Operations | Monitoring | ITSM | DevOps | Cloud

On a recent episode of FinOps In Full Bloom, host Thalia Elie sat down with Anderson Oliveira, a Senior FinOps Account Manager at CloudZero. With more than two decades in IT and deep FinOps expertise, Anderson brought clarity, humor, and a refreshingly human perspective to the conversation. Their chat covered everything from visibility and budgets to cultural friction and how to shift teams from resistance to results. Here are 13 insights and takeaways every FinOps-minded leader should hear.

This year’s AWS re:Invent was a blur of GPUs, LLMs, and infrastructure roadmap reveals — but for those listening between the keynotes, another story was unfolding. Between hallway chats, booth conversations, and live polls, a signal emerged from the noise: FinOps is growing up. Mature cloud teams aren’t just managing costs — they’re asking smarter, more strategic questions about value, forecasting, and engineering accountability.

Cloud outages in 2025 ranged from minor ones affecting some sections of users, to major ones affecting hundreds or thousands of users. Services like Cloudflare and AWS on which many other services depend experienced outages that affected many due to the cascading effect. Let's look at some of the major cloud outages in 2025.

For our last episode of 2025, we’re focusing on Datadog releases announced at AWS re:Invent. Join Jeremy to see how you can manage logs at petabyte scale in your infrastructure, eliminate unneeded costs in Amazon S3 buckets, build agentic workflows, and detect credential leaks. Later in the episode, Scott spotlights how you can connect your AI agents to Datadog tools and context with our MCP Server.

After four days of AWS re:Invent—a 65,000-step marathon that included 60,000 attendees spread across five Las Vegas campuses—and navigating the latest installment of this 13-year-old cloud pilgrimage, we’re all a little dehydrated but significantly wiser. The volume of announcements felt less like a single flood and more like a river branching into three powerful currents. Making sense of this massive technological convergence requires zooming out.

Setting up a local Deep Learning environment can be a headache. Between managing CUDA drivers, resolving Python library conflicts, and ensuring you have enough GPU power, you often spend more time configuring than coding. Google Cloud and Canonical work together to solve this with Deep Learning VM Images, which use Ubuntu Accelerator Optimized OS as the base OS. These are pre-configured virtual machines optimized for data science and machine learning tasks.

In our increasingly online world, managing cloud, AI, and other tech spend has shifted from a good idea to an absolute necessity. But even when cost management is a priority, how do you get busy development teams and engineers actively engaged in the new practices? New initiatives are often viewed as more work on the team’s plate, which is an understandable deterrent to adoption. That leaves FinOps proponents struggling to get others on board.

AI should speed up innovation, not inflate your cloud bill. But today, the biggest GenAI challenge for SaaS teams isn’t model quality; it’s cost. And increasingly, that cost comes from AI cost sprawl. That’s not because anyone is doing something wrong, but because AI operates differently from the cloud services we’ve all spent a decade learning how to manage.