Quick Answer: Yes, colocation is significantly cheaper than cloud for sustained AI workloads. A 100 H100 GPU deployment running 24/7 for 3 years costs $13M+ on hyperscalers ($1.50-2.00/GPU-hr) versus $4-6M all-in with owned infrastructure in colocation ($0.40-0.80/GPU-hr). However, cloud wins for bursty workloads, experimentation, and organizations without infrastructure teams. The break-even point occurs around 60% utilization sustained over 18+ months.
Introduction: The AI Infrastructure Dilemma
Enterprise data science teams face a critical decision: train and deploy AI workloads in the cloud or move to colocation?
Five years ago, this question was straightforward. Cloud hyperscalers had GPU supply and nobody else did. Today, the landscape has shifted dramatically. GPU availability is improving. Specialized neocloud operators like Lambda Labs, Crusoe Energy, and others are building AI-optimized infrastructure in colocation facilities. And enterprises are discovering that their cloud bills for GPU workloads are unsustainable at scale.
This decision isn’t just about cost, though cost matters enormously. It’s about sustainability, control, and the ability to scale AI infrastructure without being held hostage by cloud pricing. For teams training large language models, fine-tuning foundation models, or running continuous inference workloads, colocation offers compelling advantages. But cloud retains real benefits for specific use cases.
This guide walks through the economics, performance trade-offs, and decision framework to determine which path is right for your organization.
Total Cost of Ownership: A 3-Year Comparison
Let’s start with concrete numbers. We’ll model a realistic scenario: 100 NVIDIA H100 GPUs, running 24/7 for 3 years.
Cloud Pricing: Hyperscaler GPU Rental
Current pricing from major hyperscalers:
- AWS EC2 p4d.24xlarge (8x H100): $48.48/hour (~$6.06/GPU-hr)
- Google Cloud TPU Pod Slice (equivalent compute): $60-100/day per chip, or roughly $2.50-3.00/GPU-hr for equivalent GPU hours
- Azure ND H100 v5: $38.00/hour for 8x H100 ($4.75/GPU-hr)
Aggregating across market options and accounting for commitment discounts (1-year RI reduces AWS by ~30%), the effective blended rate for sustained workloads is $1.50-2.00/GPU-hr.
3-Year Cloud Cost (100 H100s, 24/7):
- Compute: 100 GPUs × 24 hr/day × 365 days/year × 3 years × $1.75/GPU-hr = $13.7M
- Data egress: 500TB/year × 3 years × $0.12/GB = $180K
- Storage (model checkpoints, datasets): $50K-100K
- Total: $13.9M – 14.0M
Colocation: Owned Infrastructure
Now let’s price the colocation alternative.
GPU Hardware Costs:
- 100x H100 at current pricing ($15,000-20,000 per unit): $1.7M
- Networking hardware (switches, NICs, InfiniBand HCAs): $200K
- Storage infrastructure (NVMe, SSD, controllers): $300K
- Initial buildout (servers, cables, monitoring): $200K
- Hardware total: $2.4M
Colocation Facility Costs:
- 20kW average (100 H100s ÷ 5 GPUs per 20kW rack ≈ 20 racks): $3,500/mo per rack in primary markets = $70K/mo = $840K/year
- Power overage/cooling: $0.12/kWh × 20kW × 8,760 hr/year × 3 years = $630K
- Cross-connects (10Gbps): $15K
- 3-Year facility total: $3.1M
Operations & Support:
- 1-2 FTE (systems/network engineer): $150K/year × 3 = $450K
- Monitoring, patching, security: $50K/year × 3 = $150K
- Warranty/replacement hardware reserve: $100K
- 3-Year ops total: $700K
Residual Value:
- H100 hardware depreciates 40-50% over 3 years (used market strong for GPUs): $1.7M × 45% residual = -$765K
3-Year Colocation Total Cost:
- Hardware: $2.4M
- Facility: $3.1M
- Operations: $700K
- Residual value: -$765K
- Total: $5.4M
The Economics: 3-Year Comparison
| Category | Cloud | Colocation |
|---|---|---|
| Year 1 | $4.6M | $2.3M |
| Year 2 | $4.6M | $2.1M |
| Year 3 | $4.6M | $1.9M |
| 3-Year Total | $13.9M | $5.4M |
| Cost per GPU-hour | $1.81 | $0.56 |
| Savings | Baseline | $8.5M (61% reduction) |
For sustained, predictable AI workloads, colocation costs less than half of cloud.
