Put a number on your AI spend that finance can actually defend

Defensible AI spending requires costs that can be traced to the work causing them, explained when they change, and forecast with reasonable confidence. The approach breaks spending down across organization, team, workflow, model or provider, and individual run levels, with workflow-level unit costs supporting forecasts based on expected usage volumes and run-level data helping engineers investigate unusually expensive tasks. Forecasts should be presented as ranges because individual agent runs can vary due to retries, expanding context, codebase access, or task complexity. Upsun Dispatch records estimated token costs and agent time by run, workflow, and model, while team-level tracking can be achieved through separate organizations sharing a billing profile; however, token estimates should be reconciled with provider invoices, and only total agent-time credit is capped rather than individual team or workflow budgets.

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