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No, it doesn't cost Anthropic $5k per Claude Code user
- functionmouse 8mo agoWas anyone under the impression that it does? Serious question. I've never heard that, personally.
- simianwords 7mo agoYou would be surprised because there are lots of posters here who think that the cost is so enormous that this whole industry is unviable.
- beepbooptheory 7mo agoIs that conceit somehow intrinsically absurd? Or is everyone just supposed to just know? Like I wish it was simple as "if it wasn't viable, they wouldn't be in business," but alas that argument is kinda the more naive one in this world. Right? Or is there some intuition about energy/cost here all the dump posters miss, that you could tell us about? Please, anything, my company is dying.
- simianwords 7mo agoIt’s the most simple intuition. You can run a 2 year old gpt model equivalent on your laptop today. So cost is obviously going down. Its so strange to see people still think costs are not going down..
- beepbooptheory 7mo agoWow never thought about it like that... Guess there is nothing to worry about! I feel so silly now.
- dimgl 8mo agoTwitter.
- crazygringo 8mo agoI mean, the very first paragraph of TFA is describing who is under that impression. Literally the first sentence: > My LinkedIn and Twitter feeds are full of screenshots from the recent Forbes article on Cursor claiming that Anthropic's $200/month Claude Code Max plan can consume $5,000 in compute.
- fulafel 7mo agoThat's claiming that worst case, a subscriber _can_ use that much. It's possible that's wrong too, but in any case a lot of services are built on the assumption that the average user doesn't max out the plan. So the article's title is obviously sensationalized.
- vidarh 7mo agoI have no problem believing that a Claude Max plan can consume equivalent to $5000 worth of retail Opus use, but one interesting thing you'll see if you e.g. have Claude write agents for you, is that it's pretty aggressive about setting agents to use Sonnet or even Haiku, so not only will most people not exhaust their plans, but a lot of people who do will do so in part using the cheaper models. When you then factor in Anthropics reported margins, and their ability to prioritise traffic (e.g. I'd assume that if their capacity is maxed out they'd throttle subscribers in favour of paid by the token? Maybe not, but it's what I'd do), I'd expect the real cost to them of a maximised plan to be much lower. Also, while Opus certainly is a lot better than even the best Chinese models, when I max out my Claude plan, I make do with Kimi 2.5. When factoring in the re-run of changes because of the lower quality, I'd spend maybe 2x as much per unit of work I were to pay token prices for all my monthly use w/Kimi. I'd still prefer Claude if the price comes down to 1x, as it's less hassle w/the harder changes, but their lead is effectively less than a year.
- crazygringo 7mo agoThe article discusses all of that, in great detail. The title does not seem sensationalized. It's literally a summary of the article.
- versteegen 7mo agoEd Zitron made that claim (in particular here: [1]). In the same article he admits he not a programmer, and had to ask someone else to try out Claude Code and ccusage for him. He doesn't have any understanding of how LLMs or caching works. But he's prominent because he's received leaked financial details for Anthropic and OpenAI, eg [2] [1] https://www.wheresyoured.at/anthropic-is-bleeding-out/ https://www.wheresyoured.at/anthropic-is-bleeding-out/ [2] https://www.wheresyoured.at/costs/ https://www.wheresyoured.at/costs/
- sunaurus 7mo agoMaybe I'm misreading it, but I don't see him saying it's just the cost of *inference* alone (which is the strawman that the article in the OP is arguing against). He says: > this company is wilfully burning 200% to 3000% of each Pro or Max customer that interacts with Claude Code There is of course this meme that "Anthropic would be profitable today if they stopped training new models and only focused on inference", but people on HN are smart enough to understand that this is not realistic due to model drift, and also due to comeptition from other models. So training is forever a part of the cost of doing business, until we have some fundamental changes in the underlying technology. I can only interpret Ed Zitron as saying "the cost of doing business is 200% to 3000% of the price users are paying for their subscriptions", which sounds extremely plausible to me.
- z3ugma 8mo agoThis is such a well-written essay. Every line revealed the answer to the immediate question I had just thought of
- lovecg 8mo agoI can’t get past all the LLM-isms. Do people really not care about AI-slopifying their writing? It’s like learning about bad kerning, you see it everywhere.
