Anthropic made a smart business decision to mostly focus on enterprise customers rather than regular consumers. Only around 10% of their revenue comes from their heavily subsidized subscription plans. The remaining 90%ish comes from API and enterprise usage billed at per-token rates, which is something that’s actually profitable for them. They have a decent margin on token rates. It’s R&D, hardware, and subscriptions that are putting them in the red at the moment.
Anthropics numbers are probably better than OpenAIs, but i am willing to eat my boots (with ketchup and mustard tho) if they are even close to profitable at API pricing.
They are (maybe, allegedly) “profitable on inference” i.e. the API pricing is higher than the actual compute they use just for the input/output tokens for the query. That excludes, though, training, capex, staff, etc., which… is by far the vast majority of their actual expenses.
At least according to Ed Zitron, they only appeared profitable due to creative bookkeeping. They timed the announcement of their shitty “annualized revenue” to coincide with the few months of vastly reduced rent at the datacenter Musk rented to them as well as the shift to token based billing for enterprise customers. Their inference is likely not profitable.
Yeah, hence I couched it in allegedly. Ultimately we don’t really know. But even if it were true, it wouldn’t really mean anything for their profitability as a company. They have to keep improving the models if they hope to keep what customers they have.
It’s subsidized. I will eat my hat if the number is less than 50%. It is even crazier at work. My personal account cost 3 times as much for the same prompt on my work account. Inference cost is the same for all users, you can’t mega-ultra scale something that is at maximum scale labeled as a hyper scale.
My personal account cost 3 times as much for the same prompt on my work account.
That’s interesting. My employer is too large to use subscriptions and also we have our own harnesses, so they have to pay per token. Some employees spend thousands of dollars (or more) per month, but if they were doing it at home, they’d be easily able to fit the same usage into a $200/m subscription.
I was comparing API costs at home, the plans give you a lot of value. When I had to use GitHub Copilot the API cost was closer to my home cost, but whatever Enterprise plan we have through Anthropic is crazy.
Yeah, subsidized by mainly Nvidias circular financing crap. This will cause a major recession, and it is so obvious for everyone looking at how much cash investors are throwing at this thing and how much you can actually earn with something that confidently misinforms you on the regular. Roleplaying like Character.ai is one of the better uses, because there false information isn’t that relevant. Then there’s coding where it is easy to automatically check for errors, and then… scientific uses. But i think that’s it, and you can’t prop up all that investment just with coding, RP and weather analysis (which is woke by definition in the US to boot) or interpreting CT scans.
Yeah I have the $200/m sub for work and I’m using about $200/day volume of tokens at API rate. Obviously this isn’t a sustainable business model.
Am I prepared to pay full rate? Not yet, but if models keep improving at the rate they are then probably at some point. Opus 5.5 has been a huge increase in quality at a cheaper cost to deliver a task.
With next gen gpus and better models reducing the ‘cost per task’ I think they’ll get there.
Those enterprise customers are companies like this very one, who then have to heavily discount their rates or nobody wants it, (and as we see here, even then) so that’s not a solution for anthropic.
I don’t have data to back this up but I’d say thay the majority of their customers aren’t also selling AI services themselves. It’s customers that use AI for their business processes, software development, etc.
AI is more than just chatbots though, and there’s plenty of use cases where it can be used to help people rather than replace them.
I started the year being very sceptical of AI, but it’s helping me with a bunch of things at work now. For example, at work we have an agent for data analysis that can find the correct tables to use, gather data, and produce a report, along with proof (all the SQL queries it ran plus any ad-hoc code it used for analyzing the data) so that the work is repeatable and we can verify the accuracy of the queries and logic.
I’m a developer and it’s helped a lot with side tasks too. If I’m coding and see an issue in our code (usage of deprecated code, tech debt we need to pay down, etc), I used to file a task or add a todo comment to look into it later, and eventually get to it. A lot of them are small tasks, it’s just that I don’t always have time to look into them while in the middle of a project.
These days, instead of adding it to a todo list, I just spin up an agent and get it to look at it while I’m working on other things. The resulting code doesn’t get submitted for code review until I manually look at it and ensure it’s correct. The codebase I work on has never been cleaner, because AI has helped me clean up so much legacy cruft that’s accumulated over the years, while I still deliver my regular projects on time.
Nobody has extremely good data about this because it’s in their interest to make that as opaque as possible, but the reporting I’ve seen suggests that it is, if not the majority at least an extremely large fraction of their customers which are selling AI services, and even where it’s used for in-house software development that is largely bankrolled by VC hoping to gain a competitive advantage with AI. It doesn’t really appear that the API pricing offers better cost/productivity than just hiring junior devs (lol).
I’m surprised how easy it is to blow through hundreds of dollars in Claude tokens at work. They certainly aren’t subsidizing anything. If the Qwen 3.8 27B model I run on my gaming PC at home were priced like Claude, I’d probably have a $1000 a month bill.
Edit:
I bought a 7900 XTX a couple months ago for less than 2 months worth of Claude bills at work that runs 3.8 27B quite well at 200k context that is pretty on par with Sonnet 5.