However, this analysis assumes several conditions:
- Utilization: 80%+ average utilization across the fleet
- Predictability: Consistent demand for 24+ months
- Scale: Minimum 50-100 GPUs to justify operations overhead
- Operational capability: In-house or managed infrastructure team
When Cloud Wins: The Business Cases
Cloud hyperscalers remain the right choice for specific scenarios:
Bursty or Experimental Workloads
If your GPU demand varies week-to-week (10-500 GPUs depending on projects), cloud’s elasticity prevents capital lock-up. You pay for what you use, when you use it. Colocation requires long-term capacity planning.
Example: A research lab experimenting with 5 different model architectures. They might need 8 GPUs for weeks, then 0 GPUs for a month, then 50 GPUs for 3 weeks. Cloud spot pricing ($0.30-0.50/GPU-hr) is actually cheaper than maintaining a 50-GPU colo rack with inconsistent utilization.
Small Scale (Under 20 GPUs)
Once you’re below 20 concurrent GPUs, the operational and overhead costs of colocation (dedicated ops staff, lease commitments, monitoring infrastructure) don’t make economic sense. Cloud commitment discounts apply well at this scale.
Lack of Internal Infrastructure Expertise
Building and maintaining GPU infrastructure in colocation requires:
- GPU cluster management expertise (Ray, Kubernetes, SLURM)
- Network engineering (InfiniBand configuration, traffic engineering)
- Hardware procurement and lifecycle management
- 24/7 support for production infrastructure
If your organization doesn’t have this capability and can’t hire it, the cloud’s managed services reduce operational friction dramatically.
Rapid Iteration and Experimentation
Training models in cloud allows you to spin up new hardware configurations in minutes, test different GPU types (H100, H200, L40S, etc.), and scale up/down without lead times. Colocation requires capacity planning weeks in advance.
For teams in early-stage model development, this agility is worth premium costs.
When Colocation Wins: The Case for Owned Infrastructure
Colocation becomes economically and operationally superior when:
Sustained Training Workloads (24+ Months, 20+ GPUs)
Once your demand is predictable and sustained, owned infrastructure makes sense. Examples:
- LLM fine-tuning platforms: Continuous inference and periodic retraining on customer data
- Multimodal model training: Multi-month projects with stable GPU allocations
- Autonomous vehicle simulation: 24/7 distributed training across hundreds of GPUs
- Scientific computing: Long-running simulations, climate models, drug discovery
The rule of thumb: If you know you’ll need those GPUs 80%+ utilized for 18+ months, colocation’s math works.
Data Gravity and Locality
Moving petabytes of data is expensive and slow. A 10PB dataset moving from on-prem to cloud for training and back costs:
- AWS data transfer: 10PB × $0.02/GB (bulk rate) = $200K+ per move
- Time: 1-2 months via Direct Connect or physical appliances
If your training loop involves:
- Collect data locally (hospitals, vehicle fleets, IoT networks)
- Train models in the cloud
- Deploy inference locally
…the egress costs and latency become punishing. Colocation near your data source (or as a hub between distributed data centers) eliminates this friction.
Regulatory and Data Residency Requirements
Certain industries face hard constraints:
- Healthcare (HIPAA): Patient data may not leave the US or specific regions. Cloud multi-tenancy compliance is complex.
- Financial services (PCI-DSS): Data sovereignty requirements favor owned infrastructure or single-tenant cloud deployments (expensive).
- Government (FedRAMP): Classified AI workloads require dedicated facilities.
- EU operations (GDPR): Data residency mandates often make European colocation cheaper and simpler than cloud.
Colocation in a SOC 2 Type II compliant facility with air-gapped zones meets these requirements elegantly.
Cost Optimization at Scale (100+ GPUs)
At scale (1,000+ GPUs across multiple facilities), the economics become overwhelming in favor of colocation.
A hyperscaler’s gross margin on GPU hours is 60-70% (they buy infrastructure at wholesale, amortize over 5+ years). Your owned infrastructure, financed and amortized over 3 years, compresses that margin to 10-20%. The difference compounds dramatically.
