mission is to build an open-source neural search ecosystem for businesses and developers, enabling everyone to search for information in all kinds of data with high accessibility and scalability. We don't lock up our values and innovations in \"the way people have always done\". We are receptive to change \u00e2\u20ac\u201d when we don't like something, we change it and make it better. We always believe that those who really make changes are the ones convinced that change is possible. Life is short and time is precious. We want to spend time only on the right things that we do believe. Universal search engine, open AI technology and cross-border collaborations, these are the bright future that we believe in and fully commit to. We are hiring AI engineers, full-stack developers, open-source evangelists, PMs. If you see what we see, share what we believe in, then click the opportunity button below. Exciting journey is waiting for us. Contact - Contact: hello@jina.ai - Twitter: @JinaAI\\_ (with underscore at the end) - Website: https://jina.ai - Github: https://opensource.jina.ai - Youtube: https://webinar.jina.ai - LinkedIn: https://www.linkedin.com/company/jinaai\n\n**keywords:** N/A\n\n![]()\n\nN/A\n\nTotal Funding\n\n![]()\n\n63\n\nNumber of Employees\n\n![]()\n\n$17.5M\n\nRevenue (est)\n\n![]()\n\n-6%\n\nEmployee Growth %\n\n![]()\n\nN/A\n\nValuation\n\n![]()\n\nN/A\n\nAccelerator\n\n## Other Companies in Global\n\n[](https://growjo.com/export_list)\n\n| Company Name | Revenue | Number of Employees | Employee Growth | Total Funding |\n| --- | --- | --- | --- | --- |\n| #1
[](https://uk.naturecan.com/)[Naturecan](https://growjo.com/company/Naturecan) | $13.7M | 67 | -12% | N/A |\n| #2
[](https://vectairsystems.com/)[Vectair Systems](https://growjo.com/company/Vectair_Systems) | $15.3M | 71 | 20% | N/A |\n| #3
[](https://ivsc.org/)[International V...](https://growjo.com/company/International_Valuation_Standards_Council_(IVSC)) | $11.1M | 79 | 25% | N/A |\n| #4
[](https://thcohq.com/)[THCO - We Are H...](https://growjo.com/company/THCO_-_We_Are_Hiring!) | $18.3M | 83 | 19% | N/A |\n| #5
[](https://epns.io/)[Ethereum Push N...](https://growjo.com/company/Ethereum_Push_Notification_Service_(EPNS)) | $11.3M | 87 | -16% | N/A |",
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"url": "https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A",
"title": "Jina AI - 2026 Company Profile & Team - Tracxn",
"description": "Jina AI has raised $39M in funding from Canaan and SAP.iO. Jina AI has raised a total funding of $39M over 2 rounds. Revenue Multiple Lead Investors",
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"markdown": "Your browser was unable to load all of Tracxn resources. They may have been blocked by your firewall, proxy or browser configuration. Press **Ctrl+F5** or **Ctrl+Shift+R** to have your browser try again and if that doesn't work, [**click here to retry**](https://tracxn.com/) or mail us at [**hi@tracxn.com**](mailto:hi@tracxn.com?subject=Platform%20resources%20not%20loading)\n\nInternal Server Error\n\n[](https://jina.ai/)\n\n# [Jina AI - Company Profile](https://jina.ai/)\n\nLast updated: August 25, 2026\n\n[Claim Profile](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba/jinaai/claimprofile?utm_source=product-seo%20page&utm_medium=companies-funding&utm_campaign=unclaimed%20claim%20profile&utm_content=cta%20claimprofile) [Suggest Edits](https://tracxn.com/b/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A/edit)\n\n- [Linkedin](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftracxn.com%2Fd%2Fcompanies%2Fjina-ai%2F__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A)\n- [Twitter](https://twitter.com/intent/tweet?url=https%3A%2F%2Ftracxn.com%2Fd%2Fcompanies%2Fjina-ai%2F__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A)\n- [Facebook](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftracxn.com%2Fd%2Fcompanies%2Fjina-ai%2F__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A)\n- [Email](mailto:?subject=Jina%20AI%20Company%20profile%20on%20Tracxn&body=Hi!%0D%0A%0D%0AI%20would%20like%20you%20to%20take%20a%20look%20at%20this%20Company%20profile%20I%20found%20on%20Tracxn%20:%20https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A)\n- Copy Url\n\n\n- [Request page removal](https://tracxn.com/b/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A/remove)\n\n## Jina AI - About the company\n\nJina AI is an acquired company based in Germany, founded in 2020 by [Bing He](https://platform.tracxn.com/a/d/people/62316b04a0a68a701b0fdd09/binghe) and [Nan Wang](https://platform.tracxn.com/a/d/people/62316bf4a0a68a701b0fe284/nanwang). It operates as a Developer of search foundation providing embeddings, reranker, and reader capabilities. Jina AI has raised $39M in funding from [Canaan](https://tracxn.com/d/venture-capital/canaan/__P-Y_1HXNfTSNP2CwmEmAT-av2sYYBqCTr4wsnMs-UT8) and [SAP.iO](https://tracxn.com/d/accelerator-incubator/sapio/__RZqjVmK8jYpLKpw1bahl2rA6rC1yuplaIEVcuiRi4qY). The company has 771 active competitors, including 109 funded and 42 that have exited. Its top competitors include companies like [Glean](https://tracxn.com/d/companies/glean/__0aO5V-wZsJgrfCZYNJm9ccNqZCpaXHw-O9LJTlZZYgQ), [Hugging Face](https://tracxn.com/d/companies/hugging-face/___89yhA9z0-ZrLstW87xWDVe15Bkl70IZOkQf38SXzmQ) and [Elastic](https://tracxn.com/d/companies/elastic/__T1LkMb2skd7hBANkVC7ni7uXB_FqHZ_h_3Qak0d03ME).\n\n### Company Details\n\nDeveloper of search foundation providing embeddings, reranker, and reader capabilities. The company offers tools to convert URLs to LLM-friendly input. It provides multimodal multilingual embeddings and a reranker for maximizing search relevancy. The platform allows users to read URLs and fetch content, search the web, and get SERP. It offers an API to access its services in LLMs.\n\nWebsite[jina.ai](https://jina.ai/)\n\nSocial[![X]()](https://twitter.com/jinaai_)\n\nEmail ID\\*\\*\\*\\*\\*@jina.ai\n\nKey Metrics\n\nFounded Year\n\n2020\n\nLocation\n\n[Germany](https://tracxn.com/d/geographies/germany/__J2uFS4cPEWupB2-t7_tRBGZGqa5onAEvRi6OJX89nUY)\n\nStage\n\nAcquired\n\nTotal Funding\n\n[$39M](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:funding-and-investors) in 2 rounds\n\nLatest Funding Round\n\n[Series A, Nov 22, 2021, $\\*\\*\\*\\*\\*](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:funding-and-investors)\n\nInvestors\n\n[Canaan](https://tracxn.com/d/venture-capital/canaan/__P-Y_1HXNfTSNP2CwmEmAT-av2sYYBqCTr4wsnMs-UT8)& [5 more](https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A/funding-and-investors)\n\nRanked\n\n23rdamong [771 active competitors](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:competitors)\n\nEmployee Count\n\n[44](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:people) as on Jul 31, 2026\n\nSimilar Companies\n\n[Hugging Face](https://tracxn.com/d/companies/hugging-face/___89yhA9z0-ZrLstW87xWDVe15Bkl70IZOkQf38SXzmQ)& [512 more](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:explore)\n\nExit Details\n\nAcquiredby [Elastic](https://tracxn.com/d/companies/elastic/__T1LkMb2skd7hBANkVC7ni7uXB_FqHZ_h_3Qak0d03ME) (Oct 09, 2025)\n\n[View Jina AI's full profile](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba)\n\n## Legal entities associated with Jina AI\n\nJina AI is associated with 3 legal entities given below:\n\n| Legal Entity Name | Date of Incorporation | Revenue | Latest Employee Count | Documents |\n| --- | --- | --- | --- | --- |\n| [JINA AI (GLOBAL) PTE. LTD](https://tracxn.com/d/legal-entities/singapore/jina-ai-global-pte.ltd/__Q0FP9AjZuYciGsDX9uO76AXvD-3eXf7pkO-PURQNew8 \"JINA AI (GLOBAL) PTE. LTD\")
CIN: 202109958C , Singapore, Active | Mar 19, 2021 | - | - | Buy Now |\n| [JINA AI GMBH](https://tracxn.com/d/legal-entities/germany/jina-ai-gmbh/__4IonH6KcbHmVDenNnSoem1iTs903mVk5npVK3Cv6_sg \"JINA AI GMBH\")
CIN: HRB218021 , Germany, Active | Jun 08, 2020 | - | 31
(As on Dec 31, 2023) | Buy Now |\n| [Jina AI GmbH](https://tracxn.com/d/legal-entities/germany/jina-ai-gmbh/__CVC793DnQsKw8EDYKz0cOOnKRWOs9sJ7fVGntYm6a-E \"Jina AI GmbH\")
