Cases3 cases

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1. IL-0409 AssemblyAI's async and LLM Gateway APIs returned 5xx after its auth service could not get ENIs and scaled to zero

   AssemblyAI2026

   Symptoms

   On 29 January 2026 AssemblyAI's Async Transcription API and LLM Gateway on the NA API endpoints went down for 25 minutes, returning 500 errors, and then ran degraded for two hours. The status page first reported the investigation at 17:54 UTC.

   Service outageHigh error rateHigh latencyCloud API rate limit exhausted

   Trigger

   An influx of batch transcription jobs higher than usual made autoscaling add workers until spot instances ran out, on-demand instances were used, and the AWS Elastic Network Interface limit was reached.

   Root cause

   Capacity exhaustion

   Component

   Amazon ECS

   Contributing factor

   Excessive blast radiusMonitoring blind spotAutomation amplificationIgnored early signal

   Remediation

   • Worked through the backlog of requests until turnaround times were back to normal

   Action items

   • Add alerts on ENI utilisation
   • Run the authentication service on dedicated capacity, separate from batch workers
   • Fix the Lambda function that rate-limited the AWS API during recovery
   • Run everything in multiple regions

   Lessons

   AssemblyAI calls the outage preventable: ENI utilisation was not monitored, and because the authentication service shared resources with batch workers, a traffic spike in one starved the other. The warning signs were there but were not connected, and a tool built for recovery ended up blocking recovery.

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2. IL-0220 An overloaded AWS Transit Gateway dropped packets on 4 January 2021, and Slack's web tier saturated and went down

   Slack2021

   Symptoms

   During the Americas' morning of 4 January 2021, an external monitoring service paged Slack because error rates were creeping up. While the investigation was starting, Slack's own dashboarding and alerting service became unavailable, so responders had to work without their usual dashboards and alerts.

   High error rateMonitoring outagePacket lossService outageScale-up failureFile descriptor exhaustionCloud API rate limit exhausted

   Trigger

   On the first working day after the holidays, Slack's traffic went from the quietest time of the year to one of its biggest days overnight, with cold client caches pulling more data than usual, and one of Slack's AWS Transit Gateways did not scale fast enough for it.

   Root cause

   Cloud provider outage

   Component

   Transit gateway or network interconnectAutomated capacity manager

   Contributing factor

   Circular dependencyAutomation amplification

   Detection

   An external monitoring service paged Slack when error rates started creeping up during the Americas' morning. AWS engineers were separately alerted to the packet drops by AWS's internal monitoring.

   Remediation

   • Disabled automated downscaling of the web tier to keep serving capacity and allow investigation
   • Cleared up the broken, partly provisioned instances that filled the autoscaling groups
   • Repaired provision-service so new web tier instances could be provisioned and enter service
   • AWS engineers increased the Transit Gateway capacity manually across all Availability Zones

   Action items

   • AWS reviews its Transit Gateway scaling algorithms for large packet-per-second increases
   • Request a preemptive upscaling of the Transit Gateways at the end of the next holiday season
   • Run the dashboard service instances in the same VPC as their database to remove the Transit Gateway dependency
   • Load test provision-service regularly
   • Reevaluate health-checking and autoscaling configurations so they cannot overload provision-service again

   Lessons

   Monitoring has to stay as independent as possible of the infrastructure it watches, or it fails when it is needed most. Here the dashboard and alerting services failed because they ran in a different VPC from their databases and so depended on the Transit Gateways. provision-service had been load tested before, but this event exceeded the earlier scale-ups.

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3. IL-0333 A downsized RDS PostgreSQL database maxed out its CPU at US peak and failing ELB health checks took buildkite.com down

   Buildkite2016

   Symptoms

   On 22 August 2016 at 17:21 UTC buildkite.com went offline: nobody could log in, view build logs or read the documentation, although builds kept running and GitHub and Bitbucket pull request statuses kept updating. The Buildkite team in Melbourne was asleep and woke at 21:00 UTC, almost 4 hours later, to emails, tweets and Slack messages saying Buildkite was down.

   Service outageDatabase overloadProbe failuresCloud API rate limit exhausted

   Trigger

   Over a weekend maintenance window, Buildkite downgraded its m4.10xlarge Multi-AZ RDS PostgreSQL database to an r3.2xlarge Multi-AZ instance to cut its AWS bill, and moved to a centralised pgbouncer to pool connections. The first US weekday peak after the change pushed the new instance's CPU to its maximum at about 16:00 UTC.

   Root cause

   Capacity exhaustion

   Component

   DatabaseCloud load balancerLiveness and readiness probesNode launch template

   Contributing factor

   Unverified assumptionHealth check tied to a dependencyAutomation amplificationCircular dependencyPages not deliveredOverlapping failures

   Detection

   New Relic, Pingdom, Datadog, Calibre and CloudWatch raised PagerDuty alerts, but the on-call phone call never came and only silent push notifications arrived. The team noticed at 21:00 UTC when they woke to emails, tweets and Slack messages from users.

   Remediation

   • Moved servers from the Agent API to the dashboard ELB to bring buildkite.com back online
   • Switched the Auto Scaling Groups to EC2 health checks and locked in desired counts
   • Upgraded the database to an instance type that could handle the load, using the aws-cli
   • Built a new AMI that did not depend on buildkite.com/_secret/version and rolled out servers that could self-deploy
   • Removed the centralised pgbouncer and went back to pgbouncer on each host

   Action items

   • Plan load testing after significant infrastructure changes without affecting other users of the system
   • Make the ELB health check return OK when the app answers, without checking database connectivity
   • Consider a separate health check endpoint for Pingdom that checks the database and other dependencies
   • Store the latest deployed revision as a file on S3 for newly launched servers to self-deploy from
   • Test by hand that PagerDuty calls reach every on-call team member
   • Investigate regular company-wide tests of everyone's paging setup
   • Ask PagerDuty about calling members when they go on call or requiring an immediate phone rule

   Lessons

   Buildkite listed six lessons. Watch AWS credits, because billing reports show $0 while credits are spent, so the real cost is hidden and big changes end up rushed. Load test after significant infrastructure changes. Think about what a health check should do and what happens when every server behind an ELB fails it. Do not make new servers look up the deployed revision from the service they are replacing. Make sure every on-call member has the right PagerDuty and phone settings before going on call. When servers churn in AWS, stop health checks and autoscaling, for example with the Auto Scaling feature to suspend and resume processes, so the EC2 request limit is not used up.

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