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GPUs are only one part of the cost of running AI. Every service also depends on electricity and the facilities and equipment that keep computing systems powered, connected, cooled and reliable. Those costs can be significant, but no public figure establishes one universal “biggest” hidden cost across all AI businesses: workloads, locations, data centers and accounting boundaries differ.
What does it cost to run AI besides GPUs?
An AI system runs on more than accelerators. Its physical infrastructure can include servers, storage, networking, cooling equipment, backup power and connections to the electricity grid. The provider may pay for operating that infrastructure; customers may see only a service invoice. Wider effects on power systems, water, carbon emissions and land may not appear on either bill.
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That distinction matters: electricity use is not the same as financial cost. A facility’s bill depends on local electricity prices and operating arrangements, while infrastructure investment and environmental impacts are separate cost boundaries.
Why is electricity becoming a bigger part of the AI conversation?
Data-center electricity use is rising even as hardware and software improve energy efficiency per task. The International Energy Agency (IEA) estimates that global data-center electricity consumption reached 485 TWh in 2025, up 17% from the previous year. For AI-focused data centers, it estimates electricity consumption grew 50% in 2025. These are estimates for data-center electricity—not a measure of all AI energy use, a particular company’s bill or the cost of an individual task.
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In its 2026 outlook, the IEA projects global data-center electricity consumption will reach 950 TWh in 2030, roughly double the 2025 estimate. The outlook is a projection, not a guaranteed outcome: adoption, efficiency gains and infrastructure constraints can all change the trajectory. The agency also reports that energy use per AI task is improving rapidly, while demand and energy-intensive applications are expanding. Better efficiency per task therefore does not, by itself, mean lower total electricity use.
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,” the IEA said in 2026.
Where does data-center electricity go?
Servers are a large share of facility electricity, but they are not the whole load. IEA facility data from 2024, reported in 2025, puts servers at around 60% of electricity use on average in modern data centers. Cooling’s share varies sharply: about 7% in efficient hyperscale facilities and more than 30% in less-efficient enterprise facilities. Storage accounts for around 5%, and networking can account for up to 5%.
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These are shares of a data center’s electricity consumption, not percentages of an AI company’s total costs. They also show why cooling cannot be named as the largest non-GPU cost everywhere: facility design and efficiency make a substantial difference.
What makes the hidden cost stack vary by site?
Power supply and grid access
AI facilities require dependable electricity. Having a suitable grid connection and enough available power can affect where a data center can operate and how quickly it can be developed. Backup power, on-site storage or other reliability measures may also be needed, depending on the site and service requirements.
Cooling and facility efficiency
Servers generate heat, so facilities need systems to remove it. Those systems consume electricity, but their share varies by facility type and efficiency. A hyperscale data center and a less-efficient enterprise facility should not be treated as if their overheads were identical.
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Networking and storage
AI systems need to move and retain data, making networking and storage part of the infrastructure footprint. Their electricity use is measurable at the facility level, but the cited facility shares do not establish that either is the dominant cost across AI services.
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Environmental impacts extend beyond an operator’s electricity bill. They depend in part on where a facility is built, how its electricity is generated, and how its equipment and workloads are managed. The United Nations University Institute for Water, Environment and Health discusses these location- and usage-dependent impacts in its overview of AI, water, energy and land. These wider effects are not necessarily costs an AI customer pays directly.
Why there is no reliable universal cost per AI query
A per-query figure would depend on what the query asks, which model handles it, how much computing it uses, the volume of requests and the infrastructure serving them. It would also need a clear boundary: provider operating cost, customer price, facility investment or broader environmental and grid effects.
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The U.S. Government Accountability Office says companies generally do not report detailed generative-AI energy and water use, leaving the generative-AI share of data-center electricity unclear. The IEA’s data-center totals likewise should not be read as AI-only consumption. There is no well-supported, comparable figure here for the total hidden cost of one AI query.
For context, Lawrence Berkeley National Laboratory estimated U.S. data centers used 176 TWh of electricity in 2023 in a report summarized by the U.S. Department of Energy in 2024. The laboratory’s report projected a range of 325–580 TWh for U.S. data-center electricity use in 2028. Both figures concern data centers overall, not AI alone; the 2028 range is a projection, not a measurement.
How to compare AI services or deployment options
GPU specifications alone do not make a useful cost comparison. Check the dimensions that affect the job you need and the costs you want to count:
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- Capability: Does the model handle the required task well enough?
- Workload and energy: What is known about energy per task and expected usage volume? Provider disclosures are incomplete, so treat missing figures as unknown.
- Hosting and reliability: Where is the service hosted, and how do power availability and reliability affect it?
- Facility overhead: What is known about data-center efficiency, cooling, storage and networking?
- Data movement: How much storage and networking does the workload require?
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- Cost boundary: Are you comparing the customer invoice, provider operating cost, facility investment or wider effects?
These are comparison dimensions, not a ready-made ranking. Current public sources do not provide a single comparable total-cost metric across AI providers.
What the available figures can—and cannot—tell you
The figures establish that data centers use substantial and growing amounts of electricity, and that non-server facility loads vary. They do not prove that power, cooling or any other one item is the biggest hidden cost for every AI service. The answer depends on what is being counted, where the computing happens and how the facility is built and operated.
The IEA outlook and facility breakdowns are available in Energy and AI and its analysis of AI energy demand. The U.S. figures are summarized by the Department of Energy in its 2024 report on data-center electricity demand; the disclosure limitations are discussed by the U.S. Government Accountability Office.
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