AI API Product Analytics: Measuring Usage, Quality, Cost, and Retention
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AI Product AnalyticsLLM APIUsage AnalyticsAI Metrics
AI features need product analytics, not just infrastructure logs. Teams need to know whether AI usage creates value.
Metrics to track
Track:
- feature adoption
- repeat usage
- user feedback
- regeneration rate
- edit rate
- cost per user
- cost per successful task
- retention impact
Connect cost to value
AI spend should be compared to activation, conversion, retention, or saved time.
Model mix
Track which models users actually trigger and whether premium usage maps to value.
Final thoughts
AI product analytics connects model usage to product outcomes. Without it, teams optimize cost without knowing value.