- Erem 8mo agoI don’t see the usual tells in this essay
- weird-eye-issue 8mo agoI think you're just hallucinating because this does not come across as an AI article
- NetOpWibby 8mo agoName checks out
- lovecg 7mo agoI see quite a few: “what X actually is” “the X reality check” Overuse of “real” and “genuine”: > The real story is actually in the article. … And the real issue for Cursor … They have real "brand awareness", and they are genuinely better than the cheaper open weights models - for now at least. It's a real conundrum for them. > … - these are genuinely massive expenses that dwarf inference costs. This style just screams “Claude” to me.
- hansvm 7mo agoIt was almost certainly at least heavily edited with one. Ignoring the content, every single thing about the structure and style screams LLM.
- lelanthran 7mo ago
- gmerc 8mo agoNobody gets RSI typing “iterate until tests pass”
- arthurcolle 8mo agoRecursive self improvement and Repetitive Strain Injury being the same initialism is really funny to me
- rs_rs_rs_rs_rs 7mo ago[flagged]
- brianjeong 8mo agoThese margins are far greater than the ones Dario has indicated during many of his recent podcasts appearances.
- skybrian 8mo agoWhat did he say?
- ymaws 8mo agoHow confident are you in the opus 4.6 model size? I've always assumed it was a beefier model with more active params that Qwen397B (17B active on the forward pass)
- codemog 8mo agoAlso curious if any experts can weigh in on this. I would guess in the 1 trillion to 2 trillion range.
- Chamix 7mo agoTry 10s of trillions. These days everyone is running 4-bit at inference (the flagship feature of Blackwell+), with the big flagship models running on recently installed Nvidia 72gpu rubin clusters (and equivalent-ish world size for those rented Ironwood TPUs Anthropic also uses). Let's see, Vera Rubin racks come standard with 20 TB (Blackwell NVL72 with 10 TB) of unified memory, and NVFP4 fits 2 parameters per btye... Of course, intense sparsification via MoE (and other techniques ;) ) lets total model size largely decouple from inference speed and cost (within the limit of world size via NVlink/TPU torrus caps) So the real mystery, as always, is the actual parameter count of the activated head(s). You can do various speed benchmarks and TPS tracking across likely hardware fleets, and while an exact number is hard to compute, let me tell you, it is not 17B or anywhere in that particular OOM :) Comparing Opus 4.6 or GPT 5.4 thinking or Gemini 3.1 pro to any sort Chinese model (on cost) is just totally disingenuous when China does NOT have Vera Rubin NVL72 GPUs or Ironwood V7 TPUs in any meaningful capacity, and is forced to target 8gpu Blackwell systems (and worse!) for deployment.
- aurareturn 7mo agoChina is targeting H20 because that's all they were officially allowed to buy.
- Chamix 7mo agoI generally agree, back of the napkin math shows H20 cluster of 8gpu * 96gb = 768gb = 768B parameters on FP8 (no NVFP4 on Hopper), which lines up pretty nicely with the sizes of recent open source Chinese models. However, I'd say its relatively well assumed in realpolitik land that Chinese labs managed to acquire plenty of H100/200 clusters and even meaningful numbers of B200 systems semi-illicitly before the regulations and anti-smuggling measures really started to crack down. This does somewhat beg the question of how nicely the closed source variants, of undisclosed parameter counts, fit within the 1.1tb of H200 or 1.5tb of B200 systems.
- beepbooptheory 8mo agoOk but so it does cost Cursor $5k per power-Cursor user?? Still seems pretty rough..
- unlimit 8mo agoI wonder how they are defining a power user. How many tokens, what could be the size the code base?
- dietr1ch 8mo agoThe $5k power user is the one that consistently uses all input and output tokens available under the Max subscription
- scriptsmith 8mo agoYes, you could turn it around to say that using Anthropic models in Cursor, Copilot, Junie, etc. is 'subsidising' Claude Code users.
- arthurcolle 8mo ago$5 = $5 but $5 that I amortize over 7 years might end up being $1.7 maybe if I don't rapidly combust (supply chain risk)
- oefrha 7mo agoNo, to use $5k in Cursor you have to pay $5k.
- beepbooptheory 7mo agoOh. Nice! That works out.
- dimava 7mo agoCursor may be losing money only on $200 sub people who do over $200 of usage (it grants $400) Everyone else pays them at API prices
- n_u 8mo agoGood article! Small suggestions: 1. It would be nice to define terms like RSI or at least link to a definition. 2. I found the graph difficult to read. It's a computer font that is made to look hand-drawn and it's a bit low resolution. With some googling I'm guessing the words in parentheses are the clouds the model is running on. You could make that a bit more clear.
