Incident with Copilot Availability

Lesson: Availability issues can disrupt specific AI-powered developer tools within the broader platform ecosystem.

What happened

On June 17, 2026, between approximately 03:35 UTC and 04:44 UTC, GitHub Copilot was degraded and most of its frontier chat models were temporarily unavailable across all regions. During this window, affected models either disappeared from the model picker in the web, editor, and CLI experiences, or returned a "model not available" error when selected. Customers could continue using GitHub Copilot by selecting one of the models that remained available. The incident occurred during off-peak hours, which limited the number of customers affected. This was due to a configuration change that our production system deemed invalid. We mitigated the incident by reverting the configuration change, after which the affected models returned automatically as the service reloaded the previous configuration. We are working to roll out configuration changes gradually with stronger validations, alerts on sudden drops in the number of available models, and automatically rolls back configuration changes that produce these alerts.

1. Resolved · Jun 17, 04:44 UTC
   On June 17, 2026, between approximately 03:35 UTC and 04:44 UTC, GitHub Copilot was degraded and most of its frontier chat models were temporarily unavailable across all regions. During this window, affected models either disappeared from the model picker in the web, editor, and CLI experiences, or returned a "model not available" error when selected. Customers could continue using GitHub Copilot by selecting one of the models that remained available. The incident occurred during off-peak hours, which limited the number of customers affected. This was due to a configuration change that our production system deemed invalid. We mitigated the incident by reverting the configuration change, after which the affected models returned automatically as the service reloaded the previous configuration. We are working to roll out configuration changes gradually with stronger validations, alerts on sudden drops in the number of available models, and automatically rolls back configuration changes that produce these alerts.