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The latest News and Information on Containers, Kubernetes, Docker and related technologies.

Coding Agents Write the Code. Who Verifies It Works? We Built the Answer.

Coding agents are good at reading a spec and producing code. But producing code is one step in a longer process. The real loop is Spec -> Code -> Deploy -> Test -> Verify -> Ship. Agents stop at step two. Romaric founded Qovery to make Kubernetes accessible to every engineering team. He writes about platform strategy, developer experience, and the future of cloud infrastructure.

From Visibility to Real Savings: Turning FinOps Insights into Measurable Cost Reduction

FinOps programs are maturing, and most organizations have better visibility into cloud spend than ever before. Dashboards are full of data. And yet costs keep climbing. The problem isn’t the data. It’s the gap between knowing where the waste is and actually eliminating it. In this joint session, Tangoe and Kubex come together to bridge that gap. Tangoe brings deep expertise in spend management and FinOps discipline, while Kubex delivers infrastructure-level optimization across cloud, Kubernetes, and the AI and GPU workloads that are rapidly becoming the next frontier of cost pressure.

Beneath the Stack: A Software Engineer's Journey into Infrastructure

A software engineer's hands-on journey building a private cloud on bare-metal: Incus clustering, K3s, OVN networking, the Gateway API, and everything that breaks along the way — and what it taught them about why platforms like Qovery exist. Antoine is a senior software engineer at Qovery. He writes about hands-on infrastructure engineering, Kubernetes internals, and the realities of running production systems.

Sovereign GPU cloud: Data residency across training, inference, and model weights

Sovereign cloud conversations usually center on where customer data sits at rest. The provider points at a UK data center, the contract gets signed, and procurement marks the box. For most workloads, that's a defensible position. For GPU workloads, it isn't.

GPU cloud for AI inference in production: How infrastructure requirements change after training

Training a model is a project with an end date. Inference is what happens for the rest of the model's working life. The two workloads share GPUs, frameworks, and a lot of vocabulary, but the infrastructure decisions that make sense during training are usually the wrong ones in production. Teams that treat inference as "training, but smaller" tend to discover the gap somewhere around their first traffic spike.

5 questions you should be asking about cloud dependency

Cloud infrastructure has become the backbone of modern business operations. But as organizations deepen their reliance on cloud providers, a critical question often goes unasked: just how dependent are we, and at what cost? For years, the cloud adoption narrative focused on agility, scalability, and cost efficiency. Those benefits remain real. But the landscape is shifting.

[Webinar] Building Regulated Infrastructure: How Lucis Standardized Security for Global Care

In Healthtech, downtime is more than a loss of revenue, it is a disruption to patient care. Whether supporting digital health platforms or AI-driven healthcare applications, infrastructure must remain secure, compliant, and highly available. Join Lucis and Qovery for a technical breakdown of building compliant and secure infrastructure that scales AI and healthcare workloads, handles traffic peaks, and maintains SOC 2, HDS, and HIPAA standards.

4 Best Chainguard Alternatives for Zero-CVE Images in 2026

Chainguard helped make zero-CVE and near-zero-CVE container images a mainstream topic in cloud-native security. For many engineering and security teams, the core appeal is clear: fewer vulnerabilities in base images, smaller attack surfaces, stronger software provenance, and less time wasted chasing noisy vulnerability reports.

AI inference vs. training: What they are and how they differ

AI inference and training are terms you'd run into if you have been around software engineering or even just scrolled through the news. Both are integral to delivering the AI-powered experiences we have come to expect from many of the applications we use daily. According to McKinsey, by 2030 inference will overtake training as the dominant workload in AI data centers, making up more than half of all AI compute and roughly 30-40% of total data center demand.