Operations | Monitoring | ITSM | DevOps | Cloud

If you work in FinOps, you know the feeling. You open your recommendations queue on a Monday morning. There are 47 items. You worked through 12 of them last week. You’re back up to 47 again. All represent real money leaving the building, but not all are “bad money” – of those 47, a significant share will be “this is ok, expected, we got value”. That’s what really kills FinOps enthusiasm (and is why the engineer is skeptical towards the FinOps person).

At SaaS Metrics Palooza 2025, CloudZero CEO Phil Pergola delivered a keynote on the software industry’s most pressing question: can SaaS survive the AI revolution, or will AI rewrite the SaaS playbook outright? Phil’s answer wasn’t doom and gloom, but he didn’t sugarcoat the challenges. “Churn rates are up,” he told moderator Ray Rike of Benchmarkit on Oct. 9, 2025. “The payback from a customer acquisition cost perspective is taking longer.

I’ve been an engineering leader for over a decade, and I’ve spent most of those years in private Slack groups with other engineering leaders, comparing strategies and kvetching about Kubernetes. Of the hundreds of threads I’ve taken part in, the one that got the most engagement the fastest was a recent one around AI adoption. “Where are you on this continuum?”, it read. “A. You don’t really care how people use AI; B. You push people to use AI; or C.

AI cost management is the practice of tracking, allocating, and optimizing the cloud infrastructure costs tied to building, running, and scaling AI workloads. It differs from traditional cloud cost optimization because AI infrastructure behaves differently at every layer of the stack. The biggest problem isn’t overspending. It’s that most organizations can’t see where their AI spending is going.

VPC peering can be simple and cost-effective in smaller setups. For growing multi-account platforms, Transit Gateway can offer predictable structure and centralized governance. But that’s not all. AWS VPC peering connects two VPCs directly with no hourly fee — simple and cost-effective at small scale, but it creates an unmanageable mesh as your VPC count grows.

The wrong people got the most attention from Jensen Huang’s comments last week. Huang told the All-In Podcast that he’d be “deeply alarmed” if a $500,000 engineer consumed less than $250,000 in AI tokens annually. Within 48 hours, the discourse collapsed into a compensation debate.

Earlier this month, we announced the CloudZero Claude Code Plugin and the CloudZero AI Hub — the first step toward putting your cloud cost data directly inside the AI tools your team already uses. The feedback from customers was clear. They said engineers and FinOps teams wanted more tools and more ways to get answers from CloudZero without switching context. Today, we’re delivering more.

If you’re building or running AI-powered features in production, you need a clear understanding of inference costs. Get it right, and you can turn your AI investments into profitable growth. As Larry Advey, Director of Cloud Platform and FinOps at CloudZero and a member of the FinOps Foundation Technical Advisory Council, puts it: “AI investments will only continue to grow.

Snowflake is a cloud data platform designed for large-scale analytics, data warehousing, and data processing. It allows teams across an organization to run multiple data workloads on a single platform without managing infrastructure. Snowflake’s architecture is also unique. Compute and storage are completely independent and highly elastic. However, Snowflake’s per-second pricing and elastic compute model make costs highly sensitive to usage.

For longer than I’m proud of, I was an AI skeptic. Then, over the holidays, I vibe coded an app whose sole purpose was to make me a better person. The app is a motivator. It’s programmed to send me timely reminders along certain themes, like reading every day, making healthy eating choices, and giving myself plenty of time to plan for anniversaries and birthdays.