When model costs were opaque and product usage was experimental, teams could treat AI spend as an infrastructure issue. That is getting harder. Context-heavy prompts, repeated tool calls, verification loops and long-running agent tasks now shape unit economics directly.
As a result, product leaders have to think about AI architecture and pricing at the same time. They must decide which workflows deserve a premium model, which can be routed to lower-cost systems and how much reliability overhead the customer will tolerate.