Operations | Monitoring | ITSM | DevOps | Cloud

Observability pricing often seems reasonable at the outset, but many organizations discover their real complexity only as environments scale and usage patterns change. As environments grow more complex and hybrid by default, many organizations struggle with rising costs, fragmented tools, and pricing models that complicate cost predictability and long-term planning.

The dust has settled on Las Vegas. We saw transparent TVs, cars that drive sideways, and enough “AI-powered” toothbrushes to confuse a dentist. CES is incredible at selling the dream of hardware. The demos are slick, the lighting is perfect, and everything works on the showroom floor. But as engineers, we know the dirty secret of CES: The hardware is the easy part.

Security data collection has become a first-class architectural concern for modern SOCs. Once collection is treated as a dedicated layer, separate from analytics and detection, the next question becomes practical: how should telemetry be collected in a way that aligns with this architecture? In the previous article, we examined why this shift occurred. Here, we focus on how different collection models (agent-based, agentless, and hybrid) fit into modern security data collection architectures.

As Kubernetes platforms scale, one part of the system consistently resists standardization and predictability: networking. While compute and storage have largely matured into predictable, operationally stable subsystems, networking remains a primary source of complexity and operational risk This complexity is not the result of missing features or immature technology.

January 15, 2026 – Canonical, the publisher of Ubuntu and provider of open source security, support, and services, announced today that it is a launch partner for the AWS European Sovereign Cloud, a new independent cloud for Europe, with Ubuntu and Ubuntu Pro now available. Canonical’s Ubuntu Pro delivers a securely designed, stable, and enterprise-ready foundation for open source innovation while providing customers with the same security, availability, and performance they expect from AWS.

Let’s start with a question. What is DevOps all about? I’ll tell you my answer. In retrospect, I think the entire DevOps movement was a mighty, twenty year battle to achieve one thing: a single feedback loop connecting devs with prod. On those grounds, it failed. Not because software engineers weren’t good at their jobs, or didn’t care enough. It failed because the technology wasn’t good enough.

Some releases break new ground. Others clear the path. GitKraken Desktop 11.8 does both. You know that moment when you’re three commits deep into an interactive rebase and realize you’ve made a terrible mistake? Or when you’re trying to explain what changed on a feature branch, but it means manually selecting 47 commits? Or when you just want to preview a README without opening another app?

The AI sidekick called Dossinth I'm building is starting to take shape. So far, it became a moderately useful CRM, with a nice dashboard, and event log, daily briefing (which I am also receiving by email), account management and knowledge base. It's less AI and more CRM. For now. Here's a quick tour...

Setting up on-call is simpler than it seems. It comes down to a few clear decisions about your team and what your service actually needs. This guide walks you through those decisions. You’ll learn who to add in your rotation, how long shifts should last, when to hand off, and what coverage makes sense for your service. By the end, you’ll know exactly how to set up your first schedule and move from ad-hoc firefighting to organized incident response.

TLDR: Runtime Context is live, execution-level access to a running production system. It lets engineers and AI agents ask precise questions of running code and get answers immediately, without redeploying or interrupting users. This is the new baseline for reliability.