Why current LLM costs are not sustainable

The problem space has a few aspects:

1. We're still in the "$5 airport Uber" era of LLMs. They're heavily subsidized, and everyone still complains about costs.

2. There hasn't been a real incentive to work on cost optimization for data centers and the hardware they contain. When/if price hikes happen and send people scrambling to use other models or drastically reduce AI usage, this will suddenly need to happen.

3. We're massively overusing SOTA models. As long as you're on a subsidized subscription, you can use Claude Opus 4.8 high to write blog article meta descriptions. If you paid by token, you wouldn't do that.

4. Open models are a wildcard that could completely change the calculus.

This entire discussion is on the supply side chips, available weight, and switching cost. What is missing is the demand aspect. The firms that will be the most impacted when there is a drop in price are not Uber or Microsoft; it is that small privately-held firm using spreadsheets and hunches to make business decisions and cannot afford to invest in AI due to the high costs involved currently. The minute inference becomes affordable, an entirely new set of users joins the scene. The real issue is not what will happen to the laboratories it's what gets built for that next wave and whether it's actually useful or just cheaper versions of what already exists.

> To give an example, just doing Typescript type fixes with this model across 50 files cost me $54 this afternoon.

If you can use a subscription with any of the SOTA models, do that.

Instead of around 4k EUR in token costs, my Opus usage costs me 108 EUR (with taxes) per month with their Max 5x plan. It's the same with OpenAI, those are heavily subsidized.

It doesn't make sense to pay per-token, unless you must.

> What is happening here is that leading AI labs are charging not only for inference but also for research in model architecture, training data collection and curation, model training cost (which can be tens or even hundreds of millions of dollars), paying their employees and recovering the marketing costs.

Chances are, they're never getting that money back. Best case scenario, the hype around AI slowly declines, worst case - it crashes and takes a part of the economy with it.

Also anyone doing distillation with hundreds or thousands of those subsidized attacks is probably winning big. Especially as the model architectures (e.g. DeepSeek V4) are more oriented towards efficiency.

> Last but not least and in fact the most important factor, is the ability of users to run local models. So far, almost everyone is using cloud-hosted models and local models are either too big to deploy or too slow to work with. With advancements in chips, this will change in 4-5 years’ time.

Currently beefy hardware to run them fast enough to be competitive with the cloud (at least 60 tps) is expensive and even then the small local models quite suck compared to SOTA or even DeepSeek V4 Pro and GLM 5.2, though they're way better than they used to be (compare Qwen 3.6 with 2.5 for example).

I am using perhaps 15% of usage count on Claude with just the normal subscription. And I do full time software engineering and would say I use quite a lot of AI input on thoughts, designs and code drafts.

So how these companies and people manage to use these absurd amount of tokens is a mystery to me. It feels like this are just running huge amount of non-vetted data to the LLM's and or running loops against the LLM's which only produce fractional results if not wasted results for insane cost.

So really it is the equivalent of just burning money, or heating your house in the winter while having all your windows open.

I have already seen a number of people doing the math on what it would take for hardware to self host a Q8XL quantization of GLM5.2 shared between N numbers of people.

There's additional advantages that everything you query, all of your context cache and everything it outputs stays private and can't be arbitrarily turned off by external interference.

Personally I think it would be a fairly good bet that something with the 1TB of RAM needed to properly self-host GLM5.2 will still be a very usable piece of hardware in 4 to 5 years from now. There will be even larger, newer models available, sure. But there will also be better models that continue to fit in the same size.

It's weird to see people claiming that model capabilities are plateauing. It wasn't until late last year that we even had strong coding models. Imagine if, less than a year after the first iPhone launched, people claimed that smartphone capabilities were "plateauing" because Apple hadn't yet launched a new phone. And it seems the issue is less than "models aren't getting better" than, "models are good enough to handle 99% of the coding tasks people give to them".

There is a wave of users switching over to DeepSeek Flash. There are Reddit threads of users sharing billion token spend for $20.

If all of global spend on Anthropic/OpenAI/Gemini APIs just switches over to DeepSeek then easily we can decrease total AI spend by 10x

Would prefer not to offend the author, but I do believe this article has very little for the HN audience. No new insight, and no numbers or new information.

