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AI data centers cost so much because they combine expensive compute hardware with a power-hungry, highly engineered facility. Servers and accelerators can dominate upfront spending, while electricity is a major recurring expense. But there is no single reliable “cost per megawatt” without defining what is included: a shell-and-core building, a greenfield project, or a complete facility fitted with AI equipment are different budgets.
Why an AI data center costs more than a building
An AI data center is a facility designed to supply computing equipment with dependable electricity, cooling, connectivity, physical space, and operational support. The building is only one part of the investment. A project also needs servers containing accelerators, network and storage equipment, electrical systems, cooling equipment, and often backup power and utility connections.
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Those pieces affect one another. A dense accelerator installation draws substantial power and produces heat, so it can require more capable electrical distribution and cooling than a lower-density facility. The site must have a feasible route to enough power, and the project has to procure and install equipment that may have long lead times. Once running, the facility consumes electricity and needs staff, maintenance, taxes, water, and eventual hardware replacement.
That is why construction benchmarks, complete-project estimates, and annual operating costs should not be treated as interchangeable. A quoted amount per megawatt may exclude land, utility work, servers, or some other major component.
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What does a megawatt cost? Start by checking the scope
Recent benchmarks illustrate how much the answer depends on the project boundary. JLL’s 2026 global outlook puts average shell-and-core construction at $10.7 million per MW in 2025 and forecasts $11.3 million per MW in 2026. Its benchmark excludes land and active IT equipment. JLL also says AI tenant technology fit-out can reach $25 million per MW. That fit-out figure is not a complete project price to add automatically to the shell benchmark: it describes a separate scope and should be interpreted in the context of the underlying estimate. JLL Research, 2026 Global Data Center Outlook
For a different U.S. scope, Cushman & Wakefield’s 2026 development cost guide estimates an average $17.6 million per MW for modern greenfield facilities. That figure should not be compared directly with JLL’s shell-and-core benchmark: the sources use different geographies and project boundaries. Neither figure, by itself, establishes the cost of a fully equipped AI data center. Cushman & Wakefield, 2026 U.S. Development Cost Guide
| Estimate | What it covers | How to read it |
|---|---|---|
| JLL: $10.7 million per MW (2025 average); $11.3 million per MW (2026 forecast) | Global shell-and-core construction; excludes land and active IT equipment | A facility construction benchmark, not an all-in AI data center budget |
| JLL: up to $25 million per MW | Potential AI tenant technology fit-out | A fit-out benchmark, not a universal add-on or a quoted total project price |
| Cushman & Wakefield: $17.6 million per MW | Average modern greenfield costs in its 2026 U.S. guide | A different scope and geography from JLL’s shell-and-core benchmark |
JLL reports that its shell-and-core benchmark rose from $7.7 million per MW in 2020 to $10.7 million per MW in 2025, a 7% compound annual growth rate. It also says the construction estimate assumes a single-tenant 50 MW air-cooled facility; its stated adjustments include a 10% premium for liquid cooling and a 20% construction-cost addition for multistory facilities in the Americas. These assumptions show why even a published “per MW” benchmark needs its footnotes. JLL Research, 2026 Global Data Center Outlook
Where upfront capital goes
Servers and accelerators
Compute hardware is often the largest single capital category in an AI facility because the servers contain the accelerators that perform AI workloads. Hardware spending varies with the number and type of accelerators, server configuration, negotiated purchase prices, and the time before equipment must be refreshed. A rack-mounted server is a useful illustration of the category, but ordinary server listings are not a proxy for the specialized systems or procurement terms in a hyperscale AI build.
Epoch AI’s 2026 cost model gives a sense of the scale: in a stylized U.S. hyperscaler facility with one gigawatt of IT capacity, it estimates $21.188 billion for servers out of $37.883 billion in total upfront capital expenditure. The figure is a modeled example, not an observed facility bill or a quote that can be applied to every project. Its assumptions include NVIDIA GB200 NVL72 systems, and changing the equipment, price, scale, or design changes the result. Epoch AI, AI data center cost breakdown, 2026
Facility, electrical systems, and cooling
The facility budget covers more than walls and floor space. It can include the building shell, data-hall space, mechanical and electrical systems, cooling distribution, and redundancy. The project also needs electrical systems to deliver power throughout the site. Depending on the location and design, that may include grid connections, substations, transformers, backup generation, and site distribution equipment.
