A finance-grade framework to calculate AI ROI using unit economics, full solution costs, and measurable business value.

Why AI governance fails at scale, and how MCP enables real-time coordination between FinOps, platform, and policy to control AI costs at speed.

Agentic AI shifts FinOps from reporting cloud costs to governing autonomous systems. This article explores what changes, why it matters, and how to adapt.

Traditional FinOps relies on delayed billing data. AI workloads evolve in minutes. This gap is where costs explode. In this post, I explain why agentic FinOps requires a real-time runtime, not better dashboards. Using MCP (Model Context Protocol), FinOps agents can query live cost, usage, and policy data across clouds, detect anomalies as they happen, and act before waste becomes sunk cost. MCP is not hype. It is the missing plumbing for scalable FinOps in the age of AI.

What you'll learn:  Why traditional dashboards miss 90% of AI cost activity, or how real-time intelligence and Agentic FinOps are changing the competitive landscape. Part of the FinOps for AI series . → Start here: The Dawn of Agentic FinOps ← Previous post: Why traditional FinOps breaks with AI workloads: a story of costs, tokens, and fat tails Most dashboards show you where the money went Your AWS Cost Explorer displays $47,000 in AI spending last month. The chart shows a 3

This post looks beyond feature announcements to explain what really shifted: cost efficiency is becoming a runtime concern, not a reporting exercise. From the new Cost Efficiency metric to Bedrock inference tiers and AI-driven operations, FinOps is moving closer to the control plane.

I workloads break traditional FinOps because they generate volatile, real-time costs. Learn how to adapt your practice with allocation-first, real-time ingestion, and business-level unit economics.

Learn how agentic AI and Model Context Protocol (MCP) are transforming cloud FinOps from manual cost reporting to autonomous optimization. This hands-on demo shows AI agents analyzing AWS billing data, detecting anomalies, and generating executive dashboards in minutes. Discover practical architectures using Claude, Amazon Bedrock, and AWS Cost Explorer for enterprise-grade FinOps automation with built-in security and audit capabilities.

How detachment and signal-based thinking improve FinOps decisions, inspired by insights from Frank Contrepois’ latest article.

In this blog post, explore the synergy between cloud optimization and FinOps, uncovering strategies to enhance efficiency, reduce costs, and drive sustainable IT practices for businesses seeking to maximize their cloud operations.

Mitigate anticompetitive practices with FinOps and cloud optimization by OptimNow. Unlock value in cloud adoption, fuel business growth

Explore how FinOps empowers large enterprises to navigate cloud cost complexities, improve efficiency, and enhance investment decisions

Intro to AI-ML in the Cloud, the building blocks (frameworks, foundation models, GPU Vs CPU for AI-ML, AWS Vs GCP

War in Ukraine has spurred growth; engineers are pioneering advanced drones, and cloud providers are expanding, fostering technology

A presentation from a webinar about Cloud Automation 2.0 that surfs cloud resources and right sizes during job runtime. The results are...

Exploring cloud sustainability, this post delves into the environmental impact of cloud computing, measures to assess it, and future trends.

Explore effective strategies to eliminate cloud waste: right-sizing instances, auto-scaling, optimizing storage, network efficiency

Explore cloud optimization and FinOps, driving cost-efficiency, enhancing performance, governance and agility with strategic insights

Cloud automation emerges as an essential tool for managing IT infrastructure in hybrid and multi-cloud environments.