You can't compare an API that is profitable (search) to an API that is likely a loss-leader to grab market share (hosted LLM cloud models).
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)
jstummbillig · 23m ago
There is also a lot of different models at a lot of different price points (and LLMs are fairly hard to compare to begin with). In this theory of a likely loss-leader, must we assume that all of them, from all companies, are priced below cost...? If so, that seems like a fairly wild claim. What's Step 2 for all of these companies to get ahead of this, given how model development currently works?
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.
Palmik · 35m ago
> API that is likely a loss-leader to grab market share (hosted LLM cloud models).
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.
noodletheworld · 26m ago
I don’t completely disagree, but “assertion one” [1]
[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.
> an API that is likely a loss-leader to grab market share (hosted LLM cloud models)
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.
mupuff1234 · 4m ago
Except they most likely do have a plan to make it harder to switch.
int_19h · 11m ago
The problem with this theory in general is that, given the sheer number of cloud inference providers (most of which are hosting third party models), it would be exceedingly strange if not only all of them are engaging in this same tactic, but apparently all of them have the same financial capacity to do so.
JimDabell · 1h ago
> you also don't have any evidence that they are profitable.
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.
bee_rider · 36m ago
> 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.
JimDabell · 33m ago
> The nodes used for training are rented, so that’s opex, right?
It’s capex. They are putting money in, and getting an asset out (the weights).
> The models are in some sense consumable?
Assets depreciate.
antman · 1h ago
No we don't, MS used their OpenAI position as a strategy to increase Azure adoption. I am surprised AWS didn't give ls for free
ceejayoz · 55m ago
> You think AWS are going to subsidise your usage of somebody else’s models indefinitely?
As with Costco's giant $5 roasted chickens, this is not solid evidence they're profitable. Loss-leaders exist.
lhl · 11m ago
Rather than speculating another option is to just measure things. I churned through billions of tokens for evals and synthetic data earlier this year, so I did some of that. On an H100 node, a Llama3 70B FP8 at concurrency=128 generated at about 0.4 J/token (this was estimating node power consumption and multiplying by a generous PUE, 1.2X or something like that) - it was still 120X cheaper than the 48 J/token estimates of cost to run the 175B GPT-3 on 2021-era Microsoft DC1 hardware (Li et al. 2023) and 10X cheaper than the 3-4 J/token empirical measurements to run LLaMA-65B on V100/A100 HPC nodes (Samsi et al 2023).
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".
ceejayoz · 7m ago
Yes, if we discount the billion or so Facebook spent to train Llama3.
rco8786 · 1h ago
AWS isn’t doing the training on those models.
JimDabell · 35m ago
OpenAI spends less on training than inference, so the worst case scenario is less than double the cost after factoring in training. Inference is still cheap.
ddp26 · 24m ago
I analyzed OpenAI API profitability in summer 2024 and found inference for gpt-4 class models likely pretty profitable, ~50% gross margins (ignoring capex for training models): https://futuresearch.ai/openai-api-profit
otterley · 21m ago
That’s a little like saying you can compute the profitability of the energy market by looking only at the margins of gas stations. You can’t exclude all the outlays on actually acquiring the product to sell.
pama · 2h ago
Please read the DeepSeek analysis of their API service (linked in this article): they have 500% profit margin and they are cheaper than any of the US companies serving the same model. It is conceivable that the API service of OpenAI or Anthropic have much higher profit margins yet.
(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.)
SEGyges · 17m ago
Every LLM provider caches their KV-cache, it's a publicly documented technique (go stuff that KV in redis after each request, basically) and a good engineering team could set it up in a month.
iamnotagenius · 2h ago
With all due respect to Deepseek, I would take their numbers with grain of salt, as they might as well be politically motivated.
jarym · 1h ago
Any more politically motivated than a model from anywhere else?
WithinReason · 1h ago
is that better or worse than commercially motivated?
leeoniya · 51m ago
commercial motivatation needs to show eventual profit to be sustainable, while political does not.
though at the outset (pre-profit / private) it's hard to say there's much difference.
bee_rider · 31m ago
> 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).
lumost · 48m ago
We don’t know what the marginal cost of inference is yet however. So far, users are demonstrating that they are willing to pay more for LLMs than traditional web experiences.
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.
Workaccount2 · 1h ago
Just wait till there are ads for free users, which is going to happen. Depending on how insidious these ads are, they could be extremely profitable too, like recommending products and services directly in context.
Sevii · 42m ago
They could dynamically update the system prompt with ad content on a per request basis. Lots of options.
ozim · 28m ago
I think you can make an educated guess if you check local model performance, prices of energy and hardware and price of the subscriptions.
Best part is you can make perplexity research task out of it
TZubiri · 2h ago
This is addressed in the article. Giving arguments for llms being profitable as APIs.
n4r9 · 1h ago
One of those arguments is:
> 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.
unilynx · 1h ago
> 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.
username223 · 17m ago
It's addressed poorly.
> 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?
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.
petesergeant · 8m ago
> 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.
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.
paxys · 2h ago
The entire comparison hinges on people only making simple factual searches ("what is the capital of USA") on both search engines and LLMs. I'm going to say that's far enough from the standard use case for both these sets of APIs to be entirely meaningless.
- If I'm using a search engine, I want to search the web. Yes these engines are increasingly providing answers rather than just search results, but that's a UI/product feature rather than an API one. If I'm paying Google $$ for access to their index, I'm interested in the index.
- If I'm using an LLM, it is for parsing large amounts of input data, image recognition, complex analysis, deep thinking/reasoning, coding. All of these result in significantly more token usage than a 2-line "the answer to your question is xyz" response.
The author is basically saying – a Honda Civic is cheap because it costs about the same per pound as Honeycrisp apples.
dale_glass · 1h ago
I think the issue is that the classical search engine model has increasingly become less useful.
There's less experts using search engines. Normal people treat search engines less like an index search and more like a person. Asking an old school search engine "What is the capital of USA" is actually not quite right, because the "what is" is probably quite superfluous, and you're counting on finding some sort of educative website with the answer. In fact phrasing it as "the capital of the USA is" is probably a better fit for a search engine, since that's the sort of sentence that would contain what you want to know.
