Operations | Monitoring | ITSM | DevOps | Cloud

Cloud cost intelligence has moved beyond simple cost-cutting. Now, it’s about creating value. Cloud bills continue to rise, and workloads are becoming increasingly complex. Teams also need to understand what they’re spending, why, and how that spend ties to business results. FinOps has become the framework for bringing finance and engineering together. It’s helping teams manage costs, improve margins, and plan with confidence. But challenges remain.

My wife and I spent hours this summer at home digging in the dirt. We planted new shrubs and perennials and created a small vegetable garden. We spread many square yards of fresh topsoil and grass seed over areas of lawn that needed rejuvenation. It turns out, I should have done all that landscaping with a FinOps leader’s mindset — before my water bill tripled when I wasn’t looking.

The integration of Amazon Kiro and Harness’s MCP server enables developers to manage, troubleshoot, and optimize CI/CD pipelines directly from their IDE using natural language, dramatically reducing manual effort and accelerating software delivery from code generation to production.

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.

A SaaS team once spotted their first breach not in a SIEM dashboard, but in their AWS bill. Their compute costs spiked by 400% overnight. Turns out, an attacker had spun up dozens of high-powered instances for crypto mining. Logs eventually confirmed the intrusion, but the cost anomaly was the first signal that something was wrong. This incident isn’t unusual. Cloud costs often reflect consumption, but they can also reflect compromise.

<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.