Weaviate

turbopuffer

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.