Architecture: How it works!

The Copilot Usage Advanced Dashboard offers a comprehensive suite of features designed to revolutionize how organizations track and analyze their Copilot usage. At its core, the solution leverages Elasticsearch for persistent data storage, breaking free from the traditional 28-day limitation and enabling analysis across extended time periods, from months to years. The system intelligently structures data with Organization and Team fields, facilitating granular filtering and cross-organizational insights. Data integrity is maintained through unique hash key generation for each data point, ensuring accurate and up-to-date information through automatic updates. The Grafana-based visualization layer offers complete flexibility, allowing teams to customize dashboards according to their specific needs and stakeholder requirements. Proactive usage monitoring is enabled through Grafana’s built-in alerting system, which can notify administrators about potential issues such as prolonged user inactivity. Furthermore, the platform’s open architecture supports seamless integration with third-party systems, whether for exporting data to external visualization platforms or incorporating additional data sources for comprehensive analytics, making it a truly versatile solution for enterprise-scale Copilot management. Copilot Usage Advanced Dashboard  almost fully utilizes data from Copilot APIs, The APIs used are listed below representing Copilot usage in multi organizations and teams from different dimensions:

• List teams of an organization
• Get a summary of Copilot metrics for a team
• Get Copilot seat information and settings for an organization
• List all Copilot seat assignments for an organization

Data Collection

The system utilizes four key Copilot APIs to gather comprehensive usage data:

• • Organization team listing
  • Team usage summaries
  • Organizational seat information and settings
  • Seat assignment tracking

Key Features

1. Long-term Data Persistence

• • Historical data stored in Elasticsearch beyond the 28-day limit
  • Flexible time period selection for trend analysis
  • Granular organizational and team-level data filtering

2. Advanced Analytics

• • Language and editor usage breakdown
  • Team performance comparisons
  • Seat utilization metrics
  • Activation rate tracking
  • Custom alert configuration for inactive users

3. Integration Capabilities

• • Seamless integration with existing Grafana deployments
  • Export capabilities for third-party visualization tools
  • Flexible data source additions for comprehensive DevOps metrics

Two dashboards are designed, they both can exist at the same time in Grafana.

Copilot Metrics Viewer compatible dashboard

If you are familiar with the copilot-metrics-viewer project, then please try this dashboard and use it in subsequent deployments.

• • link: https://softrin.com/d/be7hpbvvhst8gc/copilot-usage-advanced-dashboard
  • username：demouser
  • password：demouser

New designed dashboard

• • Link: https://softrin.com/d/a98455d6-b401-4a53-80ad-7af9f97be6f4/copilot-usage-advanced-dashboard-original
  • username：demouser
  • password：demouser

Supports four filtering varibales, namely

• Organization
• Team
• Language
• Editor

The choice of variables is dynamically associated with the data display

• You can view the distribution of seats, Enterprise or Business? and overall activation trends. And for users who don’t use Copilot, they are ranked based on the length of inactivity and list users who have never activated.
• Ranking Language and Teams based on usage

You can analyze the total number of recommendations and adoption rate trends based on Count Lines and Chats

You can analyze the effect of Copilot in different languages ​​and different editor combinations.

How these metrics translate into ROI insights

Let me provide two concrete example scenarios that illustrate how these metrics can translate into ROI insights:

Scenario 1: Enterprise Software Company

• Initial situation: 200 developers at $100,000 annual salary each
• Observed metrics:
  • 20% code acceptance rate
  • More conservative time savings estimate: 0.4 hours per day
  • Assuming 78% active usage remains constant

ROI calculation:

• Time savings: 0.4 hours × 200 developers = 80 hours daily
• Developer hourly cost: $100,000 ÷ 2080 working hours = ~$48/hour
• Daily productivity gain: 80 hours × $48 = $3,840
• Annual productivity gain: $998,400 (260 working days)
• Copilot cost: $19 × 200 developers × 12 months = $45,600 annually
• Net ROI: 2,089% ($998,400 gain – $45,600 cost = $952,800 benefit)

Scenario 2: Early-Stage Startup

• Initial situation: 12-person development team
• Observed metrics:
  • 20% code acceptance rate
  • 90% active user rate
  • Adjusted time savings: 15% faster completion

ROI calculation:

• Original timeline: 6 months to launch
• With Copilot: 5.1 months to launch (0.9 months saved)
• Monthly burn rate: $100,000
• Cost savings from faster launch: $90,000
• Copilot cost: $19 × 12 developers × 12 months = $2,736 annually
• Net ROI: 3,190% ($90,000 savings – $2,736 cost = $87,264 benefit)

Summary

Monitoring and visualizing GitHub Copilot metrics through ES + Grafana provides engineering leaders with real-time insights into their AI tooling investments. By tracking key metrics like code acceptance rates, active user engagement, and adoption status across teams, organizations can accurately measure the tangible impact on developer productivity and calculate precise ROI figures. Grafana’s powerful visualization capabilities transform complex usage data into clear, actionable dashboards that help justify AI investments, identify adoption patterns, and optimize tool utilization. This transparent, data-driven approach not only validates the business value of Copilot but also helps teams fine-tune their AI assistance strategy based on concrete metrics rather than anecdotal evidence. For engineering organizations looking to quantify the benefits of AI-powered development, this monitoring solution offers an essential bridge between raw metrics and meaningful business outcomes.