| Our story |
In 2021, André Zayarni and Andrey Vasnetsov collaborated on a project aimed at leveraging vector similarity search to build a matching engine for unstructured data objects. |
After exploring available options, including libraries like FAISS, it became clear that none of them met the requirements of features and scalability. |
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| Qdrant Team |
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As a result, Andrey decided to develop his own vision for a production-ready vector search engine from scratch.<br/><br/>The first version was published on GitHub, quickly attracting significant interest from developers. The overwhelming feedback and questions from developers and startups confirmed that there was a shared need for such a tool. |
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| Team |
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As the project gained traction, the decision was made to formally establish Qdrant and continue developing the vector search engine into its current form.<br/><br/>Today, Qdrant is the backbone of the most ambitious AI applications, powering everything from groundbreaking startups to enterprise-scale deployments with the best open-source vector database and enterprise-ready solutions. |
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| Team |
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Our team has grown to 75+ experts across 20+ countries, but our mission remains unchanged: building the most scalable, high-performance vector search engine to fuel the future of AI and machine learning. |
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