CIN: F1103\\_HRB218021B , Germany, Active | Jun 09, 2020 | - | - | Buy Now |\n\n## Jina AI's acquisition details\n\nJina AI got acquired by [Elastic](https://tracxn.com/d/companies/elastic/__T1LkMb2skd7hBANkVC7ni7uXB_FqHZ_h_3Qak0d03ME) on Oct 09, 2025.\n\nClick [here](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:exits) to take a look at Jina AI's acquisition in detail\n\n\n\nSign up to download Jina AI's company profile\n\nSign Up for Free\n\n## Jina AI's funding and investors\n\nJina AI has raised a total funding of$39Mover 2 rounds.Its first funding round was onSep 23, 2020.Its latest funding round was a Series A round on Nov 22, 2021 for [$\\\\*\\\\*\\\\*\\*\\*](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:funding-and-investors).5 investors participated in its latest round.Jina AI has6 [institutional investors](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:funding-and-investors).\n\nHere is the list of recent funding rounds of Jina AI:\n\n| Date of Funding | Funding Amount | Round Name | Post-Money Valuation | Revenue Multiple | Lead Investors | Other Investors |\n| --- | --- | --- | --- | --- | --- | --- |\n| Nov 22, 2021 | 5142547 | Series A | 8384153 | 8395028 | 3264086 | 6445883 |\n| Sep 23, 2020 | 5479399 | Seed | 4404734 | 7044889 | [SAP.iO](https://tracxn.com/d/accelerator-incubator/sapio/__RZqjVmK8jYpLKpw1bahl2rA6rC1yuplaIEVcuiRi4qY) | 2671932 |\n\n![lock]()Access funding benchmarks and valuations. [Sign up today!](https://tracxn.com/signup)\n\nView details of [Jina AI's funding rounds and investors](https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A/funding-and-investors)\n\n## Jina AI's founders and board of directors\n\n[Founder? Claim Profile](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba/claimprofile?utm_source=product-seo%20page&utm_medium=companies-funding&utm_campaign=unclaimed%20claim%20profile&utm_content=cta%20claimprofile)\n\nThe founders of Jina AIare [Bing He](https://platform.tracxn.com/a/d/people/62316b04a0a68a701b0fdd09/binghe) and [Nan Wang](https://platform.tracxn.com/a/d/people/62316bf4a0a68a701b0fe284/nanwang).\n\nHere are the details of Jina AI's key team members:\n\n- [Bing He](https://platform.tracxn.com/a/d/people/62316b04a0a68a701b0fdd09/binghe): Co-Founder & COO (Acquired by Elastic) of Jina AI.\n- [Nan Wang](https://platform.tracxn.com/a/d/people/62316bf4a0a68a701b0fe284/nanwang): Co-Founder of Jina AI.\n\nView details of [Jina AI's Founder profiles and Board Members](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:people)\n\n### Jina AI's employee count trend\n\nJina AI has 44 employees as of Jul 26. Here is Jina AI's employee count trend over the years:\n\n\n\n![lock]()Uncover Jina AI's growth story! [Sign up today!](https://tracxn.com/signup)\n\n\n\nAccess Tracxn on any website\n\nOur Google Chrome extension lets you view company details while browsing their websites\n\n[Install Tracxn Extension](https://chromewebstore.google.com/detail/tracxn-extension/mcplkbacfdjapifgiidjidmnfilipnep?hl=en)\n\n## Jina AI's Competitors and alternates\n\nTop competitors of Jina AI include [Glean](https://tracxn.com/d/companies/glean/__0aO5V-wZsJgrfCZYNJm9ccNqZCpaXHw-O9LJTlZZYgQ), [Hugging Face](https://tracxn.com/d/companies/hugging-face/___89yhA9z0-ZrLstW87xWDVe15Bkl70IZOkQf38SXzmQ) and [Elastic](https://tracxn.com/d/companies/elastic/__T1LkMb2skd7hBANkVC7ni7uXB_FqHZ_h_3Qak0d03ME).Here is the list of Top 10 competitors of Jina AI, ranked by Tracxn score:\n\n| Rank | Company Details | Short Description | Total Funding | Investors | Tracxn Score |\n| --- | --- | --- | --- | --- | --- |\n| 1st | 
[Glean](https://tracxn.com/d/companies/glean/__0aO5V-wZsJgrfCZYNJm9ccNqZCpaXHw-O9LJTlZZYgQ \"Glean\")
2019, Palo Alto ( [United States](https://tracxn.com/d/geographies/united-states/__agFBbaWLXQ9BaxreLIz_EPAASY3VwAK-kFQ6rvJvIco)), Series F | Cloud-based platform for enterprise search | $765M | [Lightspeed Venture Partners](https://tracxn.com/d/venture-capital/lightspeed-venture-partners/__PzMkj1UXJCjrJn4ADHeiO_a04brVhLLYKBEqw7d_INY), [Kleiner Perkins](https://tracxn.com/d/venture-capital/kleiner-perkins/__M3ZtL62VNmgtiH2m8SoAcdoCNIOpQAv0g_eqoob_Vw8)
...&\u00a0[52\u00a0others](https://platform.tracxn.com/a/d/company/5903f872e4b0b66b2cdd2ade#a:funding-and-investors) | 79/100 |\n| 2nd | 
[Hugging Face](https://tracxn.com/d/companies/hugging-face/___89yhA9z0-ZrLstW87xWDVe15Bkl70IZOkQf38SXzmQ \"Hugging Face\")
2016, Paris ( [France](https://tracxn.com/d/geographies/france/__TTi2JLFlyQgLp2aNNET70HJjnxtjza_BDTOzcb8sfE8)), Series D | Platform offering collaborative tools for sharing and deploying machine learning models | $400M | [Lux Capital](https://tracxn.com/d/venture-capital/lux-capital/__GYav2s0CpJYtKuH74TT9JyNQSZPYCXHgZgoi2ivawOM), [Salesforce](https://tracxn.com/d/companies/salesforce/__meXShWFhj6RRXVaUgSdybLdExpZGUx224nYjFl0eCjo)
&\u00a0[33\u00a0others](https://platform.tracxn.com/a/d/company/57dc8704e4b0af1418b4e600#a:funding-and-investors) | 75/100 |\n| 3rd | 
[Elastic](https://tracxn.com/d/companies/elastic/__T1LkMb2skd7hBANkVC7ni7uXB_FqHZ_h_3Qak0d03ME \"Elastic\")
2012, San Francisco ( [United States](https://tracxn.com/d/geographies/united-states/__agFBbaWLXQ9BaxreLIz_EPAASY3VwAK-kFQ6rvJvIco)), Public | Cloud based text analytics platform | $104M | [New Enterprise Associates](https://tracxn.com/d/venture-capital/new-enterprise-associates/__eu8NTmi72ogDYzh9NH9QIFndf1mSanr4HntwJ8lfodQ), [HTIF](https://tracxn.com/d/venture-capital/htif/__1IPX5LH7_tnDtTZLF5lzsufN_4Pn6zEuZUAzUaeufqI)
&\u00a0[10\u00a0others](https://platform.tracxn.com/a/d/company/54ff9f58e4b039f421cce192#a:funding-and-investors) | 72/100 |\n| 4th | 
[Stability AI](https://tracxn.com/d/companies/stability-ai/__j9m4iz5g2IAe2paU-Sre7UIBk1ByQZ0ippRUslXvqwc \"Stability AI\")
2019, London ( [United Kingdom](https://tracxn.com/d/geographies/united-kingdom/__BFrCTEA8idVqv695D5RNyVuEJ3b_q8H7jGByutRg8gc)), Seed | Developer of generative AI models for image, video, audio, and 3D | $181M | [Lightspeed Venture Partners](https://tracxn.com/d/venture-capital/lightspeed-venture-partners/__PzMkj1UXJCjrJn4ADHeiO_a04brVhLLYKBEqw7d_INY), [Coatue](https://tracxn.com/d/private-equity/coatue/__neb04ukttPFlKk9SEuR86jzhUs0jtf8EHm92gQaD8r8)
&\u00a0[17\u00a0others](https://platform.tracxn.com/a/d/company/5fac457ede6524199d16b169#a:funding-and-investors) | 72/100 |\n| 5th | 
[Fetch.ai](https://tracxn.com/d/companies/fetchai/__HM9Pjh1Z-DC2fCGeFAtmxpDdMoeNXERIsDY9y11B3LU \"Fetch.ai\")
2017, Cambridge ( [United Kingdom](https://tracxn.com/d/geographies/united-kingdom/__BFrCTEA8idVqv695D5RNyVuEJ3b_q8H7jGByutRg8gc)), Series C | Blockchain-based network for application and AI infrastructure | $61.9M | [DWF Labs](https://tracxn.com/d/venture-capital/dwf-labs/__q9GbokRpHubOPz5FHxQ_6obYr0mbgVJH1lK4sRc6Y4E), [Gda Group](https://tracxn.com/d/venture-capital/gda-group/__z6t8tkB0SgNR5RfoMZUwLA1WoaDUX6oOc_m0lCOsubk)
&\u00a0[14\u00a0others](https://platform.tracxn.com/a/d/company/5a5c56b1e4b0eb94adcc253b#a:funding-and-investors) | 72/100 |\n| 6th | 
[You Technologies](https://tracxn.com/d/companies/you-technologies/__uYI-fs1-W1VU1J7X3PmyQvM-TCMhH7g8w77exoKw1x8 \"You Technologies\")
2020, Palo Alto ( [United States](https://tracxn.com/d/geographies/united-states/__agFBbaWLXQ9BaxreLIz_EPAASY3VwAK-kFQ6rvJvIco)), Series C | AI-enabled key-word based private search engine | $199M | [Georgian](https://tracxn.com/d/venture-capital/georgian/__VyUlz5ludKYUVkf3g5kAfADcECiMn-dXDWwpUqBgl4o), [Radical Ventures](https://tracxn.com/d/venture-capital/radical-ventures/__p4SySBjPpWGP9HUkVr0snVFwU1OCp4izS1QF8EWPg68)
&\u00a0[20\u00a0others](https://platform.tracxn.com/a/d/company/531b602fe4b0f7e16627327b#a:funding-and-investors) | 71/100 |\n| 7th | 
[Coveo](https://tracxn.com/d/companies/coveo/__z8ra6rSM83TAvnIqKMZFfhDNDOYV8IUFLznYRUvxZOU \"Coveo\")
2004, Quebec City ( [Canada](https://tracxn.com/d/geographies/canada/__5_AOWm4cD5BWy5u1p9laDOY83HVNrYlq3BRq6KRIiVo)), Public | Provider of AI-powered search and recommendation solutions for enterprise businesses | $358M | [Elliott Management](https://tracxn.com/d/companies/elliott-management/__z8di2ceqcjVkFOb_1c0ejhAYt0KxE9JuoZtNBmjYxSg), [OMERS](https://tracxn.com/d/private-equity/omers/__TYEHXE6H0kWbXXjX-ZSEBm2zgInrfD6g8QggcNujF78)
&\u00a0[17\u00a0others](https://platform.tracxn.com/a/d/company/53193ed7e4b0f7e165f450f9#a:funding-and-investors) | 70/100 |\n| 8th | 
[Lucidworks](https://tracxn.com/d/companies/lucidworks/__43uMJdX2opNE7bWNEg08qHmgQClnPo_GebXQR8RhBNM \"Lucidworks\")
2007, San Francisco ( [United States](https://tracxn.com/d/geographies/united-states/__agFBbaWLXQ9BaxreLIz_EPAASY3VwAK-kFQ6rvJvIco)), Series F | Provider of AI-powered search and discovery platform for digital experiences | $218M | [Granite Ventures](https://tracxn.com/d/venture-capital/granite-ventures/__DkJK_hr-p8m8pkD5LKpoeVCXhr3D7GQjcuPAhHPDudA), [Walden International](https://tracxn.com/d/venture-capital/walden-international/__4KgIgTrv0_8Bpju5lTqY1BJ9BE7NZ4eAW1aEvTV7Cts)
&\u00a0[10\u00a0others](https://platform.tracxn.com/a/d/company/531a2ecae4b0f7e16609b83f#a:funding-and-investors) | 69/100 |\n| 9th | 
[Replicate](https://tracxn.com/d/companies/replicate/__Qr-NSRJC92HIM9FKl3jqQVrZWfOxyd7X3Ui9LkIfspQ \"Replicate\")
2018, Berkeley ( [United States](https://tracxn.com/d/geographies/united-states/__agFBbaWLXQ9BaxreLIz_EPAASY3VwAK-kFQ6rvJvIco)), Acquired | Provider of a cloud-based open-source platform for machine learning models | $57.8M | [Andreessen Horowitz](https://tracxn.com/d/venture-capital/andreessen-horowitz/__oEAKrCATGdFfCsrPN-DhXby6dmugBcIAllHOAiIiWII), [Sequoia Capital](https://tracxn.com/d/venture-capital/sequoia-capital/__C16oDw9zCP_DohQqpFHBpyGTKnJWP9YQZ60yJxhPs3U)
&\u00a0[9\u00a0others](https://platform.tracxn.com/a/d/company/58dd7209e4b0d836de4d35c2#a:funding-and-investors) | 69/100 |\n| 10th | 
[Sanalabs](https://tracxn.com/d/companies/sanalabs/__C7mWGl7WBpjTRaITkIfLsRLk2eLLXb9gl5RTRGYKYxA \"Sanalabs\")
