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Kubernetes has come a long way since its debut in 2014. It’s gone from running a couple of containerized microservices to orchestrating fleets of production workloads spanning everything from AI agents to full scale VMs running in pods. As Kubernetes adoption grows, and its use cases stretch to cover more ground, managing its increasingly complex networking and security landscape demands operational maturity and a platform that supports it.

We're thrilled to announce that Cycle's European Control Plane is now live! While a few organizations have been utilizing it over the past month, we're eager to officially open access to all teams. Before diving deeper into the "why," let's clarify what a Cycle Control Plane actually is. If you visit our status page, you'll see a list of the core services powering Cycle. These services include everything from our APIs to our 'factory' build systems.

A GPU spec sheet is a confidence trick. It looks like an objective document - numbers, units, comparable rows - but most of the numbers on it don't map cleanly to the performance a real workload will see. Teams that pick GPUs by reading the headline figures usually find out the gap between spec and reality somewhere around the first production run. This is a working guide to reading GPU cloud instance specifications against actual ML workloads. The goal isn't to recommend a card.

Introducing the AI Builder Portal - the governed alternative to Lovable and Bolt.new for enterprise. Same one-click builder experience, running on your Kubernetes cluster, under your governance. Romaric founded Qovery to make Kubernetes accessible to every engineering team. He writes about platform strategy, developer experience, and the future of cloud infrastructure.

A company burned $500M in Claude credits because nobody set a limit. Uber exhausted its 2026 AI budget. Microsoft canceled Claude Code licenses. The problem isn't AI - it's the total absence of governance around how people use it.

Moving to a new platform shouldn't mean weeks of migration work before you see any value. Qovery now lets you connect your ArgoCD server and manage your existing applications directly alongside your Terraform modules, lifecycle jobs, and Qovery-native services, from a single control plane. Alessandro leads product at Qovery. He drives the changelog, roadmap, and product strategy - turning customer feedback into platform capabilities.

If you serve LLMs on Kubernetes without inference-aware routing, your load balancer is likely wasting inference capacity. Generic HTTP traffic management blindly routes requests, assuming the backends in your cluster are interchangeable. But your model-serving backends are stateful and unevenly prepared to handle any given request. As a result, requests are often routed to the backend that’s not the one best suited to respond.

May has been one of our busiest months yet for Konstruct. Across three releases, 0.5, 0.5.1, and 0.5.2, we've shipped some of the most requested platform-level changes since we launched: a unified model for sharing resources across organizations, native support for AI-driven workflows via MCP, a completely redesigned API keys experience, and a cleanup to how permissions actually work in multi-org environments. Let's walk through what shipped and why it matters.

Every Kubernetes environment accumulates waste over time. Teams overprovision CPU and memory requests to avoid performance risk, run idle replicas to preserve headroom, and leave Horizontal Pod Autoscalers (HPAs) untouched long after workload behavior has changed. Some of this waste can be addressed at the node level, where Datadog Cluster Autoscaling helps teams rightsize capacity.