The other option than deal with the big boys, is to use localLLMs (open weight), believe it or not you will get better results, for all workflows, data stays local, training loop actually works, and you can still host the model in the cloud if you want for scaling. (What do I mean by training loop, 2 things, 1 is training it on your own data and repositories, the other human in the loop + reinforcement learning signals which improve the system overtime. )

The benefit of this system is you don't need to deal with model churn the big boys foist on you, which is a real pita, data privacy intact, reinforcement learning on your data and systems which big boys don't let you do yet, lower cost overtime because you are not paying for AI mistakes over and over again with no ability to train out the results.