Also with the plague of "SEO", there's a million sites trying to convince Google that their site is relevant even when it's not.
So LLMs are increasingly more and more relevant at informally phrased queries that don't actually contain relevant key words, and they're also much more useful in that they bypass a lot of pointless verbiage, spam, ads and requests to subscribe.
agentultra · 52m ago
Most search engines will parse the query sentence much more intelligently than that. It's not literally matching every word and hasn't for decades. I just tried a handful of popular search engines, they all return the appropriate responses and links.
dale_glass · 9m ago
They're not that literal anymore of course, but they still don't compare to an LLM. In the end it's still mostly searching for key words even if with a few tweaks here and there, and the ability to answer vague questions mostly works by finding forums and Reddit posts where people ask that specific question and hopefully get an answer.
When you're asking a standard question like the capital of whatever, that works great.
When you have one of those weird issues, it often lands you in a thread somewhere in the Ubuntu forums where people tried to help this person, nothing worked, and the thread died 3 years ago.
Just the fact that LLMs can translate between languages already adds an amazing amount of usefulness that search engines can't have. There seems to be a fair amount of obscure technical info that's only available in Russian for some reason.
atrettel · 1h ago
This is a great point. I'll add that search engines are also unclear about what kind of output they give. As you point out, search engines accept both questions and key words as queries. Arguably you'd want completely different searches/answers for those. Moreover, search engines no longer just output web sites with the key words but also give an "AI overview" in an attempt to keep you on their site, which is contrary to what search engines have traditionally done. Previously search engines were something you pass through but they now try to position themselves as destinations instead.
I'd argue that search engines should stick to just outputting relevant websites and let LLMs give you an overview. Both technologies are complimentary and fulfill different roles.
xpe · 36m ago
> The entire comparison hinges on people only making simple factual searches ... on both search engines and LLMs.
I disagree, but I can see why someone might say this, because the article's author writes:
> So let's compare LLMs to web search. I'm choosing search as the comparison since it's in the same vicinity and since it's something everyone uses and nobody pays for, not because I'm suggesting that ungrounded generative AI is a good substitute for search.
Still, the article's analysis of "is an LLM API subsidized or not?" does not _rely_ on a comparison with search engines. The fundamental analysis is straightforward: comparing {price versus cost} per unit (of something). The goal is figure out the marginal gain/loss per unit. For an LLM, the unit is often a token or an API call.
Summary: the comparison against search engine costs is not required to assess if an LLM APIs is subsidized or not.
og_kalu · 1h ago
>The entire comparison hinges on people only making simple factual searches
You have a point but no it doesn't. The article already kind of addresses it, but Open AI had a pretty low loss in 2024 for the volume of usage they get. 5B seems like a lot until you realize chatgpt.com alone even in 2024 was one of the most visited sites on the planet each month with the vast majority of those visits being entirely free users (no ads, nothing). Open AI in December last year said chatgpt had over a billion messages per day.
So even if you look at what people do with the service as a whole in general, inference really doesn't seem that costly.
phillipcarter · 1h ago
> If I'm using an LLM, it is for parsing large amounts of input data, image recognition, complex analysis, deep thinking/reasoning, coding. All of these result in significantly more token usage than a 2-line "the answer to your question is xyz" response.
Correct, but you're also not the median user. You're a power user.
fkyoureadthedoc · 2h ago
Anecdotally, I'm a paying user and do a lot of super basic queries. What is this bug, rewrite this drivel into an email to my HOA, turn me into a gnome, what is the worst state and why is it west Virginia.
This would probably increase 10x if one of the providers sold a family plan and my kids got paid access.
Most of my heavy lifting is work related and goes through my employer's pockets.
paxys · 1h ago
None of those are "basic queries", in the sense that you will not be able to solve them using the Google/Bing search API.
johnisgood · 1h ago
I love your prompts. :D
llm_nerd · 1h ago
The comparison is quite literally predicated on seeking an answer via both mechanisms. And the simple truth is that for an enormous percentage of users, that is indeed precisely how they use both search engines and LLMs: They want an answer to a question, maybe with some follow-up links so if that isn't satisfactory they can use heuristics to dig deeper.
Which is precisely why Google started adding their AI "answers". The web has kind of become a cancer -- the sites that game SEO the most seem to have the trashiest, most user-hostile behaviour, so search became unpleasant for most -- so Google just replaces the outbound visit conceptually.
WhyIsItAlwaysHN · 1h ago
There's something I don't get in this analysis.
The queries for the LLM which were used to estimate costs don't make a lot of sense for LLMs.
You would not ask an LLM to tell you the baggage size for a flight because there might be a rule added a week ago that changes this or the LLM might hallucinate the numbers.
You would ask an LLM with web search included so it can find sources and ground the answer. This applies to any question where you need factual data, otherwise it's like asking a random stranger on the street about things that can cost money.Then the token size balloons because the LLM needs to add entire websites to its context.
If you are not looking for a grounded answer, you might be doing something more creative, like writing a text. In that case, you might be iterating on the text where the entire discussion is sent multiple times as context so you can get the answer. There might be caching/batching etc but still the tokens required grow very fast.
In summary, I think the token estimates are likely quite off. But not to be all critical, I think it was a very informative post and in the end without real world consumption data, it's hard to estimate these things.
barrkel · 1h ago
Oh contraire, I ask questions about recent things all the time, because the LLM will do a web search and read the web page - multiple pages - for me, and summarize it all.
4o will always do a web search for a pointedly current question, give references in the reply that can be checked, and if it didn't, you can tell it to search.
o3 meanwhile will do many searches and look at the thing from multiple angles.
zambal · 1h ago
But in that case it's hard to argue that llm's are cheap in comparison to search (the premise of the article)
pzo · 1h ago
But that's from user perspective, check Google or openai pricing if you wanted to have grounded results in their API. Google ask $45 for 1k grounded searches on top of tokens. If you have business model based on ads you unlikely gonna have $45 CPM. Same if you want to offer so free version of you product then it's getting expensive.
