Operations | Monitoring | ITSM | DevOps | Cloud

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.

CloudZero continues to ship new features and enhancements at a fast pace. We’re publishing these product update posts to keep you up to date on our velocity and how we’re adding customer value.

AI spend is going parabolic, and the labels on the bill (OpenAI, Anthropic, Gemini) are about all a CXO gets to work with. The hard part of tying that spend to outcomes is structural. A major portion of AI spend isn’t COGS. It’s the spend on coding agents producing the software, the spend on building marketing content, the spend on custom sales tooling, the spend on Intercom agents and Sybill analysis.

Key Takeaway: The Harness MCP Server is now in the official Claude Connectors Directory. Developers using Claude can now discover and connect to Harness, gaining structured, real-time access to their pipelines, deployments, approvals, and delivery workflows. What makes this different from a typical API integration is what's underneath: the Harness Software Delivery Knowledge Graph, which gives Claude the context it needs to make decisions that are accurate, fast, and safe. ‍

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.