[svcrunch]

> but HNSW is the best 99% of the time for both performance and latency, and is implemented in almost every modern major vector store.

In my experience, HNSW indexes are very expensive to build, relative to indexes like IVF. They also have a larger memory footprint. IVF, on the other hand, is pretty trivial to parallelize across multiple machines, and while I'm aware there are techniques for doing that with HNSW, I don't know the details well enough.

Also, if you review papers like "SOAR: Improved Quantization for Approximate Nearest Neighbor Search", they hint at some of the throughput barriers faced by graph-based methods like HNSW.