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Define the comparison before doing any arithmetic
Choose one planning horizon—commonly 36, 48 or 60 months—and one workload definition. Both alternatives must provide the same GPU class, memory, interconnect performance and useful result. Comparing an owned accelerator with a cheaper rental GPU that cannot run the model is not a break-even analysis.
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For training or batch inference, express output as useful GPU-hours or completed jobs. For token workloads, use cost per million input and output tokens. Include the same service-level target, data movement, storage and support in each option.
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- Owned or colocated option: purchase and install the cluster, then pay its recurring operating costs.
- Rental option: buy equivalent GPU or instance hours and include the host VM, storage, network, support and any commitment or Spot effects.
- Output basis: count only productive work that reaches an accepted result, after idle time, failed jobs, data stalls, maintenance and preemption.
Cloud prices, electricity tariffs, colocation rates, utilization, resale values and accelerator generations change quickly. Refresh every input before relying on a numerical decision; published scenarios are illustrations rather than quotes for a particular region or workload.
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Build the owned-cluster total cost of ownership
Put every cash and economic cost on the same timeline. The core equation is:
Owned TCO = CapEx + financing or opportunity cost + power and cooling + colocation or facility + networking + staff and operations + support and maintenance + insurance + software + downtime and failure reserve − residual value.
Capital expenditure and residual value
CapEx includes GPUs, servers, CPUs and RAM, local storage, networking equipment and optics, racks, UPS systems, installation, deployment labor and commissioning. OECD’s 2026 model separates GPU and installation CapEx from recurring costs. At the end of the horizon, subtract the expected resale or residual value rather than treating the hardware as worthless by default.
Keep three concepts separate:
- Cash outlay: what is paid for equipment and deployment, including financing fees.
- Accounting depreciation: the tax or financial-reporting schedule.
- Economic depreciation: the loss of market value and usefulness as newer accelerators arrive.
A tax schedule can therefore differ sharply from the hardware’s actual resale and obsolescence curve. Use economic depreciation in the investment comparison and show accounting depreciation in a separate reporting view.
Financing and opportunity cost
If the cluster is financed, add interest and arrangement fees to the cash model. For a cash purchase, include the return the capital could have earned elsewhere. Omitting this cost makes ownership appear cheaper than a rental plan that preserves cash.
Power, cooling and facility costs
Measure complete server or rack consumption at the wall. Do not multiply GPU thermal design power by GPU count and call that facility power. Apply the facility’s power-usage effectiveness (PUE) to measured IT load, then multiply by the electricity tariff.
| Illustrative OECD 2026 input | Value | How to use it |
|---|---|---|
| GPU draw in the H100 example | 700 W per GPU | GPU component only; not the complete rack load |
| Other full-capacity draw | Another 700 W for cooling, RAM and CPU | Illustrates why wall measurement is required |
| Electricity tariff | $0.25 per kWh | Scenario assumption; replace with your tariff |
| PUE | About 1.3 | Multiply IT energy by this facility factor |
| Resulting electricity assumption | About $300 per H100 per month | Illustrative monthly value, not a quote |
| Colocation | Approximately $1,200 per H100 per month | Illustrative rack-space and facility charge |
| Network bandwidth | About $500 per H100 per month | Illustrative connectivity assumption |
Also include demand charges, backup power, water or liquid-cooling charges, rack fees and connectivity that are not captured by a simple energy-rate calculation. A GPU server electricity usage monitor is useful at this step because the spreadsheet should use measured average server draw rather than a GPU’s nameplate TDP.
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People, software and operating risk
Budget cluster engineering, scheduling and monitoring, software support, insurance, spare parts, failed-job capacity and downtime. OECD’s illustrative assumptions use one full-time engineer for its largest scenario, prorated for smaller deployments; they also include 2% annual insurance and a reserve equal to 5% of GPU value for failures, redundancy, software and financing overhead.
Count engineering time even when employees are already on payroll. The relevant question is the incremental capacity required to operate the cluster and keep jobs moving, not whether a new job title is created.
Normalize the rental alternative
Start with the provider’s rate for the same GPU class and then add every associated charge. GPU charges are added to machine-type costs, and effective rates vary by region. Commitments, sustained-use discounts and Spot pricing can materially change the result. Use the provider’s calculator or billing export to capture:
- GPU and VM host charges;
- boot disks, persistent storage and snapshots;
- data transfer and network egress;
- managed support or orchestration fees;
- commitment payments and utilization requirements; and
- Spot interruptions, preemption and restart waste.
