Cases2 cases

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1. IL-0237 A Kubernetes patch upgrade evicted every Ray Serve head and worker pod at once, and Lucidworks hosted LLMs went down

   Lucidworks2026sev1

   Symptoms

   From 16:18 UTC on 28 May 2026 the Lucidworks AI hosted LLMs in the us-southcarolina region were unavailable: queries to llama-3-8b-instruct, llama-3v2-3b-instruct and phi-4-multimodal-instruct returned 500 or 429 errors. Search, embedding models, the Lucidworks Platform UI and passthrough LLM calls kept working.

   Service outagePod evictionImage pull failures

   Trigger

   A routine Kubernetes patch upgrade on a cluster in us-southcarolina cycled its node pools one after another in quick succession.

   Root cause

   Faulty deployment

   Component

   Node poolModel inference server

   Contributing factor

   No staged rolloutOverlapping failures

   Remediation

   • Deleted the degraded replacement head node that could not pull container images
   • Waited for the LLM image pulls to finish on the replacement nodes
   • Launched new nodes with the required LLM images and redirected Lucidworks AI traffic to them

   Action items

   • Upgrade clusters that host LLM workloads in sequence, with each node pool fully healthy before the next one is upgraded
   • Investigate image pre-loading, such as pre-baked disks or image streaming, for large ML model images
   • Open a support ticket with the cloud provider about the abnormal Docker pull times
   • Coordinate Kubernetes maintenance windows with LLM service owners so they do not clash with ongoing deployments
   • Build tooling to force a Ray cluster failover to the blue or green state instead of waiting for Ray to settle it

   View case
2. IL-0235 Azure OpenAI GPT-5.2 returned HTTP 400 in seven regions after a configuration update the model engine did not support

   Microsoft Azure2026

   Symptoms

   From 23:20 UTC on 9 March 2026 Azure OpenAI Service customers received HTTP 400 and HTTP 429 error responses for the GPT-5.2 model in Australia East, Central US, East US 2, Korea Central, Norway East, Sweden Central and UK South. All other GPT models kept working.

   High error rateLoad imbalance

   Trigger

   An update to the Azure OpenAI GPT-5.2 model enabled feature settings meant to improve service efficiency and resilience. The model engine version running in production did not yet support those settings. The update was rolled out to the whole fleet in stages under Safe Deployment Practices.

   Root cause

   Configuration change

   Component

   Model inference serverModel request routing

   Contributing factor

   Unrepresentative stagingMonitoring blind spotOverlapping failures

   Detection

   Service monitoring detected the issue at 00:19 UTC on 10 March, about an hour after impact began. Detection was late because monitoring was set up for client-side HTTP 400 errors and not for service-side anomalies.

   Remediation

   • Rolled back the GPT-5.2 configuration in all affected regions instead of waiting for a hotfix
   • Completed the routing updates and restored full regional capacity information so traffic was spread normally again

   Action items

   • Train engineers on the operating procedures, including which scenarios can be rolled back quickly
   • Store a cached known-good version of the traffic routing details for when the capacity service cannot provide current data
   • Set thresholds for service-side HTTP 400 errors in monitoring
   • Improve anomaly detection on HTTP 4xx errors to alert on unusual error rates below the usual thresholds
   • Add signals to the deployment systems so problematic rollouts stop automatically
   • Make sure early rollout stages have enough backend model instances to catch such problems

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