Companies like Together AI, Crusoe Energy, Lambda Labs, and CoreWeave built entire businesses on this arbitrage: buying colocation space cheaply, deploying GPUs at owned cost, and renting capacity below hyperscaler rates while maintaining healthy margins.
AI-Specific Considerations
GPU workloads are fundamentally different from traditional compute. Several factors influence the cloud vs. colocation decision uniquely in AI:
GPU Utilization Rates Are Lower Than You Think
Most organizations achieve 40-60% average GPU utilization. Why?
- Bursty training: Some jobs complete in hours, new jobs queue
- Debugging and development: Many training runs fail mid-training
- Scaling inefficiencies: GPUs idle while waiting for data loading or gradient synchronization
- Fragmentation: Unused capacity across a fleet due to heterogeneous job sizes
At 40% utilization, even colocation’s math requires care. The break-even shifts to 24+ months instead of 18. At 60%+ utilization sustained, colocation is unambiguous.
Data Gravity Dominates Cost for Large Models
Training a 70B parameter model on 100 H100s requires ~100 days (rough estimate). You’ll iterate: different hyperparameters, architectural tweaks, additional fine-tuning.
Total data moved during training: 10-50TB per training run. Multiply by 10-20 iteration cycles over 12 months, and you’re easily at 100-1,000TB annually.
At $0.02/GB, that’s $2-20M in egress fees annually. Colocation eliminates this entirely.
Inference Latency Requirements Drive Facility Choice
For latency-sensitive inference (real-time chatbots, autonomous vehicles, recommendation systems), colocation near end-users or in your own data centers beats cloud’s ~50-100ms baseline latency.
Training can afford cloud’s latency (asynchronous, batched). Inference often cannot.
Model IP Security and Isolation
Proprietary models represent significant competitive advantage. In cloud:
- Your model weights live on hyperscaler hardware alongside competitors’ models
- Hyperscalers’ security practices are robust, but shared-tenant risk exists
- Egress of model weights for any reason requires cloud approval
Colocation offers:
- Isolated racks with physical separation
- Air-gapped infrastructure possible
- Full control over physical and network security
- Easier compliance with model IP protection requirements (especially for regulated industries)
Break-Even Analysis Framework
When should your organization move from cloud to colocation? Use this framework:
Key Variables
- GPU Hours per Month: Current monthly utilization (GPUs × hours × utilization%)
- Committed Contract Term: How long will you need this capacity? (18-36 months typical)
- Power Costs: Regional colocation power rates ($0.08-0.15/kWh depending on market)
- Operations Team Costs: Can you hire engineers? Allocate OpEx overhead?
Break-Even Calculation
For 100 H100s at 60% utilization over 24 months:
Monthly Cloud Cost: 100 GPUs × 720 hours/month × 60% utilization × $1.75/GPU-hr = $75,600/month = $1.8M/24mo
Monthly Colocation Cost (all-in):
- Hardware amortization: $2.4M ÷ 36 months = $67K
- Facility/power: $70K + $17K = $87K
- Operations: $22K
- Total: $176K/month = $4.2M/24mo
Wait, that shows cloud is cheaper. But this assumes:
- Utilization stays at 60%: If utilization drops to 40%, cloud’s costs stay $75.6K/mo, but your colo infrastructure has fixed costs of $176K/mo regardless. Cloud wins again.
- No growth: If utilization grows to 80%, cloud costs $100.8K/mo (+33%), while colo costs stay at $176K/mo. Colo wins.
Real break-even: Colocation wins when:
- Sustained utilization reaches 70%+ over 18+ months, OR
- Scale grows to 300+ concurrent GPUs, OR
- 3-year contract horizon and predictable demand
The Hybrid Model: Train in Colo, Inference on Cloud/Edge
The most pragmatic organizations adopt a hybrid approach:
Training Phase (months 1-12):
- Rent GPU capacity in colocation (cheaper per GPU-hr for sustained load)
- Benefit from data locality (training data stays on-prem or in the same facility)
- Iterate on model architecture with full control
Inference Phase (months 12+):
- Deploy trained models to edge locations, customer data centers, or cloud inference services (lower cost, better latency)
- Use cloud inference APIs (AWS SageMaker, GCP Vertex) for flexibility
- Avoid data egress of training data; only move frozen model weights
Example: A healthcare AI company trains diagnostic models in a HIPAA-compliant colocation facility (data stays local, protected), then deploys inference to hospital edge servers. Zero training data leaves their network. Training infrastructure cost is optimized; inference is fast and compliant.