These ridiculous market caps can only be justified if LLMs can ultimately produce outputs that are above human capabilities OR reduce costs and perform human tasks for cheaper than the human rate. Neither of these things have happened thus far and the incremental gains are decreasing more and more.

The OpenAIs and Anthropics are going to get eaten by open source, I don't see prices going up, prices are going to crater. The models are going to be more and more commoditized.

A 200 USD Clause 20x subscription gives you 13,000 USD equivalent in API credits. And those are believed to profitable for Anthropic while the 200 USD are properly not, if used to a large extend.

If the subscription is gutted by factor 2/5/10/20/65 to make it more profitable for Anthropic it will be harder for users to justify the subscription.

On the other hand 13,000 USD in API credits can go a very long way if used ergonomically. For instance using a max context length of 200k is multiplying your reach in comparison to 1m context.

> We are seeing improvements with each model release these days but it’s clear that the improvements are getting smaller and smaller.

This is obviously untrue, both with GPT-5.4, and Claude Fable as examples in the last 6 months.

As information flows abundantly, and as information processing flows more abundantly, where will the bottlenecks in the system emerge? It surely won't be in design and production. It probably won't be in chips, infrastructure, and energy (already commodities, increasingly competitive). So... what's the bottleneck? Political will? Human discernment/taste? Raw materials?

I am convinced that the combination of capable open weight models and specialized hardware will mean that Apple (and other hardware providers) will start shipping computers with built-in, hardwired, "LLM-on-a-chip" cards that are capable enough to meet 90% of your AI needs.

I really believe that in the near-term future we will run our LLMs in hardware, not in software. Hardwire a capable model into a device the size of a graphics card, embed it into a laptop, and you have something that uses less power, does faster inference, doesn't require additional CPU or memory, doesn't cost a monthly fee, and will probably eventually be available for under a (few) hundred bucks.

A few thoughts:

1. Chat, being 3 yr old, is a fairly mature and solved problem today. Top companies aren't even talking about it anymore! Gemma 31B does it amazingly well (for $0.4/1M token output). Practically every near-SoTA and SoTA model does simple "chat-like" QA amazingly well -- summarization, basic question answering, single- or few-step search.

2. Tasks -- or knowledge work on a computer -- are the new frontier. Computers have become competent only recently, and only for some of the tasks so far. I'd guess another 2-3 yr development cycle, after which "el cheapo" models will be virtually indistinguishable from SoTA.

As tasks are the new game in town, AI labs can still charge a premium for it. That premium has disappeared already for chat; most users cannot tell 99% correct answer from 95% correct answer; nor do they always wish for maximum accuracy.

3. What comes after Tasks? I think today's AI startups should figure that one out and solve it before everyone else.

Prices will go down one way or another. That is of course unless the market gets cornered by restricting model use, restricting supply of essential hardware components or raw materials to make this hardware, etc.

In terms of running the model locally vs a service provider, that will be down to convenience more than anything else for the same reason why not everyone is hosting their own website at home on their own box.

> What is happening here is that leading AI labs are charging not only for inference but also for research in model architecture, training data collection and curation, model training cost (which can be tens or even hundreds of millions of dollars), paying their employees and recovering the marketing costs.

Of course they do. How else do you expect them to pay for that? If you buy a Foo from Acme, Inc, you aren’t only paying construction costs, either.

> On the other hand, once an open weight model is released, any inference provider can easily host it and just do some markup on inference cost. This proves way cheaper than running a frontier AI lab.

The only logical conclusion for commercial AI labs is to never release their models as open data, and try to stay ahead of open models. One way to do that is by having better models, another by having more users (because that decreases the per-user costs of creating the models, decreasing the price difference with companies running open models). The frontier labs are aiming for a combination of both.

> doing Typescript type fixes with this model across 50 files cost me $54 this afternoon.

Not trying to be harsh, but that sounds like a skill issue. You have the language server to lean on; easy feedback loop; sub agent per type.

I don't agree with Uber & MS buring their AI consumer budget, it is hard to believe they miscalculate something this significant and not realize it within the first month itself.

> To give an example, just doing Typescript type fixes with this model across 50 files cost me $54 this afternoon.

1. How much it costs in terms of programmers' salaries?

2. Can DeepSeek do this (I bet it can) and how much it costs?

The fact the author ever had the idea of using a SOTA to solve do this means LLMs are actually quite cheap.

