Operations | Monitoring | ITSM | DevOps | Cloud

If you're building a modern web application in 2026, Vercel is almost certainly on your shortlist, and probably near the top of it. The developer experience Vercel pioneered for Next.js and the frontend ecosystem around it is a real achievement. Push a branch, get a preview URL, ship. It works, it's fast, and an entire generation of frontend teams have built their workflow around it. This article is not here to argue with any of that.

Scaling your deployments to zero is only half the battle. If your cluster autoscaler does not aggressively bin-pack and terminate the underlying worker nodes, you are still paying for idle metal. True environment sleeping requires tight integration between your ingress layer and your node provisioner to actually realize FinOps savings.

Developers and SREs who rely on Microsoft Azure DevOps often face fragmented workflows when investigating issues or reviewing code quality. Troubleshooting an error can require jumping between observability tools and source code repositories as you manually connect traces, stack frames, and commits. At the same time, security vulnerabilities, misconfigurations, and flaky tests may go undetected until later stages of the software delivery life cycle (SDLC), where they are more costly to fix.

Claude Opus 4.7 holds at $5/$25 per million tokens — but a new tokenizer inflates costs up to 35% on identical text. Here's what Opus 4.7 actually costs at production scale, how it compares to Sonnet 4.6, and the six levers that determine where your bill lands.

Make AI do the dirty work while you focus your energy on strategy. CloudZero's Ryland Bowles shows you how. Every FinOps engineer is worried that AI is going to steal their job. I’ve worried about it. But I’ve also experimented extensively with AI, and I’ve got a pretty clear sense of what it can and can’t do in a FinOps context.

AI Classify reaches general availability this month, TDM gets a Docker image, and we’ve added a few things you’ve been asking for.

GPU observability isn't one thing - it's eight connected layers from silicon to cost. See why correlation across layers is what cuts debugging from 2 hours to 2 minutes, and why most teams instrument only one or two.

1,000 nodes × 8 GPUs × 60 metrics = 1.4M time series - before you add pod names or Slurm job IDs. GPU monitoring is a cardinality problem disguised as a metrics problem. How to design for it before production OOMs your Prometheus.

WordPress powers 40% of the web but has no native observability story. Here's how to instrument it end-to-end with OpenTelemetry - PHP, browser RUM, and errors. Prathamesh works as an evangelist at Last9, runs SRE stories - where SRE and DevOps folks share their stories, and maintains o11y.wiki - a glossary of all terms related to observability.

Most service desk automation problems get misdiagnosed. You see the ticket backlog, the manual work, and the slow incident response, and assume the issue is due to process, adoption, or staffing. But at some point, the math stops working. You’ve invested in a service desk automation tool, given it time to mature, built workflows around it, and the results still don’t match what was promised.