How IPM helped a top tech brand catch an OpenAI outage before it became a crisis

Today's digital businesses are more interconnected than ever. Industry research shows that 74% of organizations now take an "API-first" approach, and the average application is powered by between 26 and 50 APIs. While this accelerates innovation, it also introduces new risks: when an external provider fails, the impact can be immediate and far-reaching.

One global enterprise experienced this firsthand when OpenAI, one of their critical third-party providers, began to falter. The company depended on OpenAI to power key AI-driven features, so even a short disruption risked cascading performance issues across their platform. Here's how they detected the issue early, avoided launching a war room, and rapidly confirmed that the problem didn't originate with their own systems.

Early detection through synthetic monitoring

During the early morning hours of May 31, 2025, synthetic tests from Catchpoint alerted a global leader in consumer technology to timeouts and degraded performance when accessing OpenAI's API.

• First failure observed: 4:30 AM PDT

• Initial locations impacted: Washington, DC and Boston, MA

• Symptoms detected: API timeouts, erratic performance, increasing test failures

Using real-time observability data, the customer's team immediately opened a support ticket with OpenAI's premium support staff.

The performance timeline

Scatterplot of OpenAI API failures observed across multiple locations

This scatterplot shows the average test time (in milliseconds) for API requests to OpenAI over the course of the day. Each point represents a synthetic test result, with higher values indicating increased latency or slower response times. Notice the clusters of elevated points and outliers. These correspond to periods of significant performance degradation and intermittent outages.

Independent monitoring makes the difference

While OpenAI's staff acknowledged the ticket within five minutes, it took over 40 minutes to verify the degradation and begin active troubleshooting. Meanwhile, Catchpoint continued to detect intermittent failures across multiple locations and providers in the U.S. for several hours:

• Ongoing failures observed: 4:30 AM PDT to 2:00 PM PDT

• Expanded geographic impact: Additional U.S. locations beyond initial sites

Independent testing was critical for early detection and for quickly confirming that the source of the problem was external. This enabled the customer's team to avoid wasting resources troubleshooting internally and focus on monitoring while the third-party provider worked toward resolution. Relying solely on vendor status pages or internal logs can leave organizations without visibility into emerging issues.

The business risks of API outages

OpenAI's powerful generative capabilities fuel many AI-powered applications, including ones that rely on real-time API calls to generate personalized content. API outages can stem from a variety of sources: sudden traffic spikes, infrastructure failures, or code deployments gone wrong. Unlike traditional downtime, these incidents often present as intermittent failures or regional slowdowns, making them harder to detect with basic uptime checks.

When third-party dependencies falter, customer-facing features become inaccessible, directly impacting user experience and revenue. Many Internet disruptions stem from failures of third-party APIs, which now serve as the backbone of digital operations across everything from e-commerce to AI-powered personalization.

Internet Performance Monitoring relies on active testing from multiple locations to simulate user interactions and continuously track API health. It's uniquely effective at catching early warning signs of degradation or failure. In this case, the platform gave the customer confidence to monitor the situation closely while OpenAI worked on resolution, avoiding unnecessary war room escalation.

Full service restored, and AI creativity resumes

Once service was fully restored, users could happily return to what matters most: generating Studio Ghibli-style portraits of themselves and their pets.

Thanks to early detection and continuous monitoring, what could have been a weekend crisis became a brief interruption.

Proactive IPM is mission-critical for digital enterprises

Incidents like OpenAI's outage underscore why Internet Performance Monitoring matters for digital enterprises. As modern platforms depend on dozens of third-party APIs, real-time, distributed observability is essential.

LM Internet Performance Monitoring enables:

• Immediate detection of API performance degradation

• Faster MTTR through independent verification

• Reduced business impact from third-party outages

• End-to-end observability across global user locations

These capabilities matter because Autonomous IT depends on visibility into the full delivery path, not just the infrastructure you control. Most monitoring platforms stop at the edge of the internal environment. But the Internet sits between your systems and your users, and it includes BGP routes, CDNs, DNS resolution, ISP peering, and third-party APIs that your team can't instrument directly. Without telemetry from that layer, any AI reasoning about root cause has a significant blind spot.

In the OpenAI outage scenario, for example, the difference between "our application is failing" and "an upstream provider is degrading" is the difference between mobilizing an engineering team and waiting for a third party to recover. That distinction requires data from the Internet path itself.

When Internet performance telemetry feeds into the same platform as infrastructure and application data, it changes how teams investigate. Edwin AI already analyzes signals across hybrid environments using a context graph that maps dependencies and prioritizes by business impact.

Adding Internet performance data to that graph means Edwin AI can correlate an internal service degradation with an upstream BGP shift or a CDN latency spike, and surface the connection in seconds rather than leaving teams to piece it together manually. Root cause identification moves from manual triage to contextual correlation across logs, metrics, topology, and Internet telemetry. Early warnings can surface Internet degradation before it cascades into customer-facing impact.

This is how observability becomes operational. Catchpoint and Edwin AI share a single telemetry pipeline and context graph, so the Internet performance layer isn't a separate feed that someone has to manually cross-reference. It's built into the same system that's already tracking infrastructure health, application performance, and service dependencies. For teams managing complex, multi-provider environments, that means faster answers to the question that matters most during an incident: is this something we can fix, or something we need to route around?

The future of API observability

Looking ahead, API ecosystems will only grow more complex. Enterprises need monitoring solutions that go beyond traditional metrics, integrating AI-driven anomaly detection, automated remediation, and unified visibility across both application and network layers. LogicMonitor is building toward this future with Autonomous IT, connecting observability, intelligence, and governed action in one system so teams can anticipate issues, reduce manual effort, and protect business performance as their API dependencies scale.