> It looks vaguely comparable on benchmarks, but it tends to be fragile and a lot of management overhead in reality.
I'm working on an self-hostable LLM (web) UI[0] that aims to provide a comparable good UX to e.g. ChatGPT, and you are right that there is a decent amount of fragility involved, and more management overhead than most people would expect.
However, we usually find that those details happen a lot more in e.g. the harness (= out application), or some prompt tuning that's required for each of the models, rather than model quality itself. We have seen customers using self-hosted LLMs with similar user satisfaction across their organization to other customers that heavily lean on latest GPT-5 models on Azure. Especially given that you have to do some level of tuning and setup anyways, you might as well invest it in "local"/self-hosted AI (if you can make the financials of the inference cost work out for you).
I think it should also be noted that the inference providers on hyperscalers also tend to be quite fragile, each in their own way (e.g. Google with a horrible rate limit system or Azure with almost weekly intermittent 500-error incidents).