Faiss (Facebook AI Similarity Search) is an open-source library developed by Meta (formerly Facebook) for efficient similarity search and clustering of dense vectors. It’s widely used in AI applications where fast nearest neighbor searches are essential, such as recommendation systems, image retrieval, and natural language processing. Faiss is optimized for handling large datasets of vectors, making it a powerful tool for searching millions—or even billions—of high-dimensional data points. It achieves this efficiency through indexing techniques such as hierarchical clustering, product quantization, and approximate nearest neighbor (ANN) search. These methods significantly reduce computational overhead while maintaining high accuracy. One of Faiss’s standout features is its GPU acceleration, allowing massive datasets to be processed quickly using NVIDIA GPUs. Developers frequently pair Faiss with vector databases like Milvus to manage and search embeddings effectively.
What is Faiss?

- Vector Database 101: Everything You Need to Know
- The Definitive Guide to Building RAG Apps with LlamaIndex
- Embedding 101
- Exploring Vector Database Use Cases
- Getting Started with Milvus
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
How is self-supervised learning used in autonomous driving?
Self-supervised learning is a type of machine learning where the model learns from unlabeled data by generating its own
What is sensor fusion in robotics?
Sensor fusion in robotics refers to the process of combining data from multiple sensors to produce more accurate, reliab
How do distributed databases provide geo-replication?
Distributed databases provide geo-replication by maintaining copies of data across multiple geographical locations. This