WhyIsItAlwaysHN · 21m ago
But that was my point, then you need to include the entire websites in the context and it won't be 506 tokens per question. It will be thousands
harperlee · 56m ago
Nitpick: Au contraire
brookst · 1h ago
Just tried asking “what is the maximum carryon size for an American Airlines flight DFW-CDG” and it used a webs search, provided the correct answer, and provided links to both the airline and FAA sites.
Why wouldn’t I use it like this?
WhyIsItAlwaysHN · 18m ago
What I was saying is that you wouldn't use a raw LLM (so 506 tokens to get an answer). You would use it with web search so you can get the links.
The LLM has to read the websites to answer you so that significantly increases the token count, since it has to include them in its input.
ceejayoz · 52m ago
That search query brings up https://www.aa.com/i18n/travel-info/baggage/carry-on-baggage... for the first result, which says "The total size of your carry-on, including the handles and wheels, cannot exceed 22 x 14 x 9 inches
(56 x 36 x 23 cm) and must fit in the sizer at the airport."
What benefit did the LLM add here, if you still had to vet the sources?
adrian_b · 1h ago
I do not see which is the added benefit provided by the LLM in such cases, instead of doing yourself that web search, and for free.
JimDabell · 44m ago
I just tried that search on Google.
The first thing I saw was the AI summary. Underneath that was a third-party site. Underneath that was “People also ask” with five different questions. And then underneath that was the link to the American Airlines site.
I followed the line to the official site. I was presented with a “We care about your privacy” consent screen, with four categories.
The first category, “Strictly necessary”, told me it was necessary for them to share info with eleven entities, such as Vimeo and LinkedIn, because it was “essential to our site operation”.
The remaining categories added up to 59 different entities that American Airlines would like to share my browsing data with while respecting my privacy.
Once I dismissed the consent screen, I was then able to get the information.
Then I tried the question on ChatGPT. It said “Searching the web”, paused for a second, and then it told me.
Then I tried it on Claude. It paused for a second, said “Searching the web”, and then it told me.
Then I tried it on Qwen. It paused for a second, then told me.
Then I tried it on DeepSeek. It paused for a second, said “Searching the web”, and then it told me.
All of the LLMs gave me the information more quickly, got the answer right, and linked to the official source.
Yes, Google’s AI answer did too… but that’s just Google’s LLM.
Websites have been choosing shitty UX for decades at this point. The web is so polluted with crap and obstacles it’s ridiculous. Nobody seems to care any more. Now LLMs have come along that will just give you the info straight away without any fuss, so of course people are going to prefer them.
ceejayoz · 30m ago
> Websites have been choosing shitty UX for decades at this point. The web is so polluted with crap and obstacles it’s ridiculous. Nobody seems to care any more. Now LLMs have come along that will just give you the info straight away without any fuss, so of course people are going to prefer them.
Do you honestly believe LLMs aren't gonna get sponsored answers/ads and "helpful" UI elements that boost their profits?
JimDabell · 27m ago
I’m talking about today’s experience, not speculating about what might happen at some arbitrary point in the future.
The web has this shitty UX. LLMs do not have this shitty UX. I’m going to judge on what I can see and use.
ceejayoz · 23m ago
> I’m talking about today’s experience…
In that case, get uBlock. The answer is in the first result, on the first screen, and the answer is even quoted in the short description from the site. (As a bonus, it also blocks the cookie consent popups on the AA site, if you like.)
The only thing getting in the way of the real, vetted, straight-from-the-source answer currently is the AI overview.
Even so, saying that the UX of the web is almost as good as the UX of an LLM after you take steps to work around the UX problems with the web isn’t really an argument.
ceejayoz · 4m ago
> Most people don’t use an ad blocker.
I mean, they should. Anyone on this site most certainly should.
I really doubt that, in an industry where chips are so hard to come by, draw so much power and are so terribly expensive, big players could at any time flip a switch and become profitable.
They burn through insane amounts of cash and are, for some reason, still called startups. Sure, they'll be around for a long time until they figure something out, but unless hardware prices and power consumption go down, they won't be turning a profit anytime soon.
Just look at YouTube: in business for 20 years, but it's still unclear whether it's profitable or not, as Alphabet chooses not to disclose YT's net income. I'd imagine any public company would do this, unless those numbers are in the red.
patapong · 1h ago
Sure, but Alphabet is insanely profitable, based on having grabbed a lot of market share in the search market and showing people ads. The AI companies är betting that AI will be similarly important to people, and that there is at least some stickiness to the product, meaning that market share can eventually be converted to revenue. I think both of these are relatively likely.
dist-epoch · 2h ago
Stock price go up is another way a company is profitable. The amazon playbook for 10+ years.
andrew_lettuce · 1h ago
Amazon made huge money as they captured more and more of the market and didn't return any of it. The company literally became worth more and more each year. Open AI continues to hemorrhage money.
wrsh07 · 33m ago
I'm confused by this claim - OpenAI has pretty meaningful revenue.
If they monetized free users, they would have even better revenue. The linked post estimates eg $1 per user per month would flip them to profitable.
bfrog · 1h ago
It's another Uber moment for VC. The bullshit ends as soon as becoming a functioning business suddenly takes precedence, and the real costs start to come out.
Etheryte · 2h ago
> OpenAI reportedly made a loss of $5B in 2024. They also reportedly have 500M MAUs. To reach break-even, they'd just need to monetize those free users for an average of $10/year, or $1/month. A $1 ARPU for a service like this would be pitifully low.
This is a tangent to the rest of the article, but this "just" is doing more heavy lifting than Atlas holding up the skies. Taking a user from $0 to $1 is immeasurably harder than taking a user from $1 to $2, and the vast majority of those active users would drop as soon as you put a real price tag on it, no matter the actual number.
jsnell · 2h ago
Ok, I clearly should have made the wording more explict since this is the second comment I got in the same vein. I'm not saying you'd convert users to $1/month subscriptions. That would indeed be an absurd idea.
I'm saying that good-enough LLMs are so cheap that they could easily be monetized with ads, and it's not even close. If you look at other companies with similar sized consumer-facing services monetized with ads, their ARPU is far higher than $1.
