4 posts tagged with "pricing"

When the team published the AI tier at "$X per seat for unlimited AI," nobody on the pricing call thought of it as a derivative position. It looked like a SaaS pricing page — a number, a tier, a CTA. But every dollar of revenue from that page is now exposed to a token-cost curve set by a vendor whose roadmap does not care about your gross margin. You did not write a pricing page. You wrote a naked short on token volatility, and the strike is whatever your vendor charges next quarter.

The math arrives quickly. A handful of power users discover the workflow and start running it on the longest context they can fit. A competitor's UX change re-trains the median user to send queries that are 40% longer. The frontier model your feature is locked to gets a price-per-million bump because the older tier you were on is being deprecated. Any one of these is a margin event you cannot reverse from the pricing page in a single quarter — and they tend to arrive together.

Cursor hit $1 billion in revenue in 2025 and lost$150 million doing it. Every dollar customers paid went straight to LLM API providers, with nothing left for engineering, support, or infrastructure overhead. This wasn't a scaling problem—it was a unit economics problem that was invisible until it was catastrophic.

Most engineering teams building AI features make the same mistake: they treat inference cost as a minor line item, ship a flat-rate subscription, and assume the economics will work out later. They don't. Variable inference costs don't behave like any other COGS in software, and the pricing architectures that work for traditional SaaS will bleed you dry the moment your heaviest users find your most expensive feature.

There is a company charging £50 per month per user. Their AI feature consumes £30 in API fees. That leaves £20 to cover hosting, support, and profit — before accounting for a single refund or churned seat. They built a product users love, grew to thousands of subscribers, and unknowingly constructed a business where more customers means more losses.

This is not a cautionary tale about a bad idea. It is a cautionary tale about a pricing architecture imported from a world where the marginal cost of serving the next user was effectively zero. That world no longer fully applies when your product calls a language model.

Traditional SaaS gross margins run 70–90%. AI-forward companies are reporting 50–60% — and the gap is mostly explained by one line item: inference. When tokens are 20–40% of your cost of goods sold, the standard SaaS playbook inverts.

Token costs dropped 280x in two years. Enterprise AI bills went up 320%. If that sounds like a paradox, you haven't looked closely at how per-token billing interacts with the features that actually make AI products valuable.

The most useful AI workflows — deep research, multi-step reasoning, iterative refinement, agentic tool use — are precisely the ones that consume the most tokens. Under pure usage-based pricing, your best features are your worst margin killers. This isn't a temporary scaling problem. It's a structural misalignment between how AI creates value and how it gets billed.