I don't think so, not anymore.
If you look at API providers that host open-source models, you will see that they have very healthy margin between their API cost and inference hardware cost (this is, of course, not the only cost) [1]. And that does not take into account any proprietary inference optimizations they have.
As for closed-model API providers like OpenAI and Anthropic, you can make an educated guess based on the not-so-secret information about their model sizes. As far as I know, Anthropic has extremely good margins between API cost and inference hardware cost.
[1]: This is something you can verify yourself if you know what it costs to run those models in production at scale, hardware wise. Even assuming use of off-the-shelf software, they are doing well.
Yeah, people tout RAG and fine tuning, but lots of people just use the base chat model, if it doesn't keep up to date on new data, it falls behind. How much are these companies spending just keeping up with the Joneses?
[1] ~ you can obviously verify this yourself by doing it yourself and seeing how expensive it is.
…is an enormously weak argument.
You suppose. You guess. We guess.
Let’s be honest, you can just stop at:
> I don’t think so.
Fair. I don’t either; but that’s about all we can really get at the moment afaik.
But:
A) it makes absolutely no difference to the fact you have no idea what the big LLM providers are actually doing.
B) Just asserting some random thing and saying “anyone competent can verify this themselves” is a weak argument. Youre saying youve done the research, but failing to provide any evidence you actual have
If youve crunched the numbers then man up and post them.
If not, then stop at “I think…”
“This is based on my experience running production workloads…” is a nice way of saying “I dont have any data to backup what Im saying”.
If you did, you could just link to it.
…by not posting data you make your argument non-falisifyable.
It is just an oppinion.
Sure we do. Go to AWS or any other hosting provider and pay them for inference. You think AWS are going to subsidise your usage of somebody else’s models indefinitely?
> All the data points we have today show that companies are spending an insane amount of capex on gaining AI dominance without the revenue to achieve profitability yet.
Yes, capex not opex. The cost of running inference is opex.
This seems sort of interesting, maybe (I don’t know business, though). I agree that the cost of running inference is part of the opex, but saying that doesn’t rule out putting other stuff in the opex bucket.
Currently these LLM companies train and models on rented Azure nodes in an attempt to stay at the head of the pack, to be well positioned for when LLMs become really useful in a “take many white collar jobs” sense, right?
So, is it really obvious what’s capex and what’s opex? In particular:
* The nodes used for training are rented, so that’s opex, right?
* The models are in some sense consumable? Or at least temporary. I mean, they aren’t cutting edge anymore after a year or so, and the open weights models are always sneaking up on them, so at least they aren’t a durable investment.
It’s capex. They are putting money in, and getting an asset out (the weights).
> The models are in some sense consumable?
Assets depreciate.
Because models are still being obsoleted every couple years, old models aren’t an asset. They are an R&D byproduct.
This is of course untrue for the same reason that people are still running Windows 2000.
What is the reason?
Because models are getting much better every couple months, I wonder if getting too attached to a process built around one in particular is a bad idea.
I don't mean to be too pointed here, but it doesn't sound like you have built anything at scale with LLMs. They are absolutely not plug n play from a behavior perspective. Yes, there is API compatibility (text in, text out) but that is not what matters.
Even frontier SOTA models have their own quirks and specialties.
Kind of like how httpds will have quirks but those aren't really a good thing and they're kind of plug and play.
It looks to me similar to the situation with that newly fashionable WWW thing in, say, 1998. Everybody tried to use it, in search of some magic advantage.
Take a look at the WWW heavyweights today: say, Amazon, Google, Facebook, TikTok, WeChat. Are the web technologies essential for their success? Very much so. But TCP/IP + HTML + CSS + JS are mere tools that enable their real technical and business advantages: logistics and cloud computing, ad targeting, the social graph, content curation for virality, strong vertical integration with financial and social systems, and other such non-trivial things.
So let's wait until a killer idea emerges for which LLMs are a key enabler, but not the centerpiece. Making an LLM the centerpiece is the same thinking that was trying to make catchy domain names the centerpiece, leading to the dot com crash.