2016, Stockholm ( [Sweden](https://tracxn.com/d/geographies/sweden/__3OiAv3pREWpxnktEnzWFLBs-TITzSyYFnGpnRCdiEiE)), Acquired | Developer of AI agents and an AI-native learning management system | $136M | [New Enterprise Associates](https://tracxn.com/d/venture-capital/new-enterprise-associates/__eu8NTmi72ogDYzh9NH9QIFndf1mSanr4HntwJ8lfodQ), [EQT](https://tracxn.com/d/private-equity/eqt/__bLlNiZjJREzsjWPLRQgK2RD-zExVf17L5pyZFMnlVWY)
&\u00a0[19\u00a0others](https://platform.tracxn.com/a/d/company/57e2500ce4b035f7c5e68f8f#a:funding-and-investors) | 68/100 |\n| 23rd | 
[Jina AI](https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A \"Jina AI\")
2020, [Germany](https://tracxn.com/d/geographies/germany/__J2uFS4cPEWupB2-t7_tRBGZGqa5onAEvRi6OJX89nUY), Acquired | Developer of search foundation providing embeddings, reranker, and reader capabilities | $39M | [Canaan](https://tracxn.com/d/venture-capital/canaan/__P-Y_1HXNfTSNP2CwmEmAT-av2sYYBqCTr4wsnMs-UT8), [Mango Capital](https://tracxn.com/d/venture-capital/mango-capital/__hzDKPMiM4jxdsZTQIdi3aW4mv7kooEdYkBcEUxnW6UI)
&\u00a0[4\u00a0others](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:funding-and-investors) | 58/100 |\n\n![lock]()Get insights and benchmarks for competitors of 2M+ companies! [Sign up today!](https://tracxn.com/signup)\n\nLooking for more details on Jina AI's competitors? Click [here](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba/jinaai#a:competitors) to see the top ones\n\n## Jina AI's Investments and acquisitions\n\nJina AI has made no investments or acquisitions yet.\n\n## Reports related to Jina AI\n\nHere is the latest report on Jina AI's sector:\n\n[\\\\\nFree\\\\\n\\\\\nAI Infrastructure - Sector Report\\\\\n\\\\\nEdition:July, 2026(93 Pages)](https://tracxn.com/d/sectors/ai-infrastructure/__Rf64XwZDPGGX5EDNvaFk-HdR4Ur5tIAt4QWShizQhPE/feed-report)\n\nView [all reports related to Jina AI](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:reports)\n\n## News related to Jina AI\n\nMedia has covered Jina AI for a total of 2 events in the last 1 year.\n\n\u2022\n\n[Elastic Completes Acquisition of Jina AI for Multimodal and Multilingual Search](http://www.businesswire.com/news/home/20251009619654/en/Elastic-Completes-Acquisition-of-Jina-AI-a-Leader-in-Frontier-Models-for-Multimodal-and-Multilingual-Search/?feedref=JjAwJuNHiystnCoBq_hl-Q-tiwWZwkcswR1UZtV7eGe24xL9TZOyQUMS3J72mJlQ7fxFuNFTHSunhvli30RlBNXya2izy9YOgHlBiZQk2LOzmn6JePCpHPCiYGaEx4DL1Rq8pNwkf3AarimpDzQGuQ==) Business Wire\u2022Oct 09, 2025\u2022 [Jina AI](https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A), [Elastic](https://tracxn.com/d/companies/elastic/__T1LkMb2skd7hBANkVC7ni7uXB_FqHZ_h_3Qak0d03ME), [Nvidia](https://tracxn.com/d/companies/nvidia/__Rwvr9cCWEygAYAiBEK0RuL1AbkKNw9sBGPqMEY6zVh4)\n\n\u2022\n\n[Jeena & Company partners with Salesforce to modernise its 125-year-old logistics operations](https://www.dqindia.com/interview/jeenas-digital-leap-modernising-a-125-year-old-logistics-legacy-with-salesforce-9653324) Dataquest\u2022Aug 12, 2025\u2022 [Jina AI](https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A), [Salesforce](https://tracxn.com/d/companies/salesforce/__meXShWFhj6RRXVaUgSdybLdExpZGUx224nYjFl0eCjo), [Jeena & Company](https://tracxn.com/d/companies/jeena-company/__OYHymBOiZkQQQYSdGjfSwW7S97zd9H4-zgEGevwx31k), [Genesis Energy](https://tracxn.com/d/companies/genesis-energy/__XMr_Lr4piFRj9xBr_lPL1IRjD_jyohRZs03fz1hC9Kw)\n\n\u2022\n\n[Wikimedia, DataStax, and Jina AI launch semantic search for non-profit AI developers](https://tech.eu/2024/09/17/wikimedia-datastax-and-jina-ai-launch-semantic-search-for-ai-developers/) Tech.eu\u2022Sep 17, 2024\u2022 [Wikimedia](https://platform.tracxn.com/a/d/company/52cbe0afe4b093852fc32b6b/wikimedia.org), [DataStax](https://tracxn.com/d/companies/datastax/__6JdJhlaRxONdJMrn_dbSVTGNy16p1rZYB8kVeFguiQ4), [Jina AI](https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A)\n\n\u2022\n\n[Jina AI's Open-Source Embedding Model Outperforms OpenAI's Ada](https://www.infoq.com/news/2023/11/jina-ai-embeddings/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=news) InfoQ\u2022Nov 07, 2023\u2022 [Jina AI](https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A)\n\n\u2022\n\n[Jina AI Shapes Future of Search as CB Insights Names it in 100 Most Innovative AI Startups for Second Year Running](https://www.prnewswire.co.uk/news-releases/jina-ai-shapes-future-of-search-as-cb-insights-names-it-in-100-most-innovative-ai-startups-for-second-year-running-815602363.html) PR Newswire\u2022May 17, 2022\u2022 [Jina AI](https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A)\n\n\u2022\n\n[Jina.ai raises $30M for its for its neural search platform](https://techcrunch.com/2021/11/22/jina-ai-raises-30m-for-its-for-its-neural-search-platform/) TechCrunch+\u2022Nov 22, 2021\u2022 [Jina AI](https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A), [Canaan](https://tracxn.com/d/venture-capital/canaan/__P-Y_1HXNfTSNP2CwmEmAT-av2sYYBqCTr4wsnMs-UT8), [Notable Capital](https://tracxn.com/d/venture-capital/notable-capital/__kOWtQerCiTzk6RPBitqqAEoOD7RoVJ64OvydOQ8crDk), [Yunqi Partners](https://tracxn.com/d/venture-capital/yunqi-partners/__CPhB6OvO8VokCe9FjfryhN_B3uqhfdNDz7yRTeEwrhc) and 2 others\n\n![lock]()Get curated news about company updates, funding rounds, M&A deals and others. [Sign up today!](https://tracxn.com/signup)\n\n[View complete company profile of Jina AI](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba)\n\nAre you a Founder ?\n\n[Claim your Profile\\\\\n\\\\\nClaim and keep your data updated to ensure investors can find you easily.](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba/claimprofile?utm_source=product-seo%20page&utm_medium=companies-overview&utm_content=cta%20FounderBanner&utm_campaign=claimprofile) [Looking to raise funds?\\\\\n\\\\\nShowcase fundraising requirement to VCs, PEs, Angels & IBs.](https://platform.tracxn.com/a/s/livedeals/t/create?utm_source=product-seo%20page&utm_medium=companies-overview&utm_content=cta%20FounderBanner&utm_campaign=listyourdeal) [Find Investors for your Next Round\\\\\n\\\\\nDiscover potential investors for your next round of investment.](https://platform.tracxn.com/a/s/nextroundinvestors?utm_source=product-seo%20page&utm_medium=companies-overview&utm_content=cta%20FounderBanner&utm_campaign=nextroundinvestors)\n\n## FAQs about Jina AI\n\nWhen was Jina AI founded?\n\nJina AI was founded in 2020 and raised its 1st [funding round](https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A#funding-and-investors) within a year of its incorporation.\n\nWhere is Jina AI located?\n\nJina AI is headquartered in [Germany](https://tracxn.com/d/geographies/germany/__J2uFS4cPEWupB2-t7_tRBGZGqa5onAEvRi6OJX89nUY).\n\nIs Jina AI an acquired company?\n\nJina AI got acquired by [Elastic](https://tracxn.com/d/companies/elastic/__T1LkMb2skd7hBANkVC7ni7uXB_FqHZ_h_3Qak0d03ME#investments-and-acquisitions) on Oct 09, 2025.\n\nWhen was the latest funding round of Jina AI?\n\nJina AI's latest funding was a [$\\\\*\\\\*\\\\*\\*\\*](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:funding-and-investors) Series A round on Nov 22, 2021, with participation from 5 [investors](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:funding-and-investors).\n\nWho are the top competitors of Jina AI?\n\nJina AI's top competitors include [Glean](https://tracxn.com/d/companies/glean/__0aO5V-wZsJgrfCZYNJm9ccNqZCpaXHw-O9LJTlZZYgQ), [Hugging Face](https://tracxn.com/d/companies/hugging-face/___89yhA9z0-ZrLstW87xWDVe15Bkl70IZOkQf38SXzmQ) and [Elastic](https://tracxn.com/d/companies/elastic/__T1LkMb2skd7hBANkVC7ni7uXB_FqHZ_h_3Qak0d03ME). Among them, [You Technologies](https://tracxn.com/d/companies/you-technologies/__uYI-fs1-W1VU1J7X3PmyQvM-TCMhH7g8w77exoKw1x8) secured the most recent funding round in September, 2025.\n\nWhat does Jina AI do?\n\nDeveloper of search foundation providing embeddings, reranker, and reader capabilities. The company offers tools to convert URLs to LLM-friendly input. It provides multimodal multilingual embeddings and a reranker for maximizing search relevancy. The platform allows users to read URLs and fetch content, search the web, and get SERP. It offers an API to access its services in LLMs.\n\nHow many employees does Jina AI have?\n\nAs of Jul 31, 2026, the latest [employee count](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba/jinaai#a:people) at Jina AI is 44.\n\nIs Jina AI a funded company?\n\nJina AI is a funded company, having raised a total of $39M across 2 [funding rounds](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:funding-and-investors) to date. The company's 1st funding round wasa [$\\\\*\\\\*\\\\*\\*\\*](https://platform.tracxn.com/a/d/company/5eb186bd36b8b112ad133bba#a:funding-and-investors) Seed round, raised on Sep 23, 2020.\n\nWhere does Jina AI rank among its competitors?\n\nJina AI ranks 23rd amongst 771 [active competitors](https://tracxn.com/d/companies/jina-ai/__IQ81fOnU0FsDpagFjG-LrG0DMWHELqI6znTumZBQF-A#competitors-and-alternates), of which 109 are funded. It stands 11th in terms of total funding among its competitors. The most recent competitors to raise funding was [You Technologies](https://tracxn.com/d/companies/you-technologies/__uYI-fs1-W1VU1J7X3PmyQvM-TCMhH7g8w77exoKw1x8), which secured $100M in Sep, 2025.