Curated List
Compare the top vector databases for storing embeddings, running similarity search, and powering RAG pipelines in production.
The open-source AI-native embedding database
Chroma is an open-source, AI-native vector database designed for building LLM applications. It supports embedding storage and retrieval with first-class Python and JavaScript SDKs, and runs in-process for local development or as a hosted service.
High-performance vector database with rich filtering for production AI
Qdrant is an open-source vector similarity search engine built in Rust. It supports dense and sparse vectors, rich payload filtering, named vectors, and advanced quantization — designed for high-throughput production AI workloads.
Cloud-native open-source vector database built for scale
Milvus is an open-source cloud-native vector database built for billion-scale similarity search. It supports multiple index types, GPU acceleration, and a distributed architecture for enterprise-grade AI retrieval workloads.
Open-source vector database with hybrid search and GraphQL API
Weaviate is an open-source vector database that combines vector and keyword search (BM25) for hybrid search. It supports multimodal data, automatic vectorization via modules, and integrates directly with OpenAI, Cohere, and Hugging Face.
Open-source vector similarity extension for PostgreSQL
pgvector adds vector similarity search to PostgreSQL with support for exact and approximate nearest neighbor search. It enables teams to store embeddings alongside relational data without a separate vector database.
