Operations | Monitoring | ITSM | DevOps | Cloud

Continuous profiling is becoming a standard part of the observability stack, and for good reason. It's the only signal that tells you why your code is slow or expensive, not just that it is. Metrics tell you CPU usage is high. Logs tell you a request was slow. Traces tell you which service is the bottleneck. But only a profile tells you which function, on which line, is burning the cycles. As systems grow more complex, that level of visibility becomes essential.

The ways you and your teams build and observe your systems are changing. It’s no longer just engineers looking at dashboards, or writing queries or config files. More often, it’s an agent interacting with the data, too, helping write code, run applications, investigate incidents, rightsize deployments, and more.

A modern web application is not a single thing. A single user request may touch a web server, a database, a cache layer, and several third-party APIs before a response comes back. And as AI tools generate more and more application traffic (API calls, background jobs, automated workflows), the volume and unpredictability of that traffic is growing. When something goes wrong, it could be any of it. When something is slow, it could be all of it at once.

A preview of leadership insights shaped by real-world experience Estate-wide clarity for leaders who still need technical depth As data estates grow, the role of technical leaders changes. Visibility becomes harder. Communication becomes more important. Decisions have broader consequences. Many leaders start their careers focused on the technical details.

Q1 2026 has been one of our most eventful quarters yet for VictoriaMetrics Cloud. We shipped something we have been building towards for a long time, crossed a few infrastructure milestones, and started clearing the path for what is coming next to the most performant observability stack.

The shift from legacy middleware to open-source innovation promises agility and cost savings, but introduces the 'Modernization Tax'—operational complexity that requires new approaches to observability, governance, and management across hybrid messaging environments.

Organisations are re-evaluating their VM infrastructure. The economics have shifted, the tooling has matured, and the case for running two separate platforms, one for containers, one for VMs, is getting harder to justify. Platform teams that spent years managing hypervisor infrastructure are being asked to consolidate, and most are landing on the same answer: Kubernetes. KubeVirt makes running VMs on Kubernetes possible.

Over the past few months, Puppet has partnered with Bryxx to host a series of leadership lunches across Europe, bringing together infrastructure, operations, and security leaders for candid, peer‑to‑peer conversations. These sessions weren’t marketing briefings. They were grounded discussions about what teams are facing right now: tighter regulation, rising security pressure, shifting cloud strategies, and the practical realities of automation and AI.

Enterprise boardrooms are not debating whether to adopt agentic AI anymore. The debate has moved to a harder question: why do so many agentic deployments stall between pilot and production? ServiceNow's Enterprise AI Maturity Index 2026 puts a number to it. Most enterprises that have invested in AI tooling report that their biggest obstacle is not model quality or compute cost. It is the infrastructure that those agents are expected to operate within. The models are capable.

Most of us run multiple virtual private servers (VPS) at a time. That’s why it’s important to keep an eye on the CPU usage and memory. However, since this step often slips our minds, there is room for automated monitoring. Open-source tools tend to be a default choice, and for a good reason. The problem is that they don't provide everything you need for monitoring in a single place. As a result, you may find yourself writing custom shell scripts for automation.