It is free and easy to use so i use it every where

Faiss is optimized to perform similarity searches on large datasets.It has strong community support.It is open-source and free to use,hassle free usage.FAISS provides multiple indexing methods like flat indexes, inverted lists, HNSW and product

The best thing about Faiss is its incredible performance in high-dimensional vector search. It’s highly optimized for speed and scalability, which makes it ideal for working with massive datasets. Its support for various algorithms, such as IVF and PQ, helps achieve the right balance between accuracy and speed. Additionally, the open-source nature of Faiss means it's well-documented and backed by an active community of users and contributors, making implementation easier. Faiss has a learning curve, but its Python bindings make basic operations straightforward. While fast once implemented, getting started with advanced features can take time. Limited to community resources; no official support team. I use Faiss regularly for large-scale vector search tasks. Faiss integrates well into machine learning pipelines, especially with Python bindings.