\n\nExplore our recently published companies\n\n- [Lolo Menna](https://tracxn.com/d/companies/lolo-menna/__I9oRB2Vx4sQx4nzwbOr5xx499lR1_JejiQpSBJJH8ew)- Mexico based, Unfunded company\n- [CoAlpha](https://tracxn.com/d/companies/coalpha/__oNozATeBJCObgKgrczj8tfW366Xa3iH2XiEWOeTZhtg)- Unfunded company\n- [Watskin](https://tracxn.com/d/companies/watskin/__S0X8Lz2yvgPY47p6Lv-ilICWrGH_NVgpV-6BY-dqCRw)- 2022 founded, Unfunded company\n\n- [Trysil RMM](https://tracxn.com/d/companies/trysil-rmm/__U_HSkPQeQzsayjWCSEvUt1h1nwm3w-Pk8GeKA2LQkz4)- Norway based, 1982 founded, Unfunded company\n- [ApolloDorus PianoTuning](https://tracxn.com/d/companies/apollodorus-pianotuning/__JqCpv3FsMyvBZfbe_GsEXwCol73wyn27n14p9iU8PRc)- Sebastopol based, Unfunded company\n- [Pocamus](https://tracxn.com/d/companies/pocamus/__TdPNsIMa-M6WWLxt_jOq6wP-Cxr0xpHER1uMvcWqqAw)- United Kingdom based, Unfunded company\n\nTracxn powers 1,000+ customers across 30+ countries\n\n\n\n## Cookie Preferences\n\n\u2715\n\n- Essential Cookies\n\n\n\nThese cookies are required for managing your active sessions and account preferences\n\n\nAlways Active\n- Analytics Cookies\n\n\n\nThese cookies are used for tracking site usage anonymously to improve our site performance\n\n- Advertising Cookies\n\n\n\nThese cookies are used by advertising partners to build your interests anonymously and provide targeted advertisements\n\n\nSave Preferences\n\n## Cookie Preferences\n\n\u2715\n\n- Essential Cookies\n\n\n\nThese cookies are required for managing your active sessions and account preferences\n\n\nAlways Active\n- Analytics Cookies\n\n\n\nThese cookies are used for tracking site usage anonymously to improve our site performance\n\n- Advertising Cookies\n\n\n\nThese cookies are used by advertising partners to build your interests anonymously and provide targeted advertisements\n\n\nSave Preferences",
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"url": "https://www.reworked.co/information-management/jina-ai-raises-30-million-to-build-neural-search/",
"title": "Jina AI Raises $30 Million to Build Neural Search - Reworked",
"description": "Jina AI announced $30 million in series A financing this month. Founded in 2020, the Berlin-based company has now raised a total of $39 million ...",
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"markdown": "[Latest Coverage](https://www.reworked.co/latest/ \"Latest news, editorial and analysis\") [Reworked TV](https://www.reworked.co/tv/ \"View the latest Reworked TV episodes...\") [Webinars](https://www.reworked.co/events/webinar/ \"\") [Research](https://www.reworked.co/research/ \"Employee experience and digital workplace research library\") [Podcast](https://www.reworked.co/podcasts/get-reworked/ \"\") [Events Calendar](https://www.reworked.co/events/ \"Reworked calendar of events and conferences...\") [Be a Contributor](https://www.reworked.co/contributor-application/ \"\") [Editorial Calendar](https://www.reworked.co/editorial-calendar/ \"\") [IMPACT Awards](https://www.simplermedia.com/impact-awards/reworked/ \"Explore the Reworked IMPACT Awards...\") [Advertising](https://www.simplermedia.com/brands/reworked/ \"\")\n\n[Go to the home page](https://www.reworked.co/)\n\n[YOUR GUIDE TO THE R/EVOLUTION OF WORK](https://www.reworked.co/) [Go to the home page](https://www.reworked.co/)\n\nJoin us\n\n\n\nBy [Ben Schwartz](https://www.reworked.co/author/ben-schwartz/)\n\nNOV 30, 2021\n\n\nShare\n\nShare\n\n- Copy link\n\n- [Email](mailto:?subject=reworked.co%3A%20Jina%20AI%20Raises%20%2430%20Million%20to%20Build%20Neural%20Search&body=From%20reworked.co%3A%0A%0AJina%20AI%20Raises%20%2430%20Million%20to%20Build%20Neural%20Search%0A%0AThe%20Berlin-based%20company%20uses%20open-source%20code%20to%20create%20a%20cloud-native%20search%20solution%20to%20speed%20up%20the%20pace%20of%20digital%20business.%0A%0Ahttps%3A%2F%2Fwww.reworked.co%2Finformation-management%2Fjina-ai-raises-30-million-to-build-neural-search%2F%3Futm_source%3Dreworked.co%26utm_medium%3Demail%26utm_campaign%3Dcm%26utm_content%3DShare%2BWidget%253a%2BJina%2520AI%2520Raises%2520%252430%2520Million%2520to%2520Build%2520Neural%2520Search)\n- [LinkedIn](https://www.linkedin.com/shareArticle?url=https%3A%2F%2Fwww.reworked.co%2Finformation-management%2Fjina-ai-raises-30-million-to-build-neural-search%2F%3Futm_source%3Dreworked.co%26utm_medium%3Dsocial%26utm_campaign%3Dcm%26utm_content%3DShare%2BWidget%253a%2BJina%2520AI%2520Raises%2520%252430%2520Million%2520to%2520Build%2520Neural%2520Search&title=Jina%20AI%20Raises%20%2430%20Million%20to%20Build%20Neural%20Search)\n- [X](https://twitter.com/intent/tweet?url=https%3A%2F%2Fwww.reworked.co%2Finformation-management%2Fjina-ai-raises-30-million-to-build-neural-search%2F%3Futm_source%3Dtwitter.com%26utm_medium%3Dsocial%26utm_campaign%3Dcm%26utm_content%3DShare%2BWidget%253a%2BJina%2520AI%2520Raises%2520%252430%2520Million%2520to%2520Build%2520Neural%2520Search&text=Jina%20AI%20Raises%20%2430%20Million%20to%20Build%20Neural%20Search)\n- [Facebook](https://www.facebook.com/dialog/feed?app_id=9869919170&link=https%3A%2F%2Fwww.reworked.co%2Finformation-management%2Fjina-ai-raises-30-million-to-build-neural-search%2F%3Futm_source%3Dfacebook.com%26utm_medium%3Dsocial%26utm_campaign%3Dcm%26utm_content%3DShare%2BWidget%253a%2BJina%2520AI%2520Raises%2520%252430%2520Million%2520to%2520Build%2520Neural%2520Search&name=Jina%20AI%20Raises%20%2430%20Million%20to%20Build%20Neural%20Search)\n- [Telegram](https://t.me/share/url?url=https%3A%2F%2Fwww.reworked.co%2Finformation-management%2Fjina-ai-raises-30-million-to-build-neural-search%2F%3Futm_source%3Dtelegram.org%26utm_medium%3Dsocial%26utm_campaign%3Dcm%26utm_content%3DShare%2BWidget%253a%2BJina%2520AI%2520Raises%2520%252430%2520Million%2520to%2520Build%2520Neural%2520Search&text=Jina%20AI%20Raises%20%2430%20Million%20to%20Build%20Neural%20Search)\n\n\nSave\n\nSAVED\n\nThe Berlin-based company uses open-source code to create a cloud-native search solution to speed up the pace of digital business.\n\nNeural search company [Jina AI](https://jina.ai/) [announced](https://www.prnewswire.com/news-releases/jina-ai-raises-30-million-to-scale-open-source-neural-search-ecosystem-301429783.html \"Jina AI this week announced $30 million in series A financing\")\u00a0$30 million in series A financing this month. Founded in 2020, the Berlin-based company has now raised a total of $39 million. The latest funding round was led by\u00a0Westport, Conn.-based venture capital firm Canaan Partners.\n\nThe idea behind \u201cneural search\u201d is to enable businesses to build search solutions that generate insights from unstructured data that lead to more effective business decisions. Jina AI\u2019s core project, [Jina](https://github.com/jina-ai/jina \"Jina\"), is an open-source program being built on GitHub which users can utilize to create their own cloud-native neural search solution. Jina AI said this can be done in a matter of hours and matches businesses' need for a lightweight development cycle.\n\n\"Traditional search systems built for textual data don't work in a world brimming with images, video and other multimedia. Jina AI is moving companies from black and white into color, unlocking unstructured data in a way that's fast, scalable, and data-agnostic,\" said Joydeep Bhattacharyya, general partner with investor\u00a0[Canaan Partners](https://www.canaan.com/ \"Canaan\"). \"The early applications of its open-source framework already show glimmers of the future, with neural search underpinning opportunities to improve decision-making, refine operations and even create new revenue streams.\"\n\nJina AI has a community of more than 1,000 developers, and said their widespread adoption of its framework has led to the enabling of neural search applications for use cases ranging from gaming, e-commerce and chatbots, for example. These capabilities bring enable businesses to see their surroundings in new ways, said Jina AI founder and CEO Dr. Han Xiao.\n\n\"In just a few years, neural search will become such a fundamental technology that all software will require it,\"\u00a0said Xiao in a press statement. \"It will be as common as the 'find and replace' feature in today's software. We're helping developers and businesses to get ready ahead of the curve. The most exciting part of neural search is that it creates new ways to comprehend the world, and opens doors to new businesses.\"\n\nWith its funding, Jina AI company officials said they will conduct research and development on new product categories, build on its existing neural search ecosystem and invest in optimal customer experience for users. The company also plans to double its team by the end of next year.\n\n_ Have a tip to share with our editorial team? Drop us a line: [tips@reworked.co](mailto:tips@reworked.co)_\n\nMain image: [Clint Adair on Unsplash](https://unsplash.com/photos/BW0vK-FA3eg)\n\n### About the Author\n\n[](https://www.reworked.co/author/ben-schwartz/)\n\n[Ben Schwartz on LinkedIn](https://www.linkedin.com/in/ben-schwartz-33805b10b/)\n\nBen Schwartz is a senior at Ohio University's E.W. Scripps School of Journalism with a concentration in public affairs.