Video coming soon
What Enterprise AI Infrastructure Actually Costs
AI infrastructure decisions are being made without rigorous cost modeling. Cloud GPU sticker prices hide networking egress, storage IOPS, and managed service premiums that add 30-50% to the real cost. On-prem deployments underestimate power, cooling, and personnel costs by 2-3x.
“A hundred H100 GPUs. Three years. Cloud vs. on-prem vs. hybrid. I built a total cost of ownership model and the numbers will change how you think about AI infrastructure decisions. The sticker price is never the real price.”
Architecture Diagrams
Build Notes
- Three scenarios: Cloud ($31.5M), Hybrid ($24.3M), On-Premises ($22.8M) for 100 H100 GPUs over 3 years
- Nine cost categories: compute, networking, storage, personnel, governance, security, facilities, software, migration
- 15 user-adjustable variables for sensitivity analysis
- Break-even analysis at 60-70% sustained GPU utilization
Lessons Learned
- On-prem becomes cost-competitive at 60-70% sustained GPU utilization over 3 years
- Hidden cloud costs (egress, IOPS, managed services) add 30-50% to GPU sticker price
- Hidden on-prem costs: power/cooling is 15-25% of total; personnel is underestimated by 2-3x
- The TCO model is the single best tool for getting infrastructure budget approved
Discussion
What surprised you most about AI infrastructure costs when your organization started deploying? Was it the GPU prices, the hidden cloud fees, the power bills, or the personnel costs?