The hidden costs of AI: How to manage GPUs and inference spend

Artificial intelligence might be the most exciting technology investment on your roadmap, but its costs can quickly spiral out of control.

Training and running large models requires vast compute resources, high-performance networking, and careful orchestration. Many organizations find that what begins as an experimental project becomes a serious line item on the balance sheet.

The issue is not just that AI is expensive, but that its costs are unpredictable. GPUs are scarce, inference demand spikes without warning, and data movement between regions creates hidden compliance and performance challenges. To build sustainable AI capabilities, leaders need to understand where these costs come from and how to manage them strategically.

Why AI spend is so hard to predict

Executives are discovering that AI workloads don’t behave like traditional cloud services. Instead of steady consumption patterns, they come with unique challenges:

• GPU scarcity and cost. Competition for advanced accelerators has pushed prices up, with overprovisioning wasting millions and underprovisioning delaying projects.
• Training versus inference. Training consumes massive compute over weeks or months, while inference requires low-latency, always-on availability. Each phase has a very different cost profile.
• Data gravity. Moving terabytes of training data between clouds or into sovereign environments not only adds cost but also raises compliance risks.
• Unpredictable spikes. Experimentation cycles create sudden bursts in demand, making traditional budgeting models unreliable.

The result? CFOs are left with surprise bills, and CTOs struggle to explain why costs escalated so quickly.

If you’re struggling with runaway GPU bills, Tenth Revolution Group can help you find trusted technology talent who know how to optimize AI infrastructure for cost and efficiency.

Bringing FinOps discipline to AI

FinOps (the practice of aligning finance, engineering, and operations) has become essential for AI. It’s no longer enough to track total spend; organizations must link every GPU hour and inference call to business value.

Key strategies include:

• Workload placement decisions. Should training happen in public cloud, a sovereign provider, or on-premises clusters? The right choice depends on sensitivity, cost, and scale.
• Cost attribution. Breaking down spend by team, project, or model clarifies who is consuming what and ensures accountability.
• Sustainability tracking. Regulators and investors increasingly expect transparency around energy use and carbon footprint.
• Security and sovereignty by design. Infrastructure choices must account for where data lives and how it flows across borders.

By embedding these practices, businesses avoid treating AI infrastructure as a technical afterthought and instead manage it as a strategic resource.

If you want to bring FinOps thinking into your AI projects, Tenth Revolution Group connects you with trusted technology talent who combine cloud expertise with financial discipline.

From cost control to competitive advantage

The goal of AI cost governance isn’t just about preventing overruns. Done well, it enables faster experimentation, smoother scaling, and stronger resilience against regulatory or supply chain shocks. Companies that master GPU orchestration and inference efficiency can deliver innovation more quickly and at lower cost than competitors who treat infrastructure reactively.

Consider how financial services firms are already applying FinOps to fraud detection models, keeping GPU costs under control while meeting audit requirements. Healthcare providers are leveraging sovereign clusters to satisfy patient data rules while scaling diagnostics. Retailers are aligning AI-driven demand forecasting with FinOps frameworks to prevent overspend during seasonal peaks. Each example shows that efficient infrastructure is an enabler, not a constraint.

What leaders should prioritize

For executives, the message is clear: AI success depends as much on cost governance as it does on model performance. Leaders should:

• Invest in AI cost observability that ties spend directly to workloads and outcomes.
• Define clear multi-cloud governance policies around placement, egress, and redundancy.
• Balance agility with sovereignty when deciding whether to rent GPUs or build private clusters.
• Create cross-functional FinOps councils that align data science, finance, and compliance priorities.

Those who act now will gain predictability, trust, and competitive edge. Those who delay risk stalled projects and wasted budgets.