Operations | Monitoring | ITSM | DevOps | Cloud

You log into your network monitoring dashboard and see the alert: “High bandwidth usage detected.” This is not just a routine message; it’s a sign that something is putting pressure on your network. Bandwidth is the backbone of modern connectivity, and when usage spikes unexpectedly, the consequences can be severe. Applications slow down, cloud costs rise, and in some cases, spikes may point to a security threat.

Six years of Qovery, and the philosophy behind every pivot.

For many engineering teams, Slack is the nerve center of daily work. It’s where incidents are discussed, decisions are made, and collaboration happens in real time. But when it comes to error alerts, Slack can quickly turn from helpful to overwhelming with noisy, context-poor notifications that developers learn to ignore.

When I was invited to join one of our customer’s end-of-year team wrap-up sessions, it came as no surprise when the meeting opened with a familiar refrain: “Next year will be different. Next year, we’ll get ahead of the noise. Next year, tickets won’t pile up while we’re still triaging yesterday’s issues.

Average response time has become the default metric on many dashboards. It's easy to compute, easy to explain, and provides a single number to track over time. Of all the metrics available in application monitoring, this one feels closest to the actual user experience. But this simplicity can create a trap if you treat the average as a complete picture of system health. In fact, it’s really the starting point for a deeper investigation.

Choosing the right tool to manage your infrastructure can shape how fast your team moves and how reliable your systems become. Two names appear in almost every conversation: Ansible and Terraform. Both help you define, manage, and scale your environment. But they solve different problems and work in very different ways. One focuses on configuration. The other focuses on provisioning. Both are powerful. Both are widely used. And both can work together in the right stack.

As AI workloads shift from training to massive-scale inference, SRE teams are about to feel even more pressure. GPU-heavy computing is breaking the assumptions today’s clusters were built on, while enterprises are beginning to trust autonomous operations and cost pressure is pushing consolidation across the cloud-infrastructure stack.

Kubernetes v1.35 dropped a couple of weeks ago, and while the headlines focus on gang scheduling and in-place resizing going GA, there’s a bigger story here that every platform team needs to understand: Kubernetes is finally acknowledging that cluster utilization is fundamentally broken. At Komodor, we work with hundreds of organizations running Kubernetes at scale.

Just as NASA’s Artemis II mission represents humanity’s return to the Moon after more than 50 years, marking a pivotal moment in space exploration, Fabrix.ai has embarked on its own transformative journey in 2025. Artemis II—targeted for launch in February 2026 completed its crucial countdown demonstration test in December 2025, symbolizing humanity’s readiness to venture beyond Earth for deep space exploration and eventually return to the lunar surface.