Operations | Monitoring | ITSM | DevOps | Cloud

In a webinar hosted by CloudZero on Oct. 30, 2025, Larry Advey, Director of Cloud Platform and FinOps and a respected voice in the FinOps community, joined Umesh Rao to deliver a practical session on cloud cost allocation. The session, titled Three Allocation Mistakes Most FinOps Teams Make, unpacked hard-earned lessons and offered a guided tour of CloudZero’s new Dimension Studio.

Imagine you have an API service composed of multiple microservices. Traffic fluctuates — sometimes light, sometimes spiking. Without Fargate, you’d have to manage EC2 instances, autoscaling, patching, and more. With Fargate, you define each microservice as a task, setting the CPU/memory, container image, network rules, and AWS schedules, and then run them as needed. The result: faster deployment, lower ops overhead, and smooth scaling.

Before 2011, cloud infrastructure was still new. AWS had launched EC2 and S3 in 2006. But to deploy applications, engineers had to manually spin up servers, configure storage, and set up networking — all by hand or with custom scripts. There were early configuration management tools, such as Chef and Puppet, but those didn’t offer full cloud orchestration. Then in 2011, AWS launched AWS CloudFormation as the first major orchestration tool.

If Meta’s (now halted) nine-figure AI talent poaching scheme was any indication, the AI talent market is pretty frothy. The number of AI-related job postings has roughly tripled since 2019, and the average salary has more than doubled (Bain). The race is on for companies to find the fastest, most sustainable routes to AI-driven business value; all companies, but especially software companies, are hotly pursuing racers. But despite what Zuckerberg & Co.

Every finance team knows this pain. The cloud bill continues to grow, but the numbers don’t quite add up. Engineering swears their usage reports are accurate. But when you ask, “Which product or customer drove last month’s 18% cost increase?” things go quiet. That silence usually means one thing. Inaccurate cost allocation. Native cloud cost allocation tools from AWS, Azure, and Google Cloud can help. However, they often stop at the average this or average that layer.

SaaS, AI, and technology spending today looks like a more intense version of how it was a decade ago when everyone first migrated to the cloud. The mentality was and, at some companies, still is to build build build and worry about controlling expenditures and optimizing costs later. We’re seeing astronomical amounts of money being raised by brand-new AI businesses that barely even existed a couple of years ago. More than $145 billion has been raised for U.S.

One of the most challenging aspects of running a SaaS business is dealing with highly variable costs. Every service you provide needs resources, and the demand for those resources fluctuates. Just when you think you’ve got this quarter’s budget nailed down, an engineer makes a restructuring decision or a new feature launch exceeds expectations (or falls flat). Suddenly, your bill looks totally different than what you were expecting.

AWS did not intend to build Amazon EKS; it simply had to. Kubernetes adoption beamed light years ahead of AWS’s own managed container orchestration service. This forced AWS to develop a managed service to accommodate customers who wanted to use upstream Kubernetes but did not want to manage it themselves. As soon as AWS got around to it, it knocked the Kubernetes-based container management service out of the park. Not only is Amazon EKS simpler than Kubernetes, but EKS pricing may also be worth it.

When I ask clients to picture cloud cost optimization, they think dashboards, policies, maybe a clever right-sizing purchase. What they don’t picture? Meetings. Misunderstandings. Mistrust. To avoid FinOps failures, we need a new starting line; one that gets to the root of spend misalignment.

In early October, we launched Dimension Studio, a new visual editor for engineers and others that brings point-and-click simplicity to the same powerful, precise allocation engine CloudZero is known for. Before that, when CloudZero users built cloud cost allocations, they got it from our YAML-based CostFormation engine, a code-driven way to describe how cloud and AI costs roll up to products, customers, or teams.

Kubernetes began as a tool to help teams keep thousands of microservices running without falling apart. It gave them a way to schedule workloads, recover from failures, and scale services without constant firefighting. Now, AI has brought back the same chaos, only magnified. Training jobs sprawl across GPUs. Inference traffic spikes without warning. Pipelines stretch across clusters, clouds, and compliance boundaries. Left unchecked, it can break both your workload and cloud budget.

The siren song of building a custom, internal cloud cost management platform is enticing. Many brilliant engineering teams are convinced they can come up with a bespoke solution that perfectly fits their needs. They look at their company’s unique infrastructure and decide they can DIY cost management without having to rely on an external vendor. Believe me, I get the temptation.

A global outage at Amazon Web Services (AWS) did more than knock popular apps offline. It laid bare the cost risks embedded in many cloud architectures. As services fail, the hidden costs of high availability, from redundancy planning to recovery operations, often multiply. For cloud cost leaders, this isn’t an issue of uptime; it’s a visibility and budget-shock issue. It’s a key reminder that architecting for resilience involves difficult trade-offs.

In 2018, attackers gained access to Tesla’s AWS cloud environment through an unprotected Kubernetes console (admin console). Because it lacked proper authentication, the hackers could see and control cluster resources. Once inside, they deployed new pods running cryptocurrency mining software, using Tesla’s compute power for profit. During the breach, the attackers also uncovered credentials stored in the cluster.

