Operations | Monitoring | ITSM | DevOps | Cloud

FinOps thrives on clarity, and clarity is built on metrics. Metrics give engineering and finance a shared language to understand costs, evaluate trade-offs, and guide innovation. The most impactful metrics go beyond “how much are we spending?” and help us answer: When we measure these things, we stretch beyond tracking progress to fueling it.

(Almost) everyone is using generative AI, and just as many aren’t seeing any benefits. Research firm Gartner calls it the “gen AI paradox” — nearly 80% of companies say they’ve invested in generative solutions, and the same number report no benefits to their bottom line. What’s more, 90% of projects are stuck in pilot mode; ready to take off, but just can’t get up to speed.

Information architecture used to be about structure. We organized menus and pages into trees, built hierarchies, and created pathways for people to follow. For years, that worked. Navigation was the interface. But that world is changing. People aren’t clicking their way through information anymore. They’re asking for it. They’re refining questions, expecting context, and assuming that systems will not only understand what they mean, but act on it.

Read a few articles on artificial intelligence and financial operations, and you’re bound to run across a sentence like this: AI enables FinOps teams to reduce TCO and boost ROI. Or one like this: The future of FinOps uses agentic AI-powered systems to detect and remediate cost issues automatically. Keep reading and you’ll find piece after piece that say a lot about AI and FinOps … without really saying anything.

I admit I was a little skeptical going into KubeCon 2025. The last time I went, in 2022, it felt tactical. I heard lots of conversations around small solutions to small problems. Practical knowledge-sharing is of course beneficial, but I’m most inspired by the big picture — ideally, a picture bigger than you can see anywhere outside of your mind. I’m heartened to say that KubeCon 2025 was exactly that.

Discover the basics of cloud efficiency as well as six advanced data-driven strategies you can use to make your cloud environment more efficient. With incredibly complex cloud architecture — that may even include Kubernetes and multi-tenant infrastructure — organizations are finding it hard to measure and monitor the performance and cost of their cloud environments.

Kubernetes has reshaped how teams build and scale infrastructure, but it’s also made cost visibility a lot harder. For platform engineers, SREs, and FinOps leads, breaking down shared cluster costs, understanding per-team usage, and driving efficient resource allocation is still a major challenge. That’s why one CloudZero webinar with Umesh Rao, Director, Tech Enablement and John Hashem, Senior Sales Engineer, stood out.

If there’s one thing we’ve learned at CloudZero, it’s that success in FinOps isn’t just about having the right tools. It’s about knowing how to use them, and understanding the “why” behind every number, dimension, and dashboard.

If you’ve ever tried to make sense of your AWS bill, you know how fast things get messy. Different accounts, hundreds of services, random tags, and suddenly, no one can say for sure who’s spending what or why the total looks so high. It’s not that teams don’t care about costs — it’s that AWS billing data isn’t always easy to interpret. Finance wants accountability. Engineering wants visibility. And somewhere between the two, ownership disappears.

The cloud has long promised limitless scalability and near-perfect uptime. But if you tried to access your Microsoft 365 dashboard or recline your smart bed last week, and got nothing but a spinning icon, you weren’t alone. In the span of 10 days, both Amazon Web Services (AWS) and Microsoft’s Azure Cloud suffered widespread outages that rippled across industries.

After three days of demos, sessions, and hallway conversations at KubeCon Atlanta, one thing became clear to CloudZero CTO Erik Peterson: the cloud-native world is shifting from cost control to value engineering. Teams aren’t just fighting bills anymore. They’re fighting complexity, GPU scarcity, Kubernetes sprawl, and pressure from the business to justify every dollar of technical investment. And this year’s KubeCon attendees? They were ready for those conversations.

Running multiple LLMs without aggregation can feel like managing five different clouds with no dashboard. Sure, you can make it work, but you won’t like the bill. And most SaaS teams didn’t start with a multi-LLM strategy. It just happened. You added one model for reasoning, another for summarization, or maybe a fine-tuned version for customer support. Fast-forward six months, and your AI stack looks like a tangle of APIs. And each charges tokens on its own terms.

