Couchbase and FAISS are both used in AI applications but serve different purposes. Couchbase is a distributed, open-source NoSQL document-oriented database that can be adapted to handle vector search functionality through Full Text Search or application level calculations. Faiss (Facebook AI Similarity Search), on the other hand, is an open-source library designed for efficient vector similarity search and clustering of dense vectors. While Couchbase provides full database features including JSON document storage, indexing, querying, ACID transactions, Faiss only has vector operations. Therefore, Couchbase is best when you need a database that can do both traditional data operations and vector search, while FAISS is the clear winner for vector search only, especially in AI and machine learning applications where high performance similarity search is key.