That does not mean, however, that inference is unprofitable. The unit economics of inference can be profitable even while the personnel costs of training next-generation models are extraordinary.
There used to be contracts with service providers, and that — IIRC — usually shielded consumers from exorbitant increases.
I slightly disagree.
My hypothesis would be that the distribution for $200 users would be bimodal.
That is there would be a one concentration of super heavy power users.
The second concentration would be of people who want the "best AI" but are not power users and feel that most expensive -> the best.
Their actual usage would be just like normal free tier of ChatGPT.
They are giving vast amounts of inference away as part of their free tier to gain market share. I said inference is cheap, not that it is free. Giving away a large amount of a cheap product costs money.
> you're saying the unit economics of inference are all good
Free tiers do not contradict positive unit economics.
Although that was posted in October, so not much time for the reasoning model costs to show up. It's also important to note their revenue is on track to more than double this year[4] and one can't make a complete picture without understanding the revenue spent on the inference provided by these reasoning models.
[1] https://techcrunch.com/2024/12/23/openais-o3-suggests-ai-mod...
[2] https://techcrunch.com/2024/12/23/openais-o3-suggests-ai-mod...
[3] https://www.reddit.com/r/singularity/comments/1g0acku/someho...
[4] https://techcrunch.com/2025/06/09/openai-claims-to-have-hit-...
There's still a cost, it's just thrown into the future.
As with Costco's giant $5 roasted chickens, this is not solid evidence they're profitable. Loss-leaders exist.
Anyway, at 0.4 J/token, at a cost of 5 cents/kWh, is about 0.5 cents/million tokens. Even at 50% utilization you're only up to 1.1 cents/M tokens. Artificial Analysis reports the current average price of Llama3.3 70B to be about $0.65/M tokens. I'd assume most of the cost you're paying for is probably the depreciation schedule of the hardware.
Note that of course, modern-day 7B class models stomp on both those older models so you could throw in another 10X lower cost if you're going to quality adjust. Also, I did minimal perf tuning - I used FP8, and W8A8-INT8 both is faster and has slightly better quality (in my functional evals). I also used -tp 8 for my system. -tp 4 w/ model parallelism and cache-aware routing you should also be able to increase throughput a fair amount. Also, speculative decode w/ a basic draft model would give you another boost. And this was tested at the beginning of the year, so using vLLM 0.6.x or so - the vLLM 1.0 engine is faster (better graph building, compilation, scheduling). I'd guess that if you were conscientious about just optimizing you could probably get at least another 2X perf free with basically just "config".
What I do for testing is that I will run a benchmark_serving sweep (I prefer ShareGPT for a standard set that is slightly more realistic for caching) with desired concurrency (eg 4-1024 or something like that) and then plot TTFT vs Total Throughput and graph Mean, P50, and P99 - this will give you a clear picture what your concurrency/throughput for a given desired latency.
If you would like to someone add that somehow as a line item, perhaps you should add the full embodied energy cost of Linux (please include the entire history of compute since it wouldn't exist without UNIX), or perhaps the full military industrial complex costs from the invention of the transistor? We could go further.
Lots of hand waving, but that’s the idea.
Yes
>indefinitely?
No, and that's the point.
What company selling a primarily AI-based service right now is making a profit on that service?
Not indefinitely or for any undetermined scale, but AWS regularly subsidise up to 100k [0] in credits. It would not surprise me in the slightest if most. Inference is much cheaper than training and 100k in compute covers a decent amount of usage. Activate is tiered over 3 years so if you want to know the full story, let’s see how many of these services are still around in 18 months. I suspect just like when Games were the flavor of the month, then Crypto, we’ll see the real story when they actually have to pay a bill and their investors aren’t seeing any growth
I think the far more reasonable assumption is: It's profitable enough to not get super nervous about the existence of your company. You have to build very costly models and build insanely costly infrastructure. Running all of that at a loss without an obvious next step, because ALL of them are pricing to not even make money at inference, seems to require a lot of weird ideas about how companies are run.
