# Qdrant

Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

Repository: https://github.com/qdrant/qdrant
Canonical: https://ross.abutalabs.com/products/qdrant
Homepage: https://qdrant.tech
Language: Rust
License: Apache-2.0
License Family: permissive
Topics: neural-network, search-engine, knn-algorithm, hnsw, vector-search, nearest-neighbor-search, image-search, embeddings-similarity, recommender-system, vector-search-engine, mlops, search-engines, neural-search, similarity-search, vector-database, machine-learning, search, ai-search, ai-search-engine, hybrid-search
Last push: 2026-08-26T15:13:55+00:00
Link (homepage): https://qdrant.tech
Link (site_page): https://qdrant.tech/documentation
Link (site_page): https://qdrant.tech/about-us
Link (site_page): https://qdrant.tech/pricing
Link (site_page): https://qdrant.tech/documentation/manage-data/payload

## Health v2 (maintenance only)
Score: 98/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 96, longevity 100
- inputs: {"age_days": 2286, "days_push": 7, "days_rel": 28, "gap_med": 14.5, "n_releases_24m": 35}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 34210, forks 2615 (observed 2026-08-28T04:11:58.745257+00:00)

## What it is
Qdrant is an open-source, production-ready vector database and similarity search engine written in Rust, supporting dense, sparse, and multivector embeddings with rich JSON payload filtering and hybrid search. It ships as a self-hostable service, a managed cloud offering, client libraries, and an MCP server for AI agent integration.

## Use cases
- build semantic search over documents with embeddings
- store and query vectors for a RAG pipeline
- recommend similar products with filtered similarity search
- hybrid search combining dense and sparse embeddings with reranking
- find nearest neighbors in image or text embedding spaces
- self-host a vector database for AI applications
- give AI agents vector search via MCP

## When to choose
- you need production-grade vector search with metadata filtering at scale
- you want hybrid search with dense, sparse, and late-interaction (ColBERT) vectors
- you need a self-hosted or managed vector store with client libraries in many languages
- you need filterable ANN search without recall loss

## When to avoid
- you only need exact k-NN on small datasets where a library like FAISS suffices
- your workload is purely relational/SQL with no vector similarity needs
- you need a full general-purpose document search engine with text analysis rather than vector retrieval

## Facets
- artifact type: service
- maturity: stable
- function: vector-database, search-engine, database, rag
- domain: databases, artificial-intelligence, machine-learning
- platform: rust, self-hosted, cloud, python, cross-platform
- tags: hnsw, embeddings, similarity-search, hybrid-search, semantic-search, recommender-systems, mcp-server, ann, search, retrieval-augmented-generation, docker

## Member repositories
- qdrant/qdrant (main) score 98
- qdrant/mcp-server-qdrant (plugin) score 69
- qdrant/qdrant-client (sdk) score 98

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:58.745257+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:27:45.112254+00:00, confidence not recorded.
  - readme: https://github.com/qdrant/qdrant (fetched 2026-08-28T04:11:58.745257+00:00, sha a657fef10581)
  - homepage: https://qdrant.tech (fetched 2026-08-29T07:48:45.104712+00:00, sha 3a940f8331f4)
  - site_page: https://qdrant.tech/documentation (fetched 2026-08-29T07:48:45.114665+00:00, sha 4376d5731049)
  - site_page: https://qdrant.tech/about-us (fetched 2026-08-29T07:48:45.116664+00:00, sha de7657e5be36)
  - site_page: https://qdrant.tech/documentation/manage-data/payload (fetched 2026-08-29T07:48:45.120279+00:00, sha a4059d7e6234)
  - site_page: https://qdrant.tech/documentation/search/hybrid-queries (fetched 2026-08-29T07:48:45.122805+00:00, sha f0b1a8d057ed)
  - site_page: https://qdrant.tech/documentation/tutorials-search-engineering/using-multivector-representations (fetched 2026-08-29T07:48:45.125807+00:00, sha 567cd3e62667)
  - site_page: https://qdrant.tech/documentation/tutorials-basics/reranking-hybrid-search (fetched 2026-08-29T07:48:45.129824+00:00, sha c6a8dfb4d283)
  - registry_crates: https://crates.io/api/v1/crates/qdrant (fetched 2026-08-29T07:48:45.132441+00:00, sha f037703c8b16)
  - registry_pypi: https://pypi.org/pypi/qdrant-client/json (fetched 2026-08-29T07:48:45.134057+00:00, sha 4683fcda01df)
- Data as of 2026-08-30T08:39:29.467469+00:00.