Operations | Monitoring | ITSM | DevOps | Cloud

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.

What is Exchange Online Archiving? Microsoft's EOA gives users an in-place archive mailbox - here's what it costs, what it covers, and where it leaves gaps.

Software moves from code to production more quickly today, but it is still difficult to tell whether delivery is actually improving or just becoming more active. Most teams rely on dashboards filled with metrics like deployments, uptime, failures, and tickets. The numbers are available, but the meaning behind them is often unclear. DevOps metrics become useful only when grouped into clear categories: DORA metrics cover only delivery speed and stability, which is just part of the picture.

Some support cases are straightforward. Others lead deep into legacy code, where a single logic bug can quietly turn a routine command into a major performance problem. This series looks at how Canonical Support and Sustaining Engineering work together to investigate, patch, and upstream difficult issues that standard troubleshooting alone cannot solve.

Every application starts the same way: One server. One database. One optimistic engineer saying: “We’ll scale later.” And honestly? That’s usually the right call. Premature scaling is how perfectly normal applications end up with: But eventually, growth happens. Traffic increases. Queries slow down. Deployments get riskier. Your infrastructure starts making unfamiliar noises. This is where scaling enters the picture. Not scaling for conference talks.

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.

The “high cardinality is expensive” sentence has become observability’s version of “in this economy” — said so often that nobody questions whether it’s true. Every vendor pricing page invokes it. Every glossary article repeats it. Every architecture diagram shows aggregation buffers placed before the storage layer.