Cases2 cases

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1. IL-0119 ChatGPT web service pods crash-looped on failing health checks after a Cosmos DB failure, leaving too few healthy pods to serve the web layer

   OpenAI2025

   Symptoms

   From 3:30am to 7:08am PT, chatgpt.com and a small number of OpenAI APIs returned elevated error rates. The status page classed the incident as a full outage.

   High error rateContainer crash loopProbe failures

   Trigger

   A failure of Cosmos DB in ChatGPT, at OpenAI's service provider.

   Root cause

   Cloud provider outage

   Component

   Application database

   Contributing factor

   Health check tied to a dependency

   Remediation

   • the service provider recovered the affected Cosmos DB databases at 4:25am PT
   • fixed the web server's health checking logic, which ended the pod crash loop

   Action items

   • Change the web systems to degrade more gracefully when they fail, including concurrency control and better load shedding
   • Make the database layer more resilient to service provider outages (described as already underway)

   Lessons

   The database came back at 4:25am PT, yet users saw errors until 7:08am PT: the web tier's own health checks kept removing pods after the dependency had recovered. The planned changes target the web tier's behaviour under failure, with concurrency control and load shedding, rather than the database alone.

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2. IL-0123 A response buffer allocated anew in a loop made OpenAI's routing layer nodes hit their memory limits and fail readiness checks on a morning with more completions than any day before, causing 502 and 503 errors across all models

   OpenAI2023

   Symptoms

   Between 5:42 AM and 7:16 AM PT on 8 November 2023 a large share of requests to OpenAI failed with 502 or 503 errors. Every model and API endpoint saw significant failures.

   High error rateService outageProbe failures

   Trigger

   A morning with dramatically more completions than any previous day pushed the routing layer nodes, which already had a chronic memory problem, past their memory limits.

   Root cause

   Software defect

   Component

   Model request routingContainer memory limit

   Contributing factor

   No alert on limitNo headroom

   Remediation

   • limited incoming traffic
   • redeployed the routing service en masse
   • ramped traffic back up slowly

   Action items

   • Pre-allocate the responseBuffer and reuse it instead of allocating a new one in a loop (3X improvement in memory and CPU usage)
   • Adjust the configured memory limits to an appropriate level so the service has significant headroom
   • Add rate limit controls to shed traffic more gracefully
   • Increase the service's capacity
   • Change alerting to catch the underlying memory behaviour before it escalates into service issues
   • Configure autoscaling for the service, which was not possible before because added capacity could hurt an upstream service

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