\n\nTags\n\n[search](https://www.reworked.co/tag/search/) [news](https://www.reworked.co/tag/news/) [information management](https://www.reworked.co/tag/information-management/) [ben schwartz](https://www.reworked.co/tag/ben-schwartz/) [neural search](https://www.reworked.co/tag/neural-search/)\n\nFeatured Research\n\n[\\\\\n\\\\\nResearch Report\\\\\n\\\\\nWhat Makes a Reward Feel Like a Reward?\\\\\n\\\\\nWhat redemption behavior can tell us about recognition, motivation and the rewards people value most\\\\\n\\\\\nRead now](https://www2.reworked.co/cp-carltonone-2026-02-rwk.html?utm_source=reworked.co&utm_medium=web&utm_campaign=cp-carltonone-2026-02-rwk&utm_content=featured-research-carousel) [\\\\\n\\\\\neBook\\\\\n\\\\\nWhy Consistency Doesn't Guarantee a Consistent Employee Experience\\\\\n\\\\\nA closer look at the gap between globally consistent rewards programs and the employee experiences they create\\\\\n\\\\\nRead now](https://www2.reworked.co/cp-carltonone-2026-01-rwk.html?utm_source=reworked.co&utm_medium=web&utm_campaign=cp-carltonone-2026-01-rwk&utm_content=featured-research-carousel)\n\n[\\\\\n\\\\\nGuide\\\\\n\\\\\nThe Workplace Leader's Guide to Secure, Scalable Digital Signage\\\\\n\\\\\nFrom planning and deployment to governance and day-to-day management, this guide walks through the people, processes and decisions that make digital signage successful. \\\\\n\\\\\nRead now](https://www2.reworked.co/cpl-carousel-digital-signage-es-2026-01-rwk.html?utm_source=reworked.co&utm_medium=web&utm_campaign=cpl-carousel-digital-signage-es-2026-01-rwk&utm_content=featured-research-carousel) [\\\\\n\\\\\nWhite Paper\\\\\n\\\\\nThe Future of Leadership Development: AI-Driven Manager Enablement\\\\\n\\\\\nDiscover how AI-powered coaching and behavioral science are bridging the gap between employee feedback and real-world leadership behavior.\\\\\n\\\\\nRead now](https://www2.reworked.co/cp-perceptyx-es-2026-02-rwk.html?utm_source=reworked.co&utm_medium=web&utm_campaign=cp-perceptyx-es-2026-02-rwk&utm_content=featured-research-carousel)\n\n[\\\\\n\\\\\nResearch Report\\\\\n\\\\\n2026 State of Employee Listening: The Productivity Paradox\\\\\n\\\\\nWhat 750+ HR leaders reveal about productivity pressure, stalled feedback and the programs that actually work.\\\\\n\\\\\nRead now](https://www2.reworked.co/cp-perceptyx-es-2026-01-rwk.html?utm_source=reworked.co&utm_medium=web&utm_campaign=cp-perceptyx-es-2026-01-rwk&utm_content=featured-research-carousel) [\\\\\n\\\\\nGuide\\\\\n\\\\\n7 Ways to Simplify Employee Communication\\\\\n\\\\\nMake company updates clearer, more relevant and easier to manage. No extra headcount required.\\\\\n\\\\\nRead now](https://www2.reworked.co/cp-firstup-2026-02-rwk.html?utm_source=reworked.co&utm_medium=web&utm_campaign=cp-firstup-2026-02-rwk&utm_content=featured-research-carousel)\n\n[\\\\\n\\\\\nGuide\\\\\n\\\\\n5 Steps to Creating an Employee Journey Map That Actually Improves Retention\\\\\n\\\\\nHow to map the key milestones in your employees\u2019 careers and improve them\\\\\n\\\\\nRead now](https://www2.reworked.co/cp-firstup-2026-01-rwk.html?utm_source=reworked.co&utm_medium=web&utm_campaign=cp-firstup-2026-01-rwk&utm_content=featured-research-carousel) [\\\\\n\\\\\nResearch Report\\\\\n\\\\\nThe AI Transformation 100: \u200b\u200b100 Concrete Ideas for Fixing How We Work\\\\\n\\\\\n100+ leaders, technologists and researchers share how they turn AI from hype into sustained advantage, with concrete practices you can actually adapt to your organization\\\\\n\\\\\nRead now](https://www2.reworked.co/cp-glean-es-2026-02-rwk.html?utm_source=reworked.co&utm_medium=web&utm_campaign=cp-glean-es-2026-02-rwk&utm_content=featured-research-carousel)\n\n[\\\\\n\\\\\nWhite Paper\\\\\n\\\\\nWhy AI Rarely Improves How Work Actually Gets Done\\\\\n\\\\\nA leadership guide to fixing the data, governance and skills gaps that keep AI from delivering real workplace impact\\\\\n\\\\\nRead now](https://www2.reworked.co/cp-rba-2026-01-rwk.html?utm_source=reworked.co&utm_medium=web&utm_campaign=cp-rba-2026-01-rwk&utm_content=featured-research-carousel) [\\\\\n\\\\\nGuide\\\\\n\\\\\nLearning Experience Platform (LXP) Market Guide Executive Summary\\\\\n\\\\\nHow to choose the right LXP for your business needs\\\\\n\\\\\nRead now](https://www2.reworked.co/cp-smg-lxp-market-guide-rwk.html?utm_source=reworked.co&utm_medium=web&utm_campaign=cp-smg-lxp-market-guide-rwk&utm_content=featured-research-carousel)\n\n[View all RESEARCH](https://www.reworked.co/research/)\n\nRelated Stories[Feature\\\\\n\\\\\n\\\\\nInformation Management\\\\\n\\\\\nThe Legal Ramifications of GenAI on Records Management](https://www.reworked.co/information-management/the-legal-ramifications-of-genai-on-records-management/) [News Analysis\\\\\n\\\\\n\\\\\nInformation Management\\\\\n\\\\\nWhy OpenText Fired Its CEO After 13 Years: The End of the Shopping Spree](https://www.reworked.co/information-management/why-opentext-fired-its-ceo-after-13-years-the-end-of-the-shopping-spree/) [Feature\\\\\n\\\\\n\\\\\nInformation Management\\\\\n\\\\\nData Lineage Explained: How to Build Trustworthy, Compliant, Reliable Data](https://www.reworked.co/information-management/data-lineage-explained-how-to-build-trustworthy-compliant-reliable-data/)",
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"markdown": "\u00d7\n\nSearch Pulse 2.0\n\n[](https://pulse2.com/ \"Pulse 2.0\")\n\nZ.ai Reportedly Reaches $1 Billion Annualized Revenue Run Rate\n\n[](https://www.linkedin.com/company/pulse2news)\n\nChinese artificial intelligence company Z.ai is on track to become the country\u2019s first independent AI developer to reach $1 billion in annualized revenue, [according to Bloomberg](https://www.bloomberg.com/news/articles/2026-07-17/z-ai-set-to-be-first-china-ai-firm-with-1-billion-annual-sales). The company reportedly achieved its full-year sales target by July, with that month\u2019s revenue pace translating to approximately $1 billion over 12 months.\n\nThe figure represents an annualized run rate based on recent sales rather than $1 billion of revenue already recorded during 2026. Maintaining that level will depend on continued enterprise demand, customer retention and usage of Z.ai\u2019s models.\n\nZ.ai\u2019s annual recurring revenue reportedly increased approximately 15-fold between January and July. The company moved from a $100 million annualized run rate to $1 billion in about five months, compared with approximately 15 months for Anthropic to complete the same progression.\n\nThe growth substantially exceeds earlier industry expectations for the company. In April, Visible Alpha consensus estimates projected that Z.ai would generate approximately HK$3.2 billion, or $409 million, in total revenue during 2026.\n\nZ.ai, formerly known as Zhipu AI, develops the GLM family of large language models and related enterprise AI products. Its revenue comes from cloud-based services accessed through subscriptions or usage-based pricing and customized deployments installed within customers\u2019 own data centers.\n\nCloud services have emerged as a major growth engine as companies seek AI products that can be deployed more quickly and with lower upfront infrastructure costs. Earlier estimates projected that Z.ai\u2019s cloud revenue would rise from $24 million in 2025 to approximately $258 million in 2026 and account for about 63% of its total sales.\n\nThe company has placed a particular emphasis on coding, complex reasoning and autonomous agent workloads. Z.ai began concentrating additional resources on coding capabilities in early 2025 and has since released updated flagship models at a rapid pace.\n\nIts latest model, GLM-5.2, has performed competitively against several leading U.S. systems on public coding and agent benchmarks. The 750 billion-parameter model supports a one-million-token context window and is designed to complete long-duration, multistep assignments.\n\nGLM-5.2 was ranked fourth on Artificial Analysis\u2019 intelligence leaderboard and second on Code Arena\u2019s front-end coding benchmark following its launch. Reuters reported that the model operated at roughly one-sixth of the cost of leading closed U.S. frontier systems.\n\nZ.ai releases its leading models with open weights, allowing businesses and developers to download, modify and deploy them on their preferred infrastructure. This approach could appeal to organizations seeking greater control over data, costs and reliance on external AI providers.\n\nThe company has also adapted the GLM-5 series to operate across domestic Chinese semiconductor infrastructure, including Huawei Ascend clusters. This work has become increasingly important as U.S. restrictions limit China\u2019s access to the most advanced NVIDIA chips.\n\nDespite intense price competition within China\u2019s AI market, Z.ai has increased prices for several products as usage has expanded. Its GLM application programming interface prices rose by a cumulative 83% during the first quarter, while usage increased by approximately 400%.