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Cloud GPU Rental vs. Local AI Workstation ROI Calculator

Calculate the exact break-even month between renting cloud GPUs (AWS, RunPod) and buying on-premise hardware factoring in kWh electricity and utilization.

Inputs USD ($)
Presets:
$
$
Hrs/day
Watts
$
Numbers stay in your browser and are never sent to a server.
Results
Hardware Break-Even Timeline
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24-Month Total Cloud Rental Cost
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24-Month Local TCO (CapEx + Electricity)
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Net 2-Year Savings (Local vs. Cloud)
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24-Month Cumulative TCO Comparison (Cloud OpEx vs. Local CapEx + Power)
24-Mo Cloud Rental Spend —
Local Hardware CapEx —
24-Mo Local Electricity Bill —
Related Service • Verified Cloud Benchmark Vast.ai & RunPod Spot GPU Marketplace

Cut cloud GPU rental bills by up to 70% compared to hyperscalers with verified spot RTX 4090, A100, and H100 instances.

Sample Calculations (2026)

Under the "Dual RTX 4090 Workstation ($4.8K)" preset (Local Hardware Purchase Cost: $4,800 • Equivalent Cloud GPU Rental Rate: $1.10 • Average Active Compute Hours per Day: 14), Hardware Break-Even Timeline is 12.14 Months and 24-Month Total Cloud Rental Cost is $11,236. At "Enterprise Scale (2.5x Load)", Hardware Break-Even Timeline reaches 25.36 Months.

Preset Inputs Hardware Break-Even Timeline 24-Month Total Cloud Rental Cost
Dual RTX 4090 Workstation ($4.8K) Local Hardware Purchase Cost: $4,800 • Equivalent Cloud GPU Rental Rate: $1.10 • Average Active Compute Hours per Day: 14 12.14 Months $11,236
Enterprise 4x A100 Server ($65K) Local Hardware Purchase Cost: $65,000 • Equivalent Cloud GPU Rental Rate: $9.50 • Average Active Compute Hours per Day: 20 11.63 Months $138,624
Compact Scale (0.5x Baseline) Local Hardware Purchase Cost: $2,400 • Equivalent Cloud GPU Rental Rate: $1.45 • Average Active Compute Hours per Day: 12 5.07 Months $12,695
Enterprise Scale (2.5x Load) Local Hardware Purchase Cost: $12,000 • Equivalent Cloud GPU Rental Rate: $1.45 • Average Active Compute Hours per Day: 12 25.36 Months $12,695

Formula

Rates updated: Nov 2026
T_{breakeven} = \frac{\text{CapEx}_{hardware}}{H_{month} \times \left( R_{cloud} - \frac{W_{system}}{1000} \times P_{kWh} \right)}

Notes & Assumptions

Choosing between cloud GPU instances and on-premise AI workstations depends heavily on utilization factor and local electricity tariffs.

If your team only runs training jobs 3–4 hours per day, on-demand or spot cloud GPUs are vastly cheaper and allow instant scaling to H100s without hardware depreciation.

Conversely, for continuous 24/7 inference pipelines, local workstations often pay for themselves within 7 to 14 months even after accounting for power and cooling.

Questions & Answers

Does this calculation include hardware resale value?

To keep estimates conservative, residual resale value is excluded. In practice, enterprise GPUs retain 35%–50% of their value after 24 months.

What utilization rate typically favors buying local GPUs?

Sustained utilization above 45% (roughly 10+ hours every day) generally tips the 18-month TCO in favor of owning hardware.

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