Qdrant (read: quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural network or semantic-based matching, faceted search, and other applications.

Qdrant is released under the open-source Apache License 2.0. Its source code is available on GitHub.

User Manual

Read more about our key concepts and visit our guides to set up and further configure Qdrant for your own use.


Check out the Tutorials section to learn more about common use cases. Qdrant is ideal for deploying applications based on the matching of embeddings produced by neural network encoders.

These can be:

  • Semantic search
  • Similar Image \ Audio \ Video search
  • Recommendation systems

In addition to this documentation, you may be interested in looking at examples of projects made with Qdrant:


Qdrant is a vector database performing an approximate nearest neighbours search on neural embeddings. It can work perfectly fine as a standalone system, yet, in some cases, you may find it easier to implement your semantic search application using some higher-level libraries. Visit our Integrations section to learn more.

Get started

Go to the Quickstart guide to get a production-ready vector search service up and running in minutes.