Cases4 cases

1 to 4 of 4

1. IL-0122 An invalid value in a configuration shared by several ChatGPT services put pods into crash loops and raised error rates

   OpenAI2025

   Symptoms

   From 7:43 PM to 8:38 PM PDT on July 15, 2025, a small percentage of ChatGPT users saw more errors in their conversations.

   Container crash loopHigh error rate

   Trigger

   An internal engineer applied a configuration change that contained an invalid value.

   Root cause

   Configuration change

   Component

   –

   Contributing factor

   Excessive blast radiusNo staged rollout

   Remediation

   • reverted the configuration to the correct value
   • started a rolling restart of the affected pods to force them to resynchronise

   Action items

   • Roll out future critical configuration changes for the service to a single cluster first, with automated health guards that stop the rollout if availability drops
   • Move to a service-specific configuration value instead of one shared between services
   • Improve deployment safeguards

   View case
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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3. IL-0118 A global change to Kubernetes namespace labels made the networking layer recompute metadata and overwhelmed the control planes of three of OpenAI's largest GPU clusters

   OpenAI2024

   Symptoms

   From 10:15 am PT on 25 November 2024 a large share of both API and ChatGPT traffic failed with timeouts and 503 errors.

   High error rateHigh latencyControl plane unresponsive

   Trigger

   A global change to Kubernetes namespace labels caused the networking layer to recompute metadata. The change had rolled out to the small clusters without problems.

   Root cause

   Configuration change

   Component

   Kubernetes API server

   Contributing factor

   Excessive blast radiusLimited debug access

   Remediation

   • moved workloads out of the impacted clusters by hand
   • with the cloud provider, scaled up the number of control plane nodes while scaling down the number of data plane nodes, which let the control planes recover

   Action items

   • Lock namespace changes so this incident cannot recur
   • Freeze certain types of infrastructure deploys
   • Allowlist identities to prevent a recurrence of this class of incidents
   • Improve the GPU load balancing logic so traffic is re-routed automatically and more effectively when possible
   • Improve change management for fleet-wide infrastructure changes

   View case
4. IL-0116 A CNI configuration change incompatible with a hardware variant that exists only in production cut network connectivity in an OpenAI GPU cluster and took text-davinci-003 fully down

   OpenAI2023

   Symptoms

   At 4:53 pm Pacific on 25 January 2023 one of OpenAI's GPU clusters had a major outage, and the text-davinci-003 model went fully down.

   Service outageThroughput degradation

   Trigger

   A configuration change to the CNI (Container Network Interface) plugins, which provide network connectivity to containers, was deployed to a GPU cluster that contained a hardware variant the staging environment did not have.

   Root cause

   Configuration change

   Component

   CNI plugin

   Contributing factor

   Unrepresentative stagingMonitoring blind spot

   Detection

   Engineers immediately identified the problem as network connectivity, but it took nearly an hour to identify CNI as the cause.

   Remediation

   • put a fix for the CNI configuration change in place, which restored 80% of traffic immediately
   • deployed models back onto GPUs over about three more hours until capacity was fully stable

   Action items

   • Make staging environments contain every type of hardware configuration so they better represent production
   • Monitor and alert on the health of the CNI system directly
   • Fix the network and storage bottlenecks that limit how quickly many replicas of a model can be deployed

   Lessons

   A change that had been deployed to other clusters over 24 hours and deemed safe still broke a cluster with different hardware, so staging needs every hardware configuration that production has.

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