Someone has to ask the question: How many vector DBs do we really need? How do the vector DB companies differentiate themselves? And why do we need a company at all when there are increasingly awesome open source options?
I genuinely ask - there are a lot of other problems in the RAG, fine tuning, AI/LLM, retireval space, to solve. And more and more vector retrieval is, while not 100% solved, at least is something the community has a grasp on the tradeoffs. Solved to the point that squeezing a bit more recall out of vector retrieval isn't the problem anymore.
Agree but then the same argument applies to RDBMSs and multiple vendors seem to be doing OK in that space.
I think it ultimately comes down to "stuff" (sales journey, price, support etc.) other than the technology itself. I am sure any RDBMS can meet most of the requirements of any given customers (in most cases) but we still see customers buying across vendors.
>Solved to the point that squeezing a bit more recall out of vector retrieval isn't the problem anymore.
I think this is a bit of a strawman.
I don't think recall is the main point these systems are trying to sell us on, it's more about robustness and ease of use compared to building something inhouse or using a lower level library to build a system on top of it just for this small part of your overall project/product (be it RAG, search, whatever).
I guess Lucene-based solutions, while very mature overall in terms of engineering, lagged behind this functionality (out of caution, trying to build what's going to be long term useful) and are also perceived a bit too cumbersome. So these stores do make sense, I think. The core functionality is nothing too complex (at least HNSW), but hiding it behind a stable black box with just a few inputs and levers, has value for people that are likely to use these stores.
qdrant is open source. Being open source is not in opposition to running a company; it is part of their strategy.
There is still work to be done in vector databases. None of the products have perfected hybrid search yet, for example, and performance varies a lot between products; they are not fungible.
I genuinely ask - there are a lot of other problems in the RAG, fine tuning, AI/LLM, retireval space, to solve. And more and more vector retrieval is, while not 100% solved, at least is something the community has a grasp on the tradeoffs. Solved to the point that squeezing a bit more recall out of vector retrieval isn't the problem anymore.