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Milvus is an open-source vector database with 33K+ GitHub stars for billion-scale similarity search. Features GPU-accelerated indexing, hybrid search combining vector and scalar filtering, multi-tenancy, partitioning, and horizontal scaling. Supports HNSW, IVF, DiskANN, and GPU index types. SDKs for Python, Java, Go, and Node.js. Zilliz Cloud offers a managed version. A production-grade foundation for RAG pipelines and recommendation systems at enterprise scale.
Purpose-built vector database for billion-scale search. 33K+ stars, most mature vector DB.
GPU-accelerated indexing. HNSW, IVF, DiskANN, GPU indexes. Hybrid vector+scalar search.
SDKs for Python, Java, Go, Node.js. LangChain and LlamaIndex integration.
Zilliz Cloud managed version with auto-scaling and free tier.
Free open-source / Zilliz Cloud free tier
Self-hosted, Docker, Kubernetes, Zilliz Cloud
Embedded vector database for multimodal AI with petabyte scale
LanceDB is an open-source embedded vector database built on the Lance columnar format for multimodal AI. It delivers near in-memory performance from disk with zero-copy architecture, supporting vector search, full-text search, and SQL. Native SDKs for Python, TypeScript, and Rust integrate with LangChain, LlamaIndex, and DuckDB. Backed by a $30M Series A, used by Harvey AI and Runway, with 18,000+ GitHub stars.
SurrealDB and Milvus both support vector similarity search but approach the problem from opposite architectural philosophies. Milvus is a purpose-built vector database engineered for billion-scale similarity search with sub-millisecond latency. SurrealDB is a multi-model database that includes vector capabilities alongside document, graph, relational, and time-series storage in a single engine with one query language.
Milvus and Pinecone target the same enterprise vector search market with different architectures. Milvus is an open-source distributed system built for billion-scale workloads with GPU acceleration and cloud-native architecture. Pinecone offers a serverless managed service that abstracts away all infrastructure complexity. This comparison helps enterprise teams choose between self-managed scale and operational simplicity.
Serverless vector and full-text search on object storage
turbopuffer is a serverless vector and full-text search engine built on object storage that delivers 10x lower costs than traditional vector databases. Used by Anthropic, Cursor, Notion, and Atlassian for production search workloads. Manages 2+ trillion vectors across 8+ petabytes with automatic scaling and no infrastructure management. Funded by Thrive Capital.
