AFAICT: Most of those are basically UIs, data management, & integrations around the same set of vector index libs like FAISS + same set of models like HF, and even the same set of inference server libs (triton, aws/gcp, fastapi, ...). So you'd be evaluating different commercializations of the same core OSS tech. There are useful evaluations to do at that level, but more like licensing, business model, UI, model management, etc.

Another commenter below recognized that regular DBs (ES, Postgres, ..) are starting to add vector indexes. As someone doing a lot of architecting for log/event/graph correlation/investigation systems, I've been tracking whether managed neural search db is a feature vs new category, and how big. Ex: those corporate sides definitely feels like an Algolia competitor, but for the 99% case we normally do, maybe just an OSS feature/lib of whatever DB/framework you're already using? Not obvious!