AI Optimization
AI Usage Monitoring
Know how AI is used across your organization.
Track AI usage by developer, team, model and workflow without reducing engineering analytics to a single vanity metric.
AI usage monitoring combines provider billing data, application telemetry and engineering context to show who uses AI, which models and workflows they use, what the usage costs and whether adoption produces useful outcomes.
What to monitor
Collect the smallest set of dimensions that supports real decisions. Identity, team, model, project and time are usually the foundation.
- Sessions, requests and active AI days
- Input, output and cached tokens
- Estimated and billed cost by model
- Usage by developer, team and repository
- Acceptance, rework and delivery signals
A privacy-aware rollout
Usage analytics should explain resource consumption, not inspect private reasoning or rank people without context.
- Document exactly which fields are collected
- Minimize prompt and source-code retention
- Apply role-based access to individual data
- Use team trends for management reporting
- Give developers access to their own data
FAQ
How do you track AI usage per developer?
Attach a stable user or cost-center identifier at the client, gateway or telemetry layer, then aggregate token and cost records using the same identity mapping as your engineering organization.
Is AI usage a productivity metric?
Usage alone is not productivity. It becomes useful only when reviewed with quality, delivery and business outcome measures.