- fnord77 8mo ago> I'm fairly confident the Forbes sources are confusing retail API prices with actual compute costs Aren't they losing money on the retail API pricing, too? > ... comparisons to artificially low priced Chinese providers... Yeah, no this article does not pass the sniff test.
- versteegen 7mo ago> Aren't they losing money on the retail API pricing, too? No, they aren't, and probably neither is anyone else offering API pricing. And Anthropic's API margins may be higher than anyone else. For example, DeepSeek released numbers showing that R1 was served at approximately "a cost profit margin of 545%" (meaning 82% of revenue is profit), see my comment https://news.ycombinator.com/item?id=46663852 https://news.ycombinator.com/item?id=46663852
- bandrami 7mo agoWeird that they're all looking for outside money then
- vidarh 7mo agoThey're all looking for outside money because they're all looking for outside money, and so need to keep up with their competitors investments in training. It's a game of chicken. Once their ability to raise more abates, they'll slow down new training runs, and fund that out of inference margins instead, but the first one to be forced to do so will risk losing market share.
- aurareturn 7mo agoInference is profitable. No one is selling at a loss. It’s training to keep up with competitors that is causing losses.
- bandrami 7mo ago> Inference is profitable Eh. We don't really know that, and the people saying that have an interest in the rest of the world believing it's true.
- hattmall 7mo agoIs it fair to say the Open Router models aren't subsidized though? They make the case that companies on there are running a business, but there are free models, and companies with huge AI budgets that want to gather training data and show usage.
- aurareturn 7mo agoBy the way, one of the charts in the article shows that Opus 4.6 is 10x costlier than Kimi K2.5. I thought there was no moat in AI? Even being 10x costlier, Anthropic still doesn't have enough compute to meet demand. Those "AI has no moat" opinions are going to be so wrong so soon.
- spiderice 7mo agoClaude Code Max obviously doesn't cost 10x more than Kimi. The article even confirms that you can get $5k worth of computer for $200 with Claude Code Max. So no, Claude would not be getting NEARLY as much usage as it's currently getting if it weren't for the $100/$200 monthly subscription. You're comparing Kimi to the price that most people aren't paying.
- aurareturn 7mo agoAPI pricing.
- jdjfnfndn 7mo ago[dead]
- eaglelamp 7mo agoIf Anthropic's compute is fully saturated then the Claude code power users do represent an opportunity cost to Anthropic much closer to $5,000 then $500. Anthropic's models may be similar in parameter size to model's on open router, but none of the others are in the headlines nearly as much (especially recently) so the comparison is extremely flawed. The argument in this article is like comparing the cost of a Rolex to a random brand of mechanical watch based on gear count.
- Aeolun 7mo agoOpportunity cost is not the same thing as actual cost. They might have made more money if they were capable of selling the API instead of CC, but I would never tell my company to use CC all the time if I didn’t have a personal subscription.
- d1sxeyes 7mo agoBut opportunity cost is not actual cost. “If everyone just kept paying but used our service less we would be more profitable” is true, but not in any meaningful way. Are Anthropic currently unable to sell subscriptions because they don’t have capacity?
- eru 7mo agoOpportunity costs are real. In many cases they are more real than 'actual costs'. However, I otherwise agree with you.
- hirako2000 7mo ago> Qwen 3.5 397B-A17B is a good comparison It is not. It's a terrible comparison. Qwen, deepseek and other Chinese models are known for their 10x or even better efficiency compared to Anthropic's. That's why the difference between open router prices and those official providers isn't that different. Plus who knows what open routed providers do in term quantization. They may be getting 100x better efficiency, thus the competitive price. That being said not all users max out their plan, so it's not like each user costs anthropic 5,000 USD. The hemoragy would be so brutal they would be out of business in months
- simianwords 7mo ago>It is not. It's a terrible comparison. Qwen, deepseek and other Chinese models are known for their 10x or even better efficiency compared to Anthropic's. I find it a good comparison because it is a good baseline since we have zero insider knowledge of Anthropic. They give me an idea that a certain size of a model has a certain cost associated. I don't buy the 10x efficiency thing: they are just lagging behind the performance of current SOTA models. They perform much worse than the current models while also costing much less - exactly what I would expect. Current Qwen models perform as good as Sonnet 3 I think. 2 years later when Chinese models catchup with enough distillation attacks, they would be as good as Sonnet 4.6 and still be profitable.
