Your AI bill just grew 340%.Now you own it.

A free, self-paced course that teaches how AI billing actually works. Five modules covering token pricing, cost attribution, anomaly detection, forecasting, and ROI measurement. Built by CloudYali, drawing on publicly available FinOps Foundation frameworks and industry best practices. Takes approximately 8 hours to complete.

Yes. All five modules are free. No credit card, no trial period, no locked content. Free because we built it for the FinOps community.

FinOps practitioners who find AI billing confusing, engineering leads who make model decisions and need cost intuition, and finance analysts who need to bring AI costs into financial reporting. Each module includes context for all three roles.

How token-based, compute-based, and capacity-based AI billing works. How to attribute AI costs to teams and products. How to detect anomalies and set governance guardrails. How to forecast AI costs in fast-changing environments. How to measure and present AI ROI to executives.

No. This course is built by CloudYali and is not affiliated with, endorsed by, or officially connected to the FinOps Foundation or its certification programs. The course content builds on the publicly available work of the FinOps Foundation and broader industry best practices in AI cost management. It is an independent educational resource created for the FinOps community.

Approximately 8 hours across five modules. Self-paced — progress saves automatically. Most people complete it across 3–4 sessions.

Applying financial operations principles to AI workloads. Unlike traditional cloud costs — compute, storage, network — AI costs involve token billing, inference pricing, and capacity reservations that need different attribution and forecasting approaches. The FinOps Foundation formally recognizes it as a distinct domain.

Traditional cloud FinOps focuses on compute, storage, and network costs billed by time or capacity. AI FinOps deals with token-based billing, three deployment models (API, managed, self-hosted), context window costs, shared inference endpoints, and rapidly changing model pricing. The attribution, forecasting, and optimization techniques are fundamentally different.

The top challenges are: 67% of AI spend cannot be attributed to a specific team, inference costs grow 4x faster than traditional cloud, shadow AI creates untracked spending through personal API keys and unapproved models, and AI cost forecasting is unreliable because model switching and optimization techniques constantly change the cost baseline.

The course covers three allocation methods: direct attribution (when workloads map 1:1 to resources), proportional allocation (splitting costs by usage metrics like tokens processed or GPU hours consumed), and fixed allocation (pre-agreed budget splits). Module 2 also covers PTU allocation for reserved capacity and tagging strategies specific to AI workloads.

AI unit economics measures cost per meaningful business outcome — cost per inference, cost per customer interaction, cost per document processed, or cost per model prediction. Module 5 covers the nine official FinOps Foundation KPIs for AI, including ROI calculation and how to translate technical metrics into business value for different stakeholders.

Key optimization strategies covered in the course include prompt engineering to reduce token usage, semantic caching to avoid redundant API calls, model routing (using smaller models for simple tasks), batch processing, context window management, and evaluating when to switch from API-based to self-hosted deployment. Module 4 explains how these optimizations affect forecasting.

The course teaches cost tracking principles that apply across tools — tagging strategies, attribution frameworks, dashboard design, and anomaly detection patterns. Module 2 includes a hands-on exercise designing a GenAI cost tracker. CloudYali is one platform that implements these principles for AWS, Azure, GCP, and AI API providers.

No formal prerequisites. The course is designed for FinOps practitioners extending to AI, engineering leads who make model decisions, and finance analysts bringing AI costs into reporting. Basic familiarity with cloud billing concepts is helpful but not required — Module 1 starts from fundamentals.