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What actually determines which option costs less?
Start with the same useful output and performance target, then compare what it costs to deliver them over the same period. For training, that might be the cost per completed run. For inference, it might be the cost per request or per million output tokens. An hourly GPU price is not enough if the systems deliver different throughput or use different software stacks.
Utilization is central. If demand is sporadic, a purchased server can sit idle while its capital and facility costs remain. If workloads are steady and the system is kept productive, those fixed costs can be spread across more work. There is no generally valid utilization threshold: it changes with the hardware quote, cloud rate, operating assumptions, workload performance, and comparison period.
How do the cost structures differ?
| Cost factor | Cloud GPUs | Owned AI hardware |
|---|---|---|
| Capacity and payment | Pay under the selected provider model, such as on-demand or a commitment; reservations can change both price and flexibility. AWS Capacity Blocks, for example, reserve capacity for particular windows. | Pay for the matched server configuration up front or through financing. Additional capacity generally requires acquiring more hardware. |
| Facility and operations | Include the full instance configuration and any required storage, data transfer, support, or other services; a GPU rate alone may not represent the bill. | Include electricity, cooling, maintenance, facility capacity or colocation, networking, storage, staffing, deployment, and downtime. |
| Idle capacity | Intermittent use can avoid paying for a server around the clock, although commitments or reservations may still incur costs outside actual work periods. | Idle time does not undo the purchase or facility costs. Higher productive utilization can distribute those costs over more output. |
| Availability and change | Rates and GPU availability vary by provider, region, zone, and purchase model, and can change over time. | Capacity is tied to the system acquired. Maintenance, useful life, refresh timing, and any resale value affect the total, but no general resale value or useful life applies to every deployment. |
What published prices and break-even examples show—and do not show
Provider prices are specific to a GPU, region, instance configuration, and purchase model. Google Cloud’s GPU pricing page lists on-demand examples of $0.35 per GPU-hour for an NVIDIA T4 and $2.48 per GPU-hour for an NVIDIA V100. These are not H100 or A100 comparisons; Google notes that prices can vary by region, and GPU availability is limited to specific zones. Its pricing guidance recommends using the Google Cloud Pricing Calculator to estimate the full instance cost, including GPU and machine configuration.
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Google Cloud says Spot GPU prices are dynamic and may change up to once every 30 days. The provider reports discounts of 60–91% off corresponding on-demand prices for most machine types and GPUs; that is provider guidance, not a guaranteed discount for every GPU or billing period.
Rates can also move materially over time. AWS reported that, relative to its May 31, 2025 baseline, P5 on-demand prices had been reduced by 44% and P4d on-demand prices by 33%. Those are AWS-reported historical reductions, not current price quotes; AWS also reported different reduction figures for Savings Plans. Use a current regional quote for an actual decision.
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A vendor comparison can illustrate the calculation, but its break-even point is not a universal buying rule. Lenovo’s June 15, 2026 usual sale prices for its described systems were $397,801.60 for an 8× H200 system and $550,475.10 for an 8× B200 system. Its comparison used selected US-region cloud rates dated July 15, 2026. The figures below belong to those specific configurations and assumptions:
| Lenovo vendor scenario | Published inputs | Modeled result |
|---|---|---|
| 8× H200 system versus Azure ND96isr H200 v5 | Lenovo listed $397,801.60 for its system and estimated owned operating costs of $9.80 per hour. Its July 15, 2026 US-region comparison listed Azure at $114.65 per hour on-demand and $50.33 per hour for its three-year reserved comparison. | Lenovo calculated break-even after about 3,793 hours (5.2 months) against its listed on-demand rate, or about 9,800 hours (13.4 months) against its three-year reserved rate. |
| 8× B200 system versus AWS on-demand | Lenovo listed $550,475.10 for its system and modeled owned operating costs of $12.84 per hour. | Lenovo estimated ownership would be cheaper above approximately 5.3 hours of use per day over five years in this scenario. |
These are Lenovo’s modeled comparisons, not independent market averages. Lenovo’s analysis excludes cloud storage, data egress, and support plans. The outcome for a real deployment can differ with the actual quote, electricity price, cooling and colocation arrangements, workload utilization, staffing, maintenance, financing, network and storage needs, and downtime or refresh assumptions. The vendor examples are useful for seeing how inputs affect the result, not as a threshold to apply to another system.
