Announcing Qdrant's $28M Series A Funding Round

Today, we are excited to announce our $28M Series A funding round, which is led by Spark Capital with participation from our existing investors Unusual Ventures and 42CAP.

We have seen incredible user growth and support from our open-source community in the past two years - recently exceeding 5M downloads. This is a testament to our mission to build the most efficient, scalable, high-performance vector database on the market. We are excited to further accelerate this trajectory with our new partner and investor, Spark Capital, and the continued support of Unusual Ventures and 42CAP. This partnership uniquely positions us to empower enterprises with cutting edge vector search technology to build truly differentiating, next-gen AI applications at scale.

The Emergence and Relevance of Vector Databases

A paradigm shift is underway in the field of data management and information retrieval. Today, our world is increasingly dominated by complex, unstructured data like images, audio, video, and text. Traditional ways of retrieving data based on keyword matching are no longer sufficient. Vector databases are designed to handle complex high-dimensional data, unlocking the foundation for pivotal AI applications. They represent a new frontier in data management, in which complexity is not a barrier but an opportunity for innovation.

The rise of generative AI in the last few years has shone a spotlight on vector databases, prized for their ability to power retrieval-augmented generation (RAG) applications. What we are seeing now, both within AI and beyond, is only the beginning of the opportunity for vector databases. Within our Qdrant community, we already see a multitude of unique solutions and applications leveraging our technology for multimodal search, anomaly detection, recommendation systems, complex data analysis, and more.

What sets Qdrant apart?

To meet the needs of the next generation of AI applications, Qdrant has always been built with four keys in mind: efficiency, scalability, performance, and flexibility. Our goal is to give our users unmatched speed and reliability, even when they are building massive-scale AI applications requiring the handling of billions of vectors. We did so by building Qdrant on Rust for performance, memory safety, and scale. Additionally, our custom HNSW search algorithm and unique filtering capabilities consistently lead to highest RPS, minimal latency, and high control with accuracy when running large-scale, high-dimensional operations.

Beyond performance, we provide our users with the most flexibility in cost savings and deployment options. A combination of cutting-edge efficiency features, like built-in compression options, multitenancy and the ability to offload data to disk, dramatically reduce memory consumption. Committed to privacy and security, crucial for modern AI applications, Qdrant now also offers on-premise and hybrid SaaS solutions, meeting diverse enterprise needs in a data-sensitive world. This approach, coupled with our open-source foundation, builds trust and reliability with engineers and developers, making Qdrant a game-changer in the vector database domain.

What’s next?

We are incredibly excited about our next chapter to power the new generation of enterprise-grade AI applications. The support of our open-source community has led us to this stage and we’re committed to continuing to build the most advanced vector database on the market, but ultimately it’s up to you to decide! We invite you to test out Qdrant for your AI applications today.