Operations | Monitoring | ITSM | DevOps | Cloud

Datadog and New Relic didn’t become some of the best observability platforms today by accident. Unlike traditional monitoring tools, both are built from the ground up to be cloud-native. This design is crucial for tracking system health across hybrid cloud infrastructure, modern applications, and microservices/containerized architectures. Both platforms also offer more flexible pricing models than the traditional subscription-based pricing you’ll see elsewhere.

Logging is essential for understanding what’s happening inside your software. It helps developers and operators catch issues, monitor system health, and track application behavior. A big part of logging is log levels—these indicate how serious a message is, from routine updates to critical errors. In this post, we’ll break down everything you need to know about log levels, how they compare to Syslog log levels, and best practices for making the most of your logs.

Modern software systems are complex, with multiple services interacting across different environments. Understanding how they behave—tracking performance, identifying bottlenecks, and diagnosing failures—requires more than just collecting data. OpenTelemetry provides a standardized way to gather logs, metrics, and traces, but the real value comes from making that data easy to interpret through visualization.

Incidents happen. Whether it’s a service outage, degraded performance, or an unexpected spike in errors, things will go wrong. The question isn’t if incidents will occur—it’s how quickly and effectively you can respond when they do. For years, incident response has been a mostly manual process: someone gets paged, scrambles to investigate, loops in the right people, and after some firefighting, hopefully resolves the issue before too many customers notice.

Following all the hype and bluster with DeepSeek’s arrival in the AI landscape––and its ability to crash the poster child of AI’s share value overnight (Nvidia), we wanted to conduct a rigorous evaluation at Komodor. We tested DeepSeek’s models head-to-head against industry leaders in solving real-world Kubernetes challenges.

Unit testing is a software testing methodology that tests the behavior of individual functional units of code. Through unit testing, developers can verify that their code performs as intended. Providing an opportunity to catch bugs, validate the implementation of logic, and assess the quality of the code, unit testing enhances the quality of applications and preemptively identifies problems before they become major issues.

Kubernetes is the standard for orchestrating containerized microservices — but it can present some monitoring challenges. Luckily, we’ve already covered why monitoring Kubernetes is a must-do, the basics of how to do it, and the options you have for collecting monitoring data from a K8s environment.