Operations | Monitoring | ITSM | DevOps | Cloud

Compare cloud GPU pricing across AWS, Azure, and GCP for AI workloads. See H100 and A100 costs per hour, hidden cost drivers, and how to track real GPU spend.

We look at the hidden economics of elastic scaling for AI inference, the scaling decisions that affect your cost per inference, and what you can do now to optimize your AI ROI.

The relationship between product managers (PMs) and engineers is due for an upgrade. The division between these personas is responsible for a healthy, if laborious, collaboration when envisioning and building new products. A PM generates the vision; engineers translate it into an architectural approach, raising the technical questions that sharpen it along the way. This back-and-forth eventually produces tight alignment, a solid PRD, and functional code.

Global IT spending is projected to reach $6.15 trillion in 2026, up 10.8% year-over-year, according to Gartner. Data center spending...

12 proven Google Cloud cost optimization strategies to cut GCP spend by 30–70%. Covers CUDs, rightsizing, labeling, and GCP pricing vs AWS.

In early March, we launched the CloudZero AI Hub and the CloudZero Claude Code plugin, giving customers a direct line to their cloud and AI cost data through natural language. Early adopters and power users have already jumped in, using the plugin to investigate cost spikes, close commitment gaps, and get to cost-per unit metrics that used to take days to pull together. What we’ve noticed over the past few weeks is pretty consistent (and predictable).

CloudZero’s Umesh Rao and Larry Advey showed what it actually looks like to connect AI to real cloud cost data, and the results are hard to unsee. On April 9, 2026, CloudZero hosted a live webinar, Cost Intelligence for the AI Era, featuring Umesh Rao, Director of Enablement, and Larry “Fred FinOps” Advey, Director of Cloud Platform & FinOps.

Welcome to April’s Cloud Economics Pulse, CloudZero’s monthly look at cloud spend as AI moves from cost problem to strategic commitment. March’s Pulse called 4.01% a record. It lasted all of 31 days. Why? February’s billing data came in at 4.84% aggregate AI/ML share. That’s another high, another acceleration. You’ve heard it before and it’s getting a bit boring now, but the story isn’t in the numbers; it’s now in the behavior.

Imagine if you had a magic box where you could keep all your business information — sales numbers, customer feedback, everything — safe and sound, but also easy to look at whenever you needed. That’s kind of what Snowflake does, but for big organizations and using the cloud. It’s a new way for companies to store and use their data without getting bogged down by the techy details.

Not long ago, adding a new cost connector to CloudZero was a serious undertaking. We’d task multiple engineers, build in extended review cycles, run a private preview period. But a single connector could take up to two months from kickoff to customer hands. For the major cloud providers, that timeline was acceptable. The size of the investment matched the scale of the integration. But the tools landscape has changed. Our customers’ teams don’t just run on AWS and Azure.