{% data variables.product.prodname_copilot_short %} usage metrics help key stakeholders and decision-makers understand how their teams are adopting and using {% data variables.product.prodname_copilot_short %}. By tracking usage patterns across the enterprise, you can measure engagement, identify opportunities to increase value, and assess how AI-assisted workflows influence pull request throughput and time to merge.

Metrics are available through:

• The {% data variables.product.prodname_copilot_short %} usage metrics APIs, which provide detailed, exportable data at the enterprise, organization, and user levels.
• The {% data variables.product.prodname_copilot_short %} usage metrics dashboard, which visualizes 28-day usage trends across your enterprise and organizations.
• The code generation dashboard, which breaks down how code is being generated by users and agents across your enterprise and organizations.
• The {% data variables.product.prodname_copilot_short %} usage metrics NDJSON export, which offers raw data for custom BI tools or long-term storage.

{% data variables.product.prodname_copilot_short %} usage metrics are derived from telemetry across multiple {% data variables.product.prodname_copilot_short %} surfaces, including IDE and {% data variables.copilot.copilot_cli_short %} activity. Because many metrics come from IDE telemetry, end users must have telemetry enabled in their IDE to be included in these metrics.

The data does not include activity from other {% data variables.product.prodname_copilot_short %} surfaces, such as:

• {% data variables.copilot.copilot_chat_short %} on {% data variables.product.prodname_dotcom_the_website %}
• {% data variables.product.prodname_mobile %}

License and seat management data are not included in {% data variables.product.prodname_copilot_short %} usage metrics reports. To view or manage license assignments, use the {% data variables.product.prodname_copilot_short %} user management API, which is the source of truth for license and seat information. See AUTOTITLE.

The following API resources expose {% data variables.product.prodname_copilot_short %}-related data, but they are not interchangeable and should not be compared directly. Each API resource is designed for a specific use case and data model, and differences in totals or coverage are expected. Use this table to understand which API resource best fits your reporting needs.

Organization-level {% data variables.product.prodname_copilot_short %} usage metrics are based on organization membership, not on where individual actions occur. To appear in an enterprise’s metrics, a user must have an active {% data variables.product.prodname_copilot_short %} seat assigned within that enterprise (in any organization that belongs to the enterprise). As a result, a single user’s usage may appear in multiple organization dashboards, while that same user is counted only once in the enterprise-level total. Organization-level analytics are intended for visibility into adoption and usage within an organization and are not designed to be directly compared to enterprise-level totals.

Organization-level {% data variables.product.prodname_copilot_short %} analytics are available starting December 12, 2025. This is the first date for which organization-level reports are provided.

Once a user has a seat in the enterprise, their usage is attributed to every organization they belong to, regardless of where the seat is assigned.

This means:

• If licenses are assigned in a dedicated “shell” organization for administrative purposes within the enterprise, users still appear in the metrics for all other organizations in the enterprise they belong to.
• If a user also has a {% data variables.product.prodname_copilot_short %} seat in a separate organization outside the enterprise, their activity is still included in the enterprise’s organization-level metrics as long as they have at least one seat within the enterprise.

In short: users must be licensed somewhere in the enterprise to appear in its metrics. Once they are, metrics reflect where they work (their organization membership), not which organization provides the {% data variables.product.prodname_copilot_short %} seat or where the activity originated.

To be included in the {% data variables.product.prodname_copilot_short %} usage metrics, end users must use one of the following IDEs and {% data variables.copilot.copilot_chat_short %} extension versions.

The data in the {% data variables.product.prodname_copilot_short %} usage metrics dashboard and API reports is updated on a regular schedule.

You can expect data to be available within two full days. This means that data for a given day is processed and made available within two full UTC days after that day closes.

{% data variables.product.prodname_copilot_short %} usage metrics can be grouped into a few main categories: Adoption, engagement, acceptance rate, Lines of Code (LoC), and pull request lifecycle metrics.

Adoption measures how many licensed developers are actively using {% data variables.product.prodname_copilot_short %}. For example, daily active users (DAU) tells you how many unique users interacted with {% data variables.product.prodname_copilot_short %} on a given day. Ideally, you'll see a consistent upward trend in these metrics during rollout.

Engagement measures describe how deeply developers use {% data variables.product.prodname_copilot_short %} once they’ve adopted it. Key engagement metrics show not only frequency of use but also breadth across features. For example, average chat requests per active user measures how often users open and interact with {% data variables.copilot.copilot_chat_short %}. You'd want to see regular and increasing chat use across languages and IDEs.

Acceptance rate measures how often developers accept {% data variables.product.prodname_copilot_short %}’s suggestions. This helps you understand whether suggestions are relevant and trusted. For example, a high inline suggestions acceptance rate indicates that suggestions are relevant and useful.

Lines of Code (LoC) metrics measure the number of lines {% data variables.product.prodname_copilot_short %} suggested, added, or deleted in the editor, providing a directional view of {% data variables.product.prodname_copilot_short %}’s tangible output. For example, "Lines added" shows how much code was actually accepted and inserted into the editor.

Pull request lifecycle metrics measure how {% data variables.product.prodname_copilot_short %} activity relates to pull request outcomes and delivery flow. These metrics include pull request creation and merge counts, median time to merge, and review suggestion activity. By comparing overall pull request activity with pull requests created by {% data variables.product.prodname_copilot_short %}, you can evaluate how AI-assisted workflows influence throughput and cycle time at the organization or enterprise level.

Pull request lifecycle metrics are available at both the organization and enterprise level. When comparing reports, keep the following in mind:

• Deduplication: Enterprise-level reports deduplicate users across organizations. Organization-level reports do not.
• Pull request-only data: Pull request lifecycle metrics may appear even if IDE usage metrics are absent, since pull request data is derived from repository activity.
• Attribution timing: If a repository or organization is transferred between owners, pull request creation, review, and merge events may be attributed to different entities depending on when each event occurred.

These metrics can be used together to answer key questions about your teams' usage of {% data variables.product.prodname_copilot_short %}.

Look for patterns across these signals rather than focusing on any single number. For example, a steady DAU paired with a rising acceptance rate indicates growing trust and value.

Now that you understand what each {% data variables.product.prodname_copilot_short %} metric measures and how to use them, you can explore the dashboards to see these metrics in action.

• To view adoption and usage trends across your enterprise and organizations, see AUTOTITLE.
• To analyze how code is being generated by users and agents, see AUTOTITLE.
• To access {% data variables.product.prodname_copilot_short %} usage metrics programmatically, see AUTOTITLE.