Neither has a universally bigger footprint: cloud computing runs on data-center infrastructure, so the categories overlap. To compare them fairly, look at the same computing workload and account for its energy use, emissions, water impacts, location and infrastructure—not simply whether it is called “cloud” or “on-premises.”
Why “data centers vs. cloud computing” is not an apples-to-apples comparison
A data center is a physical facility containing computing equipment and the systems that support it. Cloud computing is a way to use computing resources hosted on infrastructure, commonly in data centers operated by cloud providers. A cloud workload therefore has a data-center footprint; cloud is not an alternative to data centers.
| Term | What it describes | What a footprint comparison should count |
|---|---|---|
| Data center | Physical computing infrastructure and its supporting facility. | The facility and equipment included in the chosen boundary. Data-center totals cover more than cloud services alone. |
| Cloud computing | A service model for accessing computing resources hosted on infrastructure. | The resources used to deliver the particular workload, including the relevant share of facility impacts. |
| On-premises computing | Computing resources operated at an organization’s own site. | The same workload and equivalent infrastructure impacts used in the cloud comparison. |
The International Energy Agency’s 2025 analysis reports data-center-wide impacts, not a cloud-only inventory. The Lawrence Berkeley National Laboratory (LBNL) cloud case study discusses cloud’s potential efficiency mechanisms, but does not establish a current, global ranking of cloud against on-premises computing.
What the available electricity and emissions figures show
Data centers have a substantial and growing electricity demand. These global estimates describe data centers broadly, rather than cloud services in isolation:
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| Measure | Figure | Scope and qualification |
|---|---|---|
| Electricity consumed | About 415 TWh in 2024, or 1.5% of global electricity | Global data centers; IEA, 2025. |
| Growth in electricity consumption | About 12% annually since 2017 | Global data centers; IEA, 2025. |
| Electricity-related emissions | About 180 Mt “today” | Data centers; IEA, 2025. The IEA summary does not make this a cloud-only figure. |
| 2035 emissions scenarios | 300 Mt in the Base Case; 500 Mt in the Lift-Off Case | Scenario estimates for data centers, not a single forecast or cloud-only inventory; IEA, 2025. |
The IEA also estimates the electricity generation needed to supply data centers in its Base Case at 460 TWh in 2024, more than 1,000 TWh in 2030 and 1,300 TWh in 2035. These are generation figures, not the separate estimate of 415 TWh consumed in 2024; the two measures should not be treated as interchangeable. The estimates are in the IEA’s energy-supply analysis.
For a different geography and year, a 2021 U.S. study by Siddik, Shehabi and Marston estimated that data centers accounted for about 1.8% of U.S. electricity use and approximately 0.5% of total U.S. greenhouse-gas emissions. These are study-specific estimates for U.S. data centers, not current global or cloud-only figures. See the LBNL environmental-footprint study.
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What determines whether cloud computing is greener than on-premises computing?
The answer depends on the workload and the systems serving it. Cloud consolidation can place work on shared servers and reduce underused capacity; efficient facility design can also lower the energy needed to support computing equipment. LBNL identifies these as potential savings, while noting that net effects are difficult to assess across the wider system.
For a like-for-like comparison, evaluate the following factors for the same workload:
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- Utilization and consolidation: How much computing work each server performs, and whether shared resources reduce idle or duplicated capacity.
- Facility efficiency: How much additional energy the facility needs for systems such as cooling and power delivery beyond the computing equipment itself.
- Electricity supply: The carbon intensity of the electricity used and how the facility sources power. A more efficient workload can still have a different emissions impact depending on its electricity supply.
- Location: The grid and local environmental conditions where the workload runs, including whether the area faces water stress.
- Water accounting: Direct water used at the facility and indirect water associated with electricity generation are separate parts of the picture.
- Comparison boundary: Which equipment and supporting systems are included, and whether the accounting covers only operations or a broader lifecycle.
The older NRDC/WSP analysis also frames an on-premises/cloud comparison around facility efficiency and the electricity supplying the facility. It is useful for understanding the variables, not as a present-day numeric benchmark: NRDC/WSP cloud-computing analysis.
Why water and location matter as well as carbon
Electricity use alone does not capture a data center’s environmental footprint. A 2021 U.S. study by Siddik, Shehabi and Marston examined data-center water and carbon impacts, including exposure to water-stressed areas. Its findings are U.S.-specific and do not provide a global, cloud-only water estimate. The study is available from LBNL.
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When comparing two workloads, ask where each runs and distinguish water used directly by a facility from water associated with generating its electricity. A provider-wide or national total cannot, by itself, establish the water impact of a particular cloud workload or show that it is lower than the equivalent on-premises workload.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to make a useful comparison
- Define the task. Compare equivalent computing work, service levels and time periods rather than a cloud account with an entire data center.
- Set the accounting boundary. Specify which servers, facility systems and electricity-related impacts are included. Keep operational and broader lifecycle claims distinct.
- Use workload-level inputs where available. Compare utilization, consolidation and facility efficiency for the infrastructure serving that task, rather than assuming either shared cloud infrastructure or local ownership is automatically more efficient.
- Account for energy sources and place. Use the relevant electricity supply and location; consider local water stress and both direct facility water and water associated with electricity generation.
- Check the date and geography. Separate global data-center totals, U.S.-specific studies and scenario projections. Do not label any of these as a cloud-only result unless the source actually measures cloud alone.
There is no current, globally comparable cloud-only water footprint or like-for-like lifecycle estimate in the cited evidence. Without a consistent workload, boundary and location, a claim that cloud computing is categorically greener—or worse—than on-premises computing goes beyond what these figures establish.
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