The cost curve nobody models before launch

Inference cost scales with success. That is an uncomfortable property, and it is the single most common reason an AI feature gets quietly switched off nine months after a celebrated launch.

The curve is superlinear in practice

Usage grows, context windows grow with conversation depth, and retrieval grows with corpus size. Three compounding factors, each individually reasonable, produce a bill nobody forecast.

Model cost per successful outcome

Cost per request is the wrong denominator. Model cost per resolved ticket, per approved application, per completed journey — the unit the business already understands.

Design the cost controls in

Cascade routing, aggressive caching at the retrieval layer, context pruning and a hard budget ceiling per tenant. All four are far cheaper to design in than to retrofit under pressure.

Instrument before you optimise

Per-stage cost attribution from day one. Without it, optimisation is guesswork, and guesswork on a superlinear curve is expensive.