- coldtea 7mo ago> I don't buy the 10x efficiency thing: they are just lagging behind the performance of current SOTA models. They perform much worse than the current models while also costing much less - exactly what I would expect. Define "much worse". +--------------------------------------+-------------+-----------+------------------+ | Benchmark | Claude Opus | DeepSeek | DeepSeek vs Opus | +--------------------------------------+-------------+-----------+------------------+ | SWE-Bench Verified (coding) | 80.9% | 73.1% | ~90% | | MMLU (knowledge) | ~91 | ~88.5 | ~97% | | GPQA (hard science reasoning) | ~79–80 | ~75–76 | ~95% | | MATH-500 (math reasoning) | ~78 | ~90 | ~115% | +--------------------------------------+-------------+-----------+------------------+
- scuff3d 7mo agoThis article is hilariously flawed, and it takes all of 5 seconds of research to see why. Alibaba is the primary comparison point made by the author, but it's a completely unsuitable comparison. Alibab is closer to AWS then Anthropic in terms of their business model. They make money selling infrastructure, not on inference. It's entirely possible they see inference as a loss leader, and are willing to offer it at cost or below to drive people into the platform. We also have absolutely no idea if it's anywhere near comparable to Opus 4.6. The author is guessing. So the articles primary argument is based on a comparison to a company who has an entirely different business model running a model that the author is just making wild guesses about.
- simianwords 7mo agoWhat? Aws is a good comparison if you want only infra level costs which is what the post is talking about.
- jeff_antseed 7mo ago[dead]
- anonzzzies 7mo agoI calculated only last weekend that my team would cost, if we would run Claude Code on retail API costs, around $200k/mo. We pay $1400/month in Max subscriptions. So that's $50k/user... But what tokens CC is reporting in their json -> a lot of this must be cached etc, so doubt it's anywhere near $50k cost, but not sure how to figure out what it would cost and I'm sure as hell not going to try.
- jychang 7mo ago> but not sure how to figure out what it would cost and I'm sure as hell not going to try. Ask Opus to figure out how much it would cost. Lol.
- neamar 7mo agoYou can use `npx ccusage` to check your local logs and see how much it would have cost through the API.
- behehebd 7mo ago[flagged]
- scandox 7mo agoI'm fascinated to know the kind of work that allows you to intelligently allocate so much resources. I use Claude extensively and feel that I great value out of it but I reach a limit in terms of what I can do that makes sense relatively quickly it seems.
- darkwater 7mo agoWell, IDK, I have used CC with API billing pretty extensively and managed to spend ~$1000 in one month more or less. Moved to a Max 20x subscription and using it a bit less (I'm still scared) but not THAT less and I'm around 10% weekly usage. I'm not counting the tokens, though.
- himata4113 7mo agoWhat people don't realize is that cache is *free*, well not free, but compared to the compute required to recompute it? Relatively free. If you remove the cached token cost from pricing the overall api usage drops from around $5000 to $800 (or $200 per week) on the $200 max subscription. Still 4x cheaper over API, but not costing money either - if I had to guess it's break even as the compute is most likely going idle otherwise.
- eru 7mo ago> [...] if I had to guess it's break even as the compute is most likely going idle otherwise. Why would it go idle? It would go to their next best use. At least they could help with model training or let their researchers run experiments etc.
- himata4113 7mo agoinference compute is vastly different versus training, also it has to stay hot in vram which probably takes up most of it. There is limited use for THAT much compute as well, they are running things like claude code compiler and even then they're scratching the surface of the amount of compute they have. Training currently requires nvidia's latest and greatest for the best models (they also use google TPU's now which are also technically the latest and greatest? However, they're more of a dual purpose than anything afaik so that would be a correct assesment in that case) Inference can run on a hot potato if you really put your mind to it
- eru 7mo agoThey can run any number of inference experiments. Like a lot of the alignment work they have going on. I am not saying this would be a great use of their compute, but idle is far from the only alternative. (Unless electricity is the binding constraint?)
- himata4113 7mo agoElectricity is charged whenever you use it or not, so very unlikely, but sure, they can find uses for it. Although they are not going to make that much money compared to claude code subscriptions.
- overrun11 7mo agoA huge number of people are convinced that OpenAI and Anthropic are selling inference tokens at a loss despite the fact that there's no evidence this is true and a lot of evidence that it isn't. It's just become a meme uncritically regurgitated. This sloppy Forbes article has polluted the epistemic environment because now theres a source to point to as "evidence." So yes this post author's estimation isn't perfect but it is far more rigorous than the original Forbes article which doesn't appear to even understand the difference between Anthropic's API costs and its compute costs.
