Remarque
GitHub Copilot usage metrics are currently in public preview with data protection and subject to change.
The Copilot usage metrics dashboard, APIs, and export files all use the same underlying telemetry data, but they aggregate and present it differently. Understanding these differences helps you reconcile numbers across sources and trust your analysis when preparing internal reports.
Prerequisite
Copilot usage metrics depend on telemetry from users’ IDEs. If a developer has disabled telemetry in their IDE, their Copilot activity will not appear in the dashboard, API reports, or exported data.
If you notice missing users or unexpectedly low adoption numbers, verify IDE telemetry settings before troubleshooting other causes.
Metric alignment
The dashboard and APIs use shared definitions for key metrics:
| Concept | Dashboard metric | API or export field | Notes |
|---|---|---|---|
| Active users | Daily/weekly/total active users | user_initiated_interaction_count > 0 | A user is considered active if they interacted with Copilot in their IDE on that day. |
| Acceptance rate | Code completion acceptance rate | code_acceptance_activity_count ÷ code_generation_activity_count | Both sources calculate acceptance rate the same way, though rounding may differ. |
| Agent adoption | Agent adoption chart | totals_by_feature where feature = “agent” | Reflects users who interacted with the Agent Copilot. |
| Language usage | Language usage charts | totals_by_language_feature or totals_by_language_model | The dashboard visualizes these aggregated fields. |
For complete field descriptions, see Data available in Copilot usage metrics.
Discrepancies between reports
Small differences between dashboard data, API reports, and exports are expected. These variations are usually caused by differences in time windows, scope, or data freshness.
Time windows
Each data source aggregates data differently.
| Source | Time window | Aggregation method |
|---|---|---|
| Dashboard | 28-day rolling window | Metrics are aggregated continuously over the past 28 days to smooth fluctuations. |
| APIs | Daily | Each record represents a single day per user, enabling daily trend analysis. |
| NDJSON exports | Daily | Mirrors API output for BI tools and long-term reporting. |
Aligning your reporting period with the dashboard’s 28-day window ensures consistent comparisons.
Delayed telemetry
Because IDE telemetry is processed asynchronously, data for recent days may appear incomplete or missing. Data typically finalizes within three full UTC days. Apparent drops in recent daily metrics often resolve once telemetry is fully processed.
Export timing
NDJSON files reflect data available at the time of export. If a file is downloaded before new telemetry is processed, the data may lag behind the dashboard or API. Re-exporting the file after the three-day window provides the most accurate view.
Unknown values
The value Unknown appears in some API or export breakdowns when telemetry from the IDE client lacks sufficient detail to categorize the activity. This is expected behavior and does not indicate missing data.
| Breakdown | Explanation |
|---|---|
| Language | Shown as Unknown when the IDE cannot identify the programming language of the active file. |
| Feature | Appears when an older client sends a generic event without specifying a chat mode (for example, chat_panel_unknown_mode). |
| Model | Appears when the event lacks information identifying the model used. Some internal models (for example, gpt-4o-mini) may appear alongside Unknown when used for non-user-facing operations such as summarization or intent detection. |
Unknown values are excluded from dashboard visualizations but appear in API and NDJSON data for completeness. The amount of Unknown data decreases as users upgrade to newer IDE and extension versions that send richer telemetry.