A rising AI bill is a signal to investigate, not proof that token demand is the cause. Compare usage, an organization-defined charge-per-unit measure, and GPU allocation before choosing a demand control, commercial review, or capacity adjustment.
Choose the accountable scope before calculating an AI unit metric. This troubleshooting pattern separates direct charges from unresolved shared spend so token counts are useful controls rather than unsupported product-cost conclusions.
Use token-level cost as an engineering signal, then add operational and outcome measures only when the organization has evidence and definitions to support them.