On December 11, 2024, OpenAI services experienced significant downtime due to an issue stemming from a new telemetry service deployment. This incident impacted API, ChatGPT, and Sora services, resulting in service disruptions that lasted for several hours. As a company that aims to provide accurate and efficient AI solutions, OpenAI has shared a detailed post-mortem report to transparently discuss what went wrong and how they plan to prevent similar occurrences in the future.

In this article, I will describe the technical aspects of the incident, break down the root causes, and explore key lessons that developers and organizations managing distributed systems can take away from this event.

Here's a snapshot of how the events unfolded on December 11, 2024:

Figure 1: OpenAI Incident Timeline - Service Degradation to Full Recovery.

The root of the incident lay in a new telemetry service deployed at 3:12 PM PST to improve the observability of Kubernetes control planes. This service inadvertently overwhelmed Kubernetes API servers across multiple clusters, leading to cascading failures.

The telemetry service was designed to collect detailed Kubernetes control plane metrics, but its configuration unintentionally triggered resource-intensive Kubernetes API operations across thousands of nodes simultaneously.

The Kubernetes control plane, responsible for cluster administration, became overwhelmed. While the data plane (handling user requests) remained partially functional, it depended on the control plane for DNS resolution. As cached DNS records expired, services relying on real-time DNS resolution began failing.

The deployment was tested in a staging environment, but the staging clusters did not mirror the scale of production clusters. As a result, the API server load issue went undetected during testing.

When the incident began, OpenAI engineers quickly identified the root cause but faced challenges implementing a fix because the overloaded Kubernetes control plane prevented access to the API servers. A multi-pronged approach was adopted:

These measures enabled engineers to regain access to the control planes and remove the problematic telemetry service, restoring service functionality.

This incident highlights the criticality of robust testing, monitoring, and fail-safe mechanisms in distributed systems. Here's what OpenAI learned (and implemented) from the outage:

All infrastructure changes will now follow phased rollouts with continuous monitoring. This ensures issues are detected early and mitigated before scaling to the entire fleet.

By simulating failures (e.g., disabling the control plane or rolling out bad changes), OpenAI will verify that their systems can recover automatically and detect issues before impacting customers.

A "break-glass" mechanism will ensure engineers can access Kubernetes API servers even under heavy load.

To reduce dependencies, OpenAI will decouple the Kubernetes data plane (handling workloads) from the control plane (responsible for orchestration), ensuring that critical services can continue running even during control plane outages.

New caching and rate-limiting strategies will improve cluster startup times, ensuring quicker recovery during failures.

Here's an example of implementing a phased rollout for Kubernetes using Helm and Prometheus for observability.

Prometheus query for monitoring API server load:

This query helps track response times for API server requests, ensuring early detection of load spikes.

This configuration intentionally kills an API server pod to verify system resilience.

This incident underscores the importance of designing resilient systems and adopting rigorous testing methodologies. Whether you manage distributed systems at scale or are implementing Kubernetes for your workloads, here are some takeaways:

While no system is immune to failures, incidents like this remind us of the value of transparency, swift remediation, and continuous learning. OpenAI's proactive approach to sharing this post-mortem provides a blueprint for other organizations to improve their operational practices and reliability.

By prioritizing robust phased rollouts, fault injection testing, and resilient system design, OpenAI is setting a strong example of how to handle and learn from large-scale outages.

For teams that manage distributed systems, this incident is a great case study of how to approach risk management and minimize downtime for core business processes.

Let's use this as an opportunity to build better, more resilient systems together.