Qdrant
Vector similarity search is provided by Qdrant, an open-source search engine written in Rust.
About Qdrant
Qdrant is a vector search engine written in Rust for AI retrieval. It stores vectors with JSON metadata and supports filtering by nested fields, text, geo, and vector presence, plus hybrid queries that combine dense and sparse vectors.
It provides real-time indexing without rebuilding the whole index, scalar, binary and asymmetric quantization options, and reranking with score boosting, ColBERT and MMR. Deployment options listed are managed cloud on AWS, GCP or Azure, hybrid with own Kubernetes, private air-gapped setups, and edge, which is in beta.
Key features
- JSON metadata storage with nested, text and geo filtering
- Hybrid search combining dense and sparse vectors
- Support for BM25, SPLADE++ and miniCOIL retrieval
- Multiple vectors per stored object
- Filtering applied during HNSW traversal
- Reranking with score boosting, ColBERT and MMR
Best for: Developers building AI search and retrieval that use vector, keyword and filtered queries.
Good for
- Built in Rust with SIMD and custom Gridstore storage engine
- Runs on managed cloud, hybrid Kubernetes, private air-gapped and edge environments
- Combines dense, sparse and multivector search with filtering and reranking
Watch out for
- Edge deployment is listed as beta
Qdrant at a glance
- Website
- qdrant.tech
- Category
- Development › APIs & Databases
- Type
- Online tool
- Pricing
- Not confirmed. Check the site
- Platforms
- Web
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
Guides for APIs & Databases
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Qdrant FAQ
Where can Qdrant be deployed?
It lists Qdrant Cloud on AWS, GCP or Azure, Hybrid Cloud with your own Kubernetes, Private Cloud for air-gapped setups, and Qdrant Edge in beta.
What methods does hybrid search support?
Dense or sparse vectors, including BM25, SPLADE++ and miniCOIL in one query.
How is filtering handled?
Filters are applied during HNSW traversal, without separate pre- or post-filtering.
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