The Strategic Decision
"Should we build our own GPU cluster or use cloud?" It's the question every AI-forward enterprise faces. The answer isn't universal—it depends on your specific circumstances.
The True Cost of On-Premises
Capital Expenditure
- Hardware: $20-40K per high-end GPU (H100, A100)
- Networking: InfiniBand for multi-node training ($5-10K per node)
- Storage: High-speed NVMe arrays ($500-1,000 per TB)
- Infrastructure: Racks, power distribution, cooling
Operational Expenditure
- Power: 500-700W per GPU, 24/7
- Cooling: Often equals power cost
- Staff: Specialized infrastructure engineers
- Maintenance: Hardware failures, upgrades, security
Hidden Costs
- Lead Time: 6-12 months from order to production
- Obsolescence: Hardware depreciates as new generations release
- Utilization Risk: Under-used capacity is wasted capital
The True Cost of Cloud
Direct Costs
- Compute: $2-5 per GPU-hour (varies by provider and commitment)
- Storage: $0.02-0.10 per GB-month
- Networking: Egress charges add up quickly
Commitment Tradeoffs
| Commitment | Discount | Flexibility |
|---|---|---|
| On-Demand | 0% | Maximum |
| 1-Year Reserved | 30-40% | Moderate |
| 3-Year Reserved | 50-60% | Minimum |
Hidden Costs
- Data Gravity: Once your data is in cloud, moving it is expensive
- Vendor Lock-in: Cloud-specific tooling creates switching costs
- Unpredictable Bursts: Spiky workloads at on-demand rates
The Decision Framework
Choose On-Prem When:
High Sustained Utilization If you can maintain >70% utilization consistently, on-prem typically wins on cost.
Predictable Workloads Steady-state training jobs that run continuously benefit from owned hardware.
Data Sovereignty Requirements Regulatory or security requirements may mandate on-premises processing.
Long Time Horizon 3-5 year planning horizons favor capital investment.
Choose Cloud When:
Variable Demand Spiky workloads with periods of low usage favor cloud flexibility.
Rapid Experimentation Teams exploring different GPU types and configurations benefit from cloud variety.
Short Time Horizon Uncertain demand or 1-2 year planning favors operational expense.
Global Distribution Workloads that benefit from geographic distribution are cloud-native.
The Hybrid Reality
Most enterprises land on a hybrid model:
- On-prem baseline: Owned hardware handles predictable, sustained workloads
- Cloud burst capacity: Cloud absorbs spikes and experimental work
- Specialized cloud: Certain GPU types only available from specific providers
Making It Work
The hybrid model only works with complete visibility across environments. You need:
- Unified Cost Tracking: Single view of on-prem and cloud spend
- Workload Routing: Intelligent placement based on cost and requirements
- Capacity Planning: Predictive models for when to expand each tier
- Optimization Feedback: Understanding which workloads belong where
The Bottom Line
There's no universal answer to on-prem vs cloud. The right choice depends on your utilization patterns, planning horizons, and operational capabilities. What matters is having the visibility to make—and continuously validate—the right choice for your organization.
Relize provides unified visibility across on-prem and cloud GPU infrastructure. See how it works.