Operations | Monitoring | ITSM | DevOps | Cloud

Back in May of 2021, containers had already won. Kubernetes adoption was surging. ECS and EKS were powerful. But for many teams, deploying a simple containerized web service still meant stitching together clusters, networking rules, scaling policies, load balancers, IAM roles, and CI/CD pipelines. It felt heavier than it should. Developers no longer wanted more orchestration power. They wanted less operational drag.

To watch the full walkthrough video on the Pepperdata self-service install, click the link here. Many organizations struggle to efficiently manage their cloud costs, and that arises from difficulties in managing Kubernetes resources. Of the $419 billion spent on cloud infrastructure in 2025 (Synergy Research Group), Flexera estimates that 27% of all of cloud spend is wasted due to overprovisioned resources.

Like most SaaS companies, CloudZero uses its own product. When we released cost per customer reporting, we tested it on ourselves first. And today, we use cost per customer reports regularly. Why? Because they help leadership answer board and renewal questions, including customer-level margins. Cost per customer is valuable and hard to get right. Multi-tenant systems and Kubernetes can hide the link between shared infrastructure (like EC2) and the customers using it.

Modern FP&A tools help SaaS businesses analyze financial performance in real time, forecast accurately, and align spend with business priorities — especially cloud spend, which can quickly spiral without visibility. Choosing the right FP&A tool for your business means understanding what you actually need it to do — whether that’s cloud cost visibility, more accurate forecasting, or tighter alignment between finance and engineering.

Welcome to March’s Cloud Economics Pulse, CloudZero’s monthly look at cloud spend as AI moves from cost line item to cost problem. Two things landed in February that are worth holding together. Last month’s Pulse established a new baseline, calling it a floor, not a ceiling.

Amazon Web Services (AWS) has been the leader in cloud computing for more than 10 years. Despite a decade of innovation, no AWS service encapsulates cloud computing principles better than Elastic Compute Cloud (EC2). Through EC2, AWS can offer flexible and scalable virtual infrastructure that can be ‘rented’ to run applications and workloads.

In March 2026, CloudZero’s Ben Austin, Director of Product Marketing, sat down with Ray Rike, Founder and CEO of Benchmarkit, to walk through findings from FinOps in the AI Era: A Critical Recalibration, a joint survey of nearly 500 organizational leaders on how they’re managing or, rather, struggling to manage AI costs.

This month, two FinOps research reports landed in close proximity. One from the FinOps Foundation — their 6th annual State of FinOps, drawing on a broad global practitioner community. One from CloudZero: FinOps in the AI Era: A Critical Recalibration, built on responses from 475 senior leaders at cloud-mature, AI-active organizations, with a focused lens on how AI is reshaping cloud cost management. Read each alone, and you get a useful snapshot for your business.

Years in the FinOps trenches taught me one thing: The data has never been the problem. The data exists. It’s out there, scattered across provider invoices, buried in tagging gaps, locked behind dashboards that maybe three people in your org actually know how to navigate. The real problem? Nobody can get to it when they need it. Engineers ship features without understanding what they cost the business, let alone whether they improved margin.

Somewhere between a model’s first demo and its first production workload, the cost conversation changes completely. Training is a big number, but it’s a finite one. Inference isn’t. Every user interaction, every query, every API call triggers compute behind the scenes — and unlike training, inference never stops billing. That shift from one-time expense to ongoing operational cost is where inference economics begins.