Depends on what their actual costs are. Either they are losing lots of money on subscriptions, or they make absolute bank on API pricing.

Looking at the pricing of 1-2T models like Kimi or DeepSeek on the open market, I'm tempted to assume that inference costs are closer to subscription pricing than to API pricing.

Especially considering that subscriptions a) distribute load over time via rate limits, and b) will include a lot of users who get only a fraction of the possible value, whether they are on a personal account where they are on the rate limit on the weekend but barely use it during the week, or are corporate users who were issued an account they rarely use. Subscription prices are usually measured on the average case, not the most extreme value a power user can get out of it

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runtime_terror an hour ago | parent | next [-]

> I'm tempted to assume that inference costs are closer to subscription pricing than to API pricing

So just going on vibes?

While some people don't like his content, Ed Zitron shows a lot of evidence for your assumption being very wrong.

These companies are bleeding cash at ungodly rates. It's likely their API pricing is still subsidized if you look at their overall financial picture.

Related, there's a good reason those API prices keep going up a lot every new version and it's not just because the models are better.

	▲	wongarsu an hour ago \| parent [-]
		Selling inference for more than inference costs is not incompatible with bleeding cash at ungodly rates. They do in fact pay ungodly amounts of cash for other things, like training, marketing, etc. Heck, you can bleed cash while being profitable (in the accounting sense) Also, API prices going up a lot every new version is more an OpenAI thing, and even there it's a recent trend: GPT 5.0 was a big price drop compared to 4.1, and 4.1 was cheaper than 4o, which itself got a price cut at some point and is cheaper than 4. Meanwhile Anthropic's API pricing stayed stable for many versions, then got slashed to a third with the 4.2 release and have stayed at that level since.

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Forgeties79 2 hours ago | parent | prev [-]

Considering not one company is in the black yet I don’t really know how we can say anyone is making bank, unless we want to count absurd levels of VC funding (now slowing down) I guess.

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wongarsu 2 hours ago | parent | next [-]

I am conveniently not counting training costs (since they add no marginal costs, selling more tokens doesn't impact them), and hardware and DC costs only amortized

Of course they do have to "make bank" in some way to offset the insane training costs. But whether they go for high prices or high volume, or offer some services as a loss leader to drive profits elsewhere is somewhat orthogonal to that

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anthonypasq an hour ago | parent | prev [-]

https://www.wsj.com/tech/ai/mind-blowing-growth-is-about-to-...

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Forgeties79 an hour ago | parent [-]

Let’s see it first. And without omitting training/infrastructure costs at that. Until then my comment is still accurate.

	▲	anthonypasq 12 minutes ago \| parent [-]
		its a private company, what exactly do you expect to 'see'?