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AI workloads drive new FinOps challenges
As AI adoption accelerates, FinOps teams are facing new challenges in managing cloud and SaaS spend. Whether your organization is using cloud managed services or building custom infrastructure, AI can introduce unpredictable costs, complex billing, and pressure to prove ROI.
AI workloads often spike unexpectedly—especially for inference—making forecasting difficult. They typically run on expensive, GPU-backed infrastructure that is frequently overprovisioned, leading to wasted spend. Billing models vary widely across providers and deployment types, with charges often bundled into single-line items, making it hard to allocate costs accurately across teams or projects. On top of that, finance leaders need clear visibility into the business value of AI investments to justify ongoing spend.
FinOps practitioners are uniquely positioned to bring accountability, transparency, and efficiency to AI initiatives—ensuring innovation doesn’t come at the cost of financial control.
of respondents from the State of FinOps survey are investing in multiple infrastructure areas for AI
– Source: FinOps Foundation
Less than 30% of AI leaders report their CEOs are happy with AI investment return
– Source: Gartner
Bring clarity and control to AI spend
AI costs can be unpredictable and complex, but your cloud financial management doesn’t have to be. The IBM FinOps suite delivers the transparency, recommendations, and insights FinOps teams need to unlock lasting business value from AI.
Bring clarity and control to AI spend
AI costs can be unpredictable and complex, but your cloud financial management doesn’t have to be. The IBM FinOps suite delivers the transparency, recommendations, and insights FinOps teams need to unlock lasting business value from AI.
Cost transparency and accountability
- Bring together all AI spend, whether from SaaS, cloud managed services, or other deployments, into a single pane of glass.
- Use telemetry data, such as token usage and workload metrics, to allocate costs accurately across teams, projects, and business units.
- Turn monolithic charges into actionable insights that ensure financial accountability.
Optimization insights and automation
- Rightsize GPU-backed VMs and Kubernetes clusters to match underlying workloads.
- Manage commitments for inference and training jobs.
- Reduce waste and improve cost efficiency of AI workloads while ensuring performance and scalability.
Flexible budgeting and forecasting
- Enable stakeholders to plan with confidence in the dynamic world of GenAI.
- Forecast AI spend across services and workloads.
- Give teams the agility to adjust budgets and align investments with business priorities.
FinOps meets GenAI: Optimizing the cost of intelligence
Generative AI workloads are reshaping whole industries—but they’re also some of the most expensive and unpredictable to run in the cloud. As organizations race to integrate GenAI into their products and pipelines, FinOps becomes essential for keeping innovation sustainable. In this session you will learn how to demystify workloads, apply FinOps principles to manage challenges, and use unit cost metrics to measure and improve cost efficiency.
Optimize every dollar of your AI investment
The IBM FinOps Suite helps you decode spend across teams, workloads, and infrastructure. From categorization to trends to efficiency, get the insights you need to optimize investments, improve unit economics, and drive smarter decisions at scale.
- Categorization
- Trends
- Efficiency
Answers questions like:
What teams spent the most on AI infrastructure last month?
Which portion of AI costs belong to R&D and can be capitalized versus being COGS or OpEx?
What models, services, and infrastructure components are most costly?
Answers questions like:
Are sudden spikes in AI spend occurring, and what workloads are driving them?
Which services or models are showing the fastest cost growth month over month?
Are AI costs improving or hurting my overall unit economics?
Answers questions like:
Which AI workloads have the highest idle spend?
Where are GPU, CPU, or memory resources consistently underutilized?
Which teams or applications are achieving the best cost efficiency?
FinOps for AI: Enabling the Next Wave of Cloud Innovation
AI spending is projected to hit $644B by the end of 2025. Without proper cost management, budgets can spiral before value materializes. Discover how FinOps principles help align GenAI costs to actual business outcomes across GenAI lifecycle phases.
Get the full picture of AI investments
FinOps Practitioners
AI workloads introduce complex billing models and monolithic charges, making it hard for organizations to allocate costs accurately and enforce accountability across teams. FinOps practitioners require sophisticated allocation tooling to reliably charge costs back and focused dashboards to scale cost awareness.
Engineering
Engineers often run AI workloads on overprovisioned GPU-backed infrastructure without understanding the cost implications, leading to wasted spend. They need integrated cost visibility and optimization insights to guide efficient infrastructure choices and balance performance with budget constraints.
Leadership
Senior leaders face ROI transparency gaps and struggle to justify escalating AI investments due to unpredictable spend patterns and limited visibility into value creation. They need clear value metrics, benchmarking tools, and up-to-date insights to evaluate AI performance, scalability, and business impact.
Finance
Fluctuating AI usage combined with diverse billing data makes forecasting unreliable and budgeting difficult. Teams managing financial planning need predictive analytics that factors in both historical usage patterns and future plans, with tighter cross-functional alignment to help improve plan accuracy.