> To give an example, just doing Typescript type fixes with this model across 50 files cost me $54 this afternoon.

Who in hell would actually do this? That's a level of problem that any of the flash-class models can solve.

Hand that sort of thing to GPT-mini, Haiku, or DeepSeek Flash, and save the big guns for big architectural problems.

One thing missing here is the maturity of agent harnesses. I’m finding the free deepseek flash model in opencode can handle all of my simple tasks, because the harness is so good. Soon that will be a local model.

And the reality is that other industries aren’t finding the use for LLMs as much as programmers are. Sure there are some benefits but you can’t fire your marketing department and replace it with AI

Curren prices will come down. There is a lot of potential for optimization. Energy efficiency, energy generation, self hosting, model size and specialization. Etc. Rught now the state of the art is powering data centers with gas powered turbine generators. That's not very efficient.

The current costs do not have to be sustainable for the SOTA model providers as they grow their user base. But I really wonder about the future as the costs have to increase at some point (to be sustainable) but at the same time the competition and local models get better and better.

Alright, instead of all this yapping here's the real numbers you can use as a guide:

8xB200[1] costs around 250k DIY and 450k from an enterprise builder so that will be our cost factor, these consume around 7.8kw at 100% load with median load of around 7kw (optimistic) which means that a 240kwh solar installation would be enough to supply it (72kwh buffer for bad weeks / winter) and that will set you back around $240k: this includes battery storage, installation and inverters, diy cost would be lower at around $160k.

This puts the cost of the entire system anywhere from $410k to $690k. This does not take in any property tax or land ownership into account since honestly it varies too much. The solar is simply used to provide a fixed cost basis for powering hardware instead of monthly recurring payments. Financing a 5 year loan would cost anywhere from $8,313 to $15,700.

Now let's do the math for glm-5.2[3], a fully optimized theoretical build can do around 1200tok/s which means that's around 13-14 streams of ~90tok/s on average, pushing batching further and limiting context size to around ~300k with ~150k median) you can achieve up to 37 streams at around ~40tok/s pushing performance envelope to 1400tok/s. This means you are able to generate 2.5B to 2.9B tokens in 4 weeks.

Which means putting the numbers together you can serve 1m tokens at $2.86 to $3.32 per million output tokens all else being equal. Considering that glm-5.2 is approaching opus level intelligence it's pretty safe to say that same applies for frontier labs. Input/cache write/cache reads are very difficult to price, so this assumes you're providing input / cache for free[4]. As a very heavy user I generate around 2M to 5M output tokens a day which would put me at $5.72 to $6.6 of cost per day totalling $200 a month[2].

What I also don't mention is that frontier labs have BY FAR the lowest cost per token out of any provider out there due to the amount of money they also invest into efficiency gains. This was proven by the fact that anthropic saw a huge exodus of openai users put strain on their systems and with efficiency optimizations alone they managed to mitigate a bulk of capacity issues, altho they did run into limits and had to begin spreading out the duck curve, but I have zero doubts they're getting percentage points of improvements month to month.

[1]: H300's are unobtanium unless you're building rack-rooms, H200's are not that cost effective and saturate too fast while having poorer efficiency, only capable of running flash tier models.

[2]: Okay, I didn't expect to arrive at the $200, this is kind of entertaining.

[3]: fp8, z.ai serves fp8 according to openrouter.

[4]: Assuming you want to charge for input / cache, cache reads make up roughly 30% of the cost, output 20% 50% input so to price it out it would be roughly $.3 for 1m input, $.015 for cache reads and $1.5 for output. Judging by https://openrouter.ai/z-ai/glm-5.2#pricing, appears that my math checks out.

The more I think on the problem, the more I believe this will be solved with US interventions. And the interventions will increase inflation by a lot, so prices will not go down.

The other alternatives with LLMs becoming more expensive in an Uber-like move may not work due to a lot of competition. I also don't think usage will increase 10x. I don't always have coding tasks for an LLM despite it being good.

My reasons to believe so are outside of what interests HN community and I am neither endorsing this behavior, nor I think it is that simple. But US also has a huge debt that it must service. Wouldn't it be convenient if it was suddenly halved in actual value?

