Why not? Every larger "cloud" company seems to be randomly buying 1 at the moment to offer "AI" so might get some good deals. This is clearly 1 of them - panic buy.
YMMV. Sometimes a LORA is fine, but sometimes a full finetune is necessary for higher quality output.
That being said, backwards pass free training keeps making more and more progress. Seems like a short matter of time before it becomes practical.
I just fine tuned a ~30b parameter model on my 2x 3090s to check it out. It worked fantastically. I should be able to fine tune up-to 65b parameter models locally but wanted to get my dataset right on a smaller model before trying.
It seems to me that the vast majority of these people would be better off just doing semantic search with their documents chunked, run through an embeddings process, and stored in a vector database, with the search queries and results then run through an LLM at the final step to create an actual "answer". For applications where this is not practical, I agree that LoRA should be the next approach. I have a hard time believing that the future is everyone training their own domain specific LLMs from the ground up.
For SMBs, the value would be in using the LLM to generate responses to customer Q&A/search queries. But these companies aren't going to integrate some external third party service, they'll only use it if it's already baked into their CMS - Wordpress/Shopify/Wix/etc. I just don't see who the final consumer for this product would be.