Operations | Monitoring | ITSM | DevOps | Cloud

I’ve seen firsthand how persona-centric FinOps creates realized savings through synergy. I’m a Certified AWS Solutions Architect, FinOps Engineer, and Customer Success leader who’s had the joy of turning cloud confusion into clarity. I’ve added a customer story below — but hold up, we’ve got onboarding optimizing to do.

Containers and microservices are revolutionizing how distributed applications are built, run, and optimized. They enable apps to be highly scalable. You can also isolate some areas for updates and patches without shutting down the entire application or service. Yet, managing containers and microservices at scale can be tricky. That’s where a container management platform like Kubernetes comes in – or, as you’ll see below, where the top Kubernetes alternatives shine.

In collecting and analyzing trillions of events each day, Datadog ingests a massive amount of data. We spend substantially to process and store this data in the cloud, and teams across the organization are committed to optimizing the return on this investment. To this end, our FinOps analysts have always tracked the costs of delivering our services and identified opportunities for savings.

Two years ago, a “state-of-the-art” AI model could write decent copy or summarize a meeting transcript. Today, the top AI models can generate working code, analyze video in real time, and reason through complex scenarios. For SaaS teams, these changes represent a strategic crossroads. Choose the right model and you unlock new revenue streams, slash time-to-market, and wow your users.

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.

Managing the cost of foundation models is a critical challenge as AI adoption surges, particularly for teams using powerful models like Anthropic's Claude Opus and Claude Sonnet. Growing teams generate larger prompt volumes and escalating model complexity, making it difficult to have clear visibility, accountability, and control of cloud AI spending.

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.

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.