Beyond API Uptime: Modern Metrics That Matter
Imagine if a popular food delivery app suddenly took 20 seconds to display restaurant menus. Would people continue using it? Probably not.
Users don’t have the time or the patience to distinguish between an app that crashes and an app that lags. Why should they when a competitor is always a click away? Slow is the new down, and businesses need to internalize this reality.
Catchpoint, a leading provider of Internet Performance Monitoring (IPM) solutions, found in its 2024 Internet Resilience Report that 43% of companies estimate losing over $1 million per month due to outages, slowdowns and other API-related issues. Preliminary results from the 2025 report, due to be released in mid-June, suggest that percentage is even higher today at 51%.
Uptime tells you nothing about how your product feels. APIs can be 100% available and still ruin the experience. I spoke with Leo Vasiliou, director of product marketing at Catchpoint, to explore how performance metrics uncover issues that uptime overlooks, how to build monitoring that reflects user experience and the role IPM plays in API monitoring.
Why Uptime Isn’t Solely Sufficient Anymore
IT teams have historically prioritized uptime monitoring over everything else, but times have changed.
A minuscule delay (measurable in API response times) in processing API requests can be as painful to a customer as a major outage. User behavior and expectations have evolved, and performance standards need to keep up.
Traditional API monitoring tools are stuck in a binary paradigm of up versus down, despite the fact that modern, cloud native applications live in complex, distributed ecosystems. This introduces performance bottlenecks and risks, including:
- Third-party dependencies: Integrations with third-party APIs add performance variables outside the scope of your control.
- Ephemeral services: Microservices and serverless functions are very dynamic, which makes monitoring APIs for performance difficult.
- End-to-end user journeys: Customer experiences now typically involve multiple API calls and API interactions, so a lag in any step can significantly impact the entire interaction.
As Vasiliou explained, identifying performance issues across distributed systems is one of the hardest parts of IT operations.
“An outright outage is easy to detect, but slowdowns in distributed, complex API fabrics are hard to identify without the right tools. These slowdowns can be microscopic — death by a thousand cuts — or macroscopic in nature,” he said.
This raises an important question: What exactly is the role of IPM in modern API monitoring?
The Case for IPM in API Monitoring
Organizations that move beyond uptime checks adopt IPM solutions for comprehensive monitoring of API availability and performance across geographies, networks and various stages of the API life cycle. IPM combines several strengths:
Proactive, Synthetic API Testing
Synthetic testing allows teams to simulate API traffic independent of real user behavior. These checks can help proactively identify performance issues before they escalate by continuously tracking key metrics — even when users aren’t on your site.
This proactive approach is particularly valuable for preemptive diagnostics, as it supports performance validation even during deployments and third-party outages.
Global Monitoring Agents
Measuring performance from multiple locations provides a more balanced and realistic view of user experience and can help uncover metrics you need to monitor, like location-specific latency: What’s fast in San Francisco might be slow in New York and terrible in London.
“It does no good to verify API performance in the cloud — your users and customers are not in the cloud.” — Leo Vasiliou
Diverse points of presence can make a huge difference in decentralized environments where localized issues can seriously impact user experience, so IPM ensures you’re always working with the correct data.
Rich Analytics and Percentiles
Advanced observability tools increasingly use percentile-based performance metrics, instead of relying on averages, which can mask severe performance issues.
Percentiles expose the latency outliers that averages hide. Filters such as API method and device type can help you identify root causes and make better data-driven decisions so you know exactly when and where to focus your precious optimization efforts.
Experience-Level Objectives (XLOs)
XLOs are a shift from traditional service metrics to service metrics based on user experience, and can help teams align performance monitoring with real-world expectations.
XLOs enable organizations to focus on key API metrics that truly capture the user experience, shifting the emphasis away from purely internal benchmarks.
By combining synthetic monitoring, real user monitoring and observability tools into a single platform, IPM monitoring tools capture essential metrics across API endpoints and response time. They then transform raw API monitoring data into actionable performance insights that improve system reliability and optimize application performance.
What Advanced API Monitoring Looks Like in Practice
The real value of IPM comes from how its core strengths, such as proactive synthetic testing, global monitoring agents, rich analytics with percentile-based metrics and experience-level objectives, interact and complement each other, Vasiliou told me.
“IPM can proactively monitor single API URIs [uniform resource identifiers] or full API multistep transactions, even when users are not on your site or app. Many other monitors can also do this. It is only when you combine this with measuring performance from multiple locations, granular analytics and experience-level objectives that the value of the whole is greater than the sum of its parts,” Vasiliou said.
What’s fast in San Francisco might be slow in New York and terrible in London.
“It does no good to verify API performance in the cloud — your users and customers are not in the cloud. In addition to global coverage, our agents are also across different connected devices to give maximum flexibility for use cases IT operations hasn’t even thought of yet,” he continued, pointing out that IPM also enables advanced analyses, such as identifying outlier long tails and adjusting what qualifies as an outlier.
These advantages lay the foundation for the key capabilities that power performance-driven API monitoring.
What It Takes To Monitor API Performance at Scale
Modern API monitoring requires a suite of advanced capabilities to proactively detect and resolve issues that can impact user experience and business outcomes. Here’s what that includes.
- User journey monitoring: Simulating the complete user journey can pinpoint slowdowns, helping teams understand where bottlenecks are occurring in the user experience.
- Percentile-based alerts to track key API metrics efficiently: Configuring alerts based on dynamic thresholds (boundaries for alerting in monitoring systems) leads to more efficient monitoring.
- API-as-code provisioning: This approach manages API configurations and monitoring as code stored in version control. This enables automated updates whenever the API code changes, maintains alignment in monitoring and makes performance easier to track across your entire CI/CD.
