Yeah we saw faiss + es leaders for serving embeddings / vector search, and pinecone / weaviate / I think milvus as next tier, so was curious if we could improve the analysis :)
FAISS is depreciating since it is just a library that does not scale and lacks a lot of vector db functionalities like filtering. ES is often used just by default because Devs have experience with but the performance is very low compared to dedicated solutions. The Github trending for Vector Databases https://github.com/topics/vector-database
Is the analysis public? I'm curious how you determined the popularity of a product like Pinecone, since we don't have a public metric like GitHub stars.
This kind of analysis is rarely precise, but is useful for rougher tasks like tiering
My personal question is if vector indexes are/will be a good-enough general DB feature / compute lib for most users & use cases. A lucrative niche market can still happen as VC dollars disappear, similar to graph DBs, so not a knock, just important for folks deciding how to build things.
Yeah we saw faiss + es leaders for serving embeddings / vector search, and pinecone / weaviate / I think milvus as next tier, so was curious if we could improve the analysis :)