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qdrant

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High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

When to use

• Building production RAG systems requiring low latency
• Need hybrid search (vectors + metadata filtering)
• Require horizontal scaling with sharding/replication
• Want on-premise deployment with full data control
• Need multi-vector storage per record (dense + sparse)
• Building real-time recommendation systems
• **Rust-powered**: Memory-safe, high performance
• **Rich filtering**: Filter by any payload field during search

When not to use

• Simpler setup embedded use cases
• Maximum raw speed research or batch processing
• Fully managed zero ops preferred
• GraphQL preference with built-in vectorizers

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