- Automated API diagnostics: Automated diagnostics break down each test into smaller components, uncovering the root causes of problems, as opposed to their symptoms.
- Comprehensive dashboards: Comprehensive, AI-simplified dashboards with user-friendly interfaces that provide key metrics, such as API health and real-time visibility into API performance across different dimensions, enable quick identification of performance issues.
These capabilities turn raw metrics and API performance stats into actual insights, leading to faster detection and helping with user experience optimization while also optimizing application performance.
With these key capabilities in place, API monitoring needs to be integrated across the entire CI/CD pipeline to maintain consistent performance.
How to Embed API Monitoring Across Your CI/CD Pipeline
Catching bugs in production environments is always substantially more expensive than catching them earlier in the development cycle.
A good way to catch them early is to integrate API testing throughout the CI/CD pipeline. Catchpoint has dubbed this approach “shift wide,” and it involves two key steps.
Step 1: Tracking API Performance Throughout the Development Life Cycle
To shift wide means to check API performance every step of the way, rather than only early in the development life cycle (shift left) or only after release (shift right).
The key is to use the same types of checks and measurements throughout the whole process. In that way, what you’re testing before launch matches what users will experience post-launch.
During development, teams can run quick performance checks on new code to catch any slowdowns. In staging, more detailed simulations of user journeys can reveal performance issues across the full experience.
Automatic tests can run after each release to make sure API usage and performance are still on track. This helps catch problems early, roll back updates if necessary and avoid slowdowns for users.
“You need consistent, universal performance measurements at every stage of CI/CD so you’re not measuring in feet and inches in preproduction but using the metric system in production. And you need that consistent, trustworthy data across the entire life cycle. Catching performance issues earlier means you can find and fix them when it’s cheaper to do so,” Vasiliou advised.
Keeping performance checks consistent across all environments and stages helps reduce the risk of errors, guaranteeing that what performs well in testing continues to perform reliably in production.
Nevertheless, you won’t get a full picture of how your APIs behave under real-world stress without applying chaos engineering principles. But what exactly does that mean, and how can it help with resilience and performance testing?
Step 2: Applying Chaos Engineering for API Resilience Testing
Chaos engineering employs a spectrum of deliberate, controlled experiments to test system resilience and expose vulnerabilities in distributed environments.
The goal of chaos engineering is to proactively identify weak points before they impact users or violate performance benchmarks. Rather than waiting for outages to occur, teams simulate real-world stress conditions and study how systems behave under pressure.
One of the most practical and effective chaos engineering techniques is latency fault injection, where artificial delays are introduced to API endpoints or API calls to simulate issues like network congestion, CPU usage spikes or increased memory usage. This allows teams to track how these issues affect critical performance metrics (response time, API usage and overall system reliability) without bringing down the service entirely.
This practice, which can also be described as resilience testing, extends beyond traditional chaos testing by focusing on degraded performance rather than complete failure.
For instance, by adding a consistent 100-millisecond delay to a payment API, a team can analyze how latency impacts user behavior, conversion rates and downstream services that depend on real-time responses. Adding such a delay can also identify application performance monitoring gaps and uncover hidden dependencies on third-party APIs.
Integrating these experiments into a broader API monitoring strategy helps generate detailed insights about how services respond under stress. These insights can be visualized through monitoring tools and dashboards that track key metrics like API health, throughput and error tracking. Over time, this builds a foundation of actionable API performance insights and informs future data validation and infrastructure monitoring efforts.
By embracing chaos engineering as part of a holistic API performance strategy, teams optimize for both uptime and optimal performance, building system resilience under unpredictable conditions while continuously delivering a high-quality user experience.
While this is a powerful tool for stress-testing systems, achieving reliable API performance requires more than just technology. It involves instilling the right culture across the organization.
What It Takes to Build a Culture of API Performance
Sustaining reliable API performance is not just about the technologies that underpin it. It’s also about people and processes within an organization.
Or rather, it’s about building a culture of API performance, which can be summarized in the following four points.
- Secure executive buy-in: The best way for technical teams to get support from leadership is to translate API metrics into tangible business outcomes. An executive might not care about the technicalities engineering and operations teams have to deal with on a daily basis, but they do care when a delay results in a drop in conversions.
- Define API performance goals: How do you keep everyone aligned? By using user-centric metrics and defining shared objectives, instead of establishing siloed targets. It’s better to broadly agree on which measurable, user-centric metrics need to be secured so that everyone involved in delivering and supporting your APIs is aligned.
- Celebrate success and learn from API incidents: When a team detects an issue before it can impact customers, that is a reason for celebration. When an incident occurs, that is a learning opportunity. Rewarding success and reframing failure as an opportunity to improve creates a culture of accountability and growth.
- Use dedicated API monitoring tools: The best API monitoring tools provide continuous feedback loops and act as the foundation of performance strategy. They don’t just raise alarms; they keep everyone informed and provide insights when issues arise.
Combining executive support, shared objectives and the right tools creates a culture where solid API performance is everyone’s responsibility, instead of being siloed or left to a single team.
Wrapping Up: A Slow API Might as Well Be Down
The tolerance for API slowdowns has all but disappeared. Performance issues undermine trust, diminish credibility and impact revenue almost as much as outages.
Maintaining high-performing APIs means shifting focus across the entire CI/CD pipeline, embracing proactive testing and latency injections, and building a culture where performance is everyone’s responsibility.
Traditional monitoring tools struggle to provide that level of visibility, but Catchpoint’s IPM monitoring solution does not. With its global agent network and advanced analytics, Catchpoint’s IPM solution delivers end-to-end visibility into API performance across all environments, enabling proactive detection and resolution of issues.
To see Catchpoint’s IPM monitoring solution in action, schedule a one-on-one demo.
