RAG is infinitely more accessible and cheaper than finetuning. But it is true that finetuning is getting severely overlooked in situations where it would outperform alternatives like RAG.
it depends on your data access pattern. If some text goes through LLM input many times, it is more efficient for LLM to be finetuned on it once.
The budget question comes into play as well. Even if text is repetitively fed to the LLM, that might happen over a long enough time compared to finetuning which is a sort of capex that it is financially more accessible.
Now bear in mind, I'm a big proponent of finetuning where applicable and I try to raise awareness to the possibilities it opens. But one cannot deny RAG is a lot more accessible to teams which are likely developers / AI engineers compared to ML engineers/researchers.
It looks like major vendors provide simple API for fine-tuning, so you don't need ML engineers/researchers: https://platform.openai.com/docs/guides/fine-tuning
Setting RAG infra is likely more complicated than that.
Results with this method are significantly more limited compared to all the power open-weight finetuning gives you (and the skillset needed in return).
And in either case don’t forget alignment and evals.
I am not sure I understand why you are so certain that finetuned top market models, built by top researchers will be significantly worse than whatever open source model you pick.
How many epochs do you run?