I have been following the vector database trend back in 2020 and I ended up with the conclusion: vector search features are a nice to have features which adds more value on existing database (postgres) or text search services (elasticsearch) than using an entirely new framework full of hidden bugs. You could get way higher speedup when you are using the right embedding models and encoding way than just using the vector database with the best underlying optimization. And the bonus side is that you are using a stack which was battle tested (postgres, elasticsearch) vs new kids (pinecone, milvus ... )
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