So you’ve run into cloud cost challenges — incomplete visibility, overspending, thinning margins — and you’re wondering whether you have what it takes to build a cloud cost optimization platform in-house. It’s a responsible question (one that the FinOps Foundation even advises you to ask). Be warned: Building your own cloud cost optimization platform seems cheaper than adding another SaaS subscription. It’s not.

When CloudZero’s CTO Erik Peterson joined the FinOps Weekly podcast in October 2025, he didn’t hold back. Instead of going on about the usual best practices of AI cost optimization, he posed challenges to how we approach AI spending. From “zombie AI experiments” eating your budget to why you should stop apologizing for using AI, these 10 insights from the podcast are worth considering in how we approach AI FinOps. (Watch the full podcast below and keep reading for more!)

A key advantage of Kubernetes for container management is its high scalability. Kubernetes nodes are directly involved in this, and they can significantly impact your efficiency, cost-effectiveness, and service availability. This guide provides an in-depth look at Kubernetes nodes, including types of nodes and operational best practices.

Downtime is brutally expensive and damaging. Enterprises can lose about $9,000 every minute systems are down, while smaller businesses lose hundreds of dollars per minute. A single outage can often cost over $100,000, and nearly a third of companies lose customers due to downtime. That’s why many organizations turn to platforms like SolarWinds to maintain reliable systems and minimize the risk of costly disruptions.

I recently wrote about the impending SaaS crisis, driven by companies’ newfound ability to use AI to build software they used to have to buy. I predicted this phenomenon would make it even harder for SaaS vendors to drive growth, and that elite SaaS margins would fall from the mid-70s to the mid-60s as companies leaned more into their data and AI.

In July 2024, CrowdStrike — one of the world’s top cybersecurity companies — shipped a minor configuration update to its Windows security product. Within minutes, airlines, banks, hospitals, and retailers worldwide began crashing. The update wasn’t new code. It was a routine content file that slipped through with a bug in its safety checks. When Windows machines loaded it, the agent hit an out-of-bounds memory error and crashed. Devices blue-screened and got stuck in reboot loops.

Cloud computing offers several undeniable benefits to businesses. Some of the biggest ones are agility, cost savings, data recovery, and developing new apps and services to meet changing customer needs. Despite these benefits, the cloud can be complex, demand specialized skills, and require companies to follow up-to-date cloud security best practices. Why?

From the time I joined CloudZero almost two years ago, one thing was unmistakably clear: the team here had built the most powerful cost allocation engine in the FinOps industry. It was also clear that this power came with something of a tradeoff. While engineers and technical FinOps practitioners loved what our YAML-based CostFormation engine could do, the experience wasn’t always simple for non-technical people getting their hands on the product for the first time.

Your CFO just asked why cloud costs jumped 40% last quarter. You pull up your dashboards, confident in your tagging strategy, until you realize 30% of your spend is labeled ‘unallocated’. This is the moment every FinOps team dreads. It reveals a hidden complexity that even mature organizations struggle with: the challenge of accurately allocating cloud costs in a way that’s both granular enough to be actionable and trusted enough to drive decisions.

The obituary for the data center was written too soon. While the cloud dominates today’s IT headlines, traditional infrastructure hasn’t disappeared. It is evolving. Enterprises still rely on data centers for control, compliance, and reliability. However, they are increasingly needing the agility, scalability, and cost visibility that the cloud promises. Cloud managed data center services are bridging this gap.

Nearly every industry today uses AWS for different services. Developers, cloud architects, DevOps engineers, and IT teams all use it to provision servers, databases, and storage. However, doing this service by service and then wiring them together can get messy. That’s where AWS CloudFormation comes in to save time, enforce consistency, and lower the risk of misconfigurations. But beyond simplifying infrastructure management, one big question remains: at what cost?

Today, we’re making CloudZero even better with the launch of Dimension Studio, a major evolution in how CloudZero customers create and manage Dimensions — customizable “lenses” that allocate cloud and AI spend to relevant categories like products, features, teams, or customers, without relying on resource tags. At CloudZero, our mission has always been to help organizations make sense of their cloud and AI spend.

2025 is winding down, but cloud and AI spending is still heating up. September brought some big news that could impact your bottom line. Here are the can’t-miss cloud cost updates from last month: Enjoy!

Many SaaS businesses prioritize customer acquisition and retention over increasing gross margins, particularly during the startup and scale-up stages. It makes sense. A company can accelerate its revenue growth by acquiring and retaining more new customers, rather than simply selling more to existing ones. Yet, here’s the thing. Revenue growth measures the increase in the amount of money a business earns from sales.

As the Senior Manager of Finance & Accounting here at CloudZero, and with a career in FP&A that includes tenures at large public companies, I’ve spent a significant amount of time observing the interactions between the folks who plan the company’s budget and those who spend it. While engineering and operations teams focus on the execution side, my team ensures that the company has the resources required to make each endeavor a success.