Overview

Cloud and AI costs are dynamic, distributed, and often difficult to allocate today, both in single-cloud environments and in multi-cloud environments. This book turns Power BI into an end-to-end management platform and outlines a practical path for building transparent, comparable, and operationally controllable cost management from the data foundation through KPIs, reporting, and steady-state operations, from a data engineering and FinOps perspective. FOCUS provides the open standard as a shared language for cost and usage data. It standardizes billing and export data from different cloud providers and thereby enables consistent analyses, comparisons, and KPIs across cloud boundaries. AI costs are equally central. AI workloads create new cost patterns with tokens, GPU time, and multi-step workflows. Even small changes to a prompt, model version, or agent logic can shift cost profiles quickly. The book explains how token, GPU, and workflow data can be transformed into a controllable model, including practical KPIs such as cost per request, cost per feature, and version economics. Special feature: The book consistently brings FinOps, FOCUS, and Power BI together and extends this foundation with a complete AI cost model. Highlight: A structured 90-day program that describes adoption step-by-step in three phases: establishing transparency, building accountability, and embedding cost governance into steady-state operations. Download: Also included are a central KPI overview and downloadable working materials with 46 KPI fact sheets (definition, data requirements, calculation, interpretation). Target audience Organizations of any size, from single cloud (e.g., Azure) to multi-cloud FinOps owners and cloud cost managers Data engineers and BI/Power BI developers Cloud architects as well as engineering and platform teams Finance/controlling and management teams that want to steer costs transparently and prepare decisions on a solid basis Contents include: Data engineering as the foundation for FinOps Harmonizing cost data with FOCUS Single- and multi-cloud scenarios AI cost models: tokens, GPUs, workflows, and KPIs Semantic models, metrics, and reporting with Power BI Data quality, allocation, and accountability (governance) Forecasting, budgets, and early warning systems Cloud cost management in 90 days 46 KPI fact sheets as a download For everyone who wants to not only display cloud and AI costs, but truly manage them, from a data engineering, FinOps, and Power BI perspective.

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