Operations | Monitoring | ITSM | DevOps | Cloud

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.

Mission-critical networks are changing fast. Utilities, transport operators, and critical infrastructure providers are under pressure to deliver more data, more automation, and more resilience—without ever compromising reliability. The challenge is simple: legacy SDH/SONET networks were built for a different era. They still deliver reliability. But they can’t support what comes next.

Every enterprise contract I’ve signed in the last two years has the same clause. “Vendor will not use Customer Data to train machine learning models.” Sometimes it’s a paragraph. Sometimes it’s a whole section. The language varies but the intent is identical: don’t feed our production data into your AI. I get it. I sign the same clause as a vendor. But here’s what’s been bothering me: that clause is a promise, not an architecture.

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.