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Faiss is awesome but note that it's an ANN library that might not be suitable for all use cases; hence vector databases exist. Two things that might be of interest: the main contributor of Faiss on the Weaviate podcast: https://youtu.be/5o1YTp1IL5o and Weaviate as vector DB itself: https://weaviate.io

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Faiss supports FLAT aka not approx nearest neighbor. Also, all the vector databases use ANN too because nearest neighbor is hard.

Or are you referring to the ability to store data in addition to the vectors? In which case, you can pair any time tested DB with the index avoiding the hype of DBs that might be gone in a year.

Vector indexes only form a component within vector databases - you'd want the database bits and bobs (scalability, replication, caching, etc) surrounding it in addition to the index itself. Milvus, for example, supports flat indexes - as far as I know, Weaviate could support flat indexing as well if it doesn't do so already.

Most vector databases focus on ANN because of scale. Once you get to around a million vectors or so, it becomes prohibitively expensive to perform brute-force querying and search.

We used Weavite and Elastic in prod. Felt that Weavite was not yet prod ready wrt deployment ( sharding , clustering is murky ). We went with ES's dense_vector field which is serving us well.

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