A lot of people have this mental model of LLMs being so expensive that they can’t possibly be ad-supported, leaving subscriptions as the only consumer option. That might have been true two years ago, but I don't think it's true now.
andrew_lettuce · 2h ago
There are some big problems with this, mostly that openAI doesn't want to break even or be profitable, their entire setup is based on being wildly so. Building a Google sized business on ads is incredibly difficult. They need to be so much better than the competition that we have no choice but to use them, and that's not the case any more. More minor but still a major issue is the underlying IP rights. As users mature they will increasingly look for citations from LLMs, and if open AI is monetizing in this vein everyone is going to come for a piece.
NewsaHackO · 1h ago
> mostly that openAI doesn't want to break even or be profitable, their entire setup is based on being wildly so.
I’m sure you are going to provide some sort of evidence for this otherwise ridiculous claim, correct?
netdevphoenix · 59m ago
>This is a tangent to the rest of the article, but this "just" is doing more heavy lifting than Atlas holding up the skies. Taking a user from $0 to $1 is immeasurably harder than taking a user from $1 to $2, and the vast majority of those active users would drop as soon as you put a real price tag on it, no matter the actual number.
Hard indeed but they don't need everyone to pay only enough people to effectively subsidise the free users
brookst · 1h ago
Agreed. Not to mention that having 500m paid users would dramatically change usage and drive up costs.
Better math would be converting 1% of those users, but that gets you $1000/year.
chaz6 · 2h ago
I thought that services like these were run at a loss because the data that users provide is often worth more than the price of a subscription.
AndrewDucker · 1h ago
Only if you can find a way of monetisng that data or selling it on.
So, basically, ads.
paxys · 1h ago
It's easy. All OpenAI has to do to break even is checks notes replicate Google's multi-trillion dollar advertising engine and network that has been in operation for 2+ decades.
jeffbee · 2h ago
To make a billion dollars, I would simply sell a Coke to everyone in China. I have been giving away Coke in China and it is very popular, so I am sure this will work.
barrkel · 1h ago
You joke, but for food and beverages, a stand in the supermarket giving the stuff away for free is a really common (and thus successful) tactic.
andrew_lettuce · 2h ago
Of there 500M users a very small number are already paying, so it's not zero-to-one for all of them, but monetize more and take $10 a month to $100. It's unclear if this is easier or harder than what you presented, but both are hard.
eptcyka · 2h ago
500M MAU also implies that some are already paying. They need to extract 1$ more on average, not just get all of them to pay 1$ per month. This, I imagine is harder than assuming there are 500m users that pay nothing today.
barrkel · 1h ago
$1 in monetization doesn't mean $1 in subscription. It means advertising, affiliate links, traffic deals.
bboygravity · 2h ago
The entire businessmodel may only work as long as inference takes up the physical space and cost of a small building.
Last time personal computing took up an entire building, we put the same compute power into a (portable) "personal computer" a few decades later.
Can't wait to send all my data and life to my own lil inference box, instead of big tech (and NSA etc).
layer8 · 1h ago
Last time personal computing took up an entire building, we weren’t anywhere near as close to the physical limits of semiconductors as today, though. We’ll have to see how much optimization headroom there is on the model side.
relaxing · 1h ago
“Last time” we weren’t up against physical limitations for solid state electronics like the size of an atom, wavelength of light, quantum effects, thermal management, etc.
bena · 2h ago
Exactly, when the cost is free, I can ask it for whatever stupid thing I can think of.
The minute it starts costing me money, I have to make that decision: Is this worth the dollar?
holoduke · 2h ago
There are more monetization ways than just a hard paying user. You can ask Google or Facebook. I dont think its super hard to get chatgpt to a. Profitable business. Its probably the most used service currently out there. And its use and effectiveness is immense.
eptcyka · 2h ago
I wonder how many more watts does producing an answer OpenAI use than answering a Google search query.
JimDabell · 2h ago
This is a good article on the subject. Make sure you read the linked articles as well.
It’s basically the same story as this article: people incorrectly believe they use a huge amount of energy (and water), but it’s actually pretty reasonable and not out of line with anything else we do.
fsmv · 2h ago
Another problem is once they're on the pro plan using better models the users are more expensive
dist-epoch · 2h ago
This is true only because people are so dumb.
Paying $1000 for an iPhone? Sure. $10 for a Starbucks? Sure. $1 per year for LLM? Now hold on, papa is not an oil oligarch...
Retric · 1h ago
A 1000$ iPhone over 5 years is 17$/month, is it worth 17x as much as a free tier LLM?
For most people yes. Also many people are spending for less than 1000$ for their phones.
andrew_lettuce · 1h ago
People pay for the perceived value. If apple started by giving away iPhones they would balk at paying that much for them too. It's also very well know that free to anything is much harder than increasing the price
sandrello · 1h ago
It's not only that they're stupid, it's the fact that maybe they don't really need it.
Do they really need an iPhone? in a sense, yes, since the alternative still means spending a good amount of money and in no way they can do without a phone.
otabdeveloper4 · 1h ago
The LLM usually provides negative value tho. Unlike the iPhone which can theoretically play mobile games.
relaxing · 1h ago
The iphone is worth infinitely more because every time I ask it for some information it returns for me the fact I asked for, no hallucinations.
og_kalu · 2h ago
It's doing some heavy lifting but not that much. Saas subscriptions are not the be-all and end-all of software monetization. He's saying they need to get $1 more on average, not convert all users to $1 subscribers. Doable.
croes · 1m ago
By that logic streaming was cheap to operate and produce because Netflix costed way less.
