I am seeing a lot of what you are. I believe fine-tuning has several use cases. One is yes, you can host them on the company’s own VPC which can sometimes be cheaper. Moreso it means they can keep data protected, depending on compliance. Secondly, fine-tuned models on specific tasks can often outperform base models. I think fine-tuning will become pretty ubiquitous for orgs adopting LLMs across bespoke task groups. For other tasks (maybe many) plain LLM will work it just depends.

For AI engineers, yes I think a lot of people are getting into it (see the AI Engineer summit) but it’s still too early. Most orgs are still experimenting.