Cases2 cases

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1. IL-0136 A deployment that cleared an internal service's caches at peak traffic sent 20 times the normal load to the Azure OpenAI metadata backend in Sweden Central, and health checks that depended on its database restarted instances in a loop for almost six hours

   Microsoft Azure2026

   Symptoms

   From 10:03 UTC on 29 September 2026, requests to Azure OpenAI Service, Foundry Agent Service, Foundry Models and Azure AI services in Sweden Central failed intermittently, took longer, or returned HTTP 500 and 503 errors. At 10:17 UTC service monitoring raised an alert on elevated failure rates for Azure OpenAI Service requests in the region.

   High error rateHigh latencyIntermittent connection failuresIntermittent timeoutsProbe failuresOutbound connection exhaustion

   Trigger

   At about 09:38 UTC a planned deployment of an internal service in Sweden Central cleared that service's caches during the region's peak traffic hours.

   Root cause

   Faulty deployment

   Component

   In-memory cacheApplication database

   Contributing factor

   Change during peak hoursRetry stormAutomation amplificationMonitoring blind spotNo runbookHealth check tied to a dependency

   Detection

   Service monitoring detected elevated failure rates for Azure OpenAI Service requests in the region at 10:17 UTC and alerted the engineering teams; engineers were engaged at 10:28 UTC.

   Remediation

   • reconfigured the backend service's automated health check so it no longer depended on remote connectivity, which stopped the restart loop

   Action items

   • Reconfigure the backend service's automated health check so it no longer depends on connectivity to its database layer
   • Add monitoring and alerting on the load and latency of the caching layer used by the backend service
   • Update troubleshooting and procedural guidance for connection exhaustion scenarios for on-call engineers
   • Add throttling and rate limiting to protect the backend service from sudden traffic increases (estimated October 2026)
   • Increase the resource capacity of the caching layer used by the backend service across regions (estimated October 2026)
   • Improve diagnostic metrics and automated detection to identify such issues more quickly (estimated October 2026)
   • Partition the caching layer used by the backend service to increase its throughput (estimated December 2026)

   Lessons

   A health check that depends on a remote dependency turns a dependency problem into a restart loop: instances that cannot reach the database are killed, the remaining ones take more load and retries, and adding instances or resources does not help until the check itself is changed. Clearing a cache during peak traffic can multiply load on the backend behind it, especially when cache-read defects let lookups bypass the cache. Connection exhaustion was hard to see because the existing network and diagnostic metrics did not show it.

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2. 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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