Apart from search, ANNs can be use for recommendations, classification, and other information retrieval problems.

Currently, ES and Solr, both based on Lucene, can't really manage vector representations, as they are mainly based on inverted indexes to n-grams.

ANNs potential applications extend to audio, bioinformatics, video, among any modality that can be represented as a vector. All you need is an encoder! How nice.

Faiss is definitely powerful. I have been running experiments using 80 million vectors that map to legal documents, and vectorizing protein-folds (using Alphafold). While it is an interesting technology, at this moment, perhaps for my usecases, I see it more as a lib or tool than a full-featured product like ES or Solr.

For instance, ATM, updating a Faiss index is a non trivial process, with many of the workflow tools you would expect in ES missing. There is also the problem of encoding the input into vectors, which takes a few milliseconds (do you batch, parallelize, are you ok with eventual consistency?).

I recently been found with pgvector (postgres + vector support) https://github.com/ankane/pgvector. Perhaps less performant, but easier to work with for teams. With support of migrations, ORM, sharing, and all the postgres goodies.

Another interesting/product-ready alternative is https://jina.ai.

And Google's ScaNN, https://www.youtube.com/watch?v=0SvrDtnUgV4