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 established that the problem didn’t originate with their own systems.
During the early morning hours of May 31st, Catchpoint synthetic tests began alerting a global leader in consumer technology to timeouts and degraded performance when accessing OpenAI's API.
Using real-time observability data, our customer's team immediately opened a support ticket with OpenAI’s premium support staff.
This scatterplot shows the average test time (in milliseconds) for API requests to OpenAI over the course of May 31st. 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.
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:
Independent testing was crucial—not only for early detection, but also 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 blind to emerging issues.
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 even code deployments gone awry. 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, these customer-facing features become inaccessible, directly impacting user experience and revenue. According to Forrester research, the average retail company experiences 72 Internet disruptions per month. For 42% of the companies surveyed, those disruptions resulted in losses of over $500,000 in the month preceding the survey, adding up to over $6M annually.
Many of these disruptions are rooted in failures of third-party APIs, which now serve as the backbone of digital operations for everything from e-commerce to AI-powered personalization.
Internet Performance Monitoring (IPM), which leverages active testing from multiple locations to simulate user interactions and continuously track API health, is uniquely effective at catching early warning signs of degradation or failure. In this case, our IPM platform gave the customer confidence to monitor the situation closely while OpenAI worked on resolution, avoiding unnecessary war room escalation.
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 just a brief interruption.
Incidents like OpenAI’s outage highlight why IPM is now mission-critical for digital enterprises. As modern platforms rely on intricate webs of third-party APIs, real-time, distributed observability is essential.
Catchpoint IPM enables:
These capabilities empower organizations to respond swiftly, minimize disruption, and maintain trust—even in the face of complex, multi-provider incidents.
Looking ahead, the future of observability will demand even more from monitoring solutions. As API ecosystems grow more complex, enterprises must embrace tools that go beyond traditional metrics—integrating AI-driven anomaly detection, automated remediation, and unified visibility across both application and network layers. This evolution is essential to keep pace with rising user expectations and to effectively manage the growing operational and business risks that come with increasing reliance on APIs and third-party services.
Learn More
To dive deeper into API monitoring best practices and strategies for managing third-party dependencies, check out these resources:
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 established that the problem didn’t originate with their own systems.
During the early morning hours of May 31st, Catchpoint synthetic tests began alerting a global leader in consumer technology to timeouts and degraded performance when accessing OpenAI's API.
Using real-time observability data, our customer's team immediately opened a support ticket with OpenAI’s premium support staff.
This scatterplot shows the average test time (in milliseconds) for API requests to OpenAI over the course of May 31st. 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.
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:
Independent testing was crucial—not only for early detection, but also 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 blind to emerging issues.
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 even code deployments gone awry. 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, these customer-facing features become inaccessible, directly impacting user experience and revenue. According to Forrester research, the average retail company experiences 72 Internet disruptions per month. For 42% of the companies surveyed, those disruptions resulted in losses of over $500,000 in the month preceding the survey, adding up to over $6M annually.
Many of these disruptions are rooted in failures of third-party APIs, which now serve as the backbone of digital operations for everything from e-commerce to AI-powered personalization.
Internet Performance Monitoring (IPM), which leverages active testing from multiple locations to simulate user interactions and continuously track API health, is uniquely effective at catching early warning signs of degradation or failure. In this case, our IPM platform gave the customer confidence to monitor the situation closely while OpenAI worked on resolution, avoiding unnecessary war room escalation.
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 just a brief interruption.
Incidents like OpenAI’s outage highlight why IPM is now mission-critical for digital enterprises. As modern platforms rely on intricate webs of third-party APIs, real-time, distributed observability is essential.
Catchpoint IPM enables:
These capabilities empower organizations to respond swiftly, minimize disruption, and maintain trust—even in the face of complex, multi-provider incidents.
Looking ahead, the future of observability will demand even more from monitoring solutions. As API ecosystems grow more complex, enterprises must embrace tools that go beyond traditional metrics—integrating AI-driven anomaly detection, automated remediation, and unified visibility across both application and network layers. This evolution is essential to keep pace with rising user expectations and to effectively manage the growing operational and business risks that come with increasing reliance on APIs and third-party services.
Learn More
To dive deeper into API monitoring best practices and strategies for managing third-party dependencies, check out these resources:
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