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How to calculate your own rent-versus-own total
Choose a comparison period and a defined workload first. Use the same model, GPU count, serving or training stack, and required throughput on each side. Then estimate total cost and divide by the useful output delivered during that period.
- Cloud total: GPU or instance charges for expected hours, plus commitments or reservation costs, storage, networking and egress, support, and other required services.
- Owned total: purchase and financing cost, electricity and cooling, maintenance, facility or colocation, networking and storage, deployment and staffing, downtime and refresh costs, less only a defensible residual value.
- Unit cost: total cost divided by the same useful output, such as completed training runs, inference requests, or output tokens.
Run the calculation at low, expected, and high utilization rather than relying on a single forecast. Use current prices for the region and purchase model you would actually use, and check whether the cloud estimate includes the whole machine and related services. For an owned system, use your own power rate and facility quote rather than assuming a published operating-cost estimate applies to your site.
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Normalize performance before comparing GPU rates
Two hourly prices are comparable only if the systems complete equivalent work at the required performance. Hold the model, workload, GPU configuration, and software or serving stack as constant as possible, then measure delivered throughput. If one option completes the workload faster, compare total cost per completed job or unit of output—not simply the price of one hour.
NVIDIA reports an H100 inference cost of approximately $0.09 per million tokens at 66 tokens per second per user for GPT-OSS-120B using vLLM, citing SemiAnalysis InferenceX benchmarks as of April 2026. This is a specific vendor-published figure tied to that model, stack, throughput condition, and named benchmark source; it should not be treated as a general H100 cost per token.
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When cloud is the more practical economic choice
- Your GPU demand is intermittent, uncertain, or likely to change enough that owning capacity would leave substantial idle time.
- You need capacity for a defined short-term period or want a provider’s regional capacity options rather than a fixed purchased system.
- You can use a cloud purchase model that fits your schedule and price tolerance. AWS describes on-demand, Savings Plans, and Capacity Blocks; its 2026 guide says Capacity Blocks reserve GPU or accelerated instances for windows of 1 to 182 days, with the reservation fee paid up front.
- You want to avoid taking on the purchase, facility, cooling, and maintenance responsibilities of a physical system.
Commitments are not automatically discounts. AWS says Capacity Block prices reflect supply and demand and may be above or below on-demand rates; for popular GPU instances, assured availability can carry a premium. Compare the reservation’s total cost and timing with the value of the capacity assurance it provides.
When owning can be more cost-effective
- You have a steady workload and a credible plan to keep the system productively used over the period in which you expect to operate it.
- You have a suitable site or colocation arrangement and can estimate power, cooling, maintenance, staffing, networking, and storage from real quotes.
- You can match the purchased configuration to your workload and verify its throughput rather than buying based on GPU count alone.
- Your comparison still favors ownership after testing lower utilization, downtime, financing, refresh costs, and uncertain residual value.
Ownership shifts operational and lifecycle responsibility to you. A low purchase price does not by itself establish lower total cost, and the hardware’s useful life or resale value should be based on your own support and retirement assumptions rather than a general rule.
What to gather before deciding
- The GPU model and count, workload schedule, target performance, and expected useful output.
- Current cloud quotes for the relevant region and exact instance or GPU configuration, including the purchase model and any capacity reservation.
- A complete cloud estimate that accounts for machine configuration, storage, data transfer, support, and other required services.
- A purchase quote for a comparable server, plus local electricity, cooling, maintenance, facility or colocation, staffing, networking, and storage costs.
- Low, expected, and high utilization cases, along with your financing, downtime, refresh, and residual-value assumptions.
Without those inputs, an exact break-even answer cannot be established. Treat older headline comparisons as dated examples, not substitutes for current quotes.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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