Qdrant received $28 Million in Series A Funding Round

Qdrant is a vector database & vector similarity search engine. Traditional ways of retrieving data based on keyword matching are no longer sufficient. Vector databases are designed to handle complex high-dimensional data. The rise of generative AI has put Vector Databases in the spotlight. Most of the companies which handles volumes of data both structured and unstructured are in need of Vector Database so that they can get search and retrieve more relevant data. 

Qdrant engine is an open source project and it is available in Github. It written in Rust and it deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommendation etc. It has implemented a unique custom modification of the HNSW algorithm for Approximate Nearest Neighbor Search. Search with a State-of-the-Art speed and apply search filters without compromising on results.

As AI and Generative AI market is growing, Investors and VCs are interested in the open source products which are available in this space. Qdrant received $28 Million in series A funding round led by Spark Capital with participation from their existing investors Unusual Ventures and 42CAP. Qdrant plans to invest in next gen enterprise-grade AI applications and continues to build robust vector database.