I would be really interested to understand the efficiency of this learning vs a more traditional approach. If you have two models - ChessGPT and a traditional chess-playing ML model - and train them on the same number of games, which reaches a higher level of chess-playing skill?
You say you have no experience with deep learning. Start by learning about "traditional" deep learning models, then move on to LLMs. Learning LLMs before deep learning is like learning algebra before addition.
"Deep Learning with Python, Second Edition" by Google's François Chollet is a pretty good intro, and also touches on LLMs / generative models towards the end.
a) Your business model
Is the API your primary source of revenue? Something you'd be willing to lose money on in order to drive revenue somewhere else? You need to think about how it fits into the overall strategy of your business.
b) Your costs
How much will this API cost you to setup, operate, and maintain? You need to think about how much you'll make/lose at different price structures and different volumes.
c) How the customer will use it
Will they be looking to make 1 query a year? A million an hour? Probably somewhere inbetween of course, but that will make a big difference in terms of how to price it.
d) How much value does the customer get
If using this API saves the customer $1 million in costs, you should make sure they are paying you at least $100k. If it saves them $10 in costs, make sure you aren't charging them more than $10. And again, you need to think about how much they are going to pay in total, thinking about how many calls they are making of the course of a day/month/year/etc.
... I'm sure I'm missing some. But the point really is, there is no right answer unless you think carefully about your product, customers, and strategy.