Incident Library
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  1. 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.

    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
    Component
    Contributing factor
    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.

Check your cluster for this

This case involves Model request routing. kubegap audits a Kubernetes cluster for gaps like these, read-only, from Claude Code or Codex; its checks cite public reports like the ones in this library.

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