Filter:

Loading...

Events

Loading...
Get Certified Join the Community

Community

FinOps for AI

Dramatic growth in the use of Machine Learning (ML) and Artificial Intelligence (AI) has been accelerating in the past years for a wide variety of use cases in every sector. The introduction of powerful new hardware that can be used on premises, dozens of Large Language Models (LLMs) to drive generative and agentic AI use cases, and widely diverse offerings from cloud providers and AI SaaS providers, the options for using and including AI in every organization are everywhere. AI is used to augment productivity in the workforce, or replace undifferentiated heavy lifting. AI is incorporated into products and services to enhance customer value. And for companies that have been using AI for years, new opportunities abound. This has driven exponential growth in use and cost.

In many ways, the growth of AI mirrors the adoption of cloud or Kubernetes. New terminology, fast adoption, a decentralized user base, complex allocation of shared costs, collaboration between engineering and the business — these were all challenges that FinOps was established to address. But in other ways — the value economics in a scarcity market, complex utilization monitoring in new classes of hardware and services, and the fact that AI is generally additive rather than a replacement technology — require new skills to be developed.

FinOps practitioners are exploring how to apply FinOps Principles and Capabilities to the cost of AI resources and services to highlight the value that these services can deliver. This is FinOps for AI, working to manage the new usage and cost associated with AI adoption in organizations around the world.

At the same time, Generative AI is being leveraged to democratize data for non-technical FinOps Personas. Cloud providers have integrated GenAI capabilities with their FinOps tooling so users can ask questions in natural language to query the data. IDC predicts that by 2027, 75% of organizations will combine GenAI with FinOps processes.

During 2025, the FinOps Foundation will be supporting working groups developing content to help practitioners do FinOps for AI. Below you’ll find the Community Calls, and see the content and assets that can help you learn more about FinOps for AI, GenAI, and machine learning.

If you are just starting out learning about AI, review the FinOps for AI Overview.

Assets

Optimizing GenAI Usage: A FinOps Perspective on Cost, Performance, and Efficiency

This paper informs FinOps practitioners on how to manage AI workloads effectively with a strategic focus on balancing cost, scalability, and resource efficiency.

Choosing an AI Approach and Infrastructure Strategy

Use this paper to better understand how to choose an AI approach and infrastructure with FinOps principles, capabilities, and outcomes in mind.

Effect of Optimization on AI Forecasting

This Paper addresses the effects of engineering and optimization techniques for AI workloads from the perspective of both Engineering and Non-Engineering FinOps Personas.

Driving Cost Efficiency into AI Deep Learning Pipelines with FinOps

This paper gives an overview of FinOps cost and usage efficiency considerations for deep learning AI.

FinOps for AI Overview

This paper gives an overview of FinOps cost and usage efficiency considerations when using AI services.

FinOps X Sessions

Elasticity by Design: Reducing Waste and Maximizing Savings at Coinbase

Over the past year, Coinbase’s FinOps team has reduced pre-provisioned fixed costs by empowering their engineering teams with visibility into unit economics -- increasing elasticity while decreasing costs. Learn how Coinbase deployed automation to achieve maximized savings through flexible rate optimization that leverages vertical flexibility in a counterintuitive way.

Automating Anomaly Investigation with AI

Discover how to manage thousands of cost anomalies in a multi-million dollar, multi-cloud landscape using GenAI. See how Johnson&Johnson compares anomaly data with forecasting data to determine which anomalies need extra attention from our FinOps team.

Integrating GenAI with Infrastructure as Code

Rich Young and Madoc Batters of Warner Leisure Hotels explore the transformative impact of strategically integrating Generative AI with Infrastructure as Code (IAC) as a revolutionary approach to FinOps.

More Topics

Value through Optimization

FinOps is not about saving money – it’s about making money. The ultimate goal of FinOps is to drive more business value through the optimization of technology usage and costs.

Cloud Sustainability

Carbon-aware FinOps and the intersection of Cloud Sustainability with other capabilities such as Architecting for Cloud and Workload Optimization.

FinOps Scopes: Cloud+

FinOps Scopes are segments of technology-related spending to which FinOps Practitioners apply FinOps concepts.

FinOps-Aware Product Decisions

Shifting cost to a key consideration in the product design process, or "shifting left" by designing and architecting for cost requirements before deployment.

Public Sector

Apply the FinOps Framework to assist federal agencies as they begin (or mature) their FinOps journey.

FinOps is Everyone’s Job

Finance, technology, product, and business teams work together to continuously improve their technology use for efficiency by leveraging FinOps best practices.

FOCUS™

The FinOps Open Cost and Usage Specification (FOCUS™) unifies cost and usage billing data across clouds, software, and data centers with an industry-standard format.

Make Suggestions

Suggest a Resource