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Cooling is a cost and operating consideration because computing equipment turns electrical energy into heat that must be removed. The International Energy Agency reports that cooling accounts for about 7% of electricity use in efficient hyperscale data centers, compared with more than 30% in less-efficient enterprise data centers. Those figures describe a range across facility types, not a guaranteed cooling share for a new AI site. Cooling design, equipment density, climate, and operating conditions all matter. International Energy Agency, Energy and AI
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesJLL’s modeled construction adjustment for liquid cooling is one example of a design choice affecting construction cost. It is not a rule that every AI project using liquid cooling will cost precisely 10% more: the stated premium applies to JLL’s particular benchmark assumptions. Higher density may also influence electrical distribution, facility layout, and the required cooling approach.
Network, storage, land, and site preparation
Accelerators need network equipment to communicate, and AI facilities also require storage and internal and external connectivity. These are real parts of the capital stack even though a building-only estimate may not include them. In Epoch AI’s modeled one-gigawatt example, network infrastructure accounts for $4.925 billion of upfront capex, separate from the server and facility amounts.
Land and site work can add costs that a shell-and-core figure excludes. Prices depend on location, and a site with access to power can command a premium. Permitting, site preparation, access roads, and fiber extensions may also affect the project. The cited estimates do not establish a universal allowance for these items, so an all-in budget needs site-specific figures rather than a generic percentage.
A modeled one-gigawatt example: capital versus annual costs
Epoch AI’s 2026 estimate helps show why upfront investment and annual spending need separate treatment. It models a U.S. hyperscaler facility with one gigawatt of IT capacity, not total grid draw. The modeled upfront capex is $37.883 billion; annual operating expenses are about $907 million. The numbers are useful for understanding cost categories and their relative scale, but are not a universal project quote. The model assumes 71% utilization, a power usage effectiveness (PUE) of 1.14, U.S.-weighted power costs, five-year IT equipment life, and 14-year facility life, among other assumptions. Epoch AI, AI data center cost breakdown, 2026
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|---|---|---|
| Servers | $21.188 billion | Upfront capex |
| Facility | $11.433 billion | Upfront capex |
| Network infrastructure | $4.925 billion | Upfront capex |
| Total | $37.883 billion | Upfront capex |
| Energy | $594 million per year | Annual opex |
| Taxes | $143 million per year | Annual opex |
| Maintenance | $120 million per year | Annual opex |
| Labor | $40 million per year | Annual opex |
| Water | $6 million per year | Annual opex |
| Total | About $907 million per year | Annual opex |
The model’s annual figure is not the full lifecycle cost of owning the facility. Epoch AI estimates that if IT equipment life were three years rather than the model’s five, annualized total cost would rise to $12 billion; with a seven-year IT life, it would fall to $7 billion. These are outcomes under the model’s assumptions, not a forecast for a particular operator. They demonstrate how quickly refresh timing can change an annualized cost calculation.
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What drives the recurring operating bill?
Electricity and utilization
Electricity is a major recurring cost. The amount depends on how much IT equipment is running, how intensively it is used, the facility’s overhead, and the price and terms of electricity procurement. PUE is a common way of expressing facility overhead: it compares total facility energy with energy used by IT equipment. A PUE of 1.14 in Epoch AI’s model is an assumption for that scenario, not a universal figure for AI data centers.
Utilization matters because an installed fleet can consume resources even when it is not delivering its maximum possible computing output. A cost estimate needs to say whether it assumes full, partial, or variable use. Electricity rates and power arrangements are also location-dependent, so a modeled energy bill should not be carried over to another region without adjusting those inputs.
Maintenance, people, taxes, and water
Facilities require maintenance for both IT equipment and building systems, as well as staff to operate and support them. Local labor availability and wages can influence both operating budgets and construction schedules. Property, sales, payroll, and personal-property taxes depend on the jurisdiction and any applicable abatements. Water spending and consumption vary with cooling design, local water rates, and operating conditions.