There is a good and cheap alternative: R9700 32GB + Qwen 3.5 27B Won't give you SOTA performance, but will be as good as Sonnet a few months back.

> and Microsoft, Salesforce and Github are taking steps to reduce AI spend by employees.

anyone got a source? sounds juicy

i see all these claims its too expensive but why arent we comparing it to wage costs, is $10-20 an hour actually expensive?

I think companies will fire 5-10% of people and convert them to token budget.

I also believe that before any real companies are running these models locally, they will already have some kind of agentic layer.

With the current frontier model lab progress, i do not see any real company which makes real money, running local models.

Running local models is easy for me, for sure not that easy for any company. Your DC needs to be able to host GPUs, it needs the cooling power, you need to have a DC. Without a DC, you need to have someone maintaining critical infrastrucutre, taking care of model evaluation etc.

For external parties, there might become a new business model: You might not hire an external anymore, but a token budget and the 'operator of the token budget'.

The current chip fabs are full, developing a high end / cheapisch local LLM Chip will still take a few years as long as the DC GPU demand is still as high as it is.

I've posted this anecdote before, but i feel its worth posting again because I've seen this spread further.

>amusing side note: >Was in a meeting reviewing a potential new product, it was going well until they showed us that they had added AI to it (of course they have). It was pretty obviously just shoehorned in, and one part of that obviousness was that they had a column that showed how many tokens it took to make each query.

>I asked who is paying for the tokens, they said its included in the license. I said, so is there a budget or is it all you can eat. they said good question they didnt know and would get back to me. I said the reason i asked was just one query there had a 250k token burn on it. and it was a fairly simple query about one device.

>then, one of the execs on their side was heard saying out loud "Why are we even showing this to the customers?"

>it have us quite a chuckle. But lesson learned... the cost of adding AI to anything isnt really being accounted for let alone the true cost of actually running the AI.

>all things AI are going to get more expensive. even if you dont want the AI aspect.

The author understands well that Opensource is catching up but I think that the gap will remain constant - SOTA models will still be more performant.

The author mentions $54 in costs but the reality is that developers are paid around this much per hour.

What is likely to happen: LLM performance goes even higher and can do tasks that take humans days to accomplish. You then have to compare LLM cost with human cost - something the Author has forgotten in their analsys.

> GPT 5.5, for example, costs $5 per million input tokens and $30 per million output tokens. This is currently the costliest model available as per OpenRouter.

claude 5 mythos and fabled are 50$/MOutTok. Previous models were priced at 75$, so presumably they found the "too expensive" price point.

>Most AI labs have likely ingested everything available in digital and print media for the model training.

This isn't how coding models get better though. Why would this have anything to do with plateauing?

This is no surprise at all and was very predictable.

The Chinese open weight models were always winning the AI race to zero where as the likes of Anthropic and OpenAI have no choice but to increase token costs.

Even Microsoft wants to use some of the Chinese models only realizing how expensive both the frontier models are. It turns out that Jevon's paradox does not exist in the US (it exists in China).

This "Tokenmaxxing" marketing stunt was a scam for the frontier models to raise even more money at unsustainable valuations.

Not the end of the world!

OpenAI and Anthropic will just go back to entirely healthy valuations of ~$5-10B each and the industry carries on.

Spot on. From an US outsider's perspective there's so much ridiculous stuff going on that you feel like you're watching an episode of "bum fights". I don't think US knowledge workers alone can carry this bubble.

i think we have the causation backwards here. llms aren't expensive because they have to be — they're expensive because we keep reaching for the expensive model instead of putting any effort into making the cheap one good enough.

a surprisingly large fraction of production workloads can be handled by smaller models with the right scaffolding. it's often easier to switch to a larger model than to engineer those pieces, so many teams never bother.

my intuition is that a lot of the current "ai cost crisis" is really an orchestration problem rather than a model pricing problem. before asking whether frontier pricing is sustainable, i'd first ask how much of that spend is simple tasks being sent to the smartest available model by default.

my bet for the next few years is that the model itself stops being where the value is. frontier models will become more like commodities, and the real difference will be the layer around them as routing each task to the cheapest model that can do it well, verifying the output, and only escalating when needed.

eventually, asking "which model do you use?" will sound a bit like asking "which cpu do you use?" the engine still matters, but the system built around it matters a lot more.