Maybe Anthropic isn’t bleeding money as fast as they would otherwise be, but their premium pricing is pretty hard to justify. If Qwen 4 27B keeps pace with Sonnet 5.5, then we’ll basically have a Fable 5.1 class model that runs on a gaming PC.
Don’t know if it was you but someone pointed out a few months ago that the 7900 was the last 24GB card to be found for less than $1k and I grabbed one as an upgrade from a 3070. Running local models it’s less reliable than Claude at work, but not by much, and it was a good upgrade for gaming.
Before the return period was up it had gone from ~$900 to past $1.2k and I wasn’t eligible for a repair/replacement just a refund, the market is completely sideways.
The subscriptions generate lots of usage data and enabled experiments on human subjects at scale. If you are paying for API they cannot really compare two similar users and give them wildy different results because those are business trying to do their job but a random ass subscriber that does not read the ToS is fair game for experimentation.
I think Google and Meta are well-positioned to provide AI services to regular people, since they both already have a large user base, existing cash flow from other non-AI products (meaning they can subsidize AI features for as long as they need to), and can probably outlast any startup. Google especially easily have enough paid users and advertisers to cover costs for free users.
Most of thr other AI companies will probably all end up focusing on / pivoting towards enterprise use cases though, for the higher prices and stability of long-term contracts.
Exactly. Enterprise is where AI can actually earn its keep: companies will pay serious money if it cuts costs or boosts revenue. Consumer chatbots are mostly a costly distribution strategy for now. The real customers have procurement budgets, not a $20 subscription and a dream.
The funniest outcome would be hyperscalers spending billions convincing enterprises they need AI, only for those enterprises to deploy open-weight models on their own hardware. After decades of vendor lock-in, »we can run this ourselves« might be the most expensive sentence Microsoft ever hears. However, the reality in many organisations is also that they opt for the supposedly safer choice of turning to a third party, rather than taking action themselves.
Anthropic made a smart business decision to mostly focus on enterprise customers rather than regular consumers. Only around 10% of their revenue comes from their heavily subsidized subscription plans. The remaining 90%ish comes from API and enterprise usage billed at per-token rates, which is something that’s actually profitable for them. They have a decent margin on token rates. It’s R&D, hardware, and subscriptions that are putting them in the red at the moment.
I suspect they’ll discontinue subscriptions some time after their IPO. Even though subscriptions are only responsible for 10% of revenue, they consume 42% of their inference compute power (https://www.theregister.com/ai-and-ml/2026/10/06/anthropic-claude-subscription-plan-provides-more-value-than-openais-study-says/5301470)
In comparison, around 70% of OpenAI’s revenue comes from subscriptions. Without subsidized subscriptions, they’d lose most of their customers.
“Yeah, besides those integral parts of our costs, we’re profitable”
😅.
It’s like a hair dresser saying “If I don’t count the rent of my shop and all the supplies, I’m profitable”.
Anthropics numbers are probably better than OpenAIs, but i am willing to eat my boots (with ketchup and mustard tho) if they are even close to profitable at API pricing.
They are (maybe, allegedly) “profitable on inference” i.e. the API pricing is higher than the actual compute they use just for the input/output tokens for the query. That excludes, though, training, capex, staff, etc., which… is by far the vast majority of their actual expenses.
At least according to Ed Zitron, they only appeared profitable due to creative bookkeeping. They timed the announcement of their shitty “annualized revenue” to coincide with the few months of vastly reduced rent at the datacenter Musk rented to them as well as the shift to token based billing for enterprise customers. Their inference is likely not profitable.
Yeah, hence I couched it in allegedly. Ultimately we don’t really know. But even if it were true, it wouldn’t really mean anything for their profitability as a company. They have to keep improving the models if they hope to keep what customers they have.
It’s subsidized. I will eat my hat if the number is less than 50%. It is even crazier at work. My personal account cost 3 times as much for the same prompt on my work account. Inference cost is the same for all users, you can’t mega-ultra scale something that is at maximum scale labeled as a hyper scale.
That’s interesting. My employer is too large to use subscriptions and also we have our own harnesses, so they have to pay per token. Some employees spend thousands of dollars (or more) per month, but if they were doing it at home, they’d be easily able to fit the same usage into a $200/m subscription.
I was comparing API costs at home, the plans give you a lot of value. When I had to use GitHub Copilot the API cost was closer to my home cost, but whatever Enterprise plan we have through Anthropic is crazy.
Yeah, subsidized by mainly Nvidias circular financing crap. This will cause a major recession, and it is so obvious for everyone looking at how much cash investors are throwing at this thing and how much you can actually earn with something that confidently misinforms you on the regular. Roleplaying like Character.ai is one of the better uses, because there false information isn’t that relevant. Then there’s coding where it is easy to automatically check for errors, and then… scientific uses. But i think that’s it, and you can’t prop up all that investment just with coding, RP and weather analysis (which is woke by definition in the US to boot) or interpreting CT scans.
Yeah I have the $200/m sub for work and I’m using about $200/day volume of tokens at API rate. Obviously this isn’t a sustainable business model.
