Operations | Monitoring | ITSM | DevOps | Cloud

We rebuilt large parts of the Qovery Console: new navigation, overviews at every level, dark mode, and a modernized frontend architecture with TanStack Router and React Suspense. Rémi is a staff frontend engineer at Qovery. He writes about frontend architecture, developer experience, and building scalable UI systems for platform engineering tools. Théo is a senior product designer at Qovery.

Three days, 20 talks at Devoxx France 2026. The through-line wasn't AI hype - it was discipline. Context engineering, code review under AI volume, and the local-vs-remote question now shaping security, cost, and sovereignty. Fabien is a senior software engineer at Qovery. He writes about platform engineering, AI tooling, context engineering, and the practical realities of running modern developer infrastructure.

The telco industry is undergoing a fundamental change. Over the past few years, the increasing maturity of cloud-native infrastructure has accelerated the movement from manually operated and hardware-centric systems to automated, software-defined platforms. Underpinning this change are open source initiatives such as the Sylva project. Sylva is hosted by Linux Foundation Europe and heavily backed by major telecom operators and vendors.

A container reporting “up” tells you the process is running, not that the workload is healthy – but that caveat is true of any service, on a container or a bare server.

Production secrets should live in one place and stay there, whether your next deployment is triggered by a developer or an AI agent. The Secret Manager integration connects AWS Secrets Manager, AWS SSM, or GCP Secret Manager to Qovery so secrets are referenced, never copied, and enterprise governance holds regardless of who deploys. Alessandro leads product at Qovery. He drives the changelog, roadmap, and product strategy - turning customer feedback into platform capabilities.

AI SRE Platform’s new capabilities resolve structural blockers and optimize workload placement, eliminating cloud waste to drive up to 80% in total cost savings.

You’ve deployed Karpenter or Cluster Autoscaler and tightened your resource requests, but while you saw an initial dip in your cloud bill, your savings have flatlined. Organizations that thought they had the fundamentals of cloud cost under control are now seeing stagnation. The problem isn’t that they need another FinOps tool or better visibility. The problem is that the current state of enterprise cloud cost optimization strategy is fundamentally reactive.