A program manager receives complaints that Copilot features feel slow while finance questions rising AI costs tied to model calls and advanced requests. The org needs to balance performance and spend across teams. Which evaluation best assesses token usage and premium request consumption?

The correct evaluation is right because it directly measures both cost and performance drivers together at the actionable level, where token trends show model-call volume that affects latency and efficiency and premium request counts show use of advanced metered capacity that explains rising AI spend, and disaggregating both by team lets the manager identify which teams drive consumption versus experience slowness and then guide targeted optimization such as prompt refinement, model routing, or policy controls. Reviewing organization-wide token and premium totals is sound for forecasting and budget control but fails the cross-team requirement because aggregate totals conceal which teams need intervention and cannot support differentiated balancing actions. Analyzing team-level tokens with latency metrics is sound for diagnosing slowness and friction but fails the spend requirement because without premium consumption the manager cannot answer finance or govern advanced-request costs. Tracking team-level premium use with active users is sound for license assignment and feature-cost management but fails the performance requirement because without token usage the manager cannot assess model-call load behind slow experiences. As an exam caveat, separate consumption from adoption and satisfaction signals, since user counts, acceptance rates, or latency alone describe reach or experience without quantifying tokens or premium units consumed. As a practical check, filter the usage dashboard by team and period, compare token volume side by side with premium requests, and confirm outliers against help-desk slowness reports and finance chargeback data before adjusting guidance.