Colocation vs Cloud for AI Workloads

The colocation vs cloud debate takes on new dimensions when AI enters the picture. GPU compute is expensive, demand outstrips supply, and the cost differences between deployment models can mean millions of dollars annually. This guide provides an honest comparison to help you make the right infrastructure decision for your AI workloads.

The Core Tradeoff

At its simplest: cloud offers flexibility and speed; colocation offers cost control and customization.But AI workloads add nuances that make this tradeoff more complex than for traditional IT.

Cloud for AI: Pros and Cons

Advantages

• Instant availability: Spin up GPU instances in minutes (when capacity is available). No hardware procurement, no rack-and-stack, no waiting for colocation provisioning.
• Elastic scaling: Scale from 8 GPUs to 1,000 for a training run, then scale back to zero. You only pay for what you use.
• Managed infrastructure: The cloud provider handles hardware failures, driver updates, cooling, and power. Your team focuses on models, not ops.
• Integrated ecosystem: Cloud AI services (training platforms, model registries, data pipelines) are tightly integrated with GPU compute.
• No capital expenditure: OpEx model avoids large upfront hardware purchases.
• Global distribution: Deploy inference endpoints in multiple regions with minimal effort.

Disadvantages

• Cost at scale: Cloud GPU pricing runs $2-4/GPU/hour for H100 instances. At sustained utilization, this can be 2-3x more expensive than colocation.
• Availability constraints: GPU instances are frequently sold out. Reserved capacity requires long-term commitments that negate cloud flexibility.
• Vendor lock-in: Training pipelines, data storage, and tooling become tightly coupled to the cloud provider.
• Limited customization: You get the instance types the provider offers. Custom networking (InfiniBand topology), cooling, or GPU configurations aren't options.
• Data egress costs: Moving large training datasets or model artifacts in and out of cloud is expensive.

Colocation for AI: Pros and Cons

Advantages

• Cost efficiency at scale: Own your hardware, pay only for space, power, and cooling. At 60%+ utilization, colocation is typically 40-60% cheaper than cloud for equivalent compute.
• Full hardware control: Choose exact GPU models, networking fabric, storage, and system configuration. Deploy custom InfiniBand topologies optimized for your workload.
• Guaranteed capacity: Your GPUs are always available — no competing for spot instances or dealing with capacity shortages.
• No data egress costs: Your data lives on your hardware. Move it freely without per-GB charges.
• Hardware asset value: GPU servers retain significant resale value. Your CapEx isn't gone — it's an asset on your balance sheet.
• Performance consistency: No noisy neighbors. Dedicated hardware delivers consistent, predictable performance.

Disadvantages

• Upfront capital: A single DGX H100 system costs $300,000-400,000+. A 32-node cluster runs $10-15M before facility costs.
• Procurement lead times: GPU servers can take 3-6+ months to deliver. Add 2-4 weeks for rack, stack, and testing.
• Operational responsibility: You manage hardware failures, driver updates, cooling coordination, and capacity planning.
• Scaling friction: Adding capacity means buying and deploying more hardware, not clicking a button.
• Facility selection complexity: You need to choose the right data center with adequate power, cooling, and connectivity.

Cost Comparison: Real Numbers

Let's compare the total cost of running 64 NVIDIA H100 GPUs (8 servers) over 3 years:

Cloud (AWS p5.48xlarge equivalent)

• On-demand: ~$32/hour per 8-GPU instance × 8 instances × 8,760 hours/year = ~$2.24M/year
• 1-year reserved (all upfront): ~$1.57M/year
• 3-year reserved: ~$1.05M/year
• 3-year total (reserved): ~$3.15M

Colocation

• Hardware (8× DGX H100): ~$2.8M (CapEx, ~$1.4M residual value after 3 years)
• Colocation (power + space + cooling): ~$15,000/month × 8 racks = ~$120,000/month = $1.44M/year
• Networking + management: ~$5,000/month = $60K/year
• Net hardware cost: $2.8M - $1.4M residual = $1.4M
• 3-year total: ~$1.4M + $4.32M + $180K = ~$5.9M

Wait — cloud is cheaper? Not so fast. The cloud 3-year reserved pricing assumes 100% utilization of a fully committed reservation. In practice, many organizations run at 60-80% utilization. At 70% utilization, the effective cloud cost rises to ~$4.5M over 3 years. Meanwhile, colocation hardware can be repurposed, resold, or reallocated as needs change.

The break-even point shifts based on utilization rate, GPU pricing in your market, and contract terms. Check our GPU colocation pricing guide for current market rates.

The Hybrid Approach

Most mature AI organizations adopt a hybrid strategy:

• Colocation for baseline: Own hardware for your steady-state training and inference workloads running 24/7
• Cloud for burst: Use cloud GPU instances for overflow, experimentation, and one-off training runs
• Cloud for global inference: Deploy inference endpoints in cloud regions close to users
• Colocation for sensitive data: Keep proprietary training data on hardware you physically control

When to Choose Cloud

• You're in the experimentation phase and workloads are unpredictable
• You need GPU access within days, not months
• Your workloads are bursty (high demand for days, then idle for weeks)
• You lack operational staff to manage physical hardware
• Utilization will be below 50-60%

When to Choose Colocation

• Sustained GPU utilization above 60%
• Multi-year AI roadmap with predictable compute needs
• Custom networking requirements (InfiniBand fabric, specific topologies)
• Data sovereignty or security requirements mandate physical control
• Cost optimization is critical for the business case
• You want to avoid cloud vendor lock-in

Getting Started with AI Colocation

If colocation makes sense for your AI workloads, start by defining your infrastructure requirements, then evaluate facilities in key markets like Northern Virginia, Texas, or Phoenix. Our directory makes it easy to filter for AI-ready facilities with the power, cooling, and GPU support you need.