FAISS

FAISS (Facebook AI Similarity Search) is a high-performance library developed by Meta's Fundamental AI Research (FAIR) team, designed specifically for efficient similarity search and clustering of dense vectors. As of 2026, FAISS remains the foundational engine powering most modern vector databases and Retrieval-Augmented Generation (RAG) frameworks. The library is written in C++ with highly optimized CUDA kernels for GPU acceleration and features seamless Python wrappers. It excels at handling datasets that exceed RAM capacity by utilizing Product Quantization (PQ) and Inverted File (IVF) indexing strategies. Its technical architecture allows it to scale to billions of vectors while maintaining millisecond-level latency. FAISS is not a standalone database but a core computational library that provides the algorithms necessary to find the nearest neighbors in high-dimensional spaces, supporting various distance metrics including L2 (Euclidean) and Inner Product. In the 2026 AI landscape, it is the primary choice for researchers and engineers building custom vector pipelines where extreme control over memory-accuracy trade-offs is required.