Faiss

Best for: developers building semantic search or RAG who need vector similarity search that runs locally rather than through a hosted database.

Faiss (Facebook AI Similarity Search) is a library from Meta's Fundamental AI Research group for efficient similarity search and clustering of dense vectors. It is written in C++ with complete Python and NumPy wrappers, and several of its most useful algorithms have GPU implementations.

What makes it useful: it scales past memory. Faiss includes algorithms for searching vector sets of any size, including collections that do not fit in RAM, along with supporting code for evaluation and parameter tuning. For RAG pipelines this often removes the need for a separate vector database service entirely.

Where it falls short: Faiss is a library, not a database. There is no server, no persistence layer, no access control and no query language — you build those yourself. Choosing the right index type and tuning its parameters requires understanding the accuracy-versus-speed trade-offs, and the defaults are rarely optimal. If you want something operational out of the box, a managed vector store will be less work.

Details: C++ with Python bindings, MIT licensed.