About Copilot usage metrics
Copilot usage metrics help administrators and decision-makers understand how teams are adopting Copilot. You can track engagement, identify opportunities for training, and explore how AI-assisted workflows relate to pull request throughput and time to merge.
Use the dashboards to explore trends across your enterprise and organizations:
- The usage metrics dashboard shows adoption and engagement trends.
- The code generation dashboard breaks down code generated by users and agents.
- The impact dashboard connects adoption depth with pull request output.
For custom reporting or long-term storage, use the usage metrics APIs and NDJSON exports. The APIs provide enterprise- and organization-scoped reports with repository- and user-level detail. See REST API endpoints for Copilot usage metrics.
What does the data measure?
Usage metrics help you answer different questions about adoption and development workflows.
| Category | What it measures | Example question |
|---|---|---|
| Adoption | How many licensed developers actively use Copilot. | Are teams using Copilot regularly? |
| Engagement | How frequently developers use Copilot and which features they use. | Are developers exploring agent workflows after training? |
| Acceptance rate | How often developers accept suggestions. Acceptance trends can suggest whether suggestions are useful. | Are developers accepting a larger share of inline suggestions? |
| Lines of code | Code suggested, added, or deleted in the editor. | How much code is being generated and inserted into files? |
| Pull request lifecycle | Pull request creation and merge counts, time to merge, and review activity. | How does adoption relate to pull request throughput and delivery flow? |
Look for patterns across these signals rather than treating any single number as a measure of productivity. Combine trends with developer feedback to understand what is working and where teams need support.
Understanding adoption cohorts
Active-user counts show whether people use Copilot, but not how they use it. Adoption cohorts in the impact dashboard group users by the features they engage with, from code completions and edits to agent-first and multi-agent workflows. This helps you see whether usage is broadening over time.
Cohorts describe the breadth of qualifying feature use, not simply the volume of activity. A frequent completions user can remain in the code-first phase while someone using multiple agent surfaces qualifies for a later phase.
The impact dashboard uses these cohorts to compare pull request output and time to merge. Its industry comparisons provide additional context from participating peers, and its potential return on investment estimates compare cost and output across adoption phases. These comparisons show associations, not proof that adoption caused a change in productivity. Team composition, project complexity, and experience can also affect the results.
Understanding coverage and limitations
Usage metrics combine telemetry from IDEs, Copilot CLI, and other supported surfaces. They aren't a complete record of all Copilot activity. Coverage varies by metric and report.
Detailed IDE breakdowns require supported clients with telemetry enabled. Server-side telemetry can still identify active users when client-side detail is missing.
Data for a given day is available within two full UTC days after that day closes. Organization-level metrics can overlap because the same user can belong to multiple organizations, so don't add organization totals to calculate an enterprise total.
Usage metrics measure activity, not license assignments. Use the Copilot user management API for seat and license information.
Analyze your metrics in more detail
When you need to explain or validate your findings, use the reference articles to check metric coverage and adoption phase definitions, reconcile dashboard and API reports, and interpret industry comparisons. See GitHub Copilot usage metrics.