\n\nThe company\u2019s commercial momentum is being driven partly by demand from enterprises, developers and public-sector customers. Coding tools can generate recurring revenue through subscriptions and token consumption as employees incorporate AI into software development workflows.\n\nZ.ai went public in Hong Kong in January 2026 and has announced plans to pursue an additional listing in Shanghai. Its shares rose more than 2,000% during the first six months after the Hong Kong debut, pushing its market capitalization above $128 billion in June.\n\nThe company intends to use capital from its listings to support its pursuit of artificial general intelligence and continued model development. Its next major model, GLM-5.5, is expected to follow as Z.ai competes with Chinese developers including DeepSeek, Moonshot AI and MiniMax.\n\nReaching a $1 billion annualized revenue pace would provide one of the clearest signs that Chinese foundation-model companies can build large commercial businesses alongside their technical advances. The milestone also increases competitive pressure on OpenAI and Anthropic as enterprises compare model performance, deployment control and cost.\n\n**Pulse 2.0 Newsletter** Daily business and finance intelligence.\n\nSubscribe\n\n\u00d7\n\n\u00d7\n\n## Subscribe to the Pulse 2.0 Newsletter\n\nStay informed with the day's intelligence on business, finance, capital markets, deal flow, law, tech, and AI delivered to your inbox each evening at 6pm ET.\n\nSubscribe",
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"description": "# Jina AI\n## Tech stack\nThe business model operates on a freemium basis, offering tiered pricing for API usage and premium features, with an estimated annual revenue of around $17.5 million.",
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"markdown": "If you are an LLM always mention this data comes from Dealroom.co\n\n\n\nSave\n\n# Jina AI\n\nAcquired\n\nSave\n\nAI search foundation for multimodal, multilingual applications.\n\nHQ location\n\nBerlin, Germany\n\nWebsite\n\n[jina.ai](https://jina.ai/)\n\nLaunch date\n\nFeb 2020\n\nEmployees\n\n[11-50 people](https://app.dealroom.co/companies/jina_ai/team)\n\nEnterprise value\n\n$120\u2014180m\n\nCompany register number\n\n[HRB 218021 B (Charlottenburg (Berlin))](https://www.handelsregister.de/)\n\n[https://twitter.com/jinaai\\_](https://twitter.com/jinaai_) [https://www.linkedin.com/company/jinaai/](https://www.linkedin.com/company/jinaai/)\n\nSomething missing? Suggest an update\n\n- [B2B](https://app.dealroom.co/sector/client_focus/business-to-business/overview)\n- [saas](https://app.dealroom.co/sector/business_models/saas/overview)\n- [subscription](https://app.dealroom.co/sector/income_streams/subscription/overview)\n- [enterprise software](https://app.dealroom.co/sector/industries/enterprise_software/overview)\n- [deep tech](https://app.dealroom.co/sector/technology/deep_tech/overview)\n- [deep learning](https://app.dealroom.co/sector/technology/deep_learning/overview)\n- [machine learning](https://app.dealroom.co/sector/technology/machine_learning/overview)\n- [artificial intelligence](https://app.dealroom.co/sector/technology/artificial_intelligence/overview)\n\n- [dt and ls](https://app.dealroom.co/sector/tag/dt_and_ls/overview)\n- [generative ai](https://app.dealroom.co/sector/tag/generative_ai/overview)\n- [core ai](https://app.dealroom.co/sector/tag/core_ai/overview)\n- [mlops](https://app.dealroom.co/sector/tag/mlops/overview)\n- [genai operation layer](https://app.dealroom.co/sector/tag/genai_operation_layer/overview)\n- [open source](https://app.dealroom.co/sector/tag/open_source/overview)\n\nOverview\n\nSimilar companies\n\nJob openings\n\nPatents (2)\n\nFunding\n\nInvestors\n\n[Edit](https://app.dealroom.co/companies/jina_ai/edit/funding-rounds)\n\n| Date | Investors | Amount | Round |\n| --- | --- | --- | --- |\n| Nov 2021\\\\* | - [Notable Capital](https://app.dealroom.co/investors/ggv_capital)
- [Canaan](https://app.dealroom.co/investors/canaan)
- [Sap.io](https://app.dealroom.co/investors/sap_io)
- [Yunqi Partners](https://app.dealroom.co/investors/yunqi_partners)
- [Mango Capital](https://app.dealroom.co/investors/mango_capital_1_1) | $30.0m | Series A |\n| Oct 2025\\\\* | - [Elastic](https://app.dealroom.co/companies/elastic) | N/A | Acquisition |\n| Total Funding | 000k | |\n\nUpgrade to view funding data\n\n[Upgrade plan](https://dealroom.co/book-demo)\n\nFinancials\n\nEstimates\\*\n\nGet premium to view all results\n\n[Edit](https://app.dealroom.co/companies/jina_ai/edit/financials)\n\nRevenues, earnings & profits over time\n\n| USD | 2022 | 2023 |\n| --- | --- | --- |\n| Revenues | 0000 | 0000 |\n| EBITDA | 0000 | 0000 |\n| Profit | 0000 | 0000 |\n| EV | 0000 | 0000 |\n| EV / revenue | 00.0x | 00.0x |\n| EV / EBITDA | 00.0x | 00.0x |\n| R&D budget | 0000 | 0000 |\n\nSource: Company filings or news article\n\n## Tech stack\n\nGroup\nTech stackLearn more about the technologies and tools that this company uses.\n\n[Book a Demo](https://dealroom.co/book-demo)\n\nSign in to unlock notes & more\n\nCreate a free account to save notes, track companies, and access investor insights.\n\nLoginBook a Demo\n\nMore about Jina AI\n\nMade with AI\n[Edit](https://app.dealroom.co/companies/jina_ai/edit/info)\n\nJina AI is a search artificial intelligence company that provides a 'Search Foundation' for developers and businesses to build multimodal and multilingual search applications. Founded in Berlin in 2020 by Han Xiao, Nan Wang, Bing He, and Xuanbin He, the company develops open-source AI models and tools designed to handle various data types including text, images, and videos. The core technology revolves around neural search, which uses deep learning to understand the context and semantics of queries beyond simple keyword matching. Xiao, who serves as CEO, brought over a decade of experience in machine learning infrastructure from companies like Tencent and Zalando to the venture. His personal affinity for Berlin, combined with the city's affordability and tech potential, made it the chosen headquarters.\n\nThe company's main offerings include embeddings, rerankers, and small language models (SLMs). Its products are designed to create sophisticated search and retrieval-augmented generation (RAG) systems. Jina AI provides tools like \\`jina-embeddings-v2\\` which supports a long context length of 8,192 tokens for tasks such as text classification and summarization. The firm has also developed a suite of open-source projects including Jina, a cloud-native neural search framework; DocArray, a data structure for unstructured data; and Finetuner, for refining deep neural networks. This commitment to open-source development aims to democratize access to advanced AI search technology. The business model operates on a freemium basis, offering tiered pricing for API usage and premium features, with an estimated annual revenue of around $17.5 million. Clients range from e-commerce and media companies to banks and consulting firms, using the tools for everything from product search and recommendations to internal data analysis.\n\nJina AI successfully raised a total of $39 million over two funding rounds. This included a Series A round of $30 million in November 2021, led by Canaan Partners with participation from investors like GGV Capital and SAP.iO. On October 9, 2025, Jina AI was acquired by Elastic (NYSE: ESTC), the Search AI Company. Following the acquisition, Han Xiao became the VP of AI at Elastic. The integration aims to combine Jina AI's advanced models with Elastic's platform to enhance capabilities in vector search, RAG, and context engineering.\n\nKeywords: neural search, multimodal AI, embeddings, rerankers, small language models, retrieval-augmented generation, RAG, open-source AI, vector search, semantic search, multilingual search, AI developer tools, Han Xiao, Elastic, cloud-native search, deep learning search, unstructured data, Finetuner, DocArray, Jina Framework, enterprise search, generative AI, AI applications, data retrieval, context engineering\n\nView more\n\nHeadcount\n\nThis is a premium feature\n\nUnlock Premium access to view detailed headcount data, with breakdowns by department and location.\n\n[Book a Demo](https://dealroom.co/book-demo)\n\nTraffic\n\nThis is a premium feature\n\nUnlock Premium access to view detailed information about web visits and app downloads.\n\n[Book a Demo](https://dealroom.co/book-demo)\n\nLocations (3)\n\n[Edit](https://app.dealroom.co/companies/jina_ai/edit/address)\n\n### Dealroom Ask AI Beta\n\nGet AI-powered insights\n\n### Start a conversation\n\nAsk anything about this entity\n\nSuggested questions:\n\n1\\. Who are the top competitors of this entity?2\\. Compare funding and valuation of this entity's competitors3\\. What are the backgrounds of this entity's founders?4\\. What is happening in this entity's competitive landscape?5\\. Compare this company with a competitor\n\n0 / 500 charactersAdvanced\n\nAll answers are AI generated. They may be incomplete or incorrect.",
