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Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
High-performance library for similarity search on dense vectors
C++ library for efficient similarity search and clustering of dense vectors at any scale, with GPU acceleration and Python bindings. Developed by Meta's Fundamental AI Research group.
Faiss is a production-grade library designed for similarity search and clustering of dense vector representations. Built primarily in C++ with complete Python/NumPy wrappers, it enables developers and researchers to search through vector sets of any size—from thousands to billions of vectors that exceed RAM capacity.
The library supports multiple distance metrics including L2 (Euclidean), dot product, and cosine similarity. It offers a spectrum of indexing algorithms that balance search time, accuracy, memory footprint, and training requirements. Methods range from exact search baselines to compressed representations using binary vectors and quantization codes that can handle billions of vectors in main memory on a single server. Advanced structures like HNSW and NSG add indexing layers for faster retrieval.
Optional GPU support via CUDA or AMD ROCm delivers industry-leading performance for exact and approximate nearest neighbor search, Lloyd's k-means clustering, and small k-selection operations. GPU indexes work as drop-in replacements for CPU equivalents, with automatic memory management across single or multi-GPU configurations.
Developed by Meta's Fundamental AI Research group and released under the MIT license, Faiss is trusted for production workloads requiring fast, scalable vector search.
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Faiss supports L2 (Euclidean) distance, dot product, and cosine similarity (implemented as dot product on normalized vectors) for comparing vectors.
Yes, Faiss includes algorithms designed for vector sets that exceed available RAM, using compressed representations and efficient indexing structures to manage billions of vectors.
No, GPU support is optional. Faiss provides full CPU implementations with only a BLAS dependency. GPU indexes via CUDA or ROCm offer performance gains but are not required.
Exact search guarantees finding true nearest neighbors but is slower. Approximate methods trade precision for speed and memory efficiency—ideal when billions of vectors or sub-millisecond latency is required.
Yes, Faiss is production-ready with precompiled Anaconda packages, minimal dependencies, and proven scalability. It's actively maintained by Meta AI Research and widely used in industry applications.
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