Never compare a bare on-demand GPU-hour with an owned cluster’s all-in cost. If the workload is token-based, translate both options into cost per million input and output tokens (or another identical goodput metric) before calculating a crossing point.
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Count useful GPU-hours, not installed capacity
Let:
Useful GPU-hours = GPU count × hours in the period × measured utilization × goodput factor.
Measured utilization is productive scheduled time. Subtract idle reservation, data stalls, failed jobs, maintenance, queue delays and preemption. The goodput factor accounts for restarts, checkpoint recovery, inefficient batching and other work that consumes accelerator time without producing accepted output.
CUDO reports that hardware commonly represents only 30–50% of five-year all-in TCO and identifies utilization, depreciation and power geography as dominant variables. There is no universal rent-versus-build threshold: a workload’s demand pattern and operating discipline matter as much as the GPU price.
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| Useful utilization | Fixed-cost burden relative to 100% utilization | Interpretation |
|---|---|---|
| 20% | Nearly 5× | Most installed capacity is idle or unproductive |
| 40% | 2.5× | Half the burden of the 20% case, before other changes |
| 60% | About 1.67× | Still carrying substantial fixed cost per productive hour |
| 80% | 1.25× | Closer to the high-utilization economics |
| 95% | About 1.05× | Little unused capacity, but less headroom for bursts and maintenance |
The multipliers assume the same fixed cost and goodput. They are not promises that a real cluster will achieve those utilization levels.
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After summing the owned TCO for the horizon, divide by useful GPU-hours:
Owned cost per useful GPU-hour = owned TCO ÷ useful GPU-hours.
For a rental plan, divide the complete rental bill by the useful GPU-hours delivered by that plan. Apply the same goodput haircut to both alternatives. A rental cluster that is preempted or repeatedly restarted may have a higher nominal GPU-hour price but a lower cost per completed job than an apparently cheaper option.
For token services, replace GPU-hours with useful tokens:
Cost per million tokens = total cost ÷ (useful input and output tokens ÷ 1,000,000).
Keep input and output token prices separate when the provider charges them differently, then combine them using your observed traffic mix.
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Solve the utilization break-even point
Break-even utilization is the point at which the owned cluster’s fixed cost is covered by the rental cost avoided for the useful work it produces. Express both sides on the same useful-work basis.
A practical form is:
Break-even utilization = owned fixed cost ÷ (rental rate × available GPU-hours − owned variable cost per available hour).
Use a rental rate that includes the comparable host, storage, network and support charges. Use available GPU-hours before utilization and then apply the same goodput treatment to both options. If the denominator is zero or negative, ownership cannot break even under those assumptions; lower utilization only worsens the result.
Spreadsheet procedure
- Set the horizon and calendar: create one row for each month and record GPU count and hours available.
- Enter capacity: calculate available GPU-hours as GPU count multiplied by hours in the month.
- Measure productivity: multiply available hours by observed utilization and the goodput factor to obtain useful GPU-hours.
- Price the rental comparator: multiply comparable rental cost per available hour by capacity, then add VM, storage, network, support and commitment or Spot effects.
- Enter owned costs: record electricity, PUE-adjusted cooling, colocation, network, staffing, support, insurance, financing, software and downtime reserve.
- Handle capital correctly: place CapEx and deployment cash in the purchase month, record financing cash flows, and subtract residual value in the final month.
- Calculate avoided cost: treat the rental bill for the same useful output as the owned cluster’s rental-equivalent savings.
- Test scenarios: run at least 20%, 40%, 60%, 80% and 95% useful utilization, with separate goodput assumptions.
- Graph the result: plot cumulative owned cash flow against cumulative rental cost. The crossing point is the cash break-even month.
Recommended monthly columns
- Available GPU-hours
- Useful GPU-hours
- Rental-equivalent revenue or avoided cloud cost
- Electricity
- PUE-adjusted cooling
- Colocation
- Network
- Staffing
- Support
- Insurance
- Financing
- Depreciation (kept separate from cash)
- Downtime and failure reserve
- Residual value
- Net and cumulative cash flow
Find the cash payback month
Cash payback answers a different question from accounting break-even. It is the first month in which cumulative rental-equivalent savings exceed upfront CapEx and deployment cash.