Am I prepared to pay full rate? Not yet, but if models keep improving at the rate they are then probably at some point. Opus 5.5 has been a huge increase in quality at a cheaper cost to deliver a task.
With next gen gpus and better models reducing the ‘cost per task’ I think they’ll get there.
Those enterprise customers are companies like this very one, who then have to heavily discount their rates or nobody wants it, (and as we see here, even then) so that’s not a solution for anthropic.
I don’t have data to back this up but I’d say thay the majority of their customers aren’t also selling AI services themselves. It’s customers that use AI for their business processes, software development, etc.
And mostly finding out that using chatbots is more expensive than hiring people. That is not a revenue stream that’s going to last very long.
AI is more than just chatbots though, and there’s plenty of use cases where it can be used to help people rather than replace them.
I started the year being very sceptical of AI, but it’s helping me with a bunch of things at work now. For example, at work we have an agent for data analysis that can find the correct tables to use, gather data, and produce a report, along with proof (all the SQL queries it ran plus any ad-hoc code it used for analyzing the data) so that the work is repeatable and we can verify the accuracy of the queries and logic.
I’m a developer and it’s helped a lot with side tasks too. If I’m coding and see an issue in our code (usage of deprecated code, tech debt we need to pay down, etc), I used to file a task or add a todo comment to look into it later, and eventually get to it. A lot of them are small tasks, it’s just that I don’t always have time to look into them while in the middle of a project.
These days, instead of adding it to a todo list, I just spin up an agent and get it to look at it while I’m working on other things. The resulting code doesn’t get submitted for code review until I manually look at it and ensure it’s correct. The codebase I work on has never been cleaner, because AI has helped me clean up so much legacy cruft that’s accumulated over the years, while I still deliver my regular projects on time.
Nobody has extremely good data about this because it’s in their interest to make that as opaque as possible, but the reporting I’ve seen suggests that it is, if not the majority at least an extremely large fraction of their customers which are selling AI services, and even where it’s used for in-house software development that is largely bankrolled by VC hoping to gain a competitive advantage with AI. It doesn’t really appear that the API pricing offers better cost/productivity than just hiring junior devs (lol).
I’m surprised how easy it is to blow through hundreds of dollars in Claude tokens at work. They certainly aren’t subsidizing anything. If the Qwen 3.8 27B model I run on my gaming PC at home were priced like Claude, I’d probably have a $1000 a month bill.
Edit:
I bought a 7900 XTX a couple months ago for less than 2 months worth of Claude bills at work that runs 3.8 27B quite well at 200k context that is pretty on par with Sonnet 5.
Maybe Anthropic isn’t bleeding money as fast as they would otherwise be, but their premium pricing is pretty hard to justify. If Qwen 4 27B keeps pace with Sonnet 5.5, then we’ll basically have a Fable 5.1 class model that runs on a gaming PC.
That is the subsidized rate!
It costs far more for all the training and compute costs together than what you are currently paying.
Don’t know if it was you but someone pointed out a few months ago that the 7900 was the last 24GB card to be found for less than $1k and I grabbed one as an upgrade from a 3070. Running local models it’s less reliable than Claude at work, but not by much, and it was a good upgrade for gaming.
Before the return period was up it had gone from ~$900 to past $1.2k and I wasn’t eligible for a repair/replacement just a refund, the market is completely sideways.
I got one used for 600€ not long ago, sold my 6900XT for 450€, very happy
Nice, that’s quite a deal!
Yeah, it might’ve been me and glad to hear you got while the getting was still good.
The subscriptions generate lots of usage data and enabled experiments on human subjects at scale. If you are paying for API they cannot really compare two similar users and give them wildy different results because those are business trying to do their job but a random ass subscriber that does not read the ToS is fair game for experimentation.
Absolutely. AI is an enterprise product - at least: for now. And Anthropic is in the pool position; https://eicker.news/@technews/117443237284007574
I think Google and Meta are well-positioned to provide AI services to regular people, since they both already have a large user base, existing cash flow from other non-AI products (meaning they can subsidize AI features for as long as they need to), and can probably outlast any startup. Google especially easily have enough paid users and advertisers to cover costs for free users.
Most of thr other AI companies will probably all end up focusing on / pivoting towards enterprise use cases though, for the higher prices and stability of long-term contracts.
Exactly. Enterprise is where AI can actually earn its keep: companies will pay serious money if it cuts costs or boosts revenue. Consumer chatbots are mostly a costly distribution strategy for now. The real customers have procurement budgets, not a $20 subscription and a dream.
Are Enterprise companies gonna wanna be fucked by hyperscalers the same way they are fucked by Microsoft?
I’d say no, they won’t. Without the vendor lock in, it will be cheaper and more secure to run open source models locally on your own hardware.
People are done outsourcing their essential workflows to non-competitive monopoly corporations.
The funniest outcome would be hyperscalers spending billions convincing enterprises they need AI, only for those enterprises to deploy open-weight models on their own hardware. After decades of vendor lock-in, »we can run this ourselves« might be the most expensive sentence Microsoft ever hears. However, the reality in many organisations is also that they opt for the supposedly safer choice of turning to a third party, rather than taking action themselves.