Table of Contents
Overview
The build vs buy decision for GPU compute is one of the most important infrastructure choices AI-focused companies face. With H100s costing $30,000+ and cloud prices ranging from $2-100/hour, the stakes are high.
The right answer depends on your specific situation: utilization rates, capital availability, timeline, expertise, and compliance requirements. This guide breaks down the numbers and provides a framework for your decision.
TL;DR - Quick Guidance
- Build: If utilization >70%, have capital, can wait 6-12 months
- Cloud: If need flexibility, quick start, or variable workloads
- Hybrid: Best of both — own baseline, burst to cloud
- Griddly: 70% cheaper than AWS — changes the math entirely
The Build Option
Building your own GPU infrastructure means purchasing hardware, securing data center space, and managing operations. Here's what it costs:
8x H100 Node — Full Cost Breakdown
| Item | Cost | Note |
|---|---|---|
| NVIDIA H100 SXM (8-GPU node) | $300,000 | Hardware only |
| DGX H100 System | $400,000+ | Complete system |
| Networking (InfiniBand) | $50,000+ | Per node |
| Rack, cooling, UPS | $30,000+ | Infrastructure |
| Data center space (colo) | $2,000/mo | Per rack |
| Power (50kW node) | $5,000/mo | At $0.10/kWh |
| IT staff (2 FTE) | $300,000/yr | Salaries |
| Maintenance & support | $40,000/yr | 10% of hardware |
Pros of Building
- Lowest cost at high utilization
- Full control over hardware
- No ongoing cloud fees
- Data stays on-premise
- Predictable costs
Cons of Building
- High upfront capital ($500K+)
- Long lead times (6-12 months)
- Requires specialized staff
- Hardware depreciation risk
- Maintenance burden
The Cloud Option
Cloud GPU services let you rent compute on-demand. Prices vary dramatically between providers:
8x H100 Cloud Pricing Comparison
| Provider | Hourly | Monthly (24/7) | Note |
|---|---|---|---|
| AWS (p5.48xlarge) | $98.32 | $70,790 | 8x H100 |
| GCP (a3-highgpu-8g) | $87.50 | $63,000 | 8x H100 |
| Azure (ND H100 v5) | $92.00 | $66,240 | 8x H100 |
| Griddly Cloud Best Value | $15.92 | $11,462 | 8x H100 |
| Lambda Labs | $24.00 | $17,280 | 8x H100 |
| CoreWeave | $27.36 | $19,699 | 8x H100 |
The Griddly Advantage
At $1.99/hr per H100, Griddly is 70% cheaper than AWS and 80% cheaper than Azure. This fundamentally changes the build vs buy equation — cloud becomes economical even at high utilization.
Pros of Cloud
- Instant access (no wait time)
- No upfront capital
- Scale up/down on demand
- No maintenance burden
- Latest hardware available
Cons of Cloud
- Higher cost at 100% utilization (traditional)
- Ongoing operational expense
- Data leaves your premises
- Potential vendor lock-in
- Availability not guaranteed
TCO Analysis
Let's compare the 3-year Total Cost of Ownership for an 8x H100 node at 100% utilization:
| Period | Build (Own) | AWS Cloud | Griddly Cloud |
|---|---|---|---|
| Year 1 | $850,000 | $849,480 | $137,544 |
| Year 2 | $147,000 | $849,480 | $137,544 |
| Year 3 | $147,000 | $849,480 | $137,544 |
| 3-Year Total | $1,144,000 | $2,548,440 | $412,632 |
| Break-even vs AWS | 16 months | N/A | Never |
Key Insights
Decision Factors
Beyond raw costs, several factors influence the build vs buy decision:
Utilization
High utilization favors ownership, variable workloads favor cloud.
Timeline
Cloud provides instant access, hardware has long lead times.
Capital
Building requires significant capital investment.
Expertise
On-premise requires specialized staff.
Data Privacy
Some industries require on-premise for compliance.
Flexibility
Cloud scales up/down instantly.
The Hybrid Approach
65% of enterprises are adopting hybrid strategies — combining owned infrastructure with cloud services. This approach offers the best of both worlds:
Baseline on Own Hardware
Run predictable, steady-state workloads on owned GPUs for lowest cost.
Burst to Cloud
Handle demand spikes and experiments on cloud without over-provisioning.
Geographic Distribution
Use cloud for inference close to users, on-premise for training.
Risk Mitigation
Avoid vendor lock-in and hardware obsolescence risk.
Hybrid Example
A mid-size AI company might:
- Own 2x DGX H100 nodes for steady-state training (~$800K)
- Use Griddly for burst capacity during deadlines (~$5K/month variable)
- Deploy inference on cloud close to users (global distribution)
Decision Framework
Use this framework to guide your decision based on your situation:
| Scenario | Recommendation | Reasoning |
|---|---|---|
| Startup / Early Stage | Cloud Only | Preserve capital, iterate fast, scale as needed. |
| Growing AI Company | Hybrid | Own baseline capacity, burst to cloud for experiments. |
| Enterprise (>70% utilization) | Build + Cloud | TCO favors ownership at high utilization. Cloud for flexibility. |
| Regulated Industry | Build Primary | Compliance may require on-premise. Cloud for non-sensitive workloads. |
| Research / Academia | Cloud First | Variable needs, grant funding cycles, avoid maintenance burden. |
Our Recommendation
For most companies in 2025, we recommend:
Start with Cloud (Griddly)
At 70% cheaper than AWS, Griddly makes cloud economical even at high utilization. Start here, validate your workloads, and only consider building when you have proven, predictable demand exceeding what cloud can economically provide.
Evolve to Hybrid
As your needs grow and stabilize, consider adding owned capacity for baseline workloads while keeping cloud for burst and flexibility. This typically makes sense at $50K+/month sustained cloud spend.
Build for Baseline
Only build your own infrastructure when you have: (1) proven >70% utilization, (2) capital and expertise, (3) 2-3 year commitment, and (4) specific compliance requirements. Even then, maintain cloud for flexibility.