Operations | Monitoring | ITSM | DevOps | Cloud

Every Kubernetes environment accumulates waste over time. Teams overprovision CPU and memory requests to avoid performance risk, run idle replicas to preserve headroom, and leave Horizontal Pod Autoscalers (HPAs) untouched long after workload behavior has changed. Some of this waste can be addressed at the node level, where Datadog Cluster Autoscaling helps teams rightsize capacity.

The Observability Gap shows up the moment you try to reproduce a production bug locally. Your traces tell you a request was slow. Your logs tell you which line printed. Neither tells you what was actually on the wire: the headers, the JSON body, the surprise field your client started sending last Tuesday. Until now, closing that gap meant SSHing to a node, attaching a debugger, or shipping a sidecar through change review.

Kubernetes Monitoring in Grafana Cloud comes out of the box with preconfigured alert rules that notify you about issues like CPU throttling, crash-looping pods, and nodes going offline. These rules are installed automatically when you set up the app, and they start evaluating immediately. But if you've recently reinstalled the Kubernetes Monitoring app and your alert notifications stopped arriving, or started looking different, you're not alone.

In The Five Pillars of AI Agent Accountability: A Diagnostic Framework for Engineering Leaders, we walked through each pillar of AI agent accountability (traceability, authorization provenance, identity and ownership, policy at scale, and human oversight) and argued that most enterprises today sit at Level 0 or Level 1 of the Accountability Maturity Model. The most common reaction we get when we share that framework is some version of: “We’re already covered. We have network policies.

Two platforms, two teams, two procurement relationships, all doing one job. There’s a reason it ended up this way. There isn’t a reason it has to stay this way. Ask anyone at a typical enterprise why the VM platform and the container platform are separate, and they’ll give you a sensible answer. The VM estate has been there for fifteen years. It runs the workloads the business depends on.

Many of us understand the concept of Kubernetes Requests and Limits, and that by reducing over-sized resource requests we can reduce waste in our clusters. And for GKE Autopilot and EKS Fargate clusters that is true. Because you’re being billed directly for the resources you’re requesting, driving down requests can result in instantaneous savings. However in most hosted Kubernetes environments you’re not actually being billed for requests.

The low-code promise failed for 15 years. AI builders delivered in 15 months. Here's what actually changed, why the engineer in me resisted it, and what it means for every CTO. Romaric founded Qovery to make Kubernetes accessible to every engineering team. He writes about platform strategy, developer experience, and the future of cloud infrastructure.

AI tools turned everyone into a builder. Your sales team, your finance team, your CEO - they're all shipping apps now. The answer isn't to ban them. It's to give them a governed platform they actually want to use. Romaric founded Qovery to make Kubernetes accessible to every engineering team. He writes about platform strategy, developer experience, and the future of cloud infrastructure.

You’re in a board meeting. The CISO is presenting on AI risk. The CFO asks a simple question: “When that finance agent we deployed last quarter accessed a customer payment record, can we tell who authorized it, what policy permitted it, and produce the full audit trail?” The CISO looks at the head of the platform. The head of the platform looks at security. Nobody answers. If you can picture that meeting happening at your company, you’re not alone.