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GPUs are only one part of the cost of delivering AI. Power, cooling, networking, storage, reliable grid access and other data-center infrastructure can all matter—and the biggest item depends on the facility, workload, location and which costs you count. Electricity use is measurable; there is no established universal figure for an AI query’s total cost or a single hidden expense that dominates every AI service.
What it costs to run AI besides GPUs
An AI service runs on a physical system, not a processor in isolation. That system can include servers, storage and networking, plus cooling, backup power and the connections needed to draw electricity from the grid. Each layer supports the service, and some use electricity or require investment even when they are not part of the GPU’s purchase price.
It helps to distinguish four different boundaries: what a customer pays, what a provider spends to operate a service, what a facility costs to build and equip, and wider effects on electricity grids and the environment. A claim about one boundary does not automatically describe the others.
Where data-center electricity goes
The International Energy Agency (IEA), reporting facility data from 2024 in 2025, says servers account for about 60% of electricity use in modern data centers. Cooling’s share ranges from about 7% in efficient hyperscale facilities to over 30% in less-efficient enterprise facilities. Storage is around 5%, and networking can be up to 5%. These are shares of facility electricity—not percentages of an AI company’s total costs or an AI customer’s bill. IEA: Energy demand from AI
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The variation matters. Cooling is a substantial facility load, but the published figures do not establish it as the largest non-GPU cost everywhere. A facility’s design and efficiency affect its electricity use, and the electricity shares alone do not reveal equipment costs, financing or operating expenses.
Why power supply can become a strategic cost
AI facilities need dependable electricity. Having servers available is not enough if a site cannot obtain a suitable grid connection or reliable supply. The IEA identifies electricity access and infrastructure bottlenecks as important constraints on data-center development; backup power, storage or flexibility may also be needed, depending on the site and reliability requirements. Those needs and their costs are site-specific. IEA: Energy and AI
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Location also changes what electricity means in practice: availability, price, reliability and source vary. They can affect operating costs as well as carbon, water and land impacts. Those broader impacts matter, but they are not necessarily included in a customer’s invoice. The UN University Institute for Water, Environment and Health discusses how data-center impacts depend on location, electricity source, models and usage patterns. UNU-INWEH: AI and the environment
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AI electricity demand is rising, even as tasks get more efficient
The IEA’s April 2026 outlook estimates global data-center electricity consumption at 485 TWh in 2025 and projects 950 TWh in 2030 in its central scenario. It reports that data-center electricity demand grew 17% during 2025, while electricity consumption from AI-focused data centers grew 50% that year. These are global electricity figures—not a measure of one company’s bill, all AI energy use or the financial cost of a task. The 2030 figure is a projection, not a reported outcome. IEA: Energy and AI, executive summary
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Efficiency and total demand can move in different directions. The IEA reports that energy use per AI task is improving rapidly, while demand and energy-intensive applications are expanding. Lower electricity use for an individual task does not by itself guarantee lower total system consumption if the number of tasks or their energy requirements grows.
For U.S. context, the Department of Energy summarized a Lawrence Berkeley National Laboratory report estimating that U.S. data centers used 176 TWh of electricity in 2023. That report projected a range of 325–580 TWh for 2028. Both figures cover data centers, not AI alone; the 2028 range is a projection. U.S. Department of Energy: data-center electricity report summary
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Why there is no reliable universal cost per AI query
Electricity use is not the same as financial cost: converting consumption into a bill requires location-specific electricity prices and operating assumptions. A per-query estimate would also depend on the model, task, workload, data-center efficiency and accounting boundary. The U.S. Government Accountability Office (GAO) says companies generally do not report detailed generative-AI energy and water use, and the share of data-center electricity attributable to generative AI is unclear. The available figures therefore do not support a universal total cost per query or a general AI-only share of data-center electricity. GAO: Generative AI and energy and water use
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There is no single comparable cost metric in the cited sources that can rank providers across the board. Use these dimensions to frame a comparison, and check which ones each provider actually discloses:
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- Capability: Does the model handle the task you need?
- Energy and volume: What is known about energy per task and expected workload volume? Public disclosures are incomplete.
- Hosting: Where does the service run, and what is known about power availability and reliability there?
- Facility efficiency: What is disclosed about cooling and other data-center overhead?
- Data movement: How much storage, networking and movement of data does the workload require?
- Environmental scope: Are carbon, water and land impacts part of your decision?
- Cost boundary: Are you comparing an invoice, provider operating costs, facility investment or wider grid and environmental effects?
Treat these as comparison questions, not a ready-made ranking. A GPU price or an electricity estimate by itself cannot establish which service has the lower overall cost.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the headline claim does—and does not—mean
Calling GPUs only one visible input is useful because AI depends on a larger, power-hungry infrastructure system. But the evidence does not prove that electricity, cooling or any other non-GPU item is always the largest cost. The answer changes with facility type, geography, workload and the boundary used to count costs. IEA projections are scenario-based and can change as adoption, efficiency and infrastructure constraints evolve.
As IEA Executive Director Fatih Birol put it: “The IEA was early in recognising that there is no AI without energy – and that countries that provide secure, affordable and rapid access to electricity will be one step ahead,” IEA statement on its 2026 outlook
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