- bob1029 7mo agoI think the wafer scale compute is a massive deal. It's already being leveraged for models you can use right now and the reception on HN has been negligible. The entire model lives in SRAM. This is orders of magnitude faster than HBM/DRAM. I can't imagine they couldn't eventually break even using hardware like this in production.
- barrell 7mo agoDoes this not count as evidence? I would agree that it sounds a little shaky, but I would not say there is no evidence. https://www.wheresyoured.at/oai_docs/ https://www.wheresyoured.at/oai_docs/
- bodge5000 7mo ago> A huge number of people are convinced that OpenAI and Anthropic are selling inference tokens at a loss despite the fact that there's no evidence this is true Theres quite a lot of evidence, no proof I'd agree, but then there's no absolute proof I'm aware to the contrary either, so I don't know where you're getting this from. The two pieces of evidence I'm aware of is that 1) Anthropic doesn't want their subsidised plans being used outside of CC, which would imply that the money their making off it isn't enough, and 2) last time I checked, API spending is capped at $5000 a month Like I say, neither of these are proof, you can come up with reasonable arguments against them, but once again the same could be said for evidence on the contrary
- BoredomIsFun 7mo ago
- ineedaj0b 7mo agoWhat CC costs internally is not public. How efficient it is, is not public. …You could take efficiency improvement rates from previous models releases (from x -> y) and assume; they have already made “improvements” internally. This is likely closer to what their real costs are.
- faangguyindia 7mo agoClaude subscription is equivalant of spot instance And APIs are on-demand service equivalant. Priority is set to APIs and leftover compute is used by Subscription Plans. When there is no capacity, subscriptions are routed to Highly Quantized cheaper models behind the scenes. Selling subscription makes it cheaper to run such inference at scale otherwise many times your capacity is just sitting there idle. Also, these subscription help you train your model further on predictable workflow (because the model creators also controls the Client like qwen code, claude code, anti gravity etc...) This is probably why they will ban you for violating TOS that you cannot use their subscription service model with other tools. They aren't just selling subscription, but the subscription cost also help them become better at the thing they are selling which is coding for coding models like Qwen, Claude etc... I've used qwen code, codex and claude. Codex is 2x better than Qwen code and Claude is 2x better than Codex. So I'd hope the Claude Opus is atleast 4-5x more expensive to run than flagship Qwen Code model hosted by Alibaba.
- popcorncowboy 7mo ago> Claude is 2x better than Codex This hasn't been true in a long time.
- elAhmo 7mo agoMy impression as well, especially since 5.2 which I felt was on par or better than Opus 4.5
- Huppie 7mo agoimho they're mostly better at a subset of different tasks. I find codex to be better at reasoning through bugs and reviewing code when compared to Opus, but for writing code I find Claude a lot better. Maybe that's just CLAUDE.md and memory causing the difference of course. As a matter of preference however I like the way Claude Code works just a lot better, instructing it to work with parallel subagents in work trees etc. just matches the way I think these things should work I guess.
- epolanski 7mo agoNot only that, but since the release of 5.4 and 5.3 codex I've been running them in parallel and I've been let down by Opus 4.6 with maximum thinking way more than I've been let down with OpenAI models. In fact I'm more and more inclined to run my own benchmarks from now on, because I seriously distrust those I see online. Even if the benchmarks are indeed valid, they just don't reflect my use cases, usages and ability to navigate my projects and my dependencies.
- amelius 7mo agoTl;dr, their guesstimate: > Anthropic is looking at approximately $500 in real compute cost for the heaviest users.
- akhrail1996 7mo agoThe comparison with Qwen/Kimi by "comparable architecture size" is doing a lot of heavy lifting. Parameter count doesn't tell you much when the models aren't in the same league quality-wise. I wonder if a better proxy would be comparing by capability level rather than size. The cost to go from "good" to "frontier" is probably exponential, not linear - so estimating Anthropic's real cost from what it takes to serve Qwen 397B seems off.
- lyu07282 7mo agoWhat this doesn't mention is the "cost" to the public: the inevitable bailouts after it all comes crashing down again, the massive subsidies that Datacenters get from tax payers, the fresh water they consume, the electricity price hikes for everyone else, the noise, air and water pollution and the massive health impact on the surrounding population of every datacenter. The jobs that it destroys and the innocent people it kills through use of the technology in military targeting and autonomous weapons usage.
