Operations | Monitoring | ITSM | DevOps | Cloud

When CloudZero’s CTO Erik Peterson joined the FinOps Weekly podcast in October 2025, he didn’t hold back. Instead of going on about the usual best practices of AI cost optimization, he posed challenges to how we approach AI spending. From “zombie AI experiments” eating your budget to why you should stop apologizing for using AI, these 10 insights from the podcast are worth considering in how we approach AI FinOps. (Watch the full podcast below and keep reading for more!)

A key advantage of Kubernetes for container management is its high scalability. Kubernetes nodes are directly involved in this, and they can significantly impact your efficiency, cost-effectiveness, and service availability. This guide provides an in-depth look at Kubernetes nodes, including types of nodes and operational best practices.

Smarter PR Reviews: Inline comments, keyboard shortcuts, and faster diffs reduce context switching. Optimized for Scale: Instant file tree and change listing performance even in large monorepos. Seamless Navigation: Effortlessly move between branches, commits, and repos without losing context. Unified Design System: A consistent, intuitive UI across the entire Harness platform. At Harness, we know developer velocity depends on everyday workflow.

Outages don't care how many zones you have. Power failures, software updates, and backbone disruptions all have one thing in common: they do not respect architecture diagrams. Redundancy only works if it is designed at the correct layer. Every team believes they are covered, and yet, when something breaks, the failure reveals that what looked like protection was only an illusion.

Gartner recognizes Last9 in their latest Cool Vendor report for unified telemetry and agentic SDK—moving teams from reactive monitoring to proactive ops. Founder at Last9. Loves building dev tools and listening to The Beatles.

Rolling out a powerful AI tool like GitHub Copilot is a big win for any engineering leader. But because it’s such a significant investment, leadership will inevitably ask if it was worth the cost. Until now, answering this was nearly impossible. While GitHub provides adoption stats, connecting that data to real-world performance metrics like cycle time or code quality has been a manual, frustrating process. We built the Cortex AI Impact Dashboard to provide a clear answer.

Every engineering organization measures success differently. A dashboard that’s perfect for one team might be meaningless for another. While out-of-the-box views for DORA are a great starting point, leaders need the ability to define and share the specific combination of metrics that matter most to their business. Without this, you're either forcing your teams to conform to generic reports or wasting time rebuilding the same views every week.