What makes AI spend different from cloud spend?#
The unit. Cloud FinOps meters instance-hours, which are provisioned in advance and consumed steadily. AI spend is metered in tokens, counted separately for what goes into the model and what comes out, with output priced higher than input. Consumption is driven by how the work is shaped rather than by how much capacity is running, so the same team can vary its bill by an order of magnitude without changing anything it has provisioned.
Everything loaded into a session is charged again on every turn it stays in context, including tool definitions and retrieved files. That makes context the first place to look, and it is one of the reasons context engineering is a cost discipline as well as a quality one.
What should you measure it against?#
Value, not volume. Tokens consumed, agent runs and pull requests are diagnostics: they tell you what the system did, not whether it was worth doing. The measure that connects spend to outcome is human attention per outcome, which counts the human effort one useful result consumes — reviewing, briefing, catching up and fixing — alongside the machine cost of producing it.
An organisation can cut its token bill and raise its total cost, if the cheaper output needs more review. Reading the two together is what stops that trade being made by accident.