With multiple 200 a month subs you are getting a multiple of those subsidized tokens. At least if you tabulate at retail api prices.

This rent in the era of expensive hardware thing is not exclusive to inference.

I’ve needed x86 architecture for windows builds recently and have just hemmed and hawed over buying a decent windows 11 box.

I can’t make the math work against Azure instances.

I can spin up a nice one for build deallocate,spin up something cheaper for QA and then turn that off.

I can build all the devops around that, with a number of passes, with a skills based interface so working with the cloud is not too bad.

The only thing that still has me thinking about it is the prospect of price is going up even more, which is acid as far as I know.

And I’m hopefully going to need this x86 stuff enough that I don’t wanna wish I had gotten one for that high prices now.

This should be obvious but with a model running on local hardware you can do your own RLHF and mod its behavior however you see fit. With cloud hosted models you can't. A few years ago when the models were smaller there were people undoing the guardrails, censorship, and general lobotomization with some form of a RLHF training. You can't do that on larger models unless you have the hardware like this person does.

Notice all the comments saying like "omg why so expensive so just use the API??". It's a trick for lockin even with, so called, "open" models. Keep trying to run them locally, keep undoing the lobotomies, mod model behavior so that they work for you and do what you want vs only what someone else says they're allowed to do.

Anecdotally, ~$500-1500/month token spend at API OpenAI/Anthropic pricing seems pretty realistic for full-time engineers at companies with "liberal but not unlimited" LLM spend policies.

This is of course anecdata. I know plenty of outliers, too. I know a principal engineer who uses many multiples of the number I quoted above. I am sure we also know many people making do with much much smaller budgets as well, via all kinds of well-discussed methods.

But, "$500-$1500 per month per full-time developer" is just kind of the personal mental baseline I use when making my decisions with regards to thinking about whether any of this makes any economic sense.

The cloud stuff is definitely a much better economic value, but I would argue:

1. You learn a lot more running this stuff yourself (especially since you can poke at its internals if you're interested or watch the reasoning chain.) Just being a consumer of this stuff doesn't really teach you much about it other than model & harness specific tricks that become obsolete pretty quickly. (IE, your Claude.md from 6 months ago probably needs a rewrite). Which is fine, I don't think you're going to be "left behind" if you're not a hardcore AI enthusiast or anything (I'm not), but as a guy that's always been interested in computer science I want to see how it ticks.

2. You can't really depend on this subsidization lasting forever IMO. I know the financials thing has been beaten to death but I guess I'm in the camp that it's good to be in control of your tools so that you can go elsewhere if the economics change.

I like to check in with ccusage pretty frequently, and honestly like if I were paying API prices for Claude I'd probably be paying thousands a month.

I am also not sure I would choose to use the cheap and easy to run at home model, given a choice. The marketing copy says this is a frontier model, but it's not. Sol and Mythos are the frontier right now. GLM 5.3 Flash simply isn't. I'd rather use the frontier model as they waste less of my time than even Opus.