And now?
theOGognf · 2h ago
Some anecdotal data, but we recently estimated the cost of running a LLM at $WORK by looking at power usage over a bursty period of requests from our internal users and it was on the order of $10s/mil tokens. And we arent a big place, nor were our servers at max load, so I can see the cost being much lower at scale
exceptione · 2h ago
This is only the power usage?
theOGognf · 2h ago
Right, this is only power usage. Factoring in labor and all that would make it more expensive for sure. However, it’s not like it’s a complex system to maintain. We use a popular inference server and just run it with some modest rate limits . It’s been hands-off for close to a year at this point
exceptione · 1h ago
Ok! What hardware do you run? I had thought that would be the most expensive part.
dist-epoch · 2h ago
Hardware spend also need to be amortized (over 1 year? 2 years?) Unless you cloud rent.
theOGognf · 2h ago
That is true too
qoez · 2h ago
So far. Give it a few years when the core players have spent their way to market dominance and regulation kicks in and you'll see the price hikes investors have been promised behind closed doors.
hackyhacky · 2h ago
Or maybe they'll just use ads.
Whatever question you ask, the response will recommend a cool, refreshing Coca Cola soft drink.
Your AI coding project will automatically display ads collecting revenue for Anthropic, not for you.
Every tenth email sent by your AI agent will encourage the recipient to consider switching to Geico.
The opportunities are endless.
JackSlateur · 1h ago
Yes
LLM and stuff are the ultimate propaganda machine: a machine which is able to masquerade everything, to generate endless lies in the coherent manner
physicsguy · 1h ago
LLMs are heavily subsidised. If you self-host them and run them at cost, then you find that the GPU costs are high, and that's largely without the additional tools that OpenAI and Anthropic provide and which also must cost a lot to operate.
jsnell · 1h ago
If you self-host, you likely won't have anywhere near enough volume to do efficient batching, and end up bottlenecked on memory rather than compute.
Before I started self-hosting my LLMs with Ollama, I imagined that they required a ton of energy to operate. I was amazed at how quickly my local LLM operates with a relatively inexpensive GeForce RTX 4060 with 8GB VRAM and an 8b model. The 8b model isn't as smart as the hosted 70b models I've used, but it's still surprisingly useful.
prmoustache · 2h ago
LLMs aren't cheap if you consider the impact on the climate and the cost that comes from it.
PickledChris · 1h ago
I will preface this by saying that I care a lot about climate change and carbon usage and AI usage is not a big issue, it is in fact a distraction from where we should be focusing our efforts.
Watching TV isn’t cheap if you consider the impact on the climate and the cost that comes from it.
worldsayshi · 1h ago
I would like to understand if this still has truth to it.
fastball · 2h ago
I didn't realize Large Language Models have a direct impact on the climate.
tecleandor · 1h ago
Well, running them does. And, from what I get from the article, that's what they're trying to do: either running them or having someone do it for them as a service.
How big is that impact? Well, that's a complicated issue.
fastball · 15m ago
Running LLMs does not have any intrinsic impact on the climate.
If you want to talk about the impact of different power generation methods on climate change, fair enough, but I don't think this thread is the place for it. Unless of course the idea is to talk about climate change in every single thread centered on "things that consumes energy", which is approximately all of them.
johnisgood · 1h ago
How about indirect? At any rate, something is going on, because our summers are more and more hotter, and there are no snow during our winters. We are all noticing it but it gets shrugged off as "misremembering". I am not contributing it to running LLMs alone, however, but climate change seems real enough to me, I experience it. It is barely July and I am dying! We used to have more tolerable weather around this time of year, for a long time.
fastball · 16m ago
Yes, but what does climate change have to do specifically with LLMs? How are they different from any other use of energy? As far as I can tell they are better than most uses, given that (as software) they run entirely with electricity, which of course can be generated with near-zero CO2 emissions.
Given that, this interjection about climate change seems like a complete non-sequitur to the topic at hand.
pier25 · 36m ago
What about training costs?
> Training GPT-4 may have cost in the vicinity of $50 million but the overall training cost is probably more than $100 million because compute is required for trial and error before the final training run.
I think this is a good analysis but falls a little short. Sure, the price is not high for inference, but what about the cost? To be fair, the author already tries to answer this claim, but you could look more critically at this question. Something like: taking into account the insane amount of capital that is being spent and injected into AI companies, what is the strategy to break-even in a reasonable amount of time? What would be the implications for the price over time from now on? That's an interesting thought experiment that, at least in my head, raises the question if the price we're paying for inference today is actually fair.
eschatology · 41m ago
I would add a small asterisk that a given sentence may result in different number of tokens depending on the model and the tokenization method they use, so it’s unfortunately not as straightforward to get the precise dollar value for a given input.
bfrog · 1h ago
Cheap by what measure? Surely not by the carbon footprint these large capital intense datacenters are going up in droves to support them? Surely not given by the revenue being generated by one silicon design company at the moment?
I think this article is measuring all the wrong things and therefore comes to the wrong conclusion.
empiko · 1h ago
One thing that is making them cheap is the lack of moats. If anybody can provide the same service, the market will push the prices down eventuallt, as this is a model demand-supply situation. OpenAI has the advantage due to brand awareness, but that is more or less it for them. Most users would probably not notice if you would switch the product they are using. For this reason, I think that companies that already have some channels to get their products on users' screens - Google, MS, Apple - have theoretically the best position to control the market. But practically, they do not seem very keen to do so.
Havoc · 47m ago
Guess it comes down to how heavy the query is in context size. If you’re not doing RAG and instead just inlining large amounts then it won’t stay cheap.
But yeah 0.20 per million is nothing for light use.
datadrivenangel · 2h ago
low to moderate quality digital text work is now almost free!
This is going to reshape large portions of our text based communication networks.
falcor84 · 2h ago
There's of course also the issue that an increasing fraction of web content reading is being done by AI agents. I wonder what the Pareto front here is.
iwontberude · 2h ago
No one has successfully rebutted that paper about stochastic collapse of AI models which happens when models train on their own output over time. It’s just a matter of time before we find out if it was right or not.
fumeux_fume · 1h ago
Author seems comfortable basing their complicated argument entirely on speculation and only one aspect of what it means to bring these services to market. Some numbers are pulled out of thin air with no context. It just doesn't seem worth taking seriously even IF there really is a misconception about the costs of running an LLM.
fedeb95 · 1h ago
I don't think LLMs are inherently "costly" or "cheap". This doesn't really matter. Gold is pricey, but its usages justify the cost. Will LLMs, as they are used and evangelized now, have a true positive return for those using it? In some domains it will, most probably not everywhere and not for everyone.
graemep · 1h ago
> Gold is pricey, but its usages justify the cost
I understand the point, but gold is expensive because it is a traditionally agreed store of value, rather than because of its usage. Rhodium would be a better example.
bicepjai · 28m ago
I’m curious why gold usage justifies the cost ?
nelsnelson · 1h ago
Completely ignores externalized costs, and focuses entirely on purely end-user retail costs to operate, not even vendor internal operational costs. Can't even find the words "energy" or "electricity" or "scale" in the post. Whatever point this person is making, it is of such dramatic limitation that I am going to contentedly ignore it. Point people at this all you like, Juho Snellman. I for one will merely ignore you.
