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Qdrant is a high-performance, composable vector search engine built in Rust for production-grade semantic, hybrid, and agentic workloads. Combine dense vectors, sparse vectors, metadata filters, multi-vector representations, and custom scoring as primitives at query time. Written in Rust for memory efficiency, SIMD optimization, and predictable performance without garbage collection pauses. No wrappers, no bolt-ons, no legacy compromises — just a custom HNSW implementation and storage engine built specifically for vector workloads.

Deliver high-quality responses with a vector database built for advanced retrieval augmented generation (RAG) and modern search. Focus on exponential growth with an enterprise-ready vector database that comes with security, compliance, and responsible AI practices built in. Build better applications with sophisticated retrieval strategies backed by decades of research and customer validation. Quickly deploy your generative AI app with seamless platform and data integrations for data sources, AI models, and frameworks. Automatically upload data from a wide range of supported Azure and third-party sources. Streamline vector data processing with built-in extraction, chunking, enrichment, and vectorization, all in one flow. Support for multivector, hybrid, multilingual, and metadata filtering. Move beyond vector-only search with keyword match scoring, reranking, geospatial search, and autocomplete.