A water utility shares one Foundry deployment across departments and needs fair cost allocation. Which operational capability should they enable?

A water utility that shares one Foundry deployment across departments and needs fair cost allocation should enable token analytics per department consumer. Shared platforms without consumer-level metering produce mystery totals and blame cycles among billing, ops, and parks. Per-department token analytics make chargeback and FinOps conversations factual: each consumer’s usage is visible, so budgets and optimization pressure land where spend actually occurs.

Token quota limits per department consumer of the shared Foundry deployment fail by concept: they cap usage but do not measure actual token consumption, so they provide no per-department cost breakdown and cannot support fair allocation. Cost price estimation per department consumer of the shared Foundry deployment fails because estimates project hypothetical spend but do not meter actual token usage per department, so they cannot support fair cost allocation. Prompt token caching per department consumer of the shared Foundry deployment fails because caching reuses tokens to reduce consumption but does not measure or allocate actual token spend per department, so it cannot support fair cost allocation or chargeback.

Exam caveat: shared municipal Foundry cost fairness requires per-consumer token analytics—not token quota limits, cost price estimation, or prompt token caching.

Operational check: tag requests by department consumer, export token usage per tag, reconcile against the shared deployment invoice, and review chargeback reports with FinOps each billing cycle.