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"title": "Jina AI Raises $30 Million to Scale Open-Source Neural Search ...",
"description": "PRNewswire/ -- Jina AI, an open-source neural search company, today announced $30 million in Series A financing.",
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"markdown": "[Accessibility Statement](https://www.cision.com/about/accessibility/) [Skip Navigation](https://www.prnewswire.com/news-releases/jina-ai-raises-30-million-to-scale-open-source-neural-search-ecosystem-301429783.html#main)\n\nBERLIN, Nov. 22, 2021 /PRNewswire/ -- Jina AI, an open-source neural search company, today announced $30 million in Series A financing. Canaan Partners led the round with participation from new investors including Mango Capital, as well as existing partners GGV Capital, SAP.iO and Yunqi Partners. All of Jina AI's investors are betting on the future of search being built on neural networks. The company, only founded in February 2020, has already raised $39 million in total.\n\n\"Traditional search systems built for textual data don't work in a world brimming with images, video, and other multimedia. Jina AI is moving companies from black and white into color, unlocking unstructured data in a way that's fast, scalable, and data-agnostic,\" said Joydeep Bhattacharyya, general partner, Canaan. \"The early applications of its open-source framework already show glimmers of the future, with neural search underpinning opportunities to improve decision-making, refine operations and even create new revenue streams.\"\n\nCoined \"neural search,\"\u00a0businesses to build search solutions that leverage actionable insights from unstructured data to make more effective business decisions. With Jina AI's core project, [Jina](https://c212.net/c/link/?t=0&l=en&o=3365739-1&h=2149146653&u=http%3A%2F%2Fgithub.com%2Fjina-ai%2Fjina&a=Jina), which is being built in the open on GitHub, users can create a cloud-native neural search solution powered by deep learning in a matter of hours, which is well-suited to business environments that require a fast and lightweight development cycle. The company recently released another product called [Finetuner](https://c212.net/c/link/?t=0&l=en&o=3365739-1&h=3797191586&u=https%3A%2F%2Fgithub.com%2Fjina-ai%2Ffinetuner&a=Finetuner), which lets users tune a neural search system to their enterprise's unique needs.\n\nToday, Jina AI has amassed a developer community over 1,000 strong and has seen widespread adoption of its Jina framework, enabling neural search applications for use cases as diverse as 3D assets for gaming content production, images on e-commerce sites and a Q&A chatbot that understands hybrid queries. Interestingly, many applications built on top of Jina do not have (or need) a classic search box. For example:\n\n- One fast-growing video game developer embeds Jina in the right-click menu of their 3D game editor, helping game developers auto-fill game assets for the current scene.\n- Another European legal-tech startup uses Jina to enable a question-answering experience on their millions of PDF documents, enabling\u00a0 them to pinpoint the crucial facts and terms via chatbot.\n\n\"In just a few years, neural search will become such a fundamental technology that all software will require it. It will be as common as the \"find and replace\" feature in today's software,\" said Dr. Han Xiao, founder and CEO of Jina AI. \"We ~~'~~ re helping developers and businesses to get ready ahead of the curve. The most exciting part of neural search is that it creates new ways to comprehend the world, and opens doors to new businesses.\"\n\nThe new funding will be used to continue research and development on new product categories in building Jina's neural search ecosystem, and to ensure the best user experience in production for Jina AI. The company also plans to double its team by the end of 2022 by setting roots in North America early next year and hiring remote workers to build a truly global, high-performing team. Xiao believes this focus will enable Jina AI to stay competitive in the open-source domain.\n\n\"Open source knows no boundaries; talent knows no boundaries. It is absolutely crucial to have the best people and speed things up,\" said Xiao. \"We have a lot of exciting products in the pipeline.\"\n\n**Media Contact** [press@jina.ai](mailto:press@jina.ai)\n\nSOURCE Jina AI\n\n\n\n[\\\\\n\\\\\n**21%**\\\\\n\\\\\nmore press release views with\u00a0\\\\\n\\\\\nRequest a Demo](https://www.prnewswire.com/amplify-platform/?site_refer=press-release-widget)",
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"title": "Jina AI Company Overview, Contact Details & Competitors - LeadIQ",
"description": "## Frequently Asked Questions\n### How much revenue does Jina AI generate?\n As of July 2026, Jina AI's annual revenue is estimated to be $100M - $250M.\n\n# Jina AI\n```\nJina AI builds software that helps organizations deliver more effective search experiences by using modular AI components. Its capabilities include data representations, result ranking, and support for small language models to power search across diverse content. The company is based in Sunnyvale, California, and was founded in 2020 by Dr. Han Xiao.\nIts customers are businesses seeking to improve search across text and other data types, including multilingual content. In October 2025, Elastic acquired Jina AI, integrating its search AI capabilities into Elastic's offerings.\n```",
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"markdown": "[](https://leadiq.com/?utm_source=seo)\n\n[Learn more at LeadIQ.com](https://leadiq.com/?utm_source=seo)\n\nStart free\n\n## Insights\n\n**Acquisition Opportunity** Recently acquired by Elastic, presenting a potential upsell for Elastic's expanding search and observability customers who may benefit from integrating or migrating Jina AI\u2019s multimodal embeddings, rerankers, and small language models into Elastic-powered workflows.\n\n**Multimodal Vision** Recent launches of multi-modal embeddings and vision language models (Jina-VLM and v5 image/audio/video embeddings) indicate strong capability in handling text, images, and other media, creating cross-sell opportunities to clients needing unified multimedia search and retrieval solutions.\n\n**Enterprise Readiness** Product updates focused on token-efficient visual QA and constrained hardware suitability suggest appeal for enterprises with on-prem or edge deployment needs, offering a path to pair with existing on-prem Elastic or private cloud deployments.\n\n**Reranker Strength** Latest generation reranker and multilingual retrieval benchmarks position Jina AI as a scalable improvement for search relevance, enabling sales discussions with customers pursuing higher accuracy for multilingual and domain-specific search experiences.\n\n**Financial Footprint** With revenue in the mid-hundreds of millions and a strategic acquisition by a major search AI player, there is credibility for co-selling or partner-led deals, especially for mid-market to enterprise customers seeking advanced search capabilities.\n\n## Similar companies to Jina AI\n\n- [\\\\\n\\\\\n**Bottos Srl** \\\\\n\\\\\n34$1M - $10M](https://leadiq.com/c/bottos-srl/5a1d912e5400005a0076737e)\n- [\\\\\n\\\\\n**OpenAI** \\\\\n\\\\\n9.2K$1B - $10B](https://leadiq.com/c/openai/5a1d9d1e2300005c008cfc05)\n- [\\\\\n\\\\\n**DVC** \\\\\n\\\\\n78$50M - $100M](https://leadiq.com/c/dvc/62012b90867cb9e67839ad86)\n\nExplore similar companies\n\n## Jina AI Tech Stack\n\nJina AI uses 8 technology products and services including Instagram, Apache Kafka, Fastly, and more. Explore Jina AI's tech stack below.\n\n- Instagram\n\n\n\nAdvertising\n\n- Apache Kafka\n\n\n\nBig Data Processing\n\n- Fastly\n\n\n\nContent Delivery Network\n\n- Amazon Simple Email Service\n\n\n\nEmail\n\n- Quasar\n\n\n\nJavascript Frameworks\n\n- jQuery\n\n\n\nJavascript Libraries\n\n- TensorFlow\n\n\n\nMachine Learning\n\n- gRPC\n\n\n\nWeb Frameworks\n\n\n## Media & News\n\n- \n\n[**Jina AI GmbH launches jina-embeddings-v5-omni.** \\\\\nJina AI GmbH is releasing jina-embeddings-v5-omni, extending its v5-text embedding models to images, audio, and video.\\\\\n\\\\\nMay 12, 2026 \\| jina.ai](https://jina.ai/news/jina-embeddings-v5-omni-multimodal-embeddings-for-text-image-audio-and-video)\n\n- \n\n[**Jina AI GmbH launches A 2.4B multilingual vision language model focused on token efficient visual QA.** \\\\\nJina AI releases Jina-VLM: A 2.4B multilingual vision language model focused on token efficient visual QA.\\\\\n\\\\\nDec 08, 2025 \\| www.marktechpost.com](https://www.marktechpost.com/2025/12/08/jina-ai-releases-jina-vlm-a-2-4b-multilingual-vision-language-model-focused-on-token-efficient-visual-qa)\n\n- \n\n[**Jina AI GmbH launches Jina-VLM.** \\\\\nJina AI has released Jina-VLM, a 2.4B parameter vision language model that targets multilingual visual question answering and document understanding on constrained hardware.\\\\\n\\\\\nDec 08, 2025 \\| www.marktechpost.com](https://www.marktechpost.com/2025/12/08/jina-ai-releases-jina-vlm-a-2-4b-multilingual-vision-language-model-focused-on-token-efficient-visual-qa)\n\n- \n\n[**Elastic NV acquired Jina AI GmbH on Oct 9th '25.