Cases2 cases

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1. IL-0124 Capacity changes queued while a circuit breaker was engaged ran all at once when it was released, taking multiple OpenAI inference engines out of service and raising API error rates

   OpenAI2026

   Symptoms

   Between about 4:26 PM and 4:56 PM PST on 4 March 2026, some OpenAI API requests to several models failed or took longer than usual, and some Codex traffic was affected too. The overall API success rate dropped temporarily.

   High error rateHigh latency

   Trigger

   A protective circuit breaker that had paused some scheduling operations was released, and the infrastructure actions queued during the pause were executed at the same time.

   Root cause

   Software defect

   Component

   Automated capacity manager

   Contributing factor

   Automation amplificationChange with a delayed effectNo safeguard on destructive changeMonitoring blind spot

   Remediation

   • manually reassigned infrastructure resources to the affected models
   • brought the removed inference engines back online and reassigned them until error rates returned to normal

   Action items

   • Improve safeguards in the automated capacity management systems so they detect and respond to state drift
   • Improve observability of circuit breakers and scheduling systems so their status and effects are easier to identify
   • Add safeguards so queued actions cannot run in large batches after long pauses
   • Improve monitoring and alerting around capacity reconciliation problems

   Lessons

   Pausing one automated system does not stop the systems that feed it. Changes queued during the pause reflect the state at the time they were queued, and releasing the queue all at once applies those stale decisions together. The fixes OpenAI names are detecting state drift, making circuit breaker status visible, and preventing large batches from executing after a long pause.

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2. IL-0005 A new telemetry service overloaded the Kubernetes API servers of large clusters and broke DNS-based service discovery for all OpenAI services

   OpenAI2024

   Symptoms

   Alerts fired at 3:13 PM PST on 11 December 2024, and from 3:16 PM every OpenAI service was significantly degraded or completely unavailable, including ChatGPT, the API and Sora. The status page reported API calls returning errors and difficulties logging in to platform.openai.com and ChatGPT. Customer impact peaked at 3:40 PM.

   Service outageHigh error rateControl plane unresponsiveDNS resolution failures

   Trigger

   At 2:23 PM PST a change introducing a new telemetry service, which collects detailed Kubernetes control plane metrics, was merged and the deployment pipeline started. Between 2:51 PM and 3:20 PM the change was applied to all clusters.

   Root cause

   Configuration change

   Component

   Kubernetes API serverClient-side DNS resolver and cacheTelemetry agent

   Contributing factor

   Unrepresentative stagingNo staged rolloutCache-masked failureMonitoring blind spotLimited debug access

   Detection

   OpenAI's detection tools worked: alerts fired at 3:13 PM PST, a few minutes before customer impact began at 3:16 PM, and the root cause was identified at 3:16 PM.

   Remediation

   • Scaled clusters down to reduce the total Kubernetes API load
   • Blocked network access to the Kubernetes admin APIs so no new expensive requests arrived
   • Scaled up the Kubernetes API servers
   • Removed the telemetry service once enough control was back
   • Moved traffic from impacted clusters to healthy ones
   • Intervened manually in clusters where simultaneous resource downloads saturated resource limits

   Action items

   • Put every infrastructure configuration change through a phased rollout with continuous monitoring of both the service workloads and the clusters, including the Kubernetes control plane
   • Run fault injection tests in which the data plane runs without the control plane for longer, and tests that roll out bad changes on purpose to check detection and rollback
   • Build break-glass mechanisms so engineers can reach the Kubernetes API servers in any circumstances
   • Decouple the Kubernetes data plane from the control plane so that service discovery for critical workloads does not depend on the control plane
   • Speed up recovery with better caching and dynamic rate limiters for resources needed at cluster startup, and regular drills that rapidly replace an entire cluster

   Lessons

   Several systems and processes failed at once and interacted in unexpected ways: tests did not catch the change's effect on the Kubernetes control plane, DNS caching put a delay between the change and the failures, and being locked out of the control plane made remediation very slow. Because service discovery used Kubernetes DNS, the control plane was carrying load for product traffic even though the data plane was meant to be independent of it.

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