The colocation vs cloud decision has become more complex for AI workloads. Here's a practical framework for evaluating GPU colocation based on utilization, timeline, and total cost of ownership.
AI data center colocation infrastructure for GPU workloads
72hr
BYOH Deployment
70%+
Utilization Threshold
12-18mo
Typical Breakeven
50kW+
GPU Rack Density
The Colocation Renaissance
Colocation is experiencing a resurgence for AI workloads. The economics have shifted: cloud GPU costs exceeding $2-4/hour per GPU make owned hardware economically attractive at scale. For organizations with predictable, high utilization workloads, colocation offers a path to infrastructure ownership without the complexity of building facilities.
This guide provides an objective framework for evaluating whether colocation makes sense for your AI infrastructure needs.
The Four Variable Framework
Colocation decisions should be evaluated against four primary variables: utilization, timeline, power density, and total cost of ownership. Each variable has threshold values that help determine the optimal infrastructure approach.
- Utilization Rate If GPUs will run 70%+ of the time, colocation typically wins.
Below 50%, cloud's pay-per-use model is more efficient.
- Timeline Horizon 3+ year commitment? Colocation economics improve significantly.
Short term needs favor cloud flexibility.
- Power Density GPU racks require 30-50+ kW. Not all colos support this.
Verify cooling infrastructure for high density deployments.
- Total Cost of Ownership Include hardware, facility fees, power, and opportunity cost.
Factor in depreciation and refresh cycles.
BYOH Economics: The Numbers
Bring Your Own Hardware (BYOH) models are the most common colocation approach for AI workloads. Here's a realistic cost comparison for an 8x H100 server deployment:
Cost Category BYOH Colocation Cloud GPU
Upfront Hardware $200-400K $0
Monthly Operating $3-5K $15-25K
Year 1 Total $240-460K $180-300K
Year 3 Total $310-580K $540-900K
3-Year Savings 40-55% with BYOH (at high utilization)
Note: Costs are illustrative ranges based on NVIDIA H100 hardware list pricing, typical colocation power/space/bandwidth rates (JLL Data Center Outlook 2025), and cloud GPU spot/on-demand pricing from major providers (CoreWeave, Lambda, AWS, as of Q1 2026). Actual costs vary by provider, location, and configuration. Assumes 70%+ utilization for BYOH economics to favor colocation.
When Colocation Makes Sense
Production Inference at Scale
Running inference 24/7 for production APIs. High utilization makes hardware ownership economical.
Predictable Training Pipelines
Regular retraining schedules with known GPU requirements. Capacity planning is straightforward.
Data Sovereignty Requirements
Healthcare, finance, and defense workloads requiring physical control over hardware and data location.
GPU Availability Concerns
Owning hardware eliminates cloud capacity constraints. Your GPUs are always available.
When Cloud Is Better
Variable Workloads
GPU needs fluctuate significantly week-to-week. Cloud's elasticity is more cost effective.
Rapid Experimentation Phase
Testing multiple models and architectures. Need to spin up and down quickly without commitment.
Limited Ops Capacity
No team to manage physical hardware. Cloud's managed services reduce operational burden.
Evaluation Checklist
Before committing to colocation, answer these questions honestly:
1.Can you forecast GPU needs 12+ months out with reasonable accuracy?
2.Will utilization exceed 50-70% on average?
3.Do you have budget for upfront hardware purchase or financing?
4.Do you have ops capacity to manage physical hardware remotely?
5.Are your workloads stable enough for 3-year hardware refresh cycles?
6.Is data sovereignty a hard requirement?
If you answered "yes" to most of these, colocation is worth serious evaluation.
Top comments (0)