Cases2 cases

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1. IL-0248 Azure OpenAI inference requests slowed and failed in several regions as a retry storm overloaded the routing layer

   Microsoft Azure2026

   Symptoms

   From 09:39 to 17:05 UTC on 29 May 2026, customers sending inference requests to Azure OpenAI Service saw higher latency, intermittent failures, timeouts and HTTP 5XX errors. Automated monitoring detected falling request success rates at 09:39 UTC, and regions in Europe and Australia East, which processed the most affected traffic, were hit hardest.

   High latencyHigh error rateIntermittent timeoutsOOMKilled

   Trigger

   A change in an upstream API layer used by Microsoft 365 made some capacity-related failures surface as transient server failures that callers retry. It rolled out first in Australia East and later in Sweden Central.

   Root cause

   Faulty deployment

   Component

   Model request routing

   Contributing factor

   Retry stormExcessive blast radiusUnverified assumption

   Detection

   Automated monitoring detected a drop in request success rates at 09:39 UTC and started the incident investigation.

   Remediation

   • Isolated the internal workload that sent the amplified retries onto dedicated infrastructure
   • Rolled back the triggering change in the upstream service and reduced retry-driven traffic
   • Cleared the backlogged requests

   Action items

   • Move large first-party generative AI workloads onto dedicated routing infrastructure instead of the shared inference load balancing stamp
   • Improve diagnostic guidance, runbooks and alerting to tell secondary crash symptoms from the real source of overload
   • Work with upstream teams on retry policy design, dependency contracts and telemetry to catch retry amplification early
   • Add stronger overload prevention and throttling so no single internal workload can overwhelm shared components
   • Find and remove single points of failure in the inference routing layer

   Lessons

   Internal workloads were not protected the way external traffic was: their traffic skipped some rate limiting and overload controls, so the amplified retries reached shared infrastructure. The apparent recovery after disabling the suspected feature hid the real cause until the problem came back at larger scale in Sweden Central.

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2. IL-0552 CircleCI's archival database ran out of storage, slowing workflow and job starts and the display of workflows in the UI

   CircleCI2025

   Symptoms

   At about 23:00 UTC on 21 January 2025, an automated alert reported that the CircleCI database instance holding archived data was almost out of free storage space. From 23:50 UTC customers saw slow starts and cancellations of workflows and jobs, and delays and difficulty viewing workflows in the CircleCI UI, although pipelines for a specific project could still be viewed.

   High latencyIntermittent timeoutsLog floodNode disk fullCloud API rate limit exhausted

   Trigger

   A code change to a function in the archival database on 16 January 2025 unintentionally made it write an excessive number of log messages, and the database filled its storage.

   Root cause

   Faulty deployment

   Component

   DatabaseCI system

   Contributing factor

   Change with a delayed effectExcessive blast radiusOverlapping failures

   Detection

   An automated alert at about 23:00 UTC warned that the archival database was almost out of free storage, before customers were affected. The growing workflows queue was not linked to it at first because a separate alert about request volume fired at about the same time.

   Remediation

   • Scaled up the event consumer that processes the workflows queue at 23:44 UTC
   • Promoted a read replica of the archival database to a standalone primary at 23:55 UTC

   Action items

   • Fix the database function so it no longer writes an excessive number of log messages
   • Add a max duration to the workflows retry policy for archiving so it fails earlier than the default timeout
   • Move workflow archival to an event-based model that decouples it from the workflows service
   • Add forecast and anomaly monitoring for databases that alerts on unusual activity before it reaches critical levels

   Lessons

   The storage alert on the archival database did fire, but the database filled much faster than forecast, so there was little time to act before customers were affected. Because the workflows service called the archival service synchronously, a problem in the archival database slowed the whole workflows service.

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