If your dashboard says everything’s fine but your users are complaining about sluggish performance, you’re not alone. It’s time to explore a more complete approach to monitoring — one that goes beyond just checking if your APIs are “up.”
Picture a food delivery app that takes 20 seconds to show restaurant options. Would you stick around? Probably not.
From a user’s perspective, there’s little difference between an app that crashes and one that lags. And with alternatives just a tap away, performance becomes mission-critical. The old mindset — “up is enough” — doesn’t hold up anymore.
Catchpoint’s 2024 Internet Resilience Report revealed that 43% of organizations believe they lose over $1 million monthly due to API issues like outages and performance degradation. Early findings from the upcoming 2025 report push that figure to 51%.
High uptime doesn’t guarantee a smooth experience. APIs can be fully operational yet still create friction. I spoke with Leo Vasiliou, product marketing director at Catchpoint, about why that happens and how performance-centric monitoring provides the clarity that uptime metrics can’t.
Why Uptime Alone Doesn’t Cut It
IT teams have long treated uptime as the gold standard. But today’s software operates within sprawling, cloud-native environments that make performance far more complex to measure.
Even a slight delay in an API’s response time can frustrate users as much as a total failure. Here’s why conventional monitoring falls short:
- External dependencies: Many apps depend on third-party APIs, introducing unpredictability.
- Dynamic infrastructure: Microservices and serverless setups evolve constantly, complicating performance tracking.
- Complex user flows: Real interactions span multiple API calls. A bottleneck anywhere can tank the entire experience.
According to Vasiliou, pinpointing issues in these distributed systems is incredibly tough. “A total outage is obvious. Subtle slowdowns across interconnected APIs? Much harder to spot without specialized tools,” he said.
So what fills this gap? That’s where IPM — Internet Performance Monitoring — steps in.
Why IPM Is Critical for API Monitoring
IPM solutions bring a layered approach to monitoring — covering availability, speed, and consistency across the entire API lifecycle. They give teams a clearer view of real-world performance, using techniques like:
Synthetic Monitoring
Simulated traffic replicates real-world API usage, enabling proactive checks even when no users are online. This helps teams detect performance issues before they cause actual disruptions.
Global Perspective
IPM tools deploy agents around the world to track geographic variations. What performs well in one region might crawl in another — and if your users are global, your monitoring should be too.
As Vasiliou put it: “Testing in the cloud doesn’t reflect user reality. Your customers aren’t in the cloud — they’re on diverse networks, in different locations, using different devices.”
Deep Metrics and Percentiles
Relying on averages can obscure performance spikes. IPM platforms use percentiles to highlight outliers, helping teams zero in on the slowest interactions — not just the typical ones.
With filtering by device, API method, and other variables, these insights become actionable — letting teams optimize where it actually matters.
Experience-Level Objectives (XLOs)
XLOs shift the focus from internal benchmarks to metrics grounded in user perception. Instead of asking “Is this fast enough by our standards?” the question becomes “Is this fast enough for our users?”
Combining synthetic testing, real-user data, and observability, IPM tools convert raw API metrics into practical insights — improving system reliability and user experience alike.
Putting IPM into Action
The real value of IPM lies in how its components work together — synthetic checks, global agents, detailed analytics and experience-level objectives reinforcing each other.
“IPM allows you to monitor both simple API calls and complex transaction flows — even when no one is using your product,” Vasiliou explained. “What makes it powerful is when you pair that with location-based monitoring, percentile analytics and experience-based objectives. That’s when 1+1 becomes 3.”
He also emphasized that modern monitoring needs to span across devices, platforms, and infrastructure types — so operations teams have the flexibility to diagnose and respond to all kinds of issues.
Scaling API Monitoring for the Real World
To monitor API performance effectively at scale, you need more than just uptime checks. Here are some of the key capabilities that matter:
- Simulated user journeys to identify performance issues across multiple touchpoints.
- Alerts based on percentiles, not fixed thresholds, to catch outliers faster.
- API-as-code for configuration consistency and better alignment across CI/CD.
- Automated diagnostics to break down test results and pinpoint root causes.
- Unified dashboards for quick visibility into performance, errors, and health metrics.
Together, these capabilities enable a fast feedback loop that improves reliability and fine-tunes the user experience.
Integrating API Monitoring into CI/CD
The earlier you catch issues, the less costly they are. That’s why Catchpoint promotes a “shift wide” strategy — integrating monitoring across the full CI/CD pipeline, not just at the start or end.
Step 1: Monitor Performance Across the Lifecycle
Shifting wide means using consistent metrics during development, staging, and production. That consistency ensures that what you validate in testing matches what users experience in production.
By embedding checks at each stage, from early performance validation to post-deployment audits, you catch slowdowns before they impact customers — and avoid costly rollbacks later.
“Your monitoring shouldn’t switch measurement systems between environments,” Vasiliou noted. “Consistency across CI/CD helps find and fix issues early — when it’s cheaper.”
Step 2: Use Chaos Engineering for Realistic Testing
Chaos engineering introduces controlled faults to test system resilience. For API monitoring, this means simulating slowdowns — not just failures.
By injecting latency into API calls, teams can study how degraded performance affects conversion, user behavior and system reliability — without causing actual downtime.
For example, delaying a payment API by 100 milliseconds can reveal how sensitive your user experience is to lag. It can also expose blind spots in your monitoring stack and dependencies you didn’t realize were critical.
Over time, these experiments create a richer picture of API health — and help shape smarter performance strategies.
Fostering a Culture of Performance
Long-term reliability isn’t just about tools. It’s also about building the right mindset across the organization. That includes:
- Getting executive support by linking performance to revenue and business goals.
- Setting shared goals around user-centric metrics to align different teams.
- Celebrating wins and learning from failures, turning performance into a team-wide responsibility.
- Investing in the right tools that provide insights, not just alerts.
When everyone from engineering to leadership understands the impact of slow APIs, performance becomes a shared mission — not just a technical issue.
Final Thoughts: When Slow Feels Like Down
In a world of instant expectations, even a slight API lag can feel like a failure. Users are quick to judge, and slow experiences carry real costs — from lost trust to lost revenue.
Keeping APIs responsive and reliable means monitoring proactively, testing under stress and making performance a team-wide priority.
Traditional tools aren’t built for this complexity. Catchpoint’s IPM platform is. With its global infrastructure, advanced analytics and real-user simulation, it delivers the visibility teams need to identify, diagnose and fix issues before users ever notice them.
