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There is no single price for an AI data center: a shell-and-core building, a powered and cooled facility, and a fully equipped AI compute deployment have very different costs. JLL forecasts an average global shell-and-core construction cost of $11.3 million per MW in 2026, excluding land and active IT equipment. At a much larger scale, Epoch AI’s stylized model of a 1-GW U.S. hyperscaler facility estimates $37.883 billion in upfront capital expenditure and $907 million in annual operating expenses. These are benchmarks with different scopes, not project quotes.
What does an AI data center cost per MW?
The answer depends first on what the estimate includes. Shell-and-core construction does not buy the GPUs and servers that perform AI workloads; a fully equipped estimate may include those systems, networking, cooling, and other infrastructure. A per-MW figure is useful only when the capacity basis and cost boundary match.
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| Estimate | Cost per MW | Scope and qualification |
|---|---|---|
| JLL global average, 2026 | $11.3 million | Forecast shell-and-core construction cost; excludes land and active IT equipment. JLL models a single-tenant, 50-MW air-cooled facility. |
| JLL selected markets, 2026 | Chicago: $12–14 million; Northern Virginia: $11–12 million; Dallas: $10–11 million; Tokyo: $14–18 million; Mumbai: $6–7 million | Shell-and-core ranges under the same modeled 50-MW air-cooled facility assumptions; excludes land and active IT equipment. |
| Morgan Stanley Research AI data-center build estimate | $21–31 million including GPUs and servers; about $8–14 million excluding them | Analyst estimates; scope differs from JLL’s shell-and-core forecast. |
| JLL AI technology fit-out | Up to $25 million | Upper figure per MW for AI technology fit-out in JLL’s 2026 outlook; not a shell-and-core price. |
These figures should not be added together as if they were components of one quote: their methods and boundaries differ. They do show why a shell-only cost can substantially understate the investment needed for an operational AI deployment.
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Cooling and building design change the comparison
In JLL’s assumptions, liquid-cooled facilities carry a 10% construction premium; multistory facilities in the Americas can add 20% to construction costs. Those adjustments relate to JLL’s construction assumptions, not a universal surcharge for every project.
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What is included in a 1-GW AI data center estimate?
Epoch AI’s 2026 model offers a more complete, but explicitly stylized, example: a U.S. hyperscaler-owned and operated facility with 1 GW of IT nameplate capacity. It estimates upfront capital expenditure at $37.883 billion.
| Capital category | Modeled upfront cost |
|---|---|
| Servers | $21.188 billion |
| Facility costs | $11.433 billion |
| Network infrastructure | $4.925 billion |
| Land | $172 million |
| Utility works | $164 million |
| Total | $37.883 billion |
The model’s large server and network components illustrate why “building cost” can mean more than construction. Actual totals depend on server choices, facility design, location, financing, and power strategy; the modeled result is not a quote for another site.
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How much does it cost to run an AI data center each year?
Epoch AI estimates annual operating expenses of $907 million for its same 1-GW scenario. Energy is the largest listed operating item, but it is not the only one.
| Annual operating category | Modeled cost |
|---|---|
| Energy | $594 million |
| Taxes | $143 million |
| Maintenance | $120 million |
| Labor | $40 million |
| Water | $6 million |
| Total | $907 million |
The energy estimate uses an assumed electricity price of 8.34 cents per kWh, derived by weighting 2024 U.S. state industrial electricity costs by project counts. The model also assumes a power usage effectiveness (PUE) of 1.14 and 71% utilization. The 8.34-cent figure is a model input based on 2024 data, not a current tariff or a forecast bill for an arbitrary site.
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Why annual operating expense is not the full annual cost
Operating expense covers ongoing costs such as power, taxes, maintenance, labor, and water. Epoch AI separately annualizes the modeled capital investment over assumed asset lives and estimates total cost of ownership at $8.5 billion per year. In its breakdown, servers account for about 60% of that annualized total. This lifecycle measure should not be confused with the $907 million operating-expense estimate.
Replacement timing changes the annualized result substantially. Epoch assumes a five-year life for IT equipment and a 14-year life for the facility. With IT equipment lasting three years, its modeled annual total cost rises to $12 billion; with a seven-year life, it falls to $7 billion. Comparisons of lifecycle cost therefore need to state the assumed hardware refresh cycle.
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How power availability and location affect cost and schedule
A site’s electricity price matters, but the ability to secure power and connect to the grid can also determine when the facility can operate. Morgan Stanley Research reports that new-build AI data centers may take multiple years to reach power after construction begins. It quotes Lawrence Berkeley National Laboratory’s historical comparison: “projects built in 2022 took 5 years from the interconnection request to commercial operations, compared to 2 years in 2015 and <2 years in 2008.” This is a comparison of projects from those years, not a guaranteed schedule for a current project or a particular location.
The International Energy Agency reported in April 2026 that data-center electricity demand rose 17% in 2025, while worldwide electricity demand grew 3%. It identified grid connections and approvals, as well as supply constraints for transformers, gas turbines, advanced chips, and IT components, as bottlenecks to expansion. The IEA also reported that five large technology companies’ capital expenditure exceeded $400 billion in 2025 and was expected to rise a further 75% in 2026. These are sector-level indicators of scale and constraints, not a budget for an individual facility.
How to compare real project estimates
Before comparing two proposals, align the capacity and cost scope. A construction-only figure should not be compared directly with a turnkey deployment that includes GPUs, servers, and networking.
- Confirm whether capacity means IT nameplate capacity and whether both estimates use the same MW basis.
- Separate land, shell and core, utility works, cooling, and active IT equipment.
- Identify which party pays for technology fit-out and ongoing operations.
- Compare hardware configuration, network infrastructure, cooling design, and expected equipment replacement life.
- Check the site’s power price and reliability, interconnection plan and timing, and expected utilization.
- Include location-specific taxes, financing assumptions, and any differences in operating responsibility.
Company disclosures can illustrate execution risks without serving as neutral price benchmarks. For example, IREN’s fiscal 2026 filing lists data-center platform development and construction and GPU purchases among its primary cash requirements, and identifies risks including higher costs, falling GPU rental rates, counterparty exposure, and regulatory or sociopolitical conditions affecting development. Those risks are relevant context, but one company’s filing does not establish the cost of a typical AI data center.
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