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For B2B SaaS companies, downtime isn’t just an inconvenience—it’s a direct threat to customer satisfaction and revenue. Unlike consumer applications, they serve a mix of power users pushing the system to its limits and new users expecting a seamless experience from day one.
Reliability isn’t just about keeping services online—it’s about ensuring every user interaction runs smoothly. A minor hiccup for one customer might be a major disruption for another.
Every second of degraded performance increases churn risk, damages trust, and leads to overwhelmed support teams.
B2B SaaS moves fast. This leads to many unplanned breakages. The key to reliability isn’t just detecting issues quickly but fixing them fast. Small glitches can quickly snowball into major outages, impacting thousands of customers and damaging trust.
That’s why reducing Mean Time to Resolution (MTTR) is critical. The faster teams diagnose and resolve incidents, the less impact they have on users and the business as a whole.
The Challenges of Monitoring in B2B SaaS
B2B SaaS companies face unique monitoring and observability challenges that can directly impact customer experience and business growth. Some of the most common issues include:
- Flaky monitors and inconsistent failures: Alerts trigger sporadically, making it difficult to determine if an issue is real or just noise.
- Delayed issue detection: In many cases, customers discover problems before engineering teams are even aware.
- Lack of visibility for support teams: Without the right tools, support teams escalate too many tickets to engineering, slowing down response times.
- Inefficient debugging workflows: Engineers spend too much time jumping between logs, dashboards, and tracing tools to identify the root cause of an issue.
- Monitoring gaps due to scaling infrastructure: As companies grow, new services often lack proper monitoring, leading to unexpected failures.
Going from Reactive to Proactive
Render, a cloud hosting provider, faced some of these challenges before adopting Checkly. Their engineers were spending a significant chunk of their time tracking down obscure issues. They needed a better way to monitor performance, catch failures early, and diagnose issues fast.
That’s where Checkly came in.
Catching Problems Before They Escalate
Reducing MTTR starts with minimizing Mean Time to Detect (MTTD). The faster teams detect an issue, the faster they can begin resolving it. If detection is delayed, resolution time extends, increasing the impact on customers.
One of the most effective ways to reduce MTTR is by detecting issues before customers even notice. Checkly’s synthetic monitoring continuously tests critical services by simulating user interactions.
API and browser checks run at regular intervals, catching failures before they escalate into full-blown outages.
API and browser checks run at regular intervals, catching failures before they escalate into full-blown outages.
API and browser checks run at regular intervals, catching failures before they escalate into full-blown outages.
API and browser checks run at regular intervals, catching failures before they escalate into full-blown outages.
By running these checks every few minutes, Render established a performance baseline. If anything deviated from normal behavior, their team is alerted immediately—often before customers are affected. This proactive approach significantly reduced MTTR by allowing them to fix problems before they spiraled out of control.
Checkly Traces: Pinpointing Root Causes Instantly
Detecting an issue is one thing. Diagnosing it is another. Traditional debugging often means sifting through logs, analyzing dashboards, and making educated guesses. This can stretch resolution times from hours to days.
Checkly Traces eliminates the guesswork. Using OpenTelemetry to collect traces from your application, the feature highlights exactly where failures occur.
Instead of manually reconstructing events, engineers can instantly see whether the issue is a slow database query, a misconfigured API, or a network bottleneck. They can compare failing requests against normal ones, quickly spotting discrepancies.
For Render, this was a game-changer. A DNS lookup issue that previously took days to diagnose was resolved in minutes using Checkly Traces. Instead of waiting for the issue to happen again, they had instant, actionable data.
Here’s an example of a Checkly monitor that provides traces:
Scaling Observability with Infrastructure as Code
Monitoring should scale as fast as infrastructure does. That’s why Checkly integrates seamlessly with Terraform, allowing companies like Render to automate monitoring setup. Every new API, service, or database added to their platform is monitored from day one.
With Terraform, Render ensures monitoring is standardized across environments. Synthetic checks and tracing are consistently applied to all services, eliminating blind spots. This automation further reduces MTTR by ensuring no issue goes undetected due to inconsistent monitoring setups.
Another way to integrate Checkly with your Infrastructure as Code setup is through our CLI.
Another way to integrate Checkly with your Infrastructure as Code setup is through our CLI.
Empowering Support Teams to Close Tickets Faster
One of the biggest bottlenecks in incident resolution is reliance on engineering for every investigation. Support teams often lack the tools to diagnose issues themselves, leading to unnecessary escalations. Every escalation adds friction, increases response times, and slows down resolutions.
With Checkly, support teams gain access to synthetic monitoring results and traces. This means they can identify whether a failure is on Render’s side or the customer’s, analyze request traces, and provide more informed responses. Instead of saying, “We’re investigating,” they can offer clear, data-backed answers.
This shift reduced escalations, improved response times, and freed up engineers to focus on high-priority development work.
The Impact of Checkly on MTTR
Since adopting Checkly, Render has transformed how they handle incident response. Some of the most impactful changes include:
- Debugging times have dropped from days to minutes.
- Misconfigurations, like incorrect firewall rules, are caught before reaching production.
- Support teams can resolve more issues on their own, reducing engineering escalations.
- Engineers spend less time switching between tools or adding temporary debugging code.
Brian from Render summed it up: “Checkly has changed how we handle incidents. We don’t waste time hunting for answers—we have them immediately.”
Brian from Render summed it up: “Checkly has changed how we handle incidents. We don’t waste time hunting for answers—we have them immediately.”
Faster Resolutions Lead to Stronger Reliability
Reducing MTTR isn’t just about fixing problems faster—it’s about creating a resilient, scalable system that prevents issues from disrupting business. Every minute saved in troubleshooting means less downtime, happier customers, and greater confidence in platform stability.
Checkly gives B2B SaaS companies the visibility, automation, and real-time insights they need to improve incident resolution. With synthetic monitoring, deep tracing capabilities, and seamless Terraform integration, teams can:
- Detect issues before they impact customers.
- Instantly identify the root cause of failures.
- Scale monitoring alongside their infrastructure.
If your team struggles with slow debugging and unreliable monitoring, Checkly can help you take control of your incident resolution process.