Artificial Intelligence is revolutionizing industries, but its rapid growth is raising new questions about where AI workloads should run. For years, the default answer has been “in the cloud.” However, a growing number of experts are challenging this assumption.

Recent discussions suggest that for certain workloads, the cloud may not always be the most cost-effective or operationally efficient choice. GPU scarcity, unpredictable inference costs, and latency-sensitive applications are forcing organizations to rethink their infrastructure strategies.

Why This Debate Matters

Running AI in the cloud offers scalability, flexibility, and fast access to state-of-the-art hardware. But these benefits come with trade-offs:

  • High and unpredictable GPU pricing that makes budgeting difficult
  • Long lead times for GPU capacity in high-demand regions
  • Latency concerns for real-time or edge-focused AI applications
  • Regulatory restrictions that require certain workloads to remain on-premises or in specific geographies

In some cases, these factors push organizations toward hybrid or on-premises deployments.

FinOps Considerations

From a FinOps standpoint, the cloud-versus-on-prem decision is about more than infrastructure. It’s about cost governance, forecasting accuracy, and operational agility.

Key questions to ask:
- Are training workloads consuming more GPU hours than planned?
- Is inferencing cost growth aligned with expected business value?
- Could hybrid AI reduce both cost and operational risk?
- How do we ensure clear cost attribution when workloads are split across cloud and local resources?

Potential Advantages of Hybrid AI

A hybrid approach allows organizations to run workloads in the most cost-effective and performance-appropriate location.

For example:
- Model Training: Run in the cloud when large, burstable compute is needed, then scale down afterward.
- Inference: Run closer to the customer or on-premises to reduce latency and egress costs.
- Specialized Hardware: Use in-house GPU clusters when available to avoid long-term cloud GPU costs.

How FinOps Teams Can Prepare

  1. Expand Cost Models
    Include on-prem and edge infrastructure in your AI cost reporting.

  2. Forecast by Workload Type
    Separate training and inference costs in your forecasts for better accuracy.

  3. Scenario Planning
    Run “what-if” analyses comparing cloud-only, hybrid, and on-prem deployments.

  4. Negotiate Cloud GPU Commitments
    Explore committed-use discounts, capacity reservations, and multi-region options.

  5. Monitor Utilization Closely
    Idle GPUs drain budgets quickly. Set up alerts for underused capacity.

Looking Ahead

Cloud providers are already adapting to this challenge. Expect to see:
- More AI-specific pricing tiers
- Integrated hybrid deployment tools
- Dedicated regional GPU capacity for regulated workloads

For now, FinOps teams should prepare for a future where AI workloads run across cloud, hybrid, and on-prem environments. The key is not to pick one option blindly, but to align each workload with the location that delivers the best balance of cost, performance, and compliance.

Bottom line: The cloud is still a powerful home for AI, but it’s not always the only or best option. A thoughtful FinOps-driven approach ensures your AI investments deliver maximum business value while keeping spend under control.