Epoch AI’s model assigns $120 million annually to maintenance, $40 million to labor, $143 million to taxes, and $6 million to water. Those amounts belong only to its modeled one-gigawatt case; actual costs depend on how the facility is designed, where it is located, and how it is operated.
Hardware refresh and useful life
Servers and accelerators do not last forever as productive AI equipment. Replacing them is a recurring lifecycle cost even when accounting or project budgets classify purchases as capital expenditure rather than annual operating expense. The assumed refresh interval changes how much capital must be recovered each year. A cost comparison that omits equipment life can therefore make two facilities appear more alike than they are.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why location and schedule change the budget
Power availability can determine whether a site is viable and how much enabling infrastructure it needs. Cushman & Wakefield’s 2026 U.S. guide identifies grid-power scarcity, premiums for powered land, shortages of skilled trades, long lead times for critical equipment, wages, and materials as cost and schedule pressures. These factors can affect both the amount spent and when the facility can begin operating. Cushman & Wakefield, 2026 U.S. Development Cost Guide
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Equipment procurement is also a schedule risk. JLL reports an average equipment lead time of 33 weeks in its 2026 global outlook. That is a reported average, not a guarantee for every component or project. Delays can affect the construction sequence and the date the facility is ready to use; the cost effect depends on the project’s contracts, financing, and schedule. JLL says that speed to power is the primary site-selection criterion, followed by community support, latency, and proximity to customers. JLL Research, 2026 Global Data Center Outlook
How to compare an AI data-center cost estimate
Before comparing a headline figure with another estimate, align the assumptions that determine what the number actually means. A useful estimate should make its scope and denominator explicit, then show the capital and operating assumptions separately.
- Identify the project boundary. Determine whether the estimate covers shell and core, a greenfield development, tenant fit-out, active IT equipment, or the complete facility. Check whether land and utility works are included.
- Define the megawatt denominator. Ask whether MW refers to IT capacity or total facility power. Those are not equivalent: facility overhead means total power demand can exceed IT load.
- Specify the IT configuration. Record the accelerator and server configuration, network and storage scope, procurement pricing, and assumed refresh life. A generic construction benchmark cannot substitute for those hardware details.
- Set operating assumptions. Compare utilization, PUE, electricity price and procurement, maintenance, staffing, water, taxes, and equipment life. Mark which are measured, modeled, or forecast.
- Match location and date. Consider the local power connection, land, wages, labor supply, materials, taxes, incentives, and construction market. A U.S. greenfield guide and a global shell-and-core benchmark are not directly comparable.
- Check cooling and density. Identify cooling design, rack density, redundancy, and facility layout. These choices can affect both the build and the electricity bill.
For a quick sanity check, separate the estimate into two views: a capital budget for land, site, facility, utility and IT build-out; and an operating budget for energy, maintenance, labor, taxes, water, and replacements. Do not add benchmarks with overlapping scope, or treat an annual operating figure as if it includes equipment replacement unless the estimate says so.
What electricity-demand forecasts do—and do not—tell you
Large-scale energy projections help explain why power access has become a central planning issue, but they are not project cost estimates. The IEA’s 2025 analysis projects global data-center electricity consumption of around 945 TWh in 2030 in its base case. A 2025 U.S. data-center energy report update published by Lawrence Berkeley National Laboratory estimates 649 TWh for U.S. data centers in 2030 in its reference case, with uncertainty bounds of 521–843 TWh. The figures cover different geographies and scenario frameworks; neither is an observed 2030 result or a direct estimate of one AI facility’s consumption. International Energy Agency, Energy and AI and Lawrence Berkeley National Laboratory, United States Data Center Energy Usage Report, 2025 update
For a specific project, the decision-useful questions are more local: how much power can the site obtain, when can it connect, what upgrades are required, what electricity price applies, and what cooling and utilization assumptions govern the load? National and global forecasts provide context, not answers to those site-level questions.
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