The impossible business model of LLMs
SaaS companies spend on development, then serve users cheaply. LLMs spend on development AND on every single user interaction. That cost structure breaks everything we know about software pricing.
The economics of LLMs are fundamentally broken. At least by the standards we’re used to.
Traditional SaaS has a beautiful cost structure. You invest heavily in development; build the product, ship it, maintain it. But serving users is almost free. A few servers, some bandwidth, done. Whether you have a thousand users or a million, the marginal cost per user is tiny. That’s why SaaS companies can charge $10 a month and make enormous margins at scale.
LLMs flip this on its head.
Development costs are massive, not just building the model, but training it, which requires obscene amounts of compute. Then, unlike SaaS, every single user interaction costs money. Every query, every token, every response. The operational cost doesn’t flatten at scale. It grows with usage.
And here’s the cultural problem: we’ve been trained to expect fixed pricing for online software. A flat monthly fee, use it as much as you want. Netflix, Spotify, Notion, unlimited usage for a predictable price.
LLMs can’t deliver that without losing money on heavy users. The math doesn’t work. The distribution of usage is wildly uneven, a small percentage of users consume a disproportionate amount of tokens, racking up costs that the subscription fee doesn’t cover.
OpenAI is trapped in this. They need platform pricing expectations (flat, predictable) but have infrastructure cost structures (variable, per-use). And this isn’t just an OpenAI problem. It’s a structural challenge for the entire industry. Until someone figures out how to make per-token costs negligible, or finds a pricing model users will accept, the LLM business model remains a very expensive bet.