Operations | Monitoring | ITSM | DevOps | Cloud

A Kubernetes Operator is a method of packaging, deploying, and managing a Kubernetes application. It extends the native Kubernetes API by combining custom resources (CRDs) with a dedicated controller: a custom control loop that continuously watches the state of those resources. The primary purpose of an operator is to automate complex, stateful applications (like databases, message queues, or monitoring suites) that require human operational knowledge to maintain.

Today, we’re launching Kubex support for the KAI Scheduler and automated GPU sharing for inference workloads. As AI inference moves into production, platform teams are being asked to serve more models, support more teams, and control GPU costs at the same time. But many inference workloads do not need an entire GPU all the time. When teams reserve full GPUs or oversized GPU fractions to stay safe, expensive capacity can sit idle across the cluster.

An MCP server is the standardized bridge that lets AI agents like Claude Code and Cursor operate real infrastructure - deploy apps, provision databases, manage environments - through one governed API. Here's how MCP servers work for infrastructure, why they matter, and how to give agents production access without losing control. Romaric founded Qovery to make Kubernetes accessible to every engineering team. He writes about platform strategy, developer experience, and the future of cloud infrastructure.

Platform engineering has become one of the most discussed topics in cloud native infrastructure. Yet despite the rising focus, most conversations around platform engineering skip over the uncomfortable truths. What actually works at scale? When should you build versus buy? And how do you avoid the traps that trip up even experienced teams?

Base44 vs Lovable compared: features, backend, pricing, popularity, and which AI app builder fits your use case - plus how to take either prototype to production on infrastructure you control. Melanie leads content at Qovery. She covers platform engineering trends, Kubernetes operations, FinOps, and the tools that help engineering teams ship faster.

Most hybrid cloud strategies fail not at launch but at scale. The architecture works fine for the first year. The team's workloads are modest, the integration points are limited, and the operational overhead is manageable. Then the business grows. Workloads multiply, data volumes climb, the team expands, and the seams between public cloud and private infrastructure start showing.

Salesforce just put Heroku into maintenance mode and ended Enterprise sales for new customers. The AI era also flipped the migration math: what used to be a six-month project is now a one-prompt, agent-driven move to your own cloud. Here's why now is the best time to leave Heroku. Julien is a Senior Product Manager at Qovery. He bridges engineering and product, writing about deployment workflows, environment management, and DevOps patterns.

AI's environmental cost is real, and it's growing. Training a large language model can consume the electricity of hundreds of households for weeks. Inference at production scale runs continuously, with GPU clusters drawing power around the clock. The data centers that house all of this are some of the most concentrated energy consumers in the modern technology stack.