Why it matters
- Written in Rust — among the fastest vector database implementations, with low memory overhead and efficient indexing.
- Rich payload filtering: filter by complex JSON metadata conditions alongside vector similarity in a single query.
- Sparse-dense hybrid search (combining BM25 with embeddings) available out of the box — a feature many competitors lack.
- Available both self-hosted (free, no usage limits) and as managed Qdrant Cloud — flexible deployment model.
Key capabilities
- Vector storage and search: Store dense (embedding) vectors and search by cosine, dot product, or Euclidean distance.
- Payload filtering: Filter results by JSON payload conditions — ranges, keyword matches, geo-proximity, and nested conditions.
- Sparse vectors and hybrid search: Support for SPLADE sparse vectors; combine dense and sparse scores via reciprocal rank fusion (RRF).
- Named vectors: Multiple vector fields per point — store text, image, and audio embeddings for the same document.
- Quantization: Scalar and binary quantization to reduce memory 4–32× with minimal quality loss.
- On-disk indexing: Store vectors on disk instead of RAM for cost-effective large-scale deployments.
- Snapshots: Point-in-time backups of collections for disaster recovery.
- REST and gRPC API: REST for simplicity; gRPC for high-throughput programmatic access.
- SDKs: Official Python, JavaScript/TypeScript, Rust, Go, Java clients.
Technical notes
- Language: Rust (server); Python, JS/TS, Go, Java, Rust SDKs
- Deployment: Self-hosted via Docker (
docker pull qdrant/qdrant); Qdrant Cloud managed SaaS - Indexing: HNSW with configurable parameters; on-disk storage via memmap
- License: Apache 2.0 (open source)
- Qdrant Cloud free tier: 1 cluster, 1GB RAM — suitable for development
- Founded: 2021 by Andrei Vasnetsov and Andre Zayarni; Berlin-based; backed by Unusual Ventures
Ideal for
- Teams building production AI apps who want a self-hosted vector database with enterprise-grade features at no licensing cost.
- Applications requiring complex metadata filtering alongside vector search (legal, medical, enterprise search).
- Performance-critical applications where Rust's speed and memory efficiency matter at scale.
Not ideal for
- Developers who want zero-ops managed cloud without any infrastructure considerations — Pinecone is simpler.
- Very early prototyping — Chroma's in-process Python mode has even less setup overhead.
- Organizations needing deep ecosystem integrations and enterprise SLAs with dedicated support contracts.