Qdrant Vector DB
Open-source vector database and similarity search engine optimized for high-performance ANN retrieval in production ML workloads. Maintained by Qdrant (the company).
- Website: https://qdrant.tech/
- GitHub: https://github.com/qdrant/qdrant
What It Does
Qdrant stores dense vectors alongside payload (structured metadata) and supports filtered ANN search — combining vector similarity with attribute predicates efficiently via graph-integrated filtering.
Key capabilities:
- ANN search — HNSW-based index; high recall/speed tradeoff
- Filtered search — predicates integrated into graph traversal (not post-filter), avoiding recall loss on selective filters
- Quantization — Scalar Quantization (int8, 4×), Binary Quantization (1-bit, 32×), TurboQuant (rotation-based, 8×–32×, Qdrant 1.18+)
- Sparse + dense — supports sparse vectors alongside dense for hybrid retrieval
- Payload filtering — rich JSON payload per vector with indexed field filtering
- Collections — top-level namespace; each collection has its own vector config and index
Quantization Options
| Method | Compression | Notes |
|---|---|---|
| Scalar Quantization (int8) | 4× | Near-lossless; default recommended |
| Binary Quantization | 16×–32× | Significant recall loss without oversampling |
| TurboQuant (v1.18+) | 8×–32× | Rotation-based; beats BQ by 9–24 pp recall |
Related Tools
- Weaviate Vector DB — competing vector database; native cross-encoder reranking support
- Pinecone — managed-only competitor
- FAISS — library (not a service); no filtering, no persistence
Related Concepts
- HNSW — the ANN index Qdrant uses
- Vector Quantization — compression family
- TurboQuant — Qdrant’s latest quantization (1.18)
- Dense Vector Retrieval — primary use case
- Vector Filtering — Qdrant’s predicate-in-graph approach
- Hybrid Search — combining sparse and dense retrieval
Articles
-
Choosing a Vector Database for ANN Search at Reddit — head-to-head vs. Milvus at 340M vectors; Qdrant showed better raw latency at RF=1 but lost on scaling and organizational fit
People
- Ivan Pleshkov — TurboQuant implementation
- Jonas Schulz — TurboQuant co-author