Also, how hard was it to set up your own private model instance? I have yet to try that, I've just been playing around with OpenAI's API. Any good tutorials you recommend?
As for hosting the model, Hugging Face has a number of guides (ie. [1]) for this but overall the process was quite simple. All you need to do is find a model compatible with the kind of inference you want to do (ie. sentence embeddings vs. text generation). Once it's deployed, Hugging Face gives you an API endpoint similar to OpenAI's embeddings API that returns the embedding vector for a given input. For picking the model, there are a bunch of good resources with benchmarks comparing them depending on the use case (ie. [2]).
[1] https://huggingface.co/blog/getting-started-with-embeddings [2] https://supabase.com/blog/fewer-dimensions-are-better-pgvect...