IMO embedding models are some of the most likely models you’d want to self host because they are much more useful out of the box for batch or stream workloads than LLMs. You can just rent a GPU for $1-2/hr (you don’t need a B300) and run across your entire dataset.

If you are only interested in recommendations, clustering, etc and won’t have to do embedding at runtime, that’s it.

There is a lot of basic matrix stuff you can do with embedding vectors that are much easier than their LLM equivalents. I can’t remember the name for the techniques but a major one is translating questions annout a subject eg “Who is Slim Shady?”->”Marshall Mathers”so that your input distribution at runtime matches your offline distribution. Much easier to do with open weight models.