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Monthly spending on data center construction in the United States

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Artificial Intelligence

What you should know about this indicator

  • This indicator shows monthly spending on the physical construction of data center buildings in the United States. It represents the actual value of work done at construction sites each month, including materials, labor, and contractor costs.
  • This data doesn't include maintenance work, land purchases, or the computer equipment that goes inside data centers (like servers and storage systems). This means it captures only a small part of total data center investment, since IT hardware represents a significant additional cost.
  • Data centers power a wide range of online services beyond AI, such as streaming services and cloud storage.
  • The values shown are not seasonally adjusted, meaning they reflect actual monthly spending and typical seasonal patterns, including slower winter construction and busier summer periods.
  • This indicator is adjusted for inflation using the Producer Price Index (PPI) for new office building construction. Following guidance from the Bureau of Economic Analysis (BEA), we assume data center construction costs are similar to office-type buildings because both use comparable structural components, such as foundations, walls, and HVAC systems.
Monthly spending on data center construction in the United States
This only includes work done on-site to construct data center buildings each month, including materials and construction labor. It excludes the cost of IT hardware inside the buildings, like servers and storage, which can account for a large share of total investment. This data is expressed in US dollars, adjusted for inflation.
Source
United States Census Bureau (2026); U.S. Bureau of Labor Statistics (2026) – with major processing by Our World in Data
Last updated
March 24, 2026
Next expected update
April 2026
Unit
constant 2021 US$

Sources and processing

This data is based on the following sources

United States Census Bureau – Value of Private Construction Put in Place

Monthly construction spending data for private construction in the United States, broken down by construction type. The dataset includes datacenter construction spending measured as not seasonally adjusted monthly values in millions of dollars.

Data is collected through the Survey of Construction (SOC), which provides national and regional statistics on new privately-owned residential construction. The construction spending estimates are based on reports from builders, owners, and architects, along with information from building permits and Census Bureau surveys.

Retrieved on
March 24, 2026
Retrieved from
https://www.census.gov/construction/c30/historical_data.html
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
U.S. Census Bureau, Value of Construction Put in Place Survey (VIP), Private Construction Spending Time Series

Monthly construction spending data for private construction in the United States, broken down by construction type. The dataset includes datacenter construction spending measured as not seasonally adjusted monthly values in millions of dollars.

Data is collected through the Survey of Construction (SOC), which provides national and regional statistics on new privately-owned residential construction. The construction spending estimates are based on reports from builders, owners, and architects, along with information from building permits and Census Bureau surveys.

Retrieved on
March 24, 2026
Retrieved from
https://www.census.gov/construction/c30/historical_data.html
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
U.S. Census Bureau, Value of Construction Put in Place Survey (VIP), Private Construction Spending Time Series

U.S. Bureau of Labor Statistics – US PPI New Office Construction

BLS uses a method in which office building models are developed and specified to represent the offices being constructed in the marketplace. Multiple office models were developed to accommodate variations in building design. The building models are described as a series of unique production elements or "assemblies" in BLS terminology. Each building assembly represents a building construction activity that can be fully defined as a unique portion of the total project. Each assembly is made up of unit-price components that define the specific type and quantity of materials, labor, and equipment necessary for the assembly's installation.

A professional cost-estimating firm developed the building models under BLS direction. To achieve an output price, BLS combines the detailed material and installation (labor and related equipment) cost data, which are updated quarterly by the cost-estimating firm, with margin (overhead and profit) data collected monthly by BLS directly from building construction contractors. BLS aggregates output price changes captured at the assembly level each month to represent the changes in output prices for the total structure. Therefore, the BLS output indexes attempt to measure changes in the input costs for these structures, along with changes in contractor markups.

Retrieved on
March 24, 2026
Retrieved from
https://www.bls.gov/ppi/factsheets/producer-price-index-data-for-nonresidential-building-construction-sector-new-office-building-construction-naics-236223.htm
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
U.S. Bureau of Labor Statistics

BLS uses a method in which office building models are developed and specified to represent the offices being constructed in the marketplace. Multiple office models were developed to accommodate variations in building design. The building models are described as a series of unique production elements or "assemblies" in BLS terminology. Each building assembly represents a building construction activity that can be fully defined as a unique portion of the total project. Each assembly is made up of unit-price components that define the specific type and quantity of materials, labor, and equipment necessary for the assembly's installation.

A professional cost-estimating firm developed the building models under BLS direction. To achieve an output price, BLS combines the detailed material and installation (labor and related equipment) cost data, which are updated quarterly by the cost-estimating firm, with margin (overhead and profit) data collected monthly by BLS directly from building construction contractors. BLS aggregates output price changes captured at the assembly level each month to represent the changes in output prices for the total structure. Therefore, the BLS output indexes attempt to measure changes in the input costs for these structures, along with changes in contractor markups.

Retrieved on
March 24, 2026
Retrieved from
https://www.bls.gov/ppi/factsheets/producer-price-index-data-for-nonresidential-building-construction-sector-new-office-building-construction-naics-236223.htm
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
U.S. Bureau of Labor Statistics

How we process data at Our World in Data

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At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

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Notes on our processing step for this indicator

We used the Producer Price Index (PPI) for new office building construction to adjust for inflation, following guidance from the Bureau of Economic Analysis (BEA) that suggests data center construction costs are similar to office buildings due to their comparable structural components.

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Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Monthly spending on data center construction in the United States”, part of the following publication: Charlie Giattino, Edouard Mathieu, Veronika Samborska, and Max Roser (2023) - “Artificial Intelligence”. Data adapted from United States Census Bureau, U.S. Bureau of Labor Statistics. Retrieved from https://archive.ourworldindata.org/20260331-134337/grapher/monthly-spending-data-center-us.html [online resource] (archived on March 31, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

United States Census Bureau (2026); U.S. Bureau of Labor Statistics (2026) – with major processing by Our World in Data

Full citation

United States Census Bureau (2026); U.S. Bureau of Labor Statistics (2026) – with major processing by Our World in Data. “Monthly spending on data center construction in the United States” [dataset]. United States Census Bureau, “Value of Private Construction Put in Place”; U.S. Bureau of Labor Statistics, “US PPI New Office Construction” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260331-134337/grapher/monthly-spending-data-center-us.html (archived on March 31, 2026).

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https://ourworldindata.org/grapher/monthly-spending-data-center-us.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/monthly-spending-data-center-us.metadata.json?v=1&csvType=full&useColumnShortNames=false

Code examples

Examples of how to load this data into different data analysis tools.

Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/monthly-spending-data-center-us.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://ourworldindata.org/grapher/monthly-spending-data-center-us.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://ourworldindata.org/grapher/monthly-spending-data-center-us.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/monthly-spending-data-center-us.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/monthly-spending-data-center-us.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/monthly-spending-data-center-us.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear