When AI tool spend is reported only at the org level, high performers and idle licenses look the same. Engineering managers need per-developer views to run fair 1:1s, justify headcount, and decide who gets early access to new models.
What to measure per developer
- Active days per month on each integrated tool
- Token or request volume relative to team median
- Model mix (economy vs. premium)
- Spend trend over rolling 90 days
Use analytics for coaching—not surveillance
Frame metrics as enablement: developers below the adoption floor may need training or workflow templates; power users may inform internal best practices. Avoid using raw spend alone in performance reviews—context matters.
Compare teams side by side
Platform and backend teams often have different usage profiles. Side-by-side dashboards reveal where playbooks transfer and where tool policies should diverge.
See it in ForgeMeter
ForgeMeter tracks adoption, identifies power users, and surfaces developers who need support— with team comparisons and repo-level context on Pro plans.