Also laughably excludes this one from openai's pricing details:
o1-pro-2025-03-19 Price per 1M tokens
Batch API price -- Input: $150.00, Output: $600.00
And this doesn't even address quality. Results quality is also explicitly ignored. I personally find most results from cheaper models to be far, far worse than any results I find using search prior to the LLM content flood. But of course, that's 1) subjective, and 2) completely impossible to conduct any analytical comparison now since indexed search has been so completely ruined by SEO and LLM junk. Yet another externalized cost for which accounting is completely impossible, but is likely to have immeasurably negative impacts on the world's ability to share information.
postexitus · 1h ago
They are cheap as long as subsidized by VC and (in some cases) government money. We will see the real cost when free tiers disappear or start to be supported by ads.
827a · 23m ago
Here's a question I don't know the answer to right off, but we're going to discover together in real time: Which is cheaper, one million queries to fetch the value of a secret from AWS Secrets Manager, or one million tokens from a modern LLM?
Storing a secret in secrets manager: $0.40
$0.05/10,000 API calls * 1,000,000 calls = $5
Total cost: $5.40.
Gemini 2.5 Flash: $0.15/million tokens.
Well, there you have it. Storing a secret value in AWS Secrets Manager is ~36 times more expensive per API call than querying an LLM!
szczepano · 1h ago
I want to see how this pricing compares with searxng.
dangoodmanUT · 1h ago
It still boggles my mind the grip had on search, it's SO expensive (especially considering it's otherwise free for humans)
ETH_start · 2h ago
Search is narrow, used occasionally to find external information. LLMs are the single most general-purpose tool in existence. If you're using them to their full potential, you end up relying on them across writing, planning, coding, summarizing, etc.
So even if the per-query or per-token cost is lower, the total consumption is vastly higher. For that reason, while it may not be a fair comparison, due to people looking at it from the perspective of personal economics, people will compare how much it costs to use each to its full potential, respectively.
throwaway0123_5 · 1h ago
> LLMs are the single most general-purpose tool in existence.
Wouldn't this award have to go to computers? They're a prerequisite for using LLMs and can do a lot more besides running LLMs.
ETH_start · 1h ago
Yes, "tool" is probably not the right term. Application?
deadbabe · 2h ago
Given how addicted people are to using LLMs steep price hikes are almost certainly guaranteed at some point.
When this happens, what we will see is once again the rich and privileged will benefit from the use of LLMs while the poor have to just rely on their own brains. Consider how some students will have to grow up struggling through school without any LLMs while rich kids breeze their way through everything with their assistants.
hajile · 1h ago
If they are dividing a few billion dollars in model training between a small number of rich people, it quickly becomes too expensive even for them.
Meanwhile, a free model running locally is good enough for most people. This causes pricing pressure (and I think is probably going to bankrupt most of the AI companies).
More likely IMO is that AI becomes a loss-leader. It'll all be stuff like Grok or DeepSeek where the real profit is in censorship and propaganda.
worldsayshi · 1h ago
That would assume that there's a moat...
thijson · 1h ago
Not sure if you're being sarcastic. The cost of compute is perpetually going lower, it is getting harder to scale though. I feel like LLM's will become ubiquitous. When I went to University in the 90's, only the wealthy could afford cell phones, pulling one out was a flex. Now they are everywhere. Even Nvidia's sky high margins will someday be eroded.
TZubiri · 2h ago
"Data from paid API queries will also typically not be used for training or tuning the models, so getting access to more data wouldn't explain it."
> By default, we will not use your inputs or outputs from our commercial products to train our models.
> If you explicitly report feedback or bugs to us (for example via our feedback mechanisms as noted below), or otherwise explicitly opt in to our model training, then we may use the materials provided to train our models.
> When you use Paid Services, including, for example, the paid quota of the Gemini API, Google doesn't use your prompts (including associated system instructions, cached content, and files such as images, videos, or documents) or responses to improve our products
xpe · 43m ago
> Data from paid API queries will also typically not be used for training or tuning the models...
Extremely unlikely in my opinion. I would expect some forms of customer data are used for some kind of value or competitive advantage. If not used outright, this might still include transformed, summarized, aggregated, or anonymized data. In my view, various mappings from legal terms of service to the myriad ways data can be massaged leads to massive gray areas. Expecting this to tilt in favor of customer privacy does not match historical practice nor incentives.
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)
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.
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.
[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.
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.
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.
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".
(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).
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.
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.
> 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?
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.
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.
- If I'm using a search engine, I want to search the web. Yes these engines are increasingly providing answers rather than just search results, but that's a UI/product feature rather than an API one. If I'm paying Google $$ for access to their index, I'm interested in the index.
- If I'm using an LLM, it is for parsing large amounts of input data, image recognition, complex analysis, deep thinking/reasoning, coding. All of these result in significantly more token usage than a 2-line "the answer to your question is xyz" response.
The author is basically saying – a Honda Civic is cheap because it costs about the same per pound as Honeycrisp apples.
There's less experts using search engines. Normal people treat search engines less like an index search and more like a person. Asking an old school search engine "What is the capital of USA" is actually not quite right, because the "what is" is probably quite superfluous, and you're counting on finding some sort of educative website with the answer. In fact phrasing it as "the capital of the USA is" is probably a better fit for a search engine, since that's the sort of sentence that would contain what you want to know.
Also with the plague of "SEO", there's a million sites trying to convince Google that their site is relevant even when it's not.
So LLMs are increasingly more and more relevant at informally phrased queries that don't actually contain relevant key words, and they're also much more useful in that they bypass a lot of pointless verbiage, spam, ads and requests to subscribe.
When you're asking a standard question like the capital of whatever, that works great.
When you have one of those weird issues, it often lands you in a thread somewhere in the Ubuntu forums where people tried to help this person, nothing worked, and the thread died 3 years ago.
Just the fact that LLMs can translate between languages already adds an amazing amount of usefulness that search engines can't have. There seems to be a fair amount of obscure technical info that's only available in Russian for some reason.
I'd argue that search engines should stick to just outputting relevant websites and let LLMs give you an overview. Both technologies are complimentary and fulfill different roles.
I disagree, but I can see why someone might say this, because the article's author writes:
> So let's compare LLMs to web search. I'm choosing search as the comparison since it's in the same vicinity and since it's something everyone uses and nobody pays for, not because I'm suggesting that ungrounded generative AI is a good substitute for search.
Still, the article's analysis of "is an LLM API subsidized or not?" does not _rely_ on a comparison with search engines. The fundamental analysis is straightforward: comparing {price versus cost} per unit (of something). The goal is figure out the marginal gain/loss per unit. For an LLM, the unit is often a token or an API call.
Summary: the comparison against search engine costs is not required to assess if an LLM APIs is subsidized or not.
You have a point but no it doesn't. The article already kind of addresses it, but Open AI had a pretty low loss in 2024 for the volume of usage they get. 5B seems like a lot until you realize chatgpt.com alone even in 2024 was one of the most visited sites on the planet each month with the vast majority of those visits being entirely free users (no ads, nothing). Open AI in December last year said chatgpt had over a billion messages per day.
So even if you look at what people do with the service as a whole in general, inference really doesn't seem that costly.
Correct, but you're also not the median user. You're a power user.
This would probably increase 10x if one of the providers sold a family plan and my kids got paid access.
Most of my heavy lifting is work related and goes through my employer's pockets.
Which is precisely why Google started adding their AI "answers". The web has kind of become a cancer -- the sites that game SEO the most seem to have the trashiest, most user-hostile behaviour, so search became unpleasant for most -- so Google just replaces the outbound visit conceptually.
The queries for the LLM which were used to estimate costs don't make a lot of sense for LLMs.
You would not ask an LLM to tell you the baggage size for a flight because there might be a rule added a week ago that changes this or the LLM might hallucinate the numbers.
You would ask an LLM with web search included so it can find sources and ground the answer. This applies to any question where you need factual data, otherwise it's like asking a random stranger on the street about things that can cost money.Then the token size balloons because the LLM needs to add entire websites to its context.
If you are not looking for a grounded answer, you might be doing something more creative, like writing a text. In that case, you might be iterating on the text where the entire discussion is sent multiple times as context so you can get the answer. There might be caching/batching etc but still the tokens required grow very fast.
In summary, I think the token estimates are likely quite off. But not to be all critical, I think it was a very informative post and in the end without real world consumption data, it's hard to estimate these things.
4o will always do a web search for a pointedly current question, give references in the reply that can be checked, and if it didn't, you can tell it to search.
o3 meanwhile will do many searches and look at the thing from multiple angles.
Why wouldn’t I use it like this?
The LLM has to read the websites to answer you so that significantly increases the token count, since it has to include them in its input.
What benefit did the LLM add here, if you still had to vet the sources?
The first thing I saw was the AI summary. Underneath that was a third-party site. Underneath that was “People also ask” with five different questions. And then underneath that was the link to the American Airlines site.
I followed the line to the official site. I was presented with a “We care about your privacy” consent screen, with four categories.
The first category, “Strictly necessary”, told me it was necessary for them to share info with eleven entities, such as Vimeo and LinkedIn, because it was “essential to our site operation”.
The remaining categories added up to 59 different entities that American Airlines would like to share my browsing data with while respecting my privacy.
Once I dismissed the consent screen, I was then able to get the information.
Then I tried the question on ChatGPT. It said “Searching the web”, paused for a second, and then it told me.
Then I tried it on Claude. It paused for a second, said “Searching the web”, and then it told me.
Then I tried it on Qwen. It paused for a second, then told me.
Then I tried it on DeepSeek. It paused for a second, said “Searching the web”, and then it told me.
All of the LLMs gave me the information more quickly, got the answer right, and linked to the official source.
Yes, Google’s AI answer did too… but that’s just Google’s LLM.
Websites have been choosing shitty UX for decades at this point. The web is so polluted with crap and obstacles it’s ridiculous. Nobody seems to care any more. Now LLMs have come along that will just give you the info straight away without any fuss, so of course people are going to prefer them.
Do you honestly believe LLMs aren't gonna get sponsored answers/ads and "helpful" UI elements that boost their profits?
The web has this shitty UX. LLMs do not have this shitty UX. I’m going to judge on what I can see and use.
In that case, get uBlock. The answer is in the first result, on the first screen, and the answer is even quoted in the short description from the site. (As a bonus, it also blocks the cookie consent popups on the AA site, if you like.)
The only thing getting in the way of the real, vetted, straight-from-the-source answer currently is the AI overview.
https://imgur.com/a/pRUGgRx
Even so, saying that the UX of the web is almost as good as the UX of an LLM after you take steps to work around the UX problems with the web isn’t really an argument.
I mean, they should. Anyone on this site most certainly should.
The LLM UX is going to rapidly converge with the search UX as soon as these companies run out of investor funds to burn. It's already starting; https://www.axios.com/2024/12/03/openai-ads-chatgpt.
What then?
They burn through insane amounts of cash and are, for some reason, still called startups. Sure, they'll be around for a long time until they figure something out, but unless hardware prices and power consumption go down, they won't be turning a profit anytime soon.
Just look at YouTube: in business for 20 years, but it's still unclear whether it's profitable or not, as Alphabet chooses not to disclose YT's net income. I'd imagine any public company would do this, unless those numbers are in the red.
If they monetized free users, they would have even better revenue. The linked post estimates eg $1 per user per month would flip them to profitable.
This is a tangent to the rest of the article, but this "just" is doing more heavy lifting than Atlas holding up the skies. Taking a user from $0 to $1 is immeasurably harder than taking a user from $1 to $2, and the vast majority of those active users would drop as soon as you put a real price tag on it, no matter the actual number.
I'm saying that good-enough LLMs are so cheap that they could easily be monetized with ads, and it's not even close. If you look at other companies with similar sized consumer-facing services monetized with ads, their ARPU is far higher than $1.
A lot of people have this mental model of LLMs being so expensive that they can’t possibly be ad-supported, leaving subscriptions as the only consumer option. That might have been true two years ago, but I don't think it's true now.
I’m sure you are going to provide some sort of evidence for this otherwise ridiculous claim, correct?
Hard indeed but they don't need everyone to pay only enough people to effectively subsidise the free users
Better math would be converting 1% of those users, but that gets you $1000/year.
So, basically, ads.
Last time personal computing took up an entire building, we put the same compute power into a (portable) "personal computer" a few decades later.
Can't wait to send all my data and life to my own lil inference box, instead of big tech (and NSA etc).
The minute it starts costing me money, I have to make that decision: Is this worth the dollar?
https://andymasley.substack.com/p/reactions-to-mit-technolog...
It’s basically the same story as this article: people incorrectly believe they use a huge amount of energy (and water), but it’s actually pretty reasonable and not out of line with anything else we do.
Paying $1000 for an iPhone? Sure. $10 for a Starbucks? Sure. $1 per year for LLM? Now hold on, papa is not an oil oligarch...
For most people yes. Also many people are spending for less than 1000$ for their phones.
And now?
Whatever question you ask, the response will recommend a cool, refreshing Coca Cola soft drink.
Your AI coding project will automatically display ads collecting revenue for Anthropic, not for you.
Every tenth email sent by your AI agent will encourage the recipient to consider switching to Geico.
The opportunities are endless.
LLM and stuff are the ultimate propaganda machine: a machine which is able to masquerade everything, to generate endless lies in the coherent manner
E.g. based on the calculations in https://www.tensoreconomics.com/p/llm-inference-economics-fr..., increasing batch size from 1 to 64 cuts the cost per token to 1/16th.
https://www.sustainabilitybynumbers.com/p/carbon-footprint-c...
How big is that impact? Well, that's a complicated issue.
If you want to talk about the impact of different power generation methods on climate change, fair enough, but I don't think this thread is the place for it. Unless of course the idea is to talk about climate change in every single thread centered on "things that consumes energy", which is approximately all of them.
Given that, this interjection about climate change seems like a complete non-sequitur to the topic at hand.
> Training GPT-4 may have cost in the vicinity of $50 million but the overall training cost is probably more than $100 million because compute is required for trial and error before the final training run.
https://ainowinstitute.org/publications/compute-and-ai
I think this article is measuring all the wrong things and therefore comes to the wrong conclusion.
But yeah 0.20 per million is nothing for light use.
This is going to reshape large portions of our text based communication networks.
I understand the point, but gold is expensive because it is a traditionally agreed store of value, rather than because of its usage. Rhodium would be a better example.
Also laughably excludes this one from openai's pricing details:
o1-pro-2025-03-19 Price per 1M tokens Batch API price -- Input: $150.00, Output: $600.00
And this doesn't even address quality. Results quality is also explicitly ignored. I personally find most results from cheaper models to be far, far worse than any results I find using search prior to the LLM content flood. But of course, that's 1) subjective, and 2) completely impossible to conduct any analytical comparison now since indexed search has been so completely ruined by SEO and LLM junk. Yet another externalized cost for which accounting is completely impossible, but is likely to have immeasurably negative impacts on the world's ability to share information.
Storing a secret in secrets manager: $0.40
$0.05/10,000 API calls * 1,000,000 calls = $5
Total cost: $5.40.
Gemini 2.5 Flash: $0.15/million tokens.
Well, there you have it. Storing a secret value in AWS Secrets Manager is ~36 times more expensive per API call than querying an LLM!
So even if the per-query or per-token cost is lower, the total consumption is vastly higher. For that reason, while it may not be a fair comparison, due to people looking at it from the perspective of personal economics, people will compare how much it costs to use each to its full potential, respectively.
Wouldn't this award have to go to computers? They're a prerequisite for using LLMs and can do a lot more besides running LLMs.
When this happens, what we will see is once again the rich and privileged will benefit from the use of LLMs while the poor have to just rely on their own brains. Consider how some students will have to grow up struggling through school without any LLMs while rich kids breeze their way through everything with their assistants.
Meanwhile, a free model running locally is good enough for most people. This causes pricing pressure (and I think is probably going to bankrupt most of the AI companies).
More likely IMO is that AI becomes a loss-leader. It'll all be stuff like Grok or DeepSeek where the real profit is in censorship and propaganda.
Source? Is this in the API ToS?
> As of March 1, 2023, data sent to the OpenAI API is not used to train or improve OpenAI models (unless you explicitly opt in to share data with us).
Anthropic: https://privacy.anthropic.com/en/articles/7996868-is-my-data...
> By default, we will not use your inputs or outputs from our commercial products to train our models.
> If you explicitly report feedback or bugs to us (for example via our feedback mechanisms as noted below), or otherwise explicitly opt in to our model training, then we may use the materials provided to train our models.
Google: https://ai.google.dev/gemini-api/terms#paid-services
> When you use Paid Services, including, for example, the paid quota of the Gemini API, Google doesn't use your prompts (including associated system instructions, cached content, and files such as images, videos, or documents) or responses to improve our products
Extremely unlikely in my opinion. I would expect some forms of customer data are used for some kind of value or competitive advantage. If not used outright, this might still include transformed, summarized, aggregated, or anonymized data. In my view, various mappings from legal terms of service to the myriad ways data can be massaged leads to massive gray areas. Expecting this to tilt in favor of customer privacy does not match historical practice nor incentives.
Runs to shop to buy GPU rig.