FinOps is the practice of treating cloud and AI spend as something to be actively engineered, not a bill that arrives at the end of the month. It brings finance, engineering, and product together around a shared view of what a workload costs to run, so a pricing decision made at design time doesn’t turn into an unpleasant surprise at production volume.
AI workloads make this harder than traditional cloud FinOps, because cost doesn’t scale linearly with usage the way a web server’s does. Multi-agent reasoning, wide context windows, and recursive evaluation loops each multiply token consumption on their own, so a query that cost cents in testing can cost dollars at scale, and the techniques that bring that back down, caching, routing, distillation, are engineering investments that only pay off once the volume justifying them already exists.
The organisations that avoid the “proof of concept to production” margin squeeze are the ones that price the target production workload before committing to an architecture, not the ones that discover the real number from an invoice.