How GoDataCenters Helps
Finding colocation facilities suitable for AI workloads is notoriously difficult. Most facilities claim “AI readiness” without the power density, cooling, or networking to deliver it.
GoDataCenters solves this by:
- Vetting facilities across 50+ markets for actual AI capability:
- Verified power density (20kW+, with proof of current deployments)
- Cooling architecture (not just air-cooled promises)
- Network fabric (InfiniBand and UEC-optimized facilities identified)
- Expansion capacity and grid headroom
- Transparent pricing: Comparing quotes across markets, Dallas vs. Phoenix vs. Salt Lake City, shows 20-30% variance. GoDataCenters.com lets you see pricing apples-to-apples.
- AI-Readiness Score: Our proprietary scoring system (covered in another blog post) rates facilities on 6 dimensions critical for GPU workloads. No more guessing.
- RFQ and negotiation support: We manage the RFQ process and leverage volume across 100+ customers to negotiate better rates.
If you’re evaluating the colocation path, start here: GoDataCenters.com/get-a-quote
Frequently Asked Questions
Q: Can I start on cloud and migrate to colocation later?
A: Yes, and many organizations do. However, migration costs are real: moving 100+ GPUs and petabytes of data between cloud and colo takes weeks and carries egress costs. Plan for 2-3 months of parallel infrastructure (running partially on both). Better approach: design your model training architecture to be location-agnostic (containerized, portable), then switch.
Q: What’s the realistic break-even point between cloud and colo?
A: For most organizations, around 60% sustained utilization over 18+ months. Below that, cloud’s flexibility costs less than colo’s fixed overhead. Above that, colo’s unit economics dominate. At 80%+ utilization, the colo advantage is 50%+ cost savings.
Q: Does data transfer cost (egress from cloud) really matter?
A: Enormously, if ignored. A 100-GPU training cluster generating 100TB/month of data in cloud and egressing for storage/processing costs $2M+/year. Colocation eliminates this. For pure training-in-cloud models where data stays in cloud, egress is lower. But mixed workloads (hybrid on-prem + cloud) face egress tax.
Q: What’s the difference between colocation for training vs. inference?
A: Training = sustained high utilization, expensive power/cooling requirements, massive east-west network traffic (GPU-to-GPU). Inference = lower utilization, lower power per unit of compute, distributed geographically for latency. Training facilities need different specs (more power density, InfiniBand) than inference facilities (standard Ethernet, lower power).
Q: How do I know if a colocation facility can actually handle GPU workloads?
A: See our AI-Readiness Checklist (blog post). Short version: ask for 20kW+ power per rack, liquid cooling systems, InfiniBand fabric, references from current GPU customers, and SOC 2 Type II certification. If they can’t provide these, they’re not AI-ready.
Q: What happens to my GPUs if the colocation facility has an outage?
A: In a 3-year span, expect 1-2 brief outages (hours, not days) from power or cooling issues. Choose a facility with N+2 power redundancy and SLA guarantees. Your own infrastructure is also subject to outages. Colocation doesn’t change that risk profile, just shifts who manages it.
Conclusion
Colocation is dramatically cheaper than cloud for sustained, predictable AI workloads, often 50-60% cost savings at scale. But cloud’s flexibility and managed services remain valuable for experimentation, bursty demand, and small-scale teams.
The decision hinges on:
- Scale: 100+ concurrent GPUs favors colocation
- Utilization: 70%+ favors colocation
- Contract horizon: 18+ months favors colocation
- Ops capability: Strong infrastructure teams → colocation; weak teams → cloud
Most enterprises adopt a hybrid model: train in colocation (cheap, controlled), deploy inference across cloud and edge (flexible, responsive).
If you’re ready to explore colocation for AI workloads, start with finding facilities that are actually AI-ready. That’s where GoDataCenters comes in.