Operations | Monitoring | ITSM | DevOps | Cloud

<span data-mce-type="bookmark" style="display: inline-block; width: 0px; overflow: hidden; line-height: 0;" class="mce_SELRES_start"></span> Andrew Hillier sits down with Dan Ciruli, who leads the cloud native product management team at Nutanix.

Cloud costs are no longer just an engineering problem or a finance problem. It’s now an everyone problem. That was the central message from the FinOps Training At Scale webinar that took place on Sept. 25, 2025, where CloudZero’s Larry Advey (a.k.a. “Fred FinOps”) and Director of Tech Enablement Umesh Rao walked through the realities of building FinOps practices that work in the real world.

The world of cloud costs is always evolving, and AI spend is quickly becoming one of the most unpredictable and confusing cost drivers. As more organizations integrate generative AI into their products, FinOps teams are struggling to account for — and control — these new, often mind-boggling cost streams. In fact, 44% of engineering professionals say improving AI explainability is a top priority in AI budgeting, according to CloudZero’s State Of AI Costs In 2025 report.

In this guide, you will build and deploy a serverless data processing workflow using AWS Step Functions and AWS Lambda. This approach enables you to orchestrate discrete processing tasks in a scalable and cost-efficient way, leveraging the event-driven architecture that AWS offers. You will begin by creating individual Lambda functions that handle specific tasks in your data pipeline.

Cloud costs don’t wait for your finance team to catch up. They spike on product launches, dip when usage slows, and sometimes blow past forecasts overnight. Every container spun up, every gigabyte stored, and every terabyte transferred adds to the tab. The main culprit is often variable costs. In this guide, we’ll break down how variable costs affect budgeting and the strategies you can use to turn cost variability into a competitive advantage.

We are thrilled to announce that the Cortex Model Context Protocol (MCP) is now available in the Devin marketplace. This integration connects the world’s first AI software engineer with the real-time context of your entire engineering ecosystem, as managed and measured by Cortex. The rise of AI software engineers like Devin fundamentally changes how organizations tackle their biggest technical challenges.