The cost to establish an AI data center in 2025 ranges from under a million dollars for small-scale projects to over $1 billion for large, AI-optimized campuses, with hyperscale builds costing between $200million and $500 million. Factors influencing cost include size, location, hardware, and infrastructure, with the overall global investment expected to reach hundreds of billions of dollars.
Cost breakdown by scale
Small-scale: Approximately $500,000 to $1 million for basic setups, suitable for light AI experimentation.
Medium-scale: Between $1 million and $5 million for more intensive workloads, including advanced hardware and robust cooling.
Large-scale: Exceeds $10 million, with costs soaring to hundreds of millions or even billions for large enterprises and AI-optimized campuses.
Factors that influence cost
Scale and size: Larger facilities require significantly more capital for land, construction, and infrastructure.
Hardware: High-performance GPUs, TPUs, and massive storage arrays are a major expense.
Power and cooling: AI data centers have immense power requirements, making power generation, distribution, and advanced cooling solutions (like liquid cooling) significant cost drivers.
Location:
The cost of land, labor, and local regulations can vary dramatically by
geographic location.
Industry-level investment in 2025
Major tech companies like Microsoft, Google, and Amazon are investing hundreds of billions of dollars in AI data center infrastructure in 2025 alone.
Some large projects, such as OpenAI's "Stargate" initiative, have projected budgets of $500 billion over the next four years.
The cost of establishing an AI data center in 2025 varies from hundreds of thousands of dollars for a small-scale facility to over $1 billion for a large hyperscale complex. The total cost is determined by several factors, including size, hardware, location, and infrastructure.
Cost by scale
Small-scale: A small AI data center suitable for handling light-to-medium AI workloads can cost between $500,000 and $1 million. This includes basic servers and standard networking equipment.
Medium-scale: For more intensive workloads, a medium-sized facility may run $1 million to $5 million. This level requires more advanced hardware and robust infrastructure for cooling and networking.
Large-scale/Enterprise: A large data center for enterprises or research institutions can cost over $10 million. These facilities use high-performance GPUs and redundant power and cooling systems.
Hyperscale: Massive, AI-optimized data center campuses built by tech giants can cost over $1 billion. In 2025, global spending on AI infrastructure is predicted to surge to $375 billion, a 67% increase from the previous year, with companies like Google and Microsoft investing tens of billions each.
Cost
breakdown by component
The total cost to build a data center can be estimated at $8 million to $12 million per megawatt (MW) of IT load. The following cost distribution for a greenfield (new) project shows what makes up the total investment:
Powered shell (land and building): 17–21% of total cost, or $105–$235 per square foot (land is typically $25–$75/sqft).
Hardware (servers, GPUs, TPUs): The cost of specialized AI hardware, like high-performance GPUs and TPUs, can range from $200,000 to over $5 million, depending on the scale. A high-end NVIDIA GPU can cost $5,000–$50,000.
Electrical systems: At 40–45% of the total cost, this is the largest component, running $280–$460 per square foot. AI's higher power needs are increasing energy prices for everyone.
Cooling infrastructure: This is the second-largest infrastructure expense at 15–20% of the total cost, or $125–$215 per square foot. Advanced AI requires more efficient, and often more expensive, liquid or immersion cooling systems.
Real estate: Land and construction costs vary significantly by location. Building in a highly competitive market will cost more, and large data center parcels have seen price increases.
Network infrastructure: High-bandwidth networks for seamless data transfer can range from $50,000 to $500,000, depending on the center's scale.
Factors driving up AI data center costs
Specialized hardware: AI accelerators like GPUs and TPUs are more expensive than traditional CPUs.
High power density: AI workloads require far more power, necessitating more expensive electrical and cooling infrastructure.
Advanced cooling: The heat generated by AI chips often requires a shift from traditional air cooling to more efficient, but more expensive, liquid cooling solutions.
Supply chain volatility: Long lead times for specialized equipment like switchgear, chillers, and UPS systems can cause delays and budget overruns.
Alternatives
to building a private data center
For many companies, purchasing access to an AI data center through a third-party provider is a more cost-effective option than building one from the ground up.
Colocation: Renting
space in a third-party facility, where costs for infrastructure like power and
cooling are shared.
Cloud infrastructure: Utilizing cloud services from providers like AWS, Google Cloud, or Microsoft Azure for access to high-performance AI computing without the high capital investment.
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Comments
I expect that controlling the cost of building Data Centers will be the business of the investors who are funding these Data Centers. The first task in Strategic Planning is to accurately assess the current conditions. This is especially true when these investors select their Power Source. Productivity gains will be needed to make this transition. Staffing Data Centers will be critical.
Norb Leahy, Dunwoody GA Tea Party Leader
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