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Estimate an AI data center in separate steps: define whether capacity means IT load or utility service, model annual IT electricity from the equipment and its operating profile, apply an explicit PUE or a facility model, and build a capital budget from benchmarks that match the location, project type, date and scope. Keep construction, active IT equipment, land, utility work and electricity bills separate; without site, design, tariff and procurement details, the result is a planning range—not a project quote.
Define what you are estimating before using a cost per MW
A megawatt figure is not enough to compare projects. First write down the basis of estimate so every energy and cost figure refers to the same project boundary.
- Location and delivery year: country or market and the target date for delivery. Labor, equipment supply, tariffs and cost benchmarks vary by place and time.
- Capacity basis: target IT load in MW, or utility service capacity in MW. State which one you mean; they are not interchangeable.
- Project type and scope: single-tenant, colocation or enterprise; and whether the estimate covers shell/core, mechanical and electrical systems, utility works, land, active IT equipment, professional services or operating costs.
- Compute plan: quantities and expected operating power for servers and accelerators, storage and networking, plus utilization and idle-power assumptions.
- Facility design: air or liquid cooling, reliability and redundancy requirements, and the assumptions used for power distribution and cooling.
Record uncertain inputs as ranges rather than hiding them inside one number. A conceptual estimate should show what is included, excluded and unknown.
Estimate annual electricity from the equipment up
Start with an equipment schedule. For each category, estimate quantity and power draw under the expected operating profile. The model should account for both active and idle periods; multiplying the rated or peak load by all hours in a year can materially misstate consumption.
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Calculate IT energy
For a first-pass calculation, determine average IT power over the year, then multiply by operating hours:
Annual IT energy (kWh) = average IT load (kW) × operating hours
For a full non-leap year, 8,760 hours is the arithmetic basis. If utilization or power changes through the year, model separate operating periods and add their energy rather than assuming one constant load. A simple utilization model may interpolate between idle and loaded power, but use equipment-specific assumptions where available.
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Convert IT energy to facility energy
Power usage effectiveness (PUE) is total facility energy divided by IT-equipment energy. Therefore:
Estimated facility energy = IT energy × assumed PUE
For example, an explicitly hypothetical facility averaging 10 MW of IT load for a full non-leap year would use 87,600 MWh of IT energy. At an assumed PUE of 1.2, the corresponding facility-energy estimate would be 105,120 MWh. These are arithmetic illustrations, not equipment or facility forecasts.
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PUE represents facility overhead relative to IT energy; it is not a percentage saved and does not measure how efficiently the AI workload performs its computing. A detailed design estimate should model power conversion and cooling for the facility configuration and climate rather than treating PUE as a guaranteed project result.
Use PUE benchmarks as context, not promises
| Published figure | What it describes | How to use it |
|---|---|---|
| 1.145 in 2024; modeled 1.136 in 2030 | Lawrence Berkeley National Laboratory’s 2026-published national estimates for facilities serving AI equipment. PUE is facility energy, including cooling and distribution, divided by IT-equipment energy. | Use as national modeling context, not a prediction for a particular new facility. |
| 1.56 in the 2024 survey (n=526) | Uptime Institute’s industry-average survey result. Respondents were asked about their largest data center. | Use as a different population and survey measure, not as a directly interchangeable alternative to Berkeley Lab’s AI-facility estimate. |
Uptime Institute notes that site climate and business objectives vary, and that PUE does not capture water use, supply-temperature trade-offs or IT-system energy performance. A low PUE alone does not establish that a facility or its computing is efficient overall.
Build a capital estimate with explicit scope
Choose a benchmark that matches the market, project size and type, delivery date and cooling configuration as closely as possible. Then separate the budget into lines rather than multiplying one headline cost per MW and calling the result all-in.
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- Shell/core and architectural work
- Mechanical and electrical fit-out and equipment
- Contractor preliminaries, margin and contingency
- Client-direct and site costs, including abnormal groundworks where relevant
- Utility works and interconnection
- Land
- Professional services
- Active IT and technology fit-out
For each line, mark whether the benchmark includes it, excludes it or leaves it unknown. Do not add excluded costs unless you have a separate estimate for them.