- dr_dshiv 7mo agoI easily go through two pro max $200/m accounts and yesterday got a third pro account when I ran out. It’s worth it, but I know they aren’t making money on me. But, of course I’m marketing them constantly so…
- vbezhenar 7mo agoWhy does Claude charge 10x for API, compared to subscriptions? They're not a monopoly, so one would expect margins to be thinner.
- hobofan 7mo agoMonopoly isn't the only thing that allows you to charge large margins. API inference access is naturally a lot more costly to provide compared to Chat UI and Claude Code, as there is a lot more load to handle with less latency. In the products they can just smooth over load curves by handling some of the requests slower (which the majority of users in a background Code session won't even notice).
- preommr 7mo agoIt's why every integration basically tries to piggyback off of a subscription, and why Anthropic has to continuously play whack-a-mole trying to shut those services down.
- timmmmmmay 7mo agothey're obviously trying to steer customers to the monthly subscription instead of the pay-per-token API. now, the consensus of the commentards on this website, who don't have access to any of anthropics financial data, is that the monthly subscriptions are a money loser! so either the leading AI company's business dev team is wrong or the Jacker News comment section is wrong, it is a mystery
- behehebd 7mo agoDid anthropic do the oldest SaaS sales trick in the 2010s SaaS playbooks ;)
- vmykyt 7mo agoI have very naive question: People in comments have assumption that Atropic 10 times bigger than chinese models so calc cost is 10 times more. But from perspective of Big O notation only a few algorithms gives you O(N). Majority high optimized things provide O(N*Log(N)) So what is big O for any open model for single request?
- rat9988 7mo agoN*Log(N) can be approximated to O(N) for most realistic usecases. As for LLM, there is probably some cost constant added once it can fit on a single GPU, but should probably be almost linear.
- fancyfredbot 7mo agoIt's a good question. Costs will be lumpy. Inference servers will have a preferred batch size. Once you have a server you can scale number of users up to that batch size for relatively low cost. Then you need to add another server (or rack) for another large cost. However I think it's fair to say the cost is roughly linear in the number of users other than that. There may be some aspects which are not quite linear when you see multiple users submitting similar queries... But I don't think this would be significant.
- osener 7mo ago> Cost remains an ever present challenge. Cursor’s larger rivals are willing to subsidize aggressively. According to a person familiar with the company’s internal analysis, Cursor estimated last year that a $200-per-month Claude Code subscription could use up to $2,000 in compute, suggesting significant subsidization by Anthropic. Today, that subsidization appears to be even more aggressive, with that $200 plan able to consume about $5,000 in compute, according to a different person who has seen analyses on the company’s compute spend patterns. This is the relevant quote from the original article.
- ranyume 7mo agoThis changes... everything.
- deleted 7mo ago[deleted]
- d--b 7mo agoAnd on top of that, Anthropic does not run their own compute clusters do they? They probably get completely ripped by whoever is renting them the processors. $200 worth of actual computation is an awful lot of computation.
- A7OM 7mo ago[flagged]
- readthemanual 7mo agoI think the main issue I have with the article is that author whole argument is based on 'Qwen wouldn't run at a loss'. But why wouldn't it? Depsite it being a business, there might be a number of arguments why they decide to run without profit for now: from trying to expand the user base, to Chinese government sponsoring Chinese AI business.
- martinald 7mo agoHi, OP here! Even if Qwen wants to run at a loss, why would Together, DeepInfra, SiliconFlow, etc _all_ also want to run at a similar loss?
- maxdo 7mo agoI'm using API directly for software developement, i'm on path to pay ~$5k this month per user, some less , some more, with daily use is just growing more and more.
- tartoran 7mo agoWhat kind of software development do you do? Are you running a gas town? I assume you make your money back but still, are you sure you’re not wasting your tokens away?
- maxdo 7mo agoLots of greenfield sub projects around same product , bug fixes , ticket management etc . With my price per hour it still Make sense, even though it’s on the edge of been not that great. Yeah , I tried gasTown. Not using it extensively.
- zurfer 7mo agotldr: the author argues it is closer to costing 500 USD per month IF a user hits their weekly rate limits every week. Which is probably a lot more correct than other claims. However it's also true that anybody who has to use the API might pay that much, creating a real cost per token moat for Anthropics Claude code vs other models as long as they are so far ahead in terms of productivity.
- secondary_op 7mo agooff-topic, but, please stop hosting websites behind cloudflare, just nginx is enough, cloudflare is cancer.