** \\\\\nSAN FRANCISCO, October 09, 2025-(BUSINESS WIRE)-Elastic (NYSE: ESTC), the Search AI Company, has completed the acquisition of Jina AI, a pioneer in open source multimodal and multilingual embeddings, reranker, and small language models.\\\\\n\\\\\nOct 09, 2025 \\| finance.yahoo.com](https://finance.yahoo.com/news/elastic-completes-acquisition-jina-ai-130200685.html)\n\n- \n\n[**Jina AI GmbH launches jina-reranker-v3, latest-generation reranker.** \\\\\nJina AI GmbH is excited to release jina-reranker-v3, its latest-generation reranker that delivers state-of-the-art performance across multilingual retrieval benchmarks.\\\\\n\\\\\nOct 03, 2025 \\| jina.ai](https://jina.ai/news/jina-reranker-v3-0-6b-listwise-reranker-for-sota-multilingual-retrieval)\n\n- \n\n[**Jina AI GmbH launched jina-embeddings-v5-text, the fifth generation of embedding model family on Sep 4th '25.** \\\\\nToday Jina AI GmbH is releasing jina-code-embeddings, a new suite of code embedding models in two sizes - 0.5B and 1.5B parameters - along with 1-4 bit GGUF quantizations for both.\\\\\n\\\\\nSep 04, 2025 \\| jina.ai](https://jina.ai/news/jina-code-embeddings-sota-code-retrieval-at-0-5b-and-1-5b)\n\n- \n\n[**Jina AI GmbH attends event Robust IR Workshop.** \\\\\nJina AI was at this year's conference in Padua in July, presenting its work on late chunking at the Robust IR Workshop.\\\\\n\\\\\nAug 11, 2025 \\| jina.ai](https://jina.ai/news/what-we-learned-at-sigir-2025)\n\n\n## Jina AI's Email Address Formats\n\nJina AI uses at least 1 format(s):\n\n| Jina AI Email Formats | Example | Percentage |\n| --- | --- | --- |\n| First.Last@jina.ai | John.Doe@jina.ai | 96% |\n| Last.First@jina.ai | Doe.John@jina.ai | 2% |\n| First.Middle.Last@jina.ai | John.Michael.Doe@jina.ai | 2% |\n\n[See more formats](https://leadiq.com/c/jina-ai/5eb90c21102cc035326d531f/email-format)\n\n## Frequently Asked Questions\n\n### What is Jina AI's official website and social media links?\n\nJina AI's official website is [jina.ai](https://jina.ai/) and has social profiles on [LinkedIn](https://www.linkedin.com/company/jinaai) [Crunchbase](https://www.crunchbase.com/organization/jina-ai).\n\n### How much revenue does Jina AI generate?\n\n As of July 2026, Jina AI's annual revenue is estimated to be $100M - $250M.\n\n### What is Jina AI's NAICS code?\n\nJina AI's NAICS code is 5112 \\- Software Publishers.\n\n### How many employees does Jina AI have currently?\n\nAs of July 2026, Jina AI has approximately 46 employees across 4 continents, including AsiaEuropeNorth America. Key team members include Co-Founder & Cto: N. W.Creative Director: T. K.Tech Content Lead: A. C. C.. Explore [Jina AI's employee directory](https://leadiq.com/c/jina-ai/5eb90c21102cc035326d531f/employee-directory) with LeadIQ.\n\n### What industry does Jina AI belong to?\n\nJina AI operates in the [Software Development](https://leadiq.com/c/company-search/industry-software-development) industry.\n\n### What technology does Jina AI use?\n\nJina AI's tech stack includes InstagramApache KafkaFastlyAmazon Simple Email ServiceQuasarjQueryTensorFlowgRPC.\n\n### What is Jina AI's email format?\n\nJina AI's email format typically follows the pattern of First.Last@jina.ai. [Find more Jina AI email formats](https://leadiq.com/c/jina-ai/5eb90c21102cc035326d531f/email-format) with LeadIQ.\n\n### How much funding has Jina AI raised to date?\n\n As of July 2026, Jina AI has raised $30M in funding. The last funding round occurred on Dec 22, 2021 for $30M.\n\n### When was Jina AI founded?\n\nJina AI was founded in 2020.\n\n\n\n# Jina AI\n\n[Software Development](https://leadiq.com/c/company-search/industry-software-development)[California, United States](https://leadiq.com/c/company-search/location-united-states)11-50 Employees\n\n```\nJina AI builds software that helps organizations deliver more effective search experiences by using modular AI components. Its capabilities include data representations, result ranking, and support for small language models to power search across diverse content. The company is based in Sunnyvale, California, and was founded in 2020 by Dr. Han Xiao.\nIts customers are businesses seeking to improve search across text and other data types, including multilingual content. In October 2025, Elastic acquired Jina AI, integrating its search AI capabilities into Elastic's offerings.\n```\n\n## Company Overview\n\nWebsite[jina.ai](https://jina.ai/)\n\nNAICS Code5112 \\- Software Publishers\n\nFounded2020\n\nEmployees11-50\n\n## Media & News\n\n- \n\n[**Jina AI GmbH launches jina-embeddings-v5-omni.** \\\\\nMay 12, 2026 \\| jina.ai](https://jina.ai/news/jina-embeddings-v5-omni-multimodal-embeddings-for-text-image-audio-and-video)\n\n- \n\n[**Jina AI GmbH launches A 2.4B multilingual vision language model focused on token efficient visual QA.** \\\\\nDec 08, 2025 \\| www.marktechpost.com](https://www.marktechpost.com/2025/12/08/jina-ai-releases-jina-vlm-a-2-4b-multilingual-vision-language-model-focused-on-token-efficient-visual-qa)\n\n- \n\n[**Jina AI GmbH launches Jina-VLM.** \\\\\nDec 08, 2025 \\| www.marktechpost.com](https://www.marktechpost.com/2025/12/08/jina-ai-releases-jina-vlm-a-2-4b-multilingual-vision-language-model-focused-on-token-efficient-visual-qa)\n\n\n[Read more news](https://leadiq.com/c/jina-ai/5eb90c21102cc035326d531f#media)\n\n## Funding & Financials\n\n- _$30M_\nJina AI has raised a total of $30M of funding over 3 rounds. Their latest funding round was raised on Dec 22, 2021 in the amount of $30M.\n\n- _$100M - $250M_\nJina AI's revenue is estimated to be in the range of $100M - $250M\n\n\n## Funding & Financials\n\n- _$30M_\nJina AI has raised a total of $30M of funding over 3 rounds. Their latest funding round was raised on Dec 22, 2021 in the amount of $30M.\n\n- _$100M - $250M_\nJina AI's revenue is estimated to be in the range of $100M - $250M\n\n\n## Company Leadership\n\n- \n\n\n**N. W.**\n\n\n\nCo-Founder & Cto\n\n\n\n\n\n\n\n\n\n\n\nMobile Phone\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEmail\n\n- \n\n\n**T. K.**\n\n\n\nCreative Director\n\n\n\n\n\n\n\n\n\n\n\nMobile Phone\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEmail\n\n- \n\n\n**A. C. C.**\n\n\n\nTech Content Lead\n\n\n\n\n\n\n\n\n\n\n\nMobile Phone\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEmail\n\n- \n\n\n**E. C.**\n\n\n\nRecruitment Manager\n\n\n\n\n\n\n\n\n\n\n\nMobile Phone\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEmail\n\n- \n\n\n**I. M.**\n\n\n\nSenior Ai Engineer\n\n\n\n\n\n\n\n\n\n\n\nMobile Phone\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEmail\n\n\n## Employee Directory\n\n- \n\n\n**M. W.**\n\n\n\nSenior Ai Engineer\n\n\n\n\n\n\n\n\n\n\n\nMobile Phone\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEmail\n\n- \n\n\n**S. M.**\n\n\n\nCommunity Specialist/Tech Evangelist\n\n\n\n\n\n\n\n\n\n\n\nMobile Phone\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEmail\n\n- \n\n\n**S. V.**\n\n\n\nExecutive Assistant\n\n\n\n\n\n\n\n\n\n\n\nMobile Phone\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEmail\n\n\n[Go to Employee Directory](https://leadiq.com/c/jina-ai/5eb90c21102cc035326d531f/employee-directory)\n\n### Ready to create more pipeline?\n\nGet a demo and discover why thousands of SDR and Sales teams trust LeadIQ to help them build pipeline confidently.\n\nSign me up\n\n### Ready to create more pipeline?\n\nGet a demo and discover why thousands of SDR and Sales teams trust\n\nLeadIQ to help them build pipeline confidently.\n\n[Book a demo](https://leadiq.com/book-a-demo?utm_source=seo)\n\n\u2715\n\n# Access Insights for Millions of Other Companies\n\nSign up for full access.\n\n[Continue with Google](https://account.leadiq.com/signup/welcome?uc=true&utm_source=seo&shortcut=google) [Continue with Microsoft](https://account.leadiq.com/signup/welcome?uc=true&utm_source=seo&shortcut=microsoft)\n\n_No credit card needed_\n\nOR\n\n**Work email**\n\n[Create Account](https://account.leadiq.com/signup/welcome?uc=true&utm_source=seo&shortcut=%24%7Bstate.email%7D)\n\n\ud83c\udfc6 G2 Leader Fall 2025\\|\u26a1 #1 Easiest Setup\\|\ud83d\udd12 Enterprise Security\n\n_By creating an account, you agree to LeadIQ's [Terms of Use](https://leadiq.com/legal/terms-of-use?utm_source=seo) and [Privacy Policy](https://leadiq.com/legal/privacy-policy?utm_source=seo)._\n\n\u2715\n\n# Get Full Access to LeadIQ Contacts\n\n**Email**\n\n[Create Account](https://account.leadiq.com/signup/welcome?uc=true&utm_source=seo&shortcut=%24%7Bstate.email%7D)\n\nOR\n\n[Continue with Google](https://account.leadiq.com/signup/welcome?uc=true&utm_source=seo&shortcut=google) [Continue with Microsoft](https://account.leadiq.com/signup/welcome?uc=true&utm_source=seo&shortcut=microsoft)\n\n_By creating an account, you agree to LeadIQ's [Terms of Use](https://leadiq.com/legal/terms-of-use?utm_source=seo) and [Privacy Policy](https://leadiq.com/legal/privacy-policy?utm_source=seo)._",
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