Operations | Monitoring | ITSM | DevOps | Cloud

As an engineer, engineering leader, or CTO, your architectural choices shape how fast your team builds products and how efficiently you manage technology costs. Your architecture determines how much control you have over data, infrastructure, and customization. The Software-as-a-Service (SaaS) model is one of the most common ways to deliver software reliably to users anywhere.

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.

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.

When a SaaS company scales, something curious happens. The cloud bill grows. One team swears it’s Kubernetes. Another blames the Black Friday promo. But when you’re unsure whether that increase is tied to healthy SaaS growth or simply overspending, your margins are already at risk. That gap between what’s spent and what’s understood is where indirect costs live. Yet these costs rarely show up in dashboards. Well, until it’s too late.

Welcome to December’s edition of CloudZero’s Cloud Economics Pulse — your monthly read on how cloud spend is shifting across providers, services, and AI workloads. No surprises here — November continued the quiet reshaping trend we’ve seen all year. Compute softened, data layers grew, and AI/ML hit its highest share yet. AWS extended its lead, Azure and GCP nudged upward, and the emerging “AI layer” of providers continued to take shape.

A few months back, a backend team at a fast-growing SaaS company shipped what seemed like a harmless feature. Just a simple request validation layer. No new service. No major dependencies. No architectural shock. Yet two months later, their cloud costs had climbed 38% without any significant increase in traffic, storage, or compute load. What they’d missed was that the validation layer triggered a fan-out pattern.

New Relic provides full-stack observability and monitoring. It provides almost every type of system monitoring on a single platform. This includes monitoring tools for infrastructure, application performance monitoring (APM), synthetics, user, log, mobile, network, and Kubernetes components. DevOps, security, and business professionals use these capabilities to detect anomalies, analyze root causes, and fix software performance issues.

AI adoption is exploding, but margins aren’t. In fact, an MIT analysis reports that 95% of organizations have yet to see measurable ROI from GenAI. This gap becomes obvious as soon as teams push a model into production and usage begins to scale. For most workloads, the pressure comes after training. Every message, call, query, completion, or retrieval triggers compute behind the scenes. That real-time execution is what AI inference is all about.