Operations | Monitoring | ITSM | DevOps | Cloud

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?

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.

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.

July brought good news, if you’re in the mood for some fresh new opportunities to learn about cost optimization and streamline your FinOps strategy. Check out these updates, hot off the press: Enjoy!

If you’ve ever stared at your cloud bill and thought, “How on earth did this get so crazy?” — you’re not alone. Especially when AI workloads come into play, those GPU costs can feel like a runaway train. The good news? It doesn’t have to be that way. The magic happens when you’ve got someone from every team that cares about smart growth (FinOps, AI/ML, product, engineering, whatever) all in one room, looking at the same set of numbers.