KubeCon Atlanta 2025 felt different this year — and CloudZero had a full team on the ground to capture it. Engineers, product leaders, sales reps, and CTO Erik Peterson spent three days embedded across the show floor. Their vantage points were complementary: the outbound conversations, the inbound questions, the demos, the technical deep-dives, and the quieter moments between sessions. Five perspectives stood out.

Traditionally, companies had to use separate tools for ETL, data storage, and analytics. Often, this resulted in slow, complex, and expensive data workflows A good example is PwC’s Deals, Insights & Analytics (DIA) team, which faced similar challenges. According to Microsoft, bespoke solutions often took months to build and were difficult to merge, slowing projects and driving up costs. That changed when PwC adopted Azure Synapse Analytics. The result?

If you’ve ever opened your Google Cloud bill and felt confused, you’re not alone. Costs in GCP can spike up fast. One project here, a few APIs there, until the total looks nothing like what you expected. That’s because Google Cloud pricing is built for flexibility. You pay for what you use, across dozens of services, each with its own rules, discounts, and data charges. It’s robust, but also easy to lose sight of what’s driving your spend. The good news?

Welcome to CloudZero’s inaugural Cloud Economics Pulse! This is our monthly snapshot of how cloud spend is evolving across providers, services, and emerging AI workloads. Each month, we’ll surface key trends, highlight where the money’s moving in cloud spend, and offer practical insight to help FinOps and cloud cost teams stay on top of things. October — and preceding months — shows a turbulent stretch for cloud economics.

For every CFO who’s ever asked, “Can we reduce engineering costs by 15%?” there’s a CTO quietly thinking, “But at what cost?” That tug-of-war plays out inside most SaaS companies. Finance wants to tighten budgets to protect margins, while engineering and product teams push for the resources to build, test, and innovate faster. The truth is, the teams that spend most efficiently — not necessarily the least — drive the highest returns.

Kubernetes is now the backbone of modern software infrastructure, helping teams deploy, scale, and manage applications efficiently across clouds. But when it comes to understanding costs, Kubernetes remains opaque. Teams often can’t answer basic questions like: How do you solve the gap between engineering usage and financial visibility? CloudZero’s new Kubernetes capabilities are built to address this challenge.

Data has exploded — and so have the challenges that come with it. Every click, transaction, and sensor ping generates mountains of data that traditional databases can’t handle. That’s why more than 94% of organizations now rely on cloud platforms, according to CloudZero’s 2025 cloud report. The goal isn’t just to store data, but rather, to make sense of it fast. And this is exactly where tools such as Google BigQuery step in.

As companies race to ship AI features into production, FinOps teams everywhere are scrambling to keep up with the variability of their engineers’ new favorite toys. The rapid pace of AI experimentation is creating a new wave of unpredictable usage patterns and costs.

Last month was certainly eventful. (If you use AWS or Azure, you probably already know what we’re talking about). But in case you missed it, here are the must-see headlines from October 2025.

Imagine you are building an e-commerce app. Every time a customer places an order, a lot happens behind the scenes. For example, you need to charge their card, update inventory, create a shipping label, and send a confirmation email. You could try to write one giant program that does everything in the correct order, but that quickly becomes a tangled mess — especially if something fails halfway through (say, payment succeeds but inventory update fails).

For years, enterprises have turned to Oracle Cloud Infrastructure (OCI) for what it does best: powering mission-critical applications with unmatched performance, security, and predictable economics. OCI has historically staked its reputation on being the go-to platform for organizations running complex, data-intensive workloads, from core databases and ERP systems to large-scale compute clusters, while putting extra focus on security and predictable pricing.

Before 2022, Alex Hanna worked on Google’s Ethical AI team. Today, she’s the director of research at the Distributed AI Research Institute, a transition sparked by Google’s handling of a paper exposing AI’s growing environmental footprint. So, how bad is it, really? That depends on who you ask. Take Jesse Dodge, a senior research analyst at the Allen Institute for AI. Jesse told NPR that a single ChatGPT query can use as much electricity as keeping a light bulb on for 20 minutes.

On Oct. 20, the Internet woke up and seemingly chose violence. For more than 12 hours, Amazon Web Services (AWS) went down. From banking platforms to hospital communications to mobile ordering apps, digital services came to a screeching halt. The cause? Two programs are trying to write a DNS entry simultaneously, failing, and leaving the entry blank. Thus began the incredibly costly failure cascade.