FinOps — Cloud Cost Management (Including AI)
A two-day course preparing for the FinOps Certified Practitioner certification — the FinOps Foundation framework (Inform-Optimize-Operate), cloud cost optimization practices, and the new FinOps for AI domain (GPU costs, inference, token pricing), aligned with the exam scope the FinOps Foundation is launching in March 2026.
FinOps as a shared language between finance and engineering
FinOps is not just about saving on cloud spend — it is a framework that builds collaboration between finance and engineering teams around shared cost metrics. The course walks through the FinOps Foundation’s Inform-Optimize-Operate cycle and the Crawl-Walk-Run maturity phases, showing how to build a culture of cost accountability without slowing engineering teams down.
AI changes the cost rulebook
Training and inference costs for AI models follow different mechanics than classic cloud infrastructure — LLM token pricing, per-epoch GPU costs, or the self-hosted vs. managed API decision require a separate skill set. The new FinOps for AI exam, launched by the FinOps Foundation in March 2026, formalizes this area as a full FinOps domain, and the course prepares participants for its scope.
Certification as proof of practice
The final module of the course focuses on the structure of the FinOps Certified Practitioner exam, practice questions, and common mistakes — so participants leave not only with operational knowledge but genuine readiness for certification.
Benefits
- Apply the FinOps Foundation framework (Crawl-Walk-Run, Inform-Optimize-Operate) in your organization
- Build cost metrics and KPIs and a culture of cost accountability
- Extend FinOps practices to AI/ML workload costs — GPU, inference, LLM token pricing
- Prepare for the FinOps Certified Practitioner exam under its current scope, including the new FinOps for AI module
Who is this training for?
Prerequisites
- Basic understanding of the public cloud cost model (AWS, Azure, or GCP)
- Basic IT budgeting knowledge helps but is not required
Training program
The FinOps framework — fundamentals and cost culture
- The Inform-Optimize-Operate cycle and Crawl-Walk-Run maturity phases
- Building a culture of cost accountability in engineering teams
- Roles in a FinOps team: practitioners, finance, engineering, business leaders
- Cost metrics and KPIs: unit economics, cost per feature, showback vs. chargeback
Cloud cost optimization — operational practices
- Rightsizing compute and memory resources
- Committed-use strategies (Reserved Instances, Savings Plans) and spot instances
- Cost allocation (tagging, cost allocation) and reporting per team/project
- Automating optimization with Infrastructure as Code
FinOps for AI — the new exam domain for 2026
- Training and fine-tuning costs — GPU, cost per epoch
- Inference optimization: batch vs. real-time, LLM token pricing, response caching
- Comparing self-hosted vs. managed AI services on cost
- Budgeting and forecasting costs for AI projects in your organization
Preparing for the FinOps Certified Practitioner certification
- Exam structure and knowledge domains per the FinOps Foundation
- Practice questions and analysis of common mistakes
- Case study: rolling out a FinOps practice step by step in an organization
Delivery Methods
Online
- Convenience of participating from anywhere
- Interactive live sessions with trainer
- Materials available for 30 days
- No travel costs
On-site
- Direct contact with trainer and group
- Intensive hands-on workshops
- Networking with other participants
- Full focus on learning
Frequently asked questions
How is this different from FinOps Fundamentals, Advanced FinOps, and FinOps for AI in our catalog?
Those three courses are deep, separate technical workshops (framework fundamentals, advanced multi-cloud optimization, dedicated AI/ML cost management). This course combines all three areas into a single two-day, certification-oriented track — structured to match the FinOps Foundation's current exam scope, including the new FinOps for AI module launching in March 2026, and ends with exam preparation that the other three courses do not offer.
What is the new FinOps for AI exam and when is it worth taking?
The FinOps Foundation is launching a dedicated FinOps for AI exam in March 2026, extending the classic FinOps Certified Practitioner certification with an AI/ML workload cost domain — model training, inference, and LLM services. It is worth taking if your organization already carries significant AI costs (own models or LLM APIs) and needs a standardized way to report and optimize them.
Can traditional cloud FinOps and AI FinOps really fit into one two-day course?
Yes — both areas rest on the same framework (Inform-Optimize-Operate) and the same core competencies (tagging, cost allocation, automation); they mainly differ in the source of the cost. The course presents the shared methodological backbone first, then dedicated modules with specifics for classic cloud and for AI, which fits both areas without losing practical depth.
What FinOps tools will I learn about in the course?
Participants work with native cloud cost tools (AWS Cost Explorer, Azure Cost Management), and in the AI module — with methods for tracking LLM API costs (token pricing, caching) and a cost comparison of self-hosted vs. managed AI services. The course is not tied to a single cloud provider.
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