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GPU Cloud vs On-Premises: TCO Comparison for AI
Total cost of ownership analysis of GPU cloud vs on-premises GPU infrastructure for AI workloads.
Overview
Deciding between GPU cloud and on-premises GPU infrastructure is a critical decision for AI teams. This TCO comparison covers hardware costs, power, cooling, staff, and utilization factors to help you choose the right model.
GPU Cloud (Harch Corp)
Pros
- No upfront CAPEX
- Pay per use (OPEX model)
- Elastic scaling
- Latest GPUs always available
- No maintenance or staff costs
Cons
- Higher cost per GPU-hour at high utilization
- Less control over hardware
- Network dependency
Key Specs
Upfront Cost$0
Cost per H100-hour$2.50-4.00
Time to ProvisionMinutes
UtilizationPay per use
Staff Required0
Hardware RefreshAutomatic
On-Premises GPU Cluster
Pros
- Lower cost at high utilization (>70%)
- Full control over hardware
- No network dependency
- Data never leaves premises
Cons
- High upfront CAPEX ($30K-40K per GPU)
- Requires datacenter space and power
- Needs specialized staff
- Hardware depreciates (3-5 years)
- Limited scalability
Key Specs
Upfront Cost$500K-5M+
Cost per H100-hour$1.50-2.50 (at 70% utilization)
Time to ProvisionMonths
UtilizationFixed capacity
Staff Required2-5 FTE
Hardware Refresh3-5 years
Verdict
For AI workloads with <50% utilization or variable compute needs, GPU cloud is cheaper and more flexible. For >70% utilization with stable workloads, on-premises may be cheaper over 3-5 years. Harch Corp offers both GPU cloud and colocation for hybrid deployments.