Operations | Monitoring | ITSM | DevOps | Cloud

One of the biggest benefits of the cloud is that it gives engineering teams the freedom to deploy and iterate applications quickly. Unlike traditional IT environments where engineers require a series of approvals before embarking on projects, in the cloud, engineers can choose from several managed services and deploy them at the click of a button. This means your team can innovate faster and respond quickly to market demands.

AI is surging into products. And the invoices are exploding with it. The key question is no longer, “How much did we spend?” It’s now: “Was it worth it?” That shift, from totals to value, is at the heart of FinOps. The FinOps community defines the practice as bringing financial accountability to the cloud, so teams make tradeoffs with clear business context. In plain English, measure value per dollar, then optimize the system and not just the bill.

AI adoption isn’t just accelerating, it’s compounding. From GPT-5 to Claude to Llama and beyond, engineering teams are integrating diverse LLMs across products, experiments, and services. And finance teams are now grappling with a new kind of cloud complexity: token-based economics and volatile inference costs, often spread across multi-model, multi-cloud, and multi-region architectures. The modern FinOps stack needs to keep up. CloudZero was built for this moment.

I’ve spent most of my career building and optimizing cloud, on-prem, and data platforms for growing companies. It’s been an amazing journey so far. Through it all, FinOps has become more than just a methodology for me (Fred FinOps didn’t just come from my love of the Flintstones, though I do appreciate a good cartoon). It’s a community, a discipline, a tribe I’ve come to call home. Lately, some tough questions have kept me up at night: These challenges got me thinking.

Amazon SageMaker makes it easy to prepare data for machine learning (ML) and then train, deploy, and modify ML models. SageMaker is a fully managed service that automates much of the ML lifecycle. So, if you want a single partner to help you through all stages of your Artificial Intelligence (AI) lifecycle, SageMaker might be the answer. Perhaps more important for this post is the promise that Amazon SageMaker can reduce your machine learning model costs. But does SageMaker pricing reflect this?

Kinesis is an Amazon Web Services (AWS) product that collects, processes, and analyzes streaming data in real-time. It can process streaming video, audio, IoT data, application logs, and other data as it arrives from thousands of unique sources, unlike technologies like Hadoop, which utilize batch processing (waiting for a complete dataset to arrive before processing and analyzing it).

Most SaaS companies aren’t spending too much on AI. They’re just completely screwing up how they price it. You feel the budget pressure. The OpenAI and Anthropic bills keep climbing. Finance is starting to twitch. So the instinct is to cut. Trim back experiments. Cap usage. Beg your team to “optimize.” You can’t cost-cut your way out of a pricing failure though. And most of the time, that’s all this is — a pricing failure.

When you’re a global leader in AI-assisted hiring, speed matters. Not just in matching candidates to jobs, but in making the engineering and financial decisions that keep your platform running efficiently. For HireVue, fragmented infrastructure, manual processes, and sprawling spreadsheets turned cloud cost management into a time-consuming spelunking expedition.

Although container-related technology existed before 2013, Docker revolutionized and propelled it into the mainstream. Using Docker, developers could automatically create containers from application source code, share libraries, and reuse containers. Docker enables you to track container image versions, roll back to an earlier iteration, and track who built a specific one. You can even upload only the deltas between two versions.

If you want to align green awareness with bottom-line impact, start by looking at your cloud waste. Not just as a budget problem, but also as wasted energy, because that’s exactly what it is. AI, especially, is a mounting factor. Deloitte’s Tech Trends 2025 report highlights the growing energy demands of large AI models, warning that electricity use in data centers could soon rival that of entire nations like Sweden or Germany.