GitHub Outage Hits Users as Platform Reports High Error Rates

GitHub experienced widespread service disruptions Monday morning, with thousands of users reporting problems as the developer platform acknowledged elevated error rates across key services. The company said it was investigating the issue and working to identify the underlying cause.

In an update posted at 12:16 p.m. EDT, GitHub said web traffic was experiencing an error rate of about 20%, while archive and content downloads were seeing errors at roughly 50%. The company said it was continuing efforts to determine the root cause and implement measures to reduce the impact.

GitHub initially acknowledged the disruption at 9:40 a.m. EDT, saying it was investigating reports of performance problems. At the time, the company said several functions across the platform were experiencing degraded performance.

User reports on Downdetector surged shortly after 9:30 a.m. EDT, reaching thousands of complaints. The number of reported problems declined as the morning progressed, although roughly 1,000 reports were still being recorded shortly after noon.

Other Microsoft-owned services also appeared to experience smaller disruptions during the same period. Hundreds of users reported issues with Microsoft Copilot on Downdetector, with reports increasing around 11:30 a.m. EDT. Microsoft Teams also generated hundreds of outage reports.

The disruption affects a platform that GitHub says serves about 180 million users and developers. The company has experienced rapid growth in recent months as AI-assisted software development has expanded.

GitHub Chief Technology Officer Vladimir Fedorov told employees earlier this summer that June had been the company’s strongest month on record. In an April blog post, Fedorov said GitHub had started planning in October to increase its capacity tenfold, but by February the company determined that it needed to expand capacity by 30 times instead.

Fedorov attributed the dramatic change in capacity requirements to a rapid transformation in how software is developed, particularly the growing use of AI-driven workflows.