AI Cost Governance Review (Retention)

AI Cost Governance Review (Retention)

1. 01

   Pull per-client token usage and spend from the gateway or provider dashboards

   Aggregate API costs across all active projects, separating production traffic from internal experiments, and compare against the retainer's AI budget line.

2. 02

   Flag any client whose monthly AI spend deviates more than 15% from forecast

   Investigate spikes caused by model upgrades, prompt regressions, or runaway agent loops before they erode margin on the delivery contract.

3. 03

   Review model selection against current pricing tiers and benchmark data

   Check whether cheaper or faster model variants, such as those compared in recent intelligence-versus-cost analyses, could handle the workload without quality loss.

4. 04

   Identify redundant calls and caching opportunities in the request path

   Look for repeated identical prompts or retrievals that a gateway layer with response caching could eliminate, cutting both latency and token spend.

5. 05

   Confirm rate limits and fallback routing are still aligned with client SLAs

   Ensure that a provider outage or throttling event triggers a failover to a secondary model rather than failing the client request.

6. 06

   Document cost per successful outcome, not just cost per token

   Translate raw API spend into delivery metrics, such as cost per content piece or per support ticket resolved, so the client sees value beyond the line item.

7. 07

   Present a quarterly cost optimization summary to the client with concrete next actions

   Share the findings in a format that shows where spend is going, what was optimized, and what the projected savings are for the next quarter.