FinOps for AI: Govern GPU, Token & SaaS Spend

Table of contents
Do not edit: TOC will be auto-generated

AI is becoming a cornerstone of modern enterprise strategy—but its cloud cost profile is also emerging as one of the most complex and volatile in IT. From GPU-hungry model training to token-based inference pricing and sprawling AI SaaS subscriptions, it’s no wonder AI workloads are adding complexity to FinOps practices.

To stay ahead, you need a FinOps approach purpose-built for AI. That’s why we’ve embedded support for AI across our FinOps portfolio: to help teams gain visibility, optimize spend and govern AI infrastructure with precision.

The unique cost challenges of AI workloads

Understanding the unique cost challenges that come with AI workloads—and what you can do about it—is essential. Here’s what Flexera is doing to help you overcome some of these obstacles.

GPU-driven compute costs

Training and inference workloads often rely on high-performance GPUs, which are significantly more expensive than standard CPUs. These costs are compounded by:

Burstiness—AI workloads are often dynamic and spikey, making it challenging to cover them with commitment discounts
Idle GPU time—Failure to schedule off time generates unnecessary usage and cost
Inefficient bin packing—Poor bin-packing can leave expensive resources underutilized
Limited availability—Spot GPU instances are scarce, and on-demand pricing is steep

Our remedy: Spot Ocean, Flexera’s container optimization solution, supports GPU time-sharing.

Token-based pricing models

AI resources and services often charge based on token usage. This introduces a new unit of cost that is:

Unpredictable: Token usage can vary dramatically based on prompt length and model complexity
Opaque: Without proper tagging and attribution, token costs are hard to allocate

Our remedy: In the coming months, Flexera One Cloud Cost Optimization will support token-based pricing models, enabling FinOps teams to map token usage to projects and owners for better accountability.

Fragmented spend across services

AI spend goes beyond infrastructure services (e.g., compute, storage, data transfer) to SaaS (e.g., Microsoft CoPilot, ChatGPT) and PaaS (e.g., Databricks, Snowflake). Each service has its own pricing model, making data normalization and unified visibility difficult to achieve.

Our remedy: Flexera One Cloud Cost Optimization is evolving to categorize and normalize AI service costs alongside other cloud resource and service costs. Additionally, Spot Eco, Flexera’s cloud commitment management solution, offers easy, multi-cloud management of commitment discounts (e.g., Reserved Instances, Savings Plans) for AI services.

Flexera’s FinOps stack for AI

Our custom FinOps stack helps you govern AI spend with precision.

Spot Ocean: container optimization for AI inference

AI inference often runs in Kubernetes environments. Spot Ocean, Flexera’s container optimization solution, automates container infrastructure scaling, rightsizing and bin-packing. It also supports GPU time slicing, giving you maximum value for your GPU spend.

High-speed autoscaling for Amazon Elastic Kubernetes Service (EKS), Azure Kubernetes Service (AKS) and Google Kubernetes Engine (GKE)
GPU-aware scheduling to reduce idle time

Spot Elastigroup: VM optimization for AI training

Training workloads are long-running and sensitive to interruptions. Spot Elastigroup, Flexera’s virtual machine optimization solution, enables these jobs to run on spot instances with:

Predictive autoscaling, which uses machine learning to forecast demand up to two days ahead and scale resources accordingly
Stateful node support for uninterrupted model training
High-performance compute (HPC) burst autoscaling on spot instances

Eco: commitment optimization for AI services

AI services offer discounts via reserved instances and savings plans. Eco automates commitment management across more than 60 AWS, Azure and Google Cloud offerings, including AI services.

Support for Amazon SageMaker and Azure OpenAI commitment discounts
Automated micro-purchasing of commitment discounts for AI services to meet actual usage without over-committing
Integrated dashboards for tracking hourly savings and coverage of your Amazon SageMaker Savings Plans or Azure OpenAI provisioned throughput units (PTUs)

Flexera One Cloud Cost Optimization: AI spend control

Flexera One Cloud Cost Optimization plays a pivotal role in managing AI-related cloud spend. As AI workloads span across compute, storage and specialized services, Cloud Cost Optimization provides unified visibility into all cost drivers. It enables organizations to proactively govern AI spend, reduce waste and align cloud investments with business value.

Categorize AI-related services using custom fields and intelligent tagging
Detect cost anomalies using machine learning models trained on historical usage
Forecast AI service costs with high accuracy, accounting for token-based and GPU-driven pricing
Attribute AI spend to business units or projects for better accountability

Be a step ahead of your AI spend with Flexera

As AI adoption accelerates, its unique cost structures and expensive resources demand tooling tailored specifically to intelligent workloads. From navigating token-based pricing to managing volatile GPU-driven compute, you need precise tools and strategies to govern AI costs. Flexera’s comprehensive FinOps stack empowers FinOps teams to optimize resource utilization, control spend and ensure their AI investments drive measurable business value.

Want to know more?

Our new e-Book, “FinOps for AI—AI for FinOps,” helps FinOps practitioners and other decision-makers align innovation and efficiency as AI reshapes the cloud landscape. This resource takes an in-depth look at: