Qdrant
Open-source vector database built in Rust for high-performance similarity search, with 25k+ GitHub stars and a managed Qdrant Cloud service.
Qdrant is an open-source vector database written in Rust, designed for production-grade similarity search with a focus on performance and filtering accuracy. It supports dense and sparse vectors side-by-side, enabling hybrid search that combines semantic embeddings with traditional keyword scoring without additional infrastructure. Qdrant Cloud offers a fully managed service with a free tier, and the self-hosted version runs on a single binary with no runtime dependencies. Used in production at Dailymotion, Disney Streaming, and hundreds of AI startups, Qdrant has 25,000+ GitHub stars and raised a $28M Series A in 2024.
Key Features
- Dense and sparse vector support for native hybrid search without extra services
- Payload filtering for high-performance metadata-conditional vector queries
- Named vectors for storing multiple embedding models per record
- REST and gRPC APIs with Python, TypeScript, Go, Rust, and Java client SDKs
- Qdrant Cloud managed service with a free cluster tier for prototyping
- Distributed deployment with horizontal sharding and replication for production scale
Use Cases
- ML engineers building high-throughput semantic search requiring Rust-level query performance
- Teams running hybrid search combining dense embeddings with metadata filtering in one query
- Developers building production RAG systems requiring sub-millisecond vector retrieval
- Enterprises needing on-premise vector storage under a permissive Apache 2.0 license
Pros
- Rust backend delivers significantly faster queries than Python-native alternatives at scale
- Apache 2.0 license with no enterprise feature gating unlike some competitors
- Payload filtering makes metadata-conditional vector search fast without post-processing
Cons
- Rust ecosystem means fewer community plugins and integrations than Python-first alternatives
- No built-in vectorization - requires external embedding models unlike Weaviate auto-vectorization
- Cloud-managed pricing is consumption-based and can be difficult to estimate for variable workloads
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Open-source AI-native vector database for building semantic search and RAG applications, used by Cohere, Bosch, and 3M+ developers globally.
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