Know what each published construction benchmark covers
| Source and date | Published benchmark | Scope and qualification |
|---|---|---|
| Turner & Townsend, 2025 methodology | No single per-MW amount is stated here. | Modeled baseline: air-cooled, build-to-suit hyperscale facility with 30–50 MW of IT load. Cost headings include shell/core, architectural fit-out, mechanical/electrical fit-out and equipment, contractor preliminaries, margin and contingency. Excludes client-direct costs, land, utility works, abnormal groundworks, site works, active IT equipment and professional services. |
| JLL Research, 2026 outlook | Global average shell-and-core benchmark of $10.7 million per MW in 2025; forecast of $11.3 million per MW in 2026. | Excludes land and active IT equipment. JLL’s geographic examples vary, so select the relevant market and confirm capacity and cooling assumptions before applying the figure. |
| JLL Research, 2026 outlook | AI technology fit-out may cost as much as $25 million per MW. | A separate possible tenant technology fit-out cost, not a general addition that applies to every project and not shell/core construction. |
As a scale illustration only, multiplying JLL’s global 2025 shell-and-core average by 10 MW gives $107 million; using its 2026 forecast gives $113 million. This arithmetic does not turn either benchmark into an estimate for a specific 10 MW facility: the figures exclude land and active IT, and market, capacity and design assumptions must still match.
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Account for cooling without treating a premium as universal
Turner & Townsend reports that liquid-cooled facilities average a 7–10% premium over similar air-cooled benchmarks in its 2025 methodology. Its baseline is a 30–50 MW air-cooled hyperscale build-to-suit model. Treat the premium as a benchmark adjustment to check against the actual design and scope, not as a multiplier for every liquid-cooled project.
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Estimate the electricity bill separately
After estimating facility energy in kWh, multiply it by an applicable electricity-price assumption. State whether the rate is energy-only or an all-in average. Add demand charges, taxes, procurement or contract charges and other tariff components where they apply.
No site-specific tariff is established by the benchmarks discussed here, so they cannot determine a project’s electricity bill. Show low, base and high cases for both utilization and price when either is uncertain; label the assumptions and calculate each case from the same facility-energy method. Keep recurring electricity expense distinct from capital construction and equipment costs.
Use national forecasts to understand uncertainty, not price a facility
Lawrence Berkeley National Laboratory’s 2026-published U.S. data-center energy update gives a 2030 reference-case estimate of 649 TWh. Its sensitivity cases are 578, 664, 590 and 782 TWh, reflecting alternative assumptions about equipment installations, specialized chip shipments, AI-chip lifetime, and AI-server idle-power and utilization. A compounded uncertainty envelope is 521–843 TWh in 2030.
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These are model-derived national scenarios, not a count of every meter or an estimate for an individual AI data center. Their spread shows why equipment shipments, chip lifetime, utilization and idle energy should be visible in a project model rather than concealed behind one point estimate.
For historical context, a 2024 U.S. Department of Energy and Berkeley Lab report estimated 176 TWh of U.S. data-center electricity in 2023, about 4.4% of U.S. electricity, and projected 325–580 TWh for 2028. The 2026-published update is the newer national forecast; the earlier figures are useful context, not a substitute for a project equipment model.
Turn a first-pass range into a project estimate
- Fix the boundary: specify market, delivery year, IT MW versus utility MW, project type, cooling, redundancy and included cost categories.
- Build the equipment schedule: estimate quantities and operating power for accelerators and servers, storage and networking, including idle operation and utilization over time.
- Calculate annual IT energy: use time-weighted average IT load and operating hours, with separate periods where load changes.
- Model facility overhead: apply a clearly labeled PUE assumption for a first pass, or replace it with a cooling and power-distribution model suited to the site.
- Scope capital line by line: select a market- and project-matched benchmark, identify its exclusions, and estimate excluded items separately before totaling.
- Price operations: apply low/base/high tariff and utilization assumptions to facility kWh, including demand and other charges where relevant.
- Report uncertainty: show the assumptions driving the range and leave unresolved costs or inputs visibly unknown rather than implying precision.
A credible project estimate requires, at minimum, the location, capacity basis, design and redundancy, equipment schedule, delivery date, utility and interconnection scope, tariff and procurement assumptions.
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