Inference Cost Forecasting: The Capacity Plan Your Finance Team Wants and You Can't Write
Your finance team will ask for a capacity plan you cannot write. Not because you're inexperienced or because the model is new, but because the two assumptions classical capacity planning rests on — a workload distribution you can measure, and a unit cost stable on a quarter timescale — are both violated by AI workloads. The number you hand them will be wrong on day one, and when the variance hits, the conversation that follows will not be about the bill.
The 2026 State of FinOps report named AI as the fastest-growing new spend category, with a majority of respondents reporting that AI costs exceeded original budget projections — for many enterprises, inference now consumes the bulk of the AI bill. The instinct to manage this with a SaaS-style capacity plan — pick a peak QPS, multiply by a unit cost, add 30% buffer — produces a number with the texture of a forecast and the predictive power of a horoscope. The capacity plan you actually need looks more like a FinOps scenario model than a procurement spreadsheet, and the engineering work to produce it is platform work that competes with feature work until the day finance loses patience.
