Operations | Monitoring | ITSM | DevOps | Cloud

Ever had a load balancer become the bottleneck in an on-prem Kubernetes cluster? You are not alone. Traditional hardware load balancers add cost, create coordination overhead, and can make scaling painful. A Kubernetes-native approach can overcome many of those challenges by pushing load balancing into the cluster data plane.

When an incident triggers, the question is not just what broke but also how urgent it is and who on your team needs to respond. Alert Routing rules answer those questions automatically. You define the conditions once and the right response follows every time an incident triggers. Every Alert Routing rule does one or more of these three things: Three conditions drive all of it: incident payload, time of occurrence, and frequency.

The disconnect between enterprise confidence and AI capability is real. MIT reports fewer than 5% of enterprises have achieved measurable ROI from AI, yet Cisco claims 13% feel ready. The gap isn’t about AI technology—it’s about organizational rigidity and change management. More importantly, most studies focus on business intelligence rather than operational use cases, which are far less risky and more measurable.

As organizations shift away from traditional container engines to embrace Podman’s rootless and daemon-less design, visibility often becomes a challenge. Because Podman doesn't rely on a central background service, traditional monitoring tools can leave you in the dark. Applications Manager's new Podman monitoring feature bridges that gap, giving you total visibility into your Podman workloads without compromising the security model you worked so hard to build.

Most teams know they are spending money on AI in production. Far fewer realize how much they are spending outside production. It’s easy to get lost as you evaluate which model has the best responses, is fast enough, and cheap enough to run in production. That is because the AI bill usually shows up as a giant blob. It is easy to see the total.

APIs sit at the center of modern digital infrastructure. Mobile applications, SaaS platforms, microservices, and third party integrations all depend on APIs to exchange data and execute business logic in real time. When an API becomes unavailable, slows down, or returns incorrect data, users feel it immediately. Transactions fail. Dashboards stop updating. Logins break. Revenue and trust are affected within minutes.

Engineering teams are shipping faster than ever. AI coding tools like Claude Code and OpenAI’s Codex have quietly removed some of the biggest friction points in the development cycle — and the result is that FinOps teams are being asked to keep up with a pace most practitioners haven’t fully reckoned with yet. That acceleration has a cost consequence. More shipping means more services, more experiments, more infrastructure spun up without review cycles.

Modern software runs on APIs. Whether you are operating microservices, integrating third party services, or building customer facing platforms, APIs are the backbone of your architecture. As systems become more distributed, simply knowing whether an endpoint is up or down is no longer enough. Teams need deeper visibility into performance, reliability, latency, and behavior across environments. That is where API observability tools come in. API observability goes beyond basic health checks.

Modern applications are powered by APIs. Every login request, checkout transaction, mobile interaction, and third-party integration depends on APIs responding quickly and reliably. When an API slows down, the entire user experience suffers. Even a one-second delay in response time can: For ecommerce platforms, fintech systems, SaaS products, and real-time applications, slow APIs do not simply create inconvenience. They directly affect revenue, customer retention, and operational stability.

On paper, lift-and-shift VM migration to Kubernetes sounds simple. Compute can be moved. Storage can be remapped. But many migration projects stall at the network boundary. VM workloads are often tied to IP addresses, network segments, firewall rules, and routing models that already exist in the wider environment. That is where lift-and-shift becomes much harder than it first appears.