(GPUs are generally much more cost effective and energy efficient than CPU if the solution maps to both architectures. Anthropic certainly caches the KV-cache of their 24k token system prompt.)
though at the outset (pre-profit / private) it's hard to say there's much difference.
I think this is the tough part, we’re at the outset still.
Also, a political investment could could be sustainable, in the sense that China might decide they are fine running Deepseek at a loss indefinitely, if that’s what’s going on (hypothetically. Actually I have never seen any evidence to suggest Deepseek is subsidized, although I haven’t gone looking).
So when I see a company bragging about "500% profitability," I can’t help but wonder if they’re even profitable at all.
Everyone just repeats this but I never buy it.
There is literally a service that allows you to switch models and service providers seamlessly (openrouter). There is just no lock-in. It doesn't make any financial sense to "grab market share".
If you sell something with UI, like ChatGPT (the web interface) or Cursor, sure. But selling API at a loss is peak stupidity and even VCs can see that.
They'll offer consumer and enterprise integrations that will only work with their models.
The carrots are already visible - think abstractions like "projects" in ChatGPT.
People start to let their LLM parse text content. Be that mails, chats or transcriptions, the models often need to formalize their output and switching models can become burdensome, while developers might switch models on a whim.
Doesn't mean you can capture a market by selling cheap though.
And unlike gasoline, once models are trained there is no significant ongoing production cost.
At the same time, cards have gotten >8x more efficient over the last 3 years, inference engines >10x more efficient and the raw models are at least treading water if not becoming more efficient. It’s likely that we’ll lose another 10-100x off the cost of inference in the next 2 years.
A lot of the companies offering LLM services are in a race gain market share and build expertise. Right now, they can burn through millions of dollars of VC money, with the expectation that they'll turn a profit at some point in the future. If that profit comes from advertising, and critically, if users don't expect advertising in their free LLMs, because they didn't see ads in generated output in the past, that will be very insidious.
Are the free LLM providers offering their service with a contractual obligation to the users that they will not add advertising to the outputs? If not, how is it insidious?
What definition of insidious are you using per https://www.merriam-webster.com/dictionary/insidious?
AI: "Of course! I see there is a flight at 6am tomorrow morning; the next one after that is at 3pm. Will that be a problem?"
You: "I was hoping for something more around lunch time. But if that is the only option; go for it."
AI: "Consider it done. I will send you the details via email in a few minutes."
The insidious part: there was a flight; they just weren’t paying for advertising. Yes. Interests are aligned here; but not yours.
AI: "Here are all available flights, economy class: 6am (£250), 12pm (£350), (Sponsored) 3pm (£300). The 12pm flight best matches your preference.
Sponsor offer: Save £50 by booking the 3pm flight through TravelDeals and get a free upgrade to business class.
Which would you prefer?"
In any case this is disingenuous, we're discussing LLM results augmented with advertising, not a fully sponsored agent which deliberately hides information. Today's LLMs pull comprehensive data anyway. You've strawmanned contextual advertising into outright fraud.
Best part is you can make perplexity research task out of it
> there's not that much motive to gain API market share with unsustainably cheap prices. Any gains would be temporary, since there's no long-term lock-in, and better models are released weekly
The goal may be not so much locking customers in, but outlasting other LLM providers whilst maintaining a good brand image. Once everyone starts seeing you as "the" LLM provider, costs can start going up. That's what Uber and Lyft have been trying to do (though obviously without success).
Also, the prices may become more sustainable if LLM providers find ways to inject ad revenue into their products.
I'm sure they've already found ways to do that, injecting relevant ads is just a form of RAG.
But they won't risk it yet as long as they're still grabbing market share just like Google didn't run them at the start - and kept them unobtrusive until their search won.
Not at all like Uber, let it go
> First, there's not that much motive to gain API market share with unsustainably cheap prices. Any gains would be temporary, since there's no long-term lock-in,
What? If someone builds something on top of your API, they're tying themselves to it, and you can slowly raise prices while keeping each increase well below the switching cost.
> Second, some of those models have been released with open weights and API access is also available from third-party providers who would have no motive to subsidize inference.
