#Vector databases
2 tools curated for you
Scale to billions of data objects seamlessly with pure vector similarity search that delivers lightning-fast results Combine keyword-based search with vector search techniques for state-of-the-art search accuracy across your data Create next-generation search experiences by integrating any generative model directly with your stored data Accelerate development with out-of-the-box vectorization support and extensive neural search framework integrations Deploy from zero to production with robust backup and restore capabilities for data-intensive applications Access extensive learning resources and community-driven innovation through open-source principles
Cut search infrastructure costs by 10x while maintaining equivalent or superior performance, thanks to object storage architecture that eliminates expensive traditional database overhead. Deliver sub-10ms median response times for real-time AI and semantic search applications, powered by low-latency vector and full-text search engine design. Scale effortlessly from prototype to production with automated scaling that handles 10 million+ writes per second and 25 thousand+ queries per second globally. Support massive datasets of 4 trillion+ documents and billions of vectors without performance degradation, enabled by object storage-based architecture that scales linearly. Achieve 90-100% recall@10 accuracy in vector search for reliable similarity matching in recommendation systems, using high-precision vector indexing. Refine search results instantly with metadata filtering to narrow down billions of vectors by specific attributes, providing precise control over query outputs. Combine full-text and vector search in hybrid queries for comprehensive, accurate results in AI applications, leveraging dual search capabilities within a single database. Experiment and iterate faster with instant namespace branching, creating isolated search environments for testing without affecting production data. Trusted by leading AI companies including Anthropic, Notion, and Grammarly for production workloads, validated through enterprise-grade reliability and performance.