Operations | Monitoring | ITSM | DevOps | Cloud

The Qovery agent skills now create a free local Kubernetes cluster in Docker when your organization has none, then deploy your project to it. One prompt in Claude Code, Cursor or OpenCode, no cloud account, no credit card. Measured: 4 min 43 s for the cluster, 2 min 40 s to build and deploy a GitHub repository. Romaric founded Qovery to make Kubernetes accessible to every engineering team. He writes about platform strategy, developer experience, and the future of cloud infrastructure.

We’re pleased to announce that Calico is supported on Red Hat OpenShift 5, the latest version of the industry’s leading hybrid cloud application platform powered by Kubernetes. Tested and validated to run on Red Hat OpenShift 5, Calico delivers high-performance networking and zero-trust network policy for containers, virtual machines on Red Hat OpenShift Virtualization, and AI workloads, with one consistent policy model across the cluster.

Over the past year, I’ve spent a lot of time talking with engineering and platform teams about how they’re deploying AI agents. The conversations are changing. Six months ago, most were focused on getting agents into production. Now, increasingly, the question is how to secure them once they’re there. It’s a familiar transition. We’ve seen it with containers, Kubernetes, microservices, and cloud infrastructure.

Set up anthropics/claude-code-action so @claude turns issues into branches and pull requests, then let Qovery preview environments deploy each Claude PR to its own full-stack environment on your Kubernetes cluster, post the URL on the PR, and delete it on merge. Romaric founded Qovery to make Kubernetes accessible to every engineering team. He writes about platform strategy, developer experience, and the future of cloud infrastructure.

Agent-written pull requests need a running environment before anyone can review them. What a full-stack preview environment needs (services, databases, seed data, scoped secrets, cleanup, cost caps), how Vercel, Netlify, Argo CD ApplicationSets, homegrown GitHub Actions + Terraform and Qovery compare, and how to run it on your own cluster for free. Romaric founded Qovery to make Kubernetes accessible to every engineering team.

More and more, we are hearing how artificial intelligence is transforming how we build, secure, and share software. But as AI models become more powerful, questions around openness, security, and digital sovereignty are becoming harder to ignore. Can open models really be controlled? Is AI putting open-source communities under pressure? What does genuine technological independence look like? And who decides who gets access?

If ECS works for your team, no single limit forces you off it. Growing teams still hit a few hard ECS and Fargate quotas that AWS won't raise, starting with 100 target groups per load balancer for preview environments. Here are the limits, the numbers, and how to add EKS for previews without moving production. Guillaume is a Staff Solutions Architect at Qovery. He helps customers troubleshoot production infrastructure and writes about Kubernetes operations, AI agents, and reliable cloud platforms.

CPU limits are one of the first guardrails most teams add to a Kubernetes manifest. Set resources.limits.cpu: 200m, and you can trust that the container will never use more than a fifth of a core…right? That’s not always the case. A CPU limit is a request, not a restriction: the kubelet can ignore it, and if that happens, the container is free to use as much CPU time as it wants.

When people hear that Civo is building a gigawatt of AI capacity in the UK, they usually picture one thing. A vast campus in a field somewhere. Hundreds of thousands of GPUs under one roof. The kind of site that gets announced with a ministerial visit and a five-year timeline. We are doing the opposite. We are building 40 data centres, spread across the country, each one close to the people and organizations that will use it. On 22 September we unveiled the first one, in Hertfordshire.

For teams that own an application running on Kubernetes, updating its observability configuration should be as straightforward as updating any other Kubernetes resource. But configuring integration checks, log collection, and tracing often means working across multiple configuration paths. If those settings live in pod annotations, adding a metric check or changing log processing rules requires a Deployment rollout.