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Companies and cloud providers are spending on data-center infrastructure because AI and cloud workloads need more computing capacity. A growing share of AI spending is shifting from training models to running them in production, while power availability, cooling, and hardware costs are limiting how quickly and where capacity can expand.
The headline forecasts are broader than enterprise-owned facilities: Gartner’s worldwide data-center-systems total includes spending across buyer categories, including cloud providers. It should not be read as a tally of what ordinary enterprises are spending to build their own sites.
What do the spending forecasts actually measure?
Gartner’s July 2026 worldwide forecast puts data-center-systems spending at $822 billion in 2026, up 62.5% from $506 billion in 2025. These are market forecasts, not audited totals of realized spending, and the category is broader than enterprise-owned construction. Gartner separately forecasts worldwide infrastructure-as-a-service (IaaS) spending of $287 billion in 2026, up 29.3% from $222 billion in 2025. IaaS is rented cloud infrastructure, not physical data-center systems. Gartner’s 2026 spending forecast
The two categories illustrate why “data-center spending” does not mean every company is pouring money into its own buildings. A business may buy systems for facilities it operates, rent computing from a cloud provider, or use both. The figures establish growth in these market categories, not which option is cheaper for every organization.
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Why does AI keep adding demand?
AI infrastructure is a major growth area
Gartner identifies AI infrastructure, cloud platforms, and intelligent applications as drivers of growth in data-center systems and IaaS. Its July 2026 forecast expects growth to concentrate in areas that directly benefit from AI, while traditional segments show comparatively modest changes. This is a concentrated investment story, not a claim that every technology budget or data-center category is growing equally. Gartner’s 2026 spending forecast
Production use requires ongoing compute
Training a model is only one stage of using AI. Once a model is deployed in a customer-facing product or an operational workflow, it must run whenever users or systems call on it. That recurring execution is called inference, and it requires continuing access to computing capacity.
Gartner forecasts worldwide spending on AI-optimized IaaS at $42.276 billion in 2026, up 96.4% from 2025, and $66.143 billion in 2027. Within the 2026 forecast, inference accounts for $23.3 billion, or 55% of the category, versus $19 billion for training. These are forecasts for rented AI-optimized infrastructure, not totals for all AI spending or company-owned facilities. Gartner expects inference spending in this category to overtake training in 2026. Gartner’s 2026 AI-optimized IaaS forecast
That shift helps explain why demand may continue after a burst of model development: production systems need compute as they operate. It does not show that every AI project will be profitable, that every company needs its own data center, or that the forecasted demand will all materialize.
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Why not just use the cloud?
Many organizations do use cloud infrastructure rather than build and operate facilities themselves. Gartner’s 2026 forecasts show growth in both data-center systems and IaaS, but these are different spending categories: systems spending reflects infrastructure purchases, while IaaS spending reflects a service bought from providers. Cloud use therefore does not eliminate data-center construction; providers build and equip facilities to supply the capacity customers rent.
The available forecasts do not establish a universal cost winner between owning systems and renting IaaS. A decision depends on a company’s capacity needs and operating circumstances; the figures alone do not provide a total-cost, latency, or return-on-investment comparison.
What is limiting data-center expansion?
Power availability is a constraint
Gartner’s June 2026 global forecast projects data-center electricity consumption of 565 terawatt-hours (TWh) in 2026, up from 447 TWh in 2025, a 26% increase. It separately forecasts worldwide data-center power demand—the capacity required at a point in time—at 132 gigawatts (GW) in 2026, up from 104 GW in 2025, with 290 GW estimated by 2030. GW measures power demand; TWh measures electricity consumed over time, so the figures are not interchangeable. Gartner says AI capacity is constrained by power availability and identifies securing grid access as a priority. Gartner’s 2026 data-center power forecast
Servers and cooling both use electricity
Gartner forecasts that AI-optimized servers will account for 31% of data-center power consumption in 2026 and that their electricity use will surpass conventional servers’ in 2027. In its global forecast, AI-optimized servers are projected to use 175 TWh in 2026, compared with 195 TWh for conventional servers; in 2027, Gartner projects 258 TWh for AI-optimized servers and 200 TWh for conventional servers. Cooling and other infrastructure are also material: Gartner forecasts their electricity use at 195 TWh in 2026, up from 159 TWh in 2025. Gartner’s 2026 data-center power forecast
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Gartner points to efficiency improvements, higher-efficiency cooling, and edge computing as ways to address the pressure. These do not remove the need for available power; they are part of managing how capacity is supplied and used.
Budgets and hardware supply are not unlimited
Gartner also cites inflation, hardware and memory shortages and rising costs, AI funding initiatives, and shifting priorities as pressures on technology budgets. Spending growth can therefore coexist with tighter budgets: investment is flowing toward AI-related infrastructure, but it competes with other technology needs and depends on equipment supply.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Where is new U.S. hyperscale capacity going?
In a 2026 analysis of 21 major cloud and internet firms, Synergy Research Group says Texas and the Midwest together held 33% of operational U.S. hyperscale capacity at the end of 2025. The same regions accounted for 53% of identified new capacity in the pipeline for the next few years. The first figure describes capacity already operating; the second describes planned capacity, not facilities guaranteed to open. These are U.S. hyperscale figures, not global data-center shares or a measure of ordinary enterprise-owned sites. Synergy Research Group’s U.S. hyperscale analysis
Synergy identifies Northern Virginia as the largest single concentration, while Texas is the most prominent state in its future pipeline. Wisconsin, Indiana, Michigan, and Missouri each have multiple major projects in the analysis. The company attributes the inland shift in part to power availability; the pipeline figures do not establish the power timeline or completion of individual projects. Synergy Research Group’s U.S. hyperscale analysis
What should a company weigh: building or renting?
The forecasts explain why capacity is being added, but they do not decide how a particular organization should obtain it. A company evaluating infrastructure can compare these practical dimensions:
Quick Recap
- Ownership versus service: Compare operating systems in a facility with purchasing IaaS capacity. The market forecasts do not establish which costs less for a given workload.
- Power and grid access: Consider whether a site can secure the capacity and connection the workload requires. Gartner identifies power availability as a constraint on AI capacity.
- Cooling and efficiency: Account for electricity used by cooling and other facility infrastructure as well as servers; Gartner’s forecasts show these are substantial parts of data-center energy use.
- Location and proximity: Assess where computing capacity needs to be in relation to users, operations, or other requirements. The supplied market figures do not quantify a universal latency or location advantage.
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