Thanks for your input, I've only tried Chroma a little bit so far and had a pretty good experience. What they also have going for them is a big community on discord that can be helpful.
Many of them are open source and you can host them yourself. That would make it more cost effective. Also someone mentioned https://turbopuffer.com/. That seems like a good alternative if you're looking for something economical.
I quickly took a look at the redisearch ANN Benchmarks and they seem to stack up against the others (more or less same level as Milvus) in the comparison when it comes to QPS and Latency.
Yeah, that's the difference we've seen according to the QPS for the ANN Benchmarks. The same story seems to be true for other datasets too. We're looking at a 0.9 recall.
The way you explain hybrid search aligns with my understanding. Pinecone has a good article about it here https://www.pinecone.io/learn/hybrid-search-intro/. From my understanding, all vector DBs support this.
Happy to connect. The benchmark numbers are mostly from ANN Benchmarks. For my use case, the nytimes-256 dataset was most relevant so I used that for the QPS benchmark. I also took a look at the benchmarks you've made at https://qdrant.tech/benchmarks/ and there qdrant seems to be outperforming many others. If I've gotten something wrong here, I'm glad to update the article :)
I made this table to compare vector databases in order to help me choose the best one for a new project. I spent quite a few hours on it, so I wanted to share it here too in hopes it might help others as well. My main criteria when choosing vector DB were the speed, scalability, dx, community and price. You'll find all of the comparison parameters in the article.
(Disclaimer, Lukas and I are co-founders)