A big difficulty in using vector DBs in production for things like embeddings or LLMs it that there is alot that goes into converting and processing raw input into a vector form (think chunking, formatting, encoding, inference, metadata, etc). DBs like pinecone just don't handle any of that and therefore you have to build out large systems to do it yourself.

There are some platforms and open source tools that handle it end to end. https://github.com/marqo-ai/marqo is one, for example that is both open source and has a cloud offering.

We put together some stories here: https://www.pinecone.io/learn/wild/

Usually folks use a vector database alongside a doc store like Postgres, Snowflake, Elastic…

We are using dense_vector field that Elastic Search offers and it's scaling quite well.

We're using Elasticsearch with binary vectors and our own sub-token filtering approach, but if we were to start over again today we'd simply use the dense vectors and ANN built into Elasticsearch, just with float vectors.

I’ve increasingly been using pgvector. It’s an excellent workflow when you can just include similarity search as another where clause in your sql query.