Qdrant
High-performance vector database for AI applications
Qdrant is a vector similarity search engine and database built in Rust for maximum performance. With 28K+ GitHub stars, it's the go-to choice for production RAG applications. Store vectors with metadata, perform lightning-fast similarity search, and scale to billions of vectors. The foundation your AI applications need.
Why Qdrant?
RAG applications need vector storage that can handle real workloads. Generic databases aren't optimized for similarity search, resulting in slow queries and high costs. As your data grows, performance degrades. You need a purpose-built vector database that scales efficiently and integrates with your AI stack.
How It Works
Qdrant stores vectors alongside JSON payloads, enabling rich metadata filtering combined with vector search. Query by semantic similarity while filtering by price, date, category, or any business logic. SIMD acceleration and quantization reduce memory usage by up to 97% without sacrificing accuracy. Distributed deployment handles billions of vectors.
What Is Qdrant?
Qdrant is an open-source vector database written in Rust. It provides vector storage and similarity search with advanced filtering, hybrid search (dense + sparse vectors), quantization, and distributed deployment. REST and gRPC APIs with official clients for Python, JavaScript, Go, Rust, Java, and .NET.
Key Benefits
Why teams choose Qdrant
Blazing Fast
Rust performance with SIMD acceleration. Sub-millisecond search at scale.
Rich Filtering
Combine vector similarity with keyword, numeric, and geo filters.
Memory Efficient
Quantization reduces RAM by up to 97%. Run large datasets affordably.
Hybrid Search
Dense vectors + sparse vectors (BM25). Best of both worlds.
Production Ready
Write-ahead logging, replication, and zero-downtime updates.
Easy Integration
Works with LangChain, LlamaIndex, and all major AI frameworks.
Features
Everything you need to build with Qdrant
Vector Search
Similarity search with cosine, dot product, and Euclidean distance.
Payload Filtering
Filter by JSON metadata alongside vector queries.
Quantization
Scalar and product quantization for memory efficiency.
Distributed Mode
Sharding and replication for horizontal scaling.
REST & gRPC
OpenAPI 3.0 spec with typed client libraries.
Collections
Organize vectors into separate collections with different configs.
Use Cases
What you can build with Qdrant
Technology Stack
Ready to deploy Qdrant?
Get started in minutes. Deploy on your own infrastructure at actual cloud cost. No markup, no vendor lock-in.