Pinecone
Managed vector database for AI applications
Faiss
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.
Overview
Efficient Vector Similarity Search at Scale
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.
Flexible Search Methods
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.
GPU-Accelerated Performance
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.
Highlights
Scales from thousands to billions of vectors with compressed and exact search methods
GPU acceleration for fastest nearest neighbor search and k-means clustering
Multiple distance metrics: L2, dot product, and cosine similarity
Complete Python/NumPy wrappers with C++ core for production performance
Pros
- Industry-leading performance for high-dimensional vector search on both CPU and GPU
- Flexible trade-offs between speed, accuracy, and memory through diverse indexing algorithms
- Production-ready with minimal dependencies (only BLAS required for CPU)
- Active development and support from Meta AI Research with strong community
Considerations
- Compressed methods sacrifice search precision for scalability
- GPU features require CUDA or ROCm setup and compatible hardware
- Learning curve for selecting optimal index types and tuning parameters
- C++ core may require compilation for custom builds beyond precompiled packages
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Fit guide
Great for
- Machine learning applications requiring fast nearest neighbor retrieval at scale
- Recommendation systems searching through millions of item or user embeddings
- Computer vision pipelines matching image features across large datasets
- Research projects exploring similarity search algorithms and trade-offs
Not ideal when
- Sparse vector representations or non-vector data structures
- Applications requiring real-time index updates with high write throughput
- Small datasets where simple brute-force search suffices
- Projects needing distributed multi-node architectures out of the box
How teams use it
Semantic Search Engine
Search billions of document embeddings in milliseconds to return relevant results for user queries with GPU-accelerated approximate nearest neighbor algorithms
Image Similarity Matching
Index millions of image feature vectors using compressed quantization to find visually similar images while keeping memory footprint manageable on a single server
Recommendation System
Cluster user behavior vectors with fast k-means and retrieve similar users or items efficiently to power real-time personalized recommendations
Duplicate Detection Pipeline
Identify near-duplicate content across massive datasets by searching high-dimensional embeddings with configurable precision-recall trade-offs
Tech snapshot
C++58%
Python20%
Cuda17%
C2%
CMake1%
SWIG1%
Frequently asked questions
What distance metrics does Faiss support?
Faiss supports L2 (Euclidean) distance, dot product, and cosine similarity (implemented as dot product on normalized vectors) for comparing vectors.
Can Faiss handle datasets larger than RAM?
Yes, Faiss includes algorithms designed for vector sets that exceed available RAM, using compressed representations and efficient indexing structures to manage billions of vectors.
Do I need a GPU to use Faiss?
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.
How do I choose between exact and approximate search?
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.
Is Faiss suitable for production deployments?
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.
Project at a glance
Active- Stars
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LicenseMIT
Repo age8 years old
Last commit4 hours ago
Primary languageC++
Last synced 3 hours ago