- notkyle 7mo agoEquating Cloudflare and NGINX seems a bit weird, they're entirely different tools with different purposes? Cloudflare has never done me any wrong as a web host or a web user. Calling it a cancer is very disingenuous.
- AussieWog93 7mo agoHave you ever hosted a website without Cloudflare? In my case, the access logs alone from bots scanning for vulns grew so large that the server started creating. Fortunately I wasn't running anything vulnerable!
- otterley 7mo agoThe first clue that an article is likely bullshit is that it’s posted on Forbes. Forbes is now basically a contributor blog wearing the tattered remains of a designer suit.
- tartoran 7mo agoNobody seems to mention free users who have a pretty small limit but there are a lot of users in this category. Who’s subsidizing them?
- ramesh31 7mo agoAgent teams change everything. I can easily burn through 1m tokens in 15 minutes. There's no way the $200 price will hold once everyone is doing it.
- steveBK123 7mo agoOne consideration to me, regardless of the exact burn rate on inference is the assumed increase in revenues via higher fees. One of the bull cases I often see is that the hockey stick revenue growth continues longer/higher than the hockey stick cost growth. Then it all prints money because people are spending 10x/100x/1000x what they are today. In the real world .. Where I work, AI is used heavily, we are already tipping into cost management mode at a firm level. Users are being aggressively steered to cheaper models, usage throttled, and cost attribution reports sent. This is already being done at the under-$1k/mo per user cost level. So some indications of revenue per user leveling out already. Meanwhile everyone I know who works anywhere near a computer has had AI shoved down their throat, with training, usage KPIs, annual goal setting and mandated engagement. So we are already pretty saturated, it's not like theres giant new frontiers of new users.
- WhitneyLand 7mo ago“Cursor has to pay Anthropic's retail API prices (or close to it) for access to Opus 4.6. So to provide a Claude Code-equivalent experience using Opus 4.6, it would cost Cursor ~$5,000 per power user per month. But it would cost Anthropic perhaps $500 max.” Cursor seems to be in a tough spot. Just heard the swix podcast on their big new cloud agents thing, and it’s looking like a pretty small moat these days.
- bhekanik 7mo ago[dead]
- 0xbadcafebee 7mo agoThere's a huge difference between cost of inference and profit margin of the "big" providers, and the cost of inference for cloud-hosted open-weights. It's the same as R&D cost of the pharmaceutical industry, versus cost of producing generic drugs. One is massively expensive, the other is cheap. That said, for inference, the margins for OpenAI were estimated at 70% [1] [2], and the margins for Anthropic were estimated between 90% and 40% [3] [4], last year. They will not be profitable for years. [1] https://phemex.com/news/article/openais-ai-profit-margin-climbs-to-70-in-october-outpacing-rivals-47258 https://phemex.com/news/article/openais-ai-profit-margin-cli... [2] https://www.saastr.com/have-ai-gross-margins-really-turned-the-corner-the-real-math-behind-openais-70-compute-margin-and-why-b2b-startups-are-still-running-on-a-treadmill/ https://www.saastr.com/have-ai-gross-margins-really-turned-t... [3] https://www.theinformation.com/articles/anthropic-projects-70-billion-revenue-17-billion-cash-flow-2028 https://www.theinformation.com/articles/anthropic-projects-7... [4] https://www.investing.com/news/stock-market-news/anthropic-trims-profit-margin-outlook-as-ai-operating-costs-rise--the-information-4459316 https://www.investing.com/news/stock-market-news/anthropic-t...
- vessenes 7mo agoThank you for real data. Please moderate the use of the word profitable talking to engineers! We get the same circle jerk over and over here. Profit implies a GAAP accrual of some sort. On any accrual schedule tied to reality, the companies are profitable now - that is, inference margin on each given model has more than paid for capital costs of training and deploying those models. That the companies get to show a loss is a feature of cash-basis accounting: they made $100m net on that last model? Good news, We’re spending $1b on the next! Infinite tax losses! The companies will not be cashflow positive for years. Why does this persnickety difference matter? It matters to me because I care about the engineers here - and they seem collectively likely to either short every AI company IPOing, or just quietly ignore AI impact on their livelihood, or head off into a corner and go catatonic - all based on a worldview that “this is collective insanity and everything here is going to eventually go bankrupt” — none of those are good outcomes. Shorting might be, but it should be done judiciously, and understanding the financial factors at play. So, anyway, long plea over - but, allow me to plead: cashflow positive if you want to make the point you were making.
