Operations | Monitoring | ITSM | DevOps | Cloud

Deployments are one of the few moments where software development still feels risky. Teams may have tests, a staging environment, and careful review processes, yet the final step still carries uncertainty. Will this change behave the same way in production? Will it interact cleanly with existing data, traffic, and infrastructure? Will it introduce regressions no one anticipated? Preview environments exist to reduce that uncertainty.

Most IT teams don’t lack AI policies. They lack policies that survive a Git push. In many organizations, AI governance is a paper tiger. There are comprehensive documents outlining data usage, approved models, and risk management. On an auditor's desk, these policies look complete. But inside the workflow, the reality is different. AI tools are being embedded directly into IDEs, CI pipelines, and internal automation scripts.

AI agents are quickly shifting from experimentation into day-to-day operations. That shift is showing up in the data. McKinsey’s latest State of AI research highlights both broader AI use and the growing focus on “agentic AI,” even as many organizations still struggle to scale safely. For mid-market IT teams, agents can feel like the unlock: automate repetitive workflows, reduce backlog pressure, and deliver more output without expanding headcount.

Tags have long been the backbone of cloud cost visibility and governance. They help teams understand who owns what, where spend comes from, and how infrastructure maps back to the value the business delivers. However, AI workloads have altered that model, and exposed the limitations of traditional AI tags in the process. In fact, many of the most expensive AI operations don’t run on taggable cloud resources at all.

You may have heard that keeping an existing customer is five times cheaper than acquiring a new one. But that isn’t always true. “Hidden costs” often accompany customer retention, loyalty, and increasing “share of customer”. Could you be spending more on customer retention than on winning new customers? This quick guide will walk you through the meaning of Customer Retention Cost (CRC), why it’s important to calculate it, and how to calculate it.

Kubernetes has become the default foundation for a lot of modern application infrastructure. It’s powerful, flexible, and widely supported, which makes it an obvious starting point for many teams building a cloud-native application platform (a standardized way for teams to deploy, run, secure, and operate applications in production). But there’s a distinction that often gets lost early in the decision process: Kubernetes is a framework. It is not a platform.

While there are many benefits to traditional cloud storage solutions, sometimes enterprises need a more scalable way to manage and access large amounts of unstructured data. So while cloud storage may be the perfect solution for small businesses, larger teams or enterprises should consider object storage to meet their storage needs without worrying about high costs, data loss, or compliance issues.

London, February 3, 2026 — Canonical and AWS are pleased to announce the release of the new snap for AWS IoT Greengrass, making the deployment of your IoT solutions easy and seamless all the way from silicon to the cloud. With the AWS IoT Greengrass agent now available as a snap package from the Canonical Snap Store, Ubuntu Core has become the ideal operating system for all your AWS IoT edge workloads and data ingress.

In 2026, outages are certain. Master Resilience Velocity to survive the Complexity Horizon and turn a 15-hour AWS outage into a non-event.