By Joe Dahlquist, Principal Product Marketing Manager & Andrew Midgley, Principal Product Marketing Manager

The Expanding Role of GPUs in AI

GPUs have become the backbone of AI and machine learning by handling complex workloads through parallel processing. As more organizations scale their AI initiatives, managing these specialized resources brings new challenges in cost, efficiency, and sustainability across cloud and on-prem environments.

Like CPUs, GPUs require monitoring and cost attribution to prevent waste, although getting this data can require extra effort. Without clear visibility into GPU usage, teams often face underutilization, overspending, and unnecessary energy consumption that can significantly increase operational costs.

Challenges in GPU Monitoring

Despite their importance, GPUs remain difficult to monitor and manage for several key reasons:

1. Cost Attribution Complexities

AI workloads typically run across multiple GPUs, models, and datasets, making accurate cost assignment difficult. Many organizations cannot produce detailed breakdowns of GPU spending, leading to unpredictable budgeting. This lack of clear attribution makes it hard to hold teams financially accountable, manage costs effectively or justify new infrastructure investments.

2. Limited Visibility into Utilization

Teams often lack real-time insights into GPU usage, allowing resources to sit idle or underutilized. AI workloads naturally fluctuate, requiring different processing power at various stages. Without proper monitoring, organizations risk either over-provisioning (creating waste) or under-provisioning (causing performance bottlenecks).

3. Unclear Optimization Options

Whether the GPUs are tied to a specific server- such as in the case of a VM running on a public cloud- or operate within a resource pool such as in the case of Kubernetes, it's often not straightforward to identify the best optimization option in the case of a utilization mismatch. Picking the right instance type or scaling mechanism requires expertise and detailed insights.

Why GPU Monitoring Matters

Optimizing GPU usage goes beyond cost control. Better visibility into GPU-based workloads supports performance, scalability, and long-term AI growth. Organizations that track GPU usage effectively can:

• Improve Performance by identifying bottlenecks and optimizing resource allocation.
• Control Costs by reducing waste and reallocating unused resources.
• Reduce Environmental Impact by lowering idle time and power consumption.
• Plan for Growth by analyzing usage trends to forecast future needs.
  

Applying FinOps Strategies to GPU Management

FinOps practices help organizations bring financial accountability to the variable, consumption-based spend model of public cloud. When applied to GPU management, and supported with specialized tools, these practices enable teams to:

• Track Costs Accurately by breaking down GPU usage and associated spend, assigning it to the teams, projects and departments responsible. FinOps practitioners commonly rely on dedicated tooling to implement these assignment rules and break apart cluster-based workloads.
• Minimize Waste by reallocating underutilized GPUs to workloads that need them.
• Optimize Spending Over Time by monitoring utilization and adjusting resources accordingly. FinOps tooling can help practitioners by matching alternative VM types and sizes to existing workloads and recommending scaling actions for clusters.
  

A financial and operational approach to GPU management improves decision-making and prevents budget overruns while maintaining AI performance.

Beyond Cost: GPU Monitoring and Sustainability

GPUs are among the most energy-intensive components in data centers. Without proper oversight, they contribute to excessive power consumption and carbon emissions. Many regions now enforce stricter sustainability regulations, requiring organizations to monitor and report energy use.

Reducing idle time, optimizing workloads, and improving efficiency all contribute to sustainability goals. As regulations evolve, organizations with strong GPU monitoring practices will find it easier to comply with new standards. FinOps tools are already helping on this front by providing users detailed carbon emission reporting across public cloud footprints.

The Future of GPU Monitoring

As AI workloads scale, organizations need better strategies to manage GPU resources efficiently. Several trends are shaping the future of GPU monitoring:

• AI-Driven Optimization - Machine learning models are improving GPU scheduling, allowing workloads to run more efficiently. Organizations are moving toward automated workload balancing, where AI predicts resource demand and adjusts allocation in real time to prevent over-provisioning and idle GPUs.
• Predictive Scaling & Cost Forecasting - AI infrastructure requires proactive planning. Instead of reacting to GPU shortages or cost spikes, teams are using historical usage patterns to predict future needs. This approach helps organizations make smarter purchasing decisions and allocate resources more effectively.
• Sustainability & Compliance - GPUs consume significant power, and many governments are tightening regulations on data center energy use. Businesses must track carbon impact and energy efficiency metrics alongside cost data to meet sustainability targets and comply with new standards, particularly in the EU.
• Real-Time Cost Attribution & Budgeting - As GPU costs rise, organizations are integrating real-time financial tracking into their workflows. Teams are using automated tagging, budget policies, and FinOps practices to keep GPU spending aligned with business goals.
  

Companies that invest in visibility, automation, and cost governance will stay ahead as AI-driven workloads continue to expand. GPU monitoring will shift from being a reactive necessity to a strategic advantage, helping businesses control costs while maximizing performance.

Conclusion

As AI workloads continue to expand, effective GPU monitoring becomes essential for controlling costs, optimizing performance, and achieving sustainability goals. Organizations that implement robust monitoring practices gain the ability to eliminate waste, enhance operational efficiency, and scale their AI infrastructure strategically. By closely tracking resource utilization, applying sound financial management principles, and prioritizing environmental considerations, teams can make more informed decisions about GPU deployment and usage - ultimately supporting both innovation and responsible resource management.

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ABOUT THE AUTHORS

Joe Dahlquist, Principal Product Marketing Manager

Joe Dahlquist is the Principal Product Marketing Manager at Kubecost, with experience spanning cybersecurity, cloud, fintech, and edtech, helping deliver products used by companies like FedEx, HSBC, Amazon, and Microsoft.

Andrew Midgley, Principal Product Marketing Manager

Andrew Midgley is a Principal Product Marketing Manager working on Cloudability. He was involved in the early development of FinOps and has worked with customers around the globe on maximizing their cloud investments.