So large companies are getting billed a lot more than those discount subscription plans.

Claude can be very good but enterprise pricing doesn't make sense to me.

A lot of startups pile up enormous amount of accounts, companies don't need the Enterprise Anthropic solution, they can just subscribe to many accounts and have their own staff KYC for each (1 codex, 1 claude, 1 google and so-on).

Common talking point. There's enough evidence for the counter argument that this is essentially misinformation. I have no idea why it's so often repeated with confidence.

> No evidence is shared

Help an open-minded critic out.

How much is Waymo burning a year? 3B on 300M ARR? Anthropic is what 5B on 20B ARR? Waymo is 3x older. Why don't we hear such confident statements about how subsidized their rides are?

It's one thing to speculate it's another to parade it as fact. Even if the S1 reveals an unprofitable business today, you can still only claim it's unlikely.

Also, we do have some evidence for my position:

- We know that the consumer Claude plans provide _way_ more tokens than you could get if you were paying API prices. This is a huge part of why Anthropic's limits on other harnesses for subscription customers is such a big deal. So either their profit margin on API tokens is absurdly high, most consumer subscribers don't come anywhere near their rate limits, or they're losing money on the consumer subscriptions. - It appears that complains about people running into rate limits are common, which suggests the "consumers usually don't use much of their subscription" explanation is incorrect. - We also know that Anthropic has just become profitable, almost certainly driven mostly by enterprise customers. This rules out the "they make a very high profit margin on the API" explanation, since if that was the case they'd likely have been profitable much earlier.

Taken together, I think the case that their consumer subscriptions lose them money on net is pretty strong, even though their enterprise subscriptions (and API pricing) does make them a profit.

To be clear I'm not arguing against this position, just questioning the confidence with which people claim that the current consumer subs are not a sustainable offering and a merely temporary.

We do. We hear it less often because no-one is talking about how Waymo changes how we all need to work or whatever, that's all.

With this kind of opaque billing, how can I reasonably deploy any AI?

I'm only doing a cursory search, but it seems OpenWebUI doesn't support Anthropic caching, and they don't intend to? Other providers handle caching automatically (apparently?) but caching has to be specifically managed by the client with Anthropic. If that's correct that OpenWebUI doesn't support it, it would really send your costs spiralling, because you're being billed for all the tokens in the entire multi-turn conversation on every turn:

https://github.com/open-webui/open-webui/issues/4887

I have no experience with OpenWebUI though (honestly, first time I've heard of it). Just trying to be helpful. If I'm completely incorrect then apologies in advance for sending you down the wrong path.

The real cost effective way is giving a team $20 cursor $20-100 Claude $20-200 codex.

I'm spending 1k on Claude enterprise easily and that's with trying to spread it on codex and cursor using pi.

Can large enterprises just not use the API ? I have audit logs and what seem to be enterprise features through my anthropic account (platform.claude.ai)

Longer term, you also have to be careful about building things around details which could change at any time. OpenAI and Anthropic have a ton of pressure to start banking huge profits and they very closely monitor customer activity. A time-honored strategy in this space is to shuffle the features enterprise customers depend on but which aren’t deal-breakers for most other customers into expensive enterprise plans. There’s possibly some counter pressure from companies like Google which have healthier finances but I wouldn’t count on that since they also have MBAs who’d be all too happy to invent pretexts to hike their prices to match.

What's your source for Opus being a 5T model?

> and tiny distillations from DeepSeek that perform well only in benchmarks.

I don't think you know what you're talking about. Local models aren't “distillations from Deepseek”.

And they don't perform well “only in benchmarks”, Qwen 3.6 is a very decent model (obviously it's not Opus, but it's also much faster and speed is a quality of its own).

Elon Musk tweeted that Grok is 0.5T or 1/10th the size of Opus. https://xcancel.com/elonmusk/status/2042123561666855235#m

While this source's reliability is certainly debatable, the size matches the results of this paper, in which researchers estimated the parameter count from model knowledge. https://01.me/research/ikp/

Massive understatement. Nowadays it has become hard to find a single Musk statement that doesn't contain at least one lie.

> the size matches the results of this paper, in which researchers estimated the parameter count from model knowledge. https://01.me/research/ikp/

Thanks for the pointer. This estimation has Grok 6 times bigger than Musk claims it is, so maybe that's where the lie is.

(I'm quite skeptical about that number though, it would be quite disappointing for the US tech if their flagship models had to be that much larger than the Chinese ones for such a small edge in performance. Because I don't think US labs are incompetent, I'd bet that US flagships aren't more than 2/3 times faster than Chinese flagship. Otherwise it really doesn't bode well.)

  Elon Musk tweeted

It's certainly a better sell that Grok sucks because it's small and Opus is impressive because it's large, than the alternative that Grok is also large and sucks which points to xAI incompetence and mismanagement.

Particularly when you're trying to IPO a rocket company based on rosy forecasted valuations of Grok dominating the market.

Probably Elon Musk: https://eu.36kr.com/en/p/3760679047267075

Like "Full Self-Driving" from coast-to-coast by 2016?

He's lagging the AI race despite having tons of compute available, so he tries to make a narrative about how it's not that the model is behind, it's just smaller than the competition.

From this paper

    Claude Opus 4.6 Anthropic 68.0% ∼5.3T [1.8–15.6T]
    Claude Opus 4.7 Anthropic 66.4% ∼4.0T [1.4–12.0T]
    Claude Opus 4.5 Anthropic 65.2% ∼3.4T [1.1–10.0T]
    Claude Opus 4.1 Anthropic 64.9% ∼3.2T [1.1–9.5T]
    Claude Opus 4 Anthropic 59.7% ∼1.4T [478B–4.2T

The fact that there's absolutely no consistency in the predicted size between models from the same lab should tell you all you need about the predictive power of this method (and they aren't really lying about their numbers, their confidence interval is huge enough to fit anything in it, but their prose is making very strong claims out of their statistical nothingburger).

(somebody already posted this paper earlier, and I spent some time reading it, and this paper is really not that good even though there are a bunch of interesting ideas in it).

This is a temporary phenomenon. Expect either drastic price increases or draconian throttling or both in the coming months.

These companies are operating at huge loses and have hundreds of billions in liabilities and commitments. They need to turn on the money faucet sooner than later.

We're already seeing slash their AI budgets. I expect that will increase till we hit more of an equilibrium.

What about human understanding of the codebase that's essential to any project's long term health? Even "superperformer developers" eventually leave the company.

Or so they say. You'll have to trust those vibes blindly, because double-checking these claims apparently makes you an anti-science luddite.

If prices keep going up, watch for companies to exit frontier models and go to local llama.cpp instances for 6-month-ago SOTA, with the flex of being housed within the office - no more privacy leakage, no more price gouging.

To be honest, I’m not sure why a Y-Combinator backed company hasn’t come out yet flooding the market with highly capable OPAI (pronounced “Oh-pah” as in what Greeks shout as the drink shots), which stands for “On-Prem AI”

… yes, I just made up OPAI right now lol

If we momentarily disregard the fact that YC itself owns billions of dollars worth of OpenAI shares[1], YC would plan to find demo-day investors willing to drive down the value of frontier labs. The coöpetition among VCs and the existing web of AI investments will mean no VC will be interested in investing in local AI...until after the frontier labs IPO.

1. Thanks to the self-dea^w foresight of former YC president Sam Altman

If we're able to see some big increases in hardware capabilities that can be self-hosted, that will be an accelerant.

That said, most companies just want to pay a provider to delegate responsibility in exchange for cost and control.

That or just hiring people to do the work! I hear rumours that this is already starting to happen in some places (perhaps those that were a little overzealous with AI-hype driven layoffs).

edit: I see in other comments on this thread you think Ed Zitron is a reliable pundit so that explains everything.

You can dismiss Ed (and me vicariously) but what's your compelling evidence to counter their extremely uphill battle towards profitability?

Either way it will be very interesting to see their S1 when they try and IPO.

If it's anything like SpaceX's then I suspect my post will age better than yours.

I haven't heard anyone claim their S1 will show that but that it will show how poorly their revenue figures look against their costs.

And you think it is unreasonable to consider this unsustainable?

For context, ChatGPT business subscriptions give you a fixed pool of credits to use, after which you get billed a la carte at inflated 1.75x rates vs API, or if you don't want to pay, you get access to anything but the non-reasoning models turned off for the month.

We also tried Claude Enterprise, which was unusable as people blew through their monthly limits in a matter of hours.

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

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

So just going on vibes?

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

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

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

Also, API prices going up a lot every new version is more an OpenAI thing, and even there it's a recent trend: GPT 5.0 was a big price drop compared to 4.1, and 4.1 was cheaper than 4o, which itself got a price cut at some point and is cheaper than 4. Meanwhile Anthropic's API pricing stayed stable for many versions, then got slashed to a third with the 4.2 release and have stayed at that level since.

Their business model is selling inference but the training and other costs have to be accounted for somehow. Unless I'm missing something obvious, inference costs must go up drastically if these companies are going to survive beyond the subsidy stage.

If that doesn't work, then yes, then prices will have to go up

There was a lot of hype and exploration of capabilities, but models aren't evolving fast enough to keep that going, so I'm settling down into a familiarity with what an LLM can and can't do that means I am using them less overall that I was 6 months ago when I was throwing everything under the sun at it just to see what happened.

Without either new model breakthroughs or dramatically _lower_ costs, I will be very surprised if the ultimate market doesn't end up within an order of magnitude of where it is today.

I think this is minimally likely. While as individuals on the bleeding edge, we're perhaps using these tools less and less, and our echo chamber reinforces that, the penetration of AI into the normal corporate workplace is still very low - emails rewritten with ChatGPT, meeting notes summaries generated by default, etc. There are a million use cases for LLMs which are not yet built out. The tokenmaxxers will begin using AI less, but the penetration into the mass market will continue at a huge velocity.

Reminds me of the crypto hype but where the hype agents are some of the largest companies in the world.

pure speculation. about as valuable as my linked wsj reporting i suppose. given thats the case, maybe you shouldnt claim so confidently that they are money incinerators.

Back to the point: No one is profitable yet. This is a statement of fact. And if you are going to lean on “they will be soon” then I can absolutely say they’re going to IPO soon.

Ease off the gas. We’re just discussing a tech company.

One thing that really stinks to me is that various AI boosters have been claiming insane profit margins (40%, 50%, ...), yet apparently Anthropic stands to (possibly) make $500M profit on $11B in expenses, that's clearly nowhere near 50%. Not to mention that they're not making profit on inference now.

So where do people get this confidence to pull random numbers from?

That's why talking about the profitability of inference without accounting for model training is interesting, because that is the deciding factor in whether more customers would help getting them in the green

But it's just that. A gut feeling.

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

Anything can also be run on a cheap computer.

The difference is in speed. A cheap computer may run a big model up to a few orders of magnitude slower than datacenter hardware, depending on whether the LLM is small enough to fit in GPU memory, or it is small enough to fit in CPU memory or it is so big that it must spill on SSDs.

Depending on the application, the tradeoff between run time and run cost may happen to favor using local hardware, despite a much slower speed.

There are plenty of applications where doing them for negligible cost during an overnight job can be preferable to obtaining faster results at a very high price, for instance scanning for bugs in a mature code base using a great number of different open-weights LLMs, which can achieve similar bug coverage like using a single, but overpriced and unavailable SOTA LLM, e.g. Mythos.

You do realize that a model like Opus is (estimated to be) around 5T parameters, and uses around 5TB of GPU memory?

These kind of things are just impossible to run locally.

Like I have said, the problem is not that they cannot be run, but that they may run more slowly than it is acceptable for a given application. Depending on the model, the speeds reported for inference with weights stored on SSDs vary from one token every few seconds to at most a few tokens per second.

Computers could solve relatively huge problems even in the early days of vacuum tube computers, when the main memories were measured in kilobytes, because at that time it was not expected that the data needed for problem solving must fit inside the main memory or even in the next tier of memory, with magnetic drums or magnetic disks, but the really big problems were solved by a great number of passes over data stored on magnetic tapes.

An LLM whose inference could not be run on a small mini-PC would have to be one hundred times bigger than the biggest existing SOTA LLMs.

Any LLM that exists today can be run on almost any PC, just extremely slowly in comparison with datacenter hardware.

I think 1 token/second is optimistic here - and even then it's over 11 days per million tokens.

Giving strong “640k is enough for anyone” vibes here.

Cloud should have more compute and efficiency than local. I wouldn't be 100% sure, as I don't know what I might not be seeing, but still.

Whether that comparative advantage will matter, though, is a completely different question.

Even very cheap mini-PCs and laptops can run any of the models run by cloud providers, albeit at a much lower speed (i.e. with the weights stored on SSDs).

Whether such a low speed is useful, depends on the application. For something like a coding assistant or bug scanning, an instant response is desirable, but certainly not necessary.

For model training, the requirements are very different, and the training of a big LLM cannot be done with home equipment. On the other hand, inference can be done on almost any PC, even for LLMs with thousands of billions of parameters, just very slowly.

The only problem is that the inference becomes limited by the SSD reading throughput. Most of the cheap new personal computers available today can read simultaneously only 2 SSDs (if there are more they share a reading path), which are typically 1 PCIe 5.0 SSD and 1 PCIe 4.0 SSD. This has an upper throughput limit of 24 Gbyte/s, with 15 to 20 GB/s achievable in practice.

Then the speed in token/s is limited by the amount of weights that must be read per inference cycle. The ratio between output tokens and the amount of weights that must be read can be improved by various methods, like batching multiple tasks or using speculative decoding.

Some might say the price wouldn't be great if you could actually process and validate it...

My hunch is that this is the source of much of the variability in outcomes upstream of HN commenters claiming extremes of, "This model changes everything!" to "This[same] model is crap."

We haven't operationalized what it means to "be good at prompting," nor developed proxies/heuristics/shibboleths for accessing prompting skill. There's community skepticism over whether prompting skill even exists. Besides even if prompting skill is real, who wants to hear, "Actually you kinda suck at prompting."

https://www.youtube.com/watch?v=9bBfYX8X5aU&t=48s

Power users are always going to have to take the messaging companies send out to the masses with a grain of salt.

These are loss leaders that will not be maintained over the long term. Already we see moves to restrict their usage and redirect people back to API pricing.