- Record equipment, installation, commissioning and any initial financing fees in month zero or the purchase month.
- For each month, subtract owned operating cash costs from the rental cost avoided for the same useful output.
- Add each month’s net savings to the prior cumulative balance.
- Identify the first month when the balance becomes positive.
Do not call depreciation a cash payment in this test. Conversely, do not omit interest, maintenance or a realistic failure reserve merely because they are not part of the equipment invoice.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use published scenarios as sanity checks, not promises
OECD 2026 token scenarios
OECD’s 2026 scenarios find that private hosting is not economical for workloads below 100 million tokens per month. Against cloud APIs, the modeled break-even occurs after about 30 months at 1 billion tokens per month, about two months at 10 billion, and about one month at 50 billion. These are scenario outputs built on stated assumptions, not universal thresholds. Your result will move with accelerator pricing, power, utilization, model efficiency, staffing and residual value.
NVIDIA’s 2023 infrastructure illustration
An October 2023 NVIDIA illustration uses 16,000 GPUs over four years, about $1 billion of infrastructure cost and about $2.5 billion of rental opportunity at $4 per GPU-hour. It labels a 15% utilization increase as worth more than $350 million. Treat this as a vendor illustration, not a current market quote or a forecast for your cluster.
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Decide whether to buy, colocate or rent
Ownership is favored when
- Useful utilization is sustained and demand is predictable enough to keep capacity busy.
- The organization can secure power, cooling and network capacity at known rates.
- Data-governance or latency requirements make external execution difficult.
- The team can operate schedulers, drivers, observability, repairs and capacity planning.
- Financing, residual value and accelerator-refresh risks are acceptable.
Rental is favored when
- Demand is bursty, experimental or difficult to forecast.
- Deployment speed and geographic flexibility matter more than the lowest steady-state unit cost.
- Capital is scarce or the hardware generation may become obsolete before it pays back.
- The workload benefits from managed infrastructure and can tolerate provider pricing, egress and preemption risk.
A hybrid plan can reduce the largest risks
Reserve a smaller owned baseline for predictable workloads and rent bursts, new accelerator types or regional capacity. Model the handoff explicitly: the owned cluster should receive the hours it can use productively, while overflow is priced as rental capacity. A hybrid plan is not automatically cheaper; it is useful when it prevents either idle ownership or expensive emergency capacity.
Common mistakes and how to correct them
Using GPU TDP as facility consumption
Symptom: the power line equals GPU wattage multiplied by count. Correction: meter the complete server or rack at the wall, apply PUE and use the actual tariff.
Comparing nominal hours instead of useful output
Symptom: the model counts every powered-on hour. Correction: remove idle, data-stall, maintenance, failed-job and preemption time, then apply a goodput factor.
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Leaving out host and data charges
Symptom: rental looks artificially cheap because only the accelerator line is used. Correction: add VM, storage, network, support, commitment and Spot effects.
Mixing depreciation with cash flow
Symptom: accounting depreciation is treated as the purchase payment, or the model ignores resale. Correction: keep accounting depreciation separate, model financing cash, and subtract expected residual value.
Assuming one utilization threshold fits every workload
Symptom: a rule such as “buy above X% utilization” is applied without testing demand and goodput. Correction: run the five utilization cases, include bursts and downtime, and solve for the workload’s own crossing point.
Ignoring operational capacity
Symptom: no staffing, monitoring, spare parts or failure reserve appears in owned TCO. Correction: add incremental engineering and support effort, insurance and a reserve for failed hardware and jobs.
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A compact decision checklist
- Are both options delivering the same accelerator class, memory, interconnect and useful output?
- Is the horizon long enough to include deployment, refresh and residual value?
- Are CapEx, financing, opportunity cost and installation included?
- Was complete wall power measured and adjusted for PUE?
- Are colocation, demand charges, connectivity and cooling included?
- Are staff, software, insurance, maintenance, spare parts and downtime reserved?
- Does rental include VM, storage, network, support, commitments and preemption waste?
- Are utilization and goodput measured rather than assumed from scheduler uptime?
- Are 20%, 40%, 60%, 80% and 95% cases shown?
- Are economic depreciation and accounting depreciation separate?
- Is the result expressed as cost per useful GPU-hour or per million tokens?
- Does the cash-flow graph identify the first positive payback month?
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