See above. Just like any other Cloud service, you tie clients to your API.
> Third, Deepseek released actual numbers on their inference efficiency in February. Those numbers suggest that their normal R1 API pricing has about 80% margins when considering the GPU costs, though not any other serving costs.
80% margin on GPU cost? What about after paying for power, facilities, admin, support, marketing, etc.? Are GPUs really more than half the cost of this business?
(EDIT: This is 80% margin on top of GPU rental, i.e. total compute cost. My bad.)
Guessing about costs based on prices makes no sense at this point. OpenAI's $20/mo and $200/mo tiers have nothing to do with the cost of those services -- they're just testing price points.
That's not really how the LLM API market works. The interfaces themselves are pretty trivial and have no real lock-in value, and there's plenty of adapters around anyway. (Often first-party, e.g. both Anthropic and Google provide OpenAI-compatible APIs). There might initially have been theories that you could not easily move to a different model, creating lock-in, but in practice LLMs are so flexible and forgiving about the inputs that a different model can be just dropped in an work without any model-specific changes.
> 80% margin on GPU cost? What about after paying for power, facilities
The market price of renting that compute on the market. That's fully loaded, so would include a) pro-rated recouping the capital cost of the GPUs, b) the power, cooling, datacenter buildings, etc, c) the hosting provider's margin.
> admin, support, marketing, etc.? Are GPUs really more than half the cost of this business?
Pretty likely! In OpenAI's leaked 2024 financial plan the compute costs were like 75% of their projected costs.
Sorry, I totally misread your post. Charging 80% on top of server rental isn't so bad, especially since I'm guessing there are significant markups on GPU rental given all the AI demand.
It's kind of unfair how little lock in factor there is at the base layer. Those doing the hardest, most innovative work have no way to differentiate themselves in the medium or long run. It's just unlikely that one person or company will keep making all the innovations. There is an endless stream of newcomers who will monetize on top of someone else's work. If anyone obtains a lock-in, it will not be through innovation. But TBH, it kind of mirrors the reality of the tech industry as a whole. Those who have been doing the innovation tend to have very little lock in. They are often left on the streets. In the end, what counts financially is the ability to capture eyeballs and credit cards. Innovation only provides a temporary spike.
With AI, even for a highly complex system, you'll end up using maybe 3 API endpoints; one for embeddings, one for inference and one for chat... You barely need to configure any params. The interface to LLMs is actually just human language; you can easily switch providers and take all your existing prompts, all your existing infra with you... Just change the three endpoint names, API key and a couple of params and you're done. Will take a couple of hours at most to switch providers.
Have you used any of these APIs? There's very little lock-in for inference. This isn't like setting up all your automation on S3, if you use the right library it's changing a config file.
Regardless of maturity lifecycle, by definition loss-leaders are cheap. If I go to the grocery store and milk is $1, I don't think I'm being swindled. I know it's a loss-leader and I buy it because it's cheap.
We are currently in the early-Netflix-massive-library-for-five-dollars-a-month era of LLMs and I'm here for it. Take all you can grab right now because prices will 100x over the next two years.
Ultimately, it may turn out that dumber models may be more economically efficient than smarter models once you ignore the investment subsidy factor.
Maybe, given the current state of AI, the economically efficient situation is to have lots of dumb LLMs to solve small, well-defined problems and leave the really difficult problems to humans.
Current approach, looking at pricing is assuming another AI breakthrough is just around the corner.
Sure there might not be any analysis that proves that they subsidized, but you also don't have any evidence that they are profitable. All the data points we have today show that companies are spending an insane amount of capex on gaining AI dominance without the revenue to achieve profitability yet.
You're also comparing two products in very different spots in the maturity lifecycle. There's no way to justify losing money on a decade-old product that's likely declining in overall usage -- ask any MBA (as much as engineers don't like business perspectives).
(Also you can reasonably serve search queries off of CPUs with high rates of caching between queries. LLM inference essentially requires GPUs and is much harder to cache between users since any one token could make a huge difference in the output)