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.

Managing technology in K12 schools means juggling dozens of critical platforms simultaneously. When Google Workspace goes down during morning classes, Canvas experiences issues during exam submissions, or PowerSchool becomes unavailable during grade entry periods, the impact ripples through entire school communities. The ability to monitor multiple school platforms from a centralized dashboard has become essential for educational IT teams.

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.

Explore IaaS, PaaS, and SaaS cloud models to accelerate your shipping. This guide defines each, compares their benefits and drawbacks, and introduces a modern approach to get IaaS power with PaaS simplicity.

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).

Applying DORA principles to improve software delivery and operational performance with Google Cloud We’re thrilled to announce that Elastic has been honored with the 2025 Google Cloud DORA Award for Architecting for the Future with AI. Google Cloud DORA awards recognize organizations that have demonstrated significant advancements by applying DORA principles to improve their software delivery and operational performance with Google Cloud.