FinOps Fundamentals: AI, GenAI & Cloud Cost Mastery Course

This course contains the use of artificial intelligence.

Cloud costs are no longer simple to predict - and AI and generative AI workloads have made cost management even harder. This course is designed for a general audience: no prior FinOps or deep cloud-cost background is required, and every concept is explained from first principles before building toward practical application.

Across three comprehensive parts, you will start with the fundamentals - how cloud billing actually works, the FinOps lifecycle, key cost metrics and unit economics, and the different cloud pricing models available to you. From there, you will explore why AI and GenAI workloads break traditional cost models: the real cost of training versus inference, GPU and accelerator economics, token economics for LLM APIs, and how to allocate and forecast unpredictable AI spend.

The course then turns to practical optimization and governance: cost optimization techniques for both cloud infrastructure and AI/GenAI workloads, tagging and visibility tooling, governance guardrails and budget alerts, vendor negotiation and commitment strategy, and a look at where FinOps for AI is headed next with automation and AI-driven cost optimization.

Whether you are an engineer, a finance professional, or simply responsible for keeping cloud and AI costs under control, this course gives you a clear, practical, and honest foundation - including the real limitations and trade-offs, not just the promise.

This course contains the use of artificial intelligence.

Curriculum Outline

Part 1: Foundations of Cloud Cost Management & FinOps

• What Is FinOps? Culture, Principles, and the Cost Accountability Shift

• Cloud Cost Fundamentals: How Cloud Billing Actually Works

• The FinOps Lifecycle: Inform, Optimize, Operate

• Key Cost Metrics and Unit Economics for Cloud Workloads

• Cloud Pricing Models: On-Demand, Reserved, Spot, and Savings Plans

• Building a Cost-Aware Culture Across Engineering and Finance

Part 2: FinOps for AI & GenAI Workloads

• Why AI/GenAI Breaks Traditional Cloud Cost Models

• The Real Cost of Training vs. Inference

• GPU/Accelerator Economics: Pricing, Utilization, and Waste

• Token Economics: Understanding LLM API Cost Structures

• Cost Allocation and Chargeback for AI Products and Teams

• Forecasting and Budgeting for Unpredictable AI Workloads

Part 3: Optimization, Governance & the Future

• Practical Cost Optimization Techniques for Cloud Infrastructure

• Practical Cost Optimization Techniques for AI/GenAI Workloads

• Tagging, Visibility, and Cost Allocation Tooling

• Governance, Guardrails, and Budget Alerts

• Vendor Negotiation, Multi-Cloud, and Commitment Strategy

• The Future of FinOps: Automation and AI-Driven Cost Optimization

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