We all know that Checkly is a ‘secret weapon’ for engineering teams who want to shorten their mean time to detection (MTTD). With Checkly, you can know within minutes if your service is unavailable for users, or acting unexpectedly. In this article we’ll talk about how Checkly traces can help you expand on the benefits of Checkly, adding insights that will help you diagnose root causes, and further reduce your mean time to resolution (MTTR) for outages and other incidents.

The Kubernetes API server is a key component in the control plane. Every interaction, whether deploying applications, scaling workloads, or monitoring system health, depends on the API server. Consider the human body: We have the brain as the critical organ, and the nerves function as the control system. The Kubernetes API server is like the nerve center of cluster management.

Checkly is the industry’s best tool to monitor your production applications. With the power of playwright, developers can test the systems they’ve developed, and roll out those tests as production monitors running from multiple geographies on the Checkly system. And Checkly monitors thousands of API endpoints with complex validation, setup and cleanup scripts, and reliable alerting. So why are we expanding into TCP-based checks?

Checkly enables engineers to automate the monitoring of their production services. Using the automation framework Playwright, you can run an end-to-end test on a regular cadence to make sure every feature is working for your users. But once you’ve got your check set up, either with Playwright scripting, a Terraform template, or an OpenAPI spec, we come to the question of what frequency you should run these checks. Should you be checking every few minutes, or every hour?

It’s every ops team’s biggest anxiety: a monitoring system detects a failure, but the notification either isn’t delivered or isn’t noticed by the team. Now we have to wait for users to complain before our team knows about the problem. Checkly sends an alert every time the system detects a failure, but how can you be sure you’re getting those alerts, and that those alerts are going to the right people?

Imagine its Black Friday and your e-commerce platform suddenly stops processing payments. The culprit? A critical API connection to your payment processor has failed, and you had no idea until angry customers started flooding your support channels. By the time your team identifies and fixes the issue, you’ve already lost thousands in potential sales and damaged your brand reputation.