- ajstars 7mo agoThe compute cost debate misses a subtler point: the real cost multiplier isn't inference, it's context length. Most agent frameworks naively stuff 6-8k tokens into every prompt turn. If you route intelligently and compress memory hierarchically, you can bring that down to 200-400 tokens per turn with no quality loss. The model cost then becomes almost irrelevant.
- sheepscreek 7mo agoTL;DR the premise for the calculation is completely flawed even if the conclusion is correct > Qwen 3.5 397B-A17B is a good comparison point. It's a large MoE model, broadly comparable in architecture size to what Opus 4.6 is likely to be. I stopped reading here. Frontier models have been rumoured to be in TRILLIONS of parameters since the days of GPT-4. Besides, with agents, I think they’re using more specialized models under the hood for certain tasks like exploration and web searches. So while their cost won’t be $5000 or anywhere close, I still think it would be in the hundreds for heavy users. They may very well be losing money to the top 5-10% MAX users. Their real margin likely comes from business API customers. Here’s an interesting bit - OpenAI filed a document with the SEC recently that gave us a peek into its finances. The cost of all infrastructure stood at just ~30% of all revenue generated. That is a phenomenal improvement. I fell off the chair when I first learned that.
- elbasti 7mo ago"Any conversation about token costs devolves into an ad-hoc, informally-specified, bug-ridden implementation of half of generally accepted accounting principles." We have a way of determining if Anthropic is, or has the capability of being profitable, and what the levers to that may be. AI may be world-changing, but the accounting principles behind AI labs are no different than those behind a Pizza Hut. Even if the cost of "inference + serving" is lower than the cost of selling a token, the relevant question is what is the depreciation schedule of the cost of training. ie, if I spend $1 on training, how long do I have before I have to spend $1 again? Almost certainly, any reasonable depreciation schedule of the cost of training will result in leading labs being presently wildly unprofitable. So the question is: What can be done to make training depreciate more slowly? Perhaps users can be persuaded to stick around using non-fronteir models for longer, although then there's a shift in the competitive landscape. If users cannot be persuaded (forced?) to use legacy models, then the entire business model is thrown into question, because there's no reason why training frontier models would ever get cheaper: even if it gets cheaper on the margin, surely that will result in more compute used to generate an even "better" model, resulting in more spend in the aggregate. This doesn't mean that the AI industry is "doomed". A couple things could happen, and this is where the fronteir labs should be focusing their attention: 1. They could find a way to climb up the value chain and capture more of the consumer surplus. 2. There could be a paradigm shift in compute architecture/compute cost. 3. We could reach a limit of marginal utility, shifting consumption to legacy models, thereby lengthening the depreciation/utility of training. Edit: My assertion of "Almost certainly, any reasonable depreciation schedule of the cost of training will result in leading labs being presently wildly unprofitable." is made with no real information, just a gut feeling, and should not be taken seriously.
- lokar 7mo agoOf course a model does not really depreciate, the problem is they are forced by competitive pressure to offer newer/better models at the same price. This is what the elites of the gilded age called "ruinous competition", and the solution today will be the same as back then: monopoly power. This has been the business plan of the tech VC industry for 25+ years.
- dreis_sw 7mo agoWe'll only know for sure when either of these companies goes public. Google serves inference and Google Cloud is profitable but we don't know how much inference is costing. If they never go public, there's our answer as well.
- tom_m 7mo agoYea, it costs more than that.
- nickcoffee 7mo agoBeen running Claude Code and the $200/month has been one of the better value decisions I've made as a founder. The more interesting question is where the margins go as inference costs keep dropping. At some point the pricing pressure flows to users.
- august11 7mo agoThey are clearly making money overall.
- SilverBirch 7mo agoWhilst this is interesting I find the topic bought up on odd lots is more interesting. The idea was this: Once you've built a model, if you can sell tokens for a profit, this is a great business - just sell more tokens. But you can't just build a model and sell tokens. You need to build the best model to sell new tokens. So the question is much more "How much does it cost you to build a new SotA model" and then "How effectively can you monetize it". And since you need a SotA model, your only option if you have a bad model that isn't selling is to invest billions more into building a better model whose tokens you can sell. So this turns into a death march. If you are behind, the only thing you can do is make massive capital investments to catch up. Once you're ahead you can sell tokens until someone else catches up. And, breaking the model of normal of places like chip fabrication, your billions of investment may only keep you ahead for 2 months. So you have a tiny window to sell those tokens.
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