Most of the clients I'm working with aren't interested in the base level of service. They are looking to further train the models for their specific use cases. That's a much higher barrier to switch than replacing an API. You've got to understand how the underlying models are handling and building context. This sort of customer is paying far more than the advertised token rates and are locked in more tightly.
not really. fine tuning generally just involves running tailored training data through the model - the actual training algorithm is fairly generalized.
For example, the Dreambooth fine tuning algorithm was originally designed for Google's image, but was quickly applied to Stable Diffusion.
