# Qdrant

> Build vector search with filtering, hybrid retrieval, and flexible deployment

Qdrant is an open-source vector database and search engine for applications that retrieve information by similarity. It stores embeddings with JSON metadata and combines vector search with filters, helping developers narrow results by attributes such as location, text, or business rules. Dense and sparse vectors can work together in hybrid retrieval, while multivector support and reranking offer additional ways to improve relevance.

Developers access Qdrant through REST, gRPC, or client libraries and can run it on their own infrastructure or use its managed cloud service. Common uses include retrieval-augmented generation, semantic search, recommendation systems, and persistent context for AI agents.

## Listing details

- Canonical BetaNeon page: https://betaneon.com/products/qdrant
- Official website: https://qdrant.tech/
- Category: Developer tools / Databases & database tools
- Status: Launched
- Topics: Databases, Open source, Self hosted, Semantic search, Vector search
- Listed: 2026-11-10T12:04:53.914Z
- Content updated: 2026-11-10T12:04:54.017Z
- Official source: https://qdrant.tech/
This is a product introduction, not an endorsement. Confirm current availability, features, and prices on the official website. Community upvotes on BetaNeon are not star ratings.