Edit: I see you run a vectordb company, so your question makes more sense
We generally avoid using embedding services to beginwith.. outside calls is the special case. Imagine something heavy like video transcription via Google APIs, not the typical one of 'just' text embedding. The actual embedding is generally one step of broader data wrangling, so there needs to be a good reason for doing something heavy and outside our control... Which has been rare.
Doing in the DB tier is nice for tiny projects, simplifying occasional business logic, etc, but generally it's not a big deal for us to run encode(str) when building a DB query.
Where DB embedding support gets more interesting to me here is layering on additional representation changes on top, like IVF+PQ... But that can be done after afaict? (And supporting raw vectors generically here, vs having to align our python & model deps to our DB's, is a big feature)
Their simple suggestion to extend to longer texts is to pool/avg sentence embeddings - but I'm not so sure that I want that; for instance eg that implies order between sentences didn't matter. If I were forced to use sentence transformers for my use case, then the real fix would be to train an actual pooling model atop of the sentence embedder, but I didn't want to do that either. At that point I stopped looking into it, but I'm certain there are newer models out nowadays that have both better encoders and handle much longer texts. The one nice thing about the sentence transformer models though is that they are much more lightweight than eg a 7B param language model
https://www.sbert.net/docs/pretrained_models.html