Berlin-based Qdrant, developer of a high-performance open source vector database, has raised $28 million in Series A funding to meet surging demand for its technology. The round was led by Spark Capital, with participation from existing Qdrant investors Unusual Ventures and 42CAP.
The funding comes on the heels of massive growth for the vector database startup. Qdrant has exceeded 5 million downloads in the past year and inked deals with Fortune 500 companies like Deloitte, Hewlett Packard Enterprise, and Bayer. It also recently expanded its managed cloud offering through collaborations with AWS, Google Cloud, and Microsoft Azure.
Qdrant is capitalizing on the AI revolution's voracious appetite for vector search capabilities. Vector databases, which structure data based on contextual relationships rather than predefined fields, are integral to generative AI applications. As unstructured data composes over 90% of new enterprise information, per Gartner, the need for efficient vector search will only intensify.
"We have seen incredible user growth and support from our open-source community in the past two years, a testament to our mission of building the most efficient, scalable, high-performance vector database on the market," said Qdrant CEO and Co-Founder Andre Zayarni. "We are excited to further accelerate this trajectory with our new partner Spark Capital and the continued support of Unusual Ventures and 42CAP."
The new funding will focus primarily on team growth, allowing Qdrant to scale its business operations and better support global enterprise customers with its database technology. This infrastructure provides the foundation for companies to build innovative AI applications leveraging large-scale vector search capabilities.
Qdrant stands out from proprietary alternatives with its commitment to open source access along with unparalleled speed, low latency, and advanced features like multitenancy, query filtering, and compression options that reduce infrastructure costs. Its recent introduction of binary quantization compression reduced memory needs by up to 32X while boosting query speeds by 40X. Committed to privacy and security, Qdrant now provides on-premise and hybrid SaaS solutions to meet diverse data sensitivity requirements.
"All of us at Spark are thrilled to partner with the Qdrant team as they continue to build the most powerful vector search database and infrastructure," said Spark Capital General Partner Yasmin Razavi. "As the volume of vectorized data multiplies, Qdrant will stand out as the only technology built from scratch with ease of use, speed, and unparalleled scalability in mind."
With the new funding, Qdrant plans to expand its business development and sales team to keep pace with customer demand. As modern AI relies increasingly on efficient vector data search, Qdrant appears poised to become a leader powering next-generation applications at scale.