인공지능(AI) 데이터센터 시장 : 점유율 분석, 업계 동향과 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the artificial intelligence data center market size is expected to grow from USD 39.49 billion in 2025 to USD 49.49 billion in 2026 and is forecast to reach USD 152.91 billion by 2031 at 25.32% CAGR over 2026-2031.

This report is Segmented by Data Center Type (Cloud Service Providers, Colocation Data Centers, and More), Component (Hardware, Software, and Services), Tier Standard (Tier III and Tier IV), End-User Industry (IT and ITES, Internet and Digital Media, Telecom Operators, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Artificial Intelligence (AI) Data Center Market Trends and Insights

Explosive Growth in AI Model-Training Compute Requirements

Training frontier models such as GPT-4 class networks can involve more than 25,000 NVIDIA H100 GPUs operating continuously, pushing per-rack power above 100 kW and outstripping legacy facility limits. Operators must deploy purpose-built interconnect fabrics, such as NVLink or InfiniBand, which dictate dedicated white-space layouts. Meta is constructing clusters with over 100,000 H100 GPUs, underscoring how parameter scale directly converts to physical capacity needs. Mixture-of-experts architectures increase total token throughput, paradoxically enlarging the infrastructure footprint. As model training becomes a primary differentiation lever among cloud providers, AI-ready capacity experiences multi-year reservation cycles. Those reservations lock in demand that cannot revert to traditional CPU workloads, cementing long-run utilization.

Rising Adoption of Hyperscale Cloud Services Integrating AI Accelerators

Amazon EC2 P5 instances integrate eight H100 GPUs per node, while Microsoft Azure combines custom Maia chips with NVIDIA devices, shifting facility design toward heterogeneous accelerator bays. Google's TPU v5 pods call for unique cooling loops and high-amperage busways that diverge from x86 rack profiles. AI-as-a-Service models result in continuous GPU occupancy, which justifies capital-intensive builds. Microsoft's USD 13 billion outlay with OpenAI illustrates the stakes in securing multi-year AI workloads. The result is a capacity arms race where differentiation hinges on accelerator availability, low-latency interconnects, and regional coverage rather than core count.

Soaring Electricity Costs and Grid Congestion

Peak power in Germany hit EUR 0.40 per kWh (USD 0.43) in 2024, eroding the economics of multi-month training runs. Northern Virginia utilities have frozen interconnections exceeding 50 MW, delaying new hyperscale builds. California ISO issued flex alerts during AI training peaks, illustrating how unrelenting GPU clusters can strain regional grids. Operators respond with on-site battery storage and load-shifting algorithms; however, these add to capital expenses. Smaller firms lacking bulk-power contracts face disproportionate cost pressure, potentially concentrating AI R&D within a handful of well-capitalized players.

Other drivers and restraints analyzed in the detailed report include:

1. Government Incentives for Green and Energy-Efficient Data Centers
2. Emergence of AI-Specific Liquid Cooling Shaping Facility Design
3. Supply-Chain Constraints for High-End GPUs and Power Electronics

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Cloud Service Providers controlled 55.12% of the Artificial Intelligence Data Center market share in 2025 as hyperscalers capitalized on direct silicon supply lines and proprietary network fabrics. Colocation, however, is projected to log a 27.29% CAGR, reflecting enterprise appetite for low-latency AI inference without owning facilities. Hybrid strategies emerge where hyperscalers lease wholesale suites inside carrier-neutral campuses to place training backends near data gravity.

Colocation operators refine liquid-cooling floor layouts to host racks with power capacities of 50 kW or more and bundle cross-connects into AI-ready service catalogs. Enterprises running predictive maintenance or real-time personalization prefer these proximity advantages. Edge locations in tier-2 cities extend AI coverage to autonomous vehicle testbeds and industrial IoT gateways, prompting colocation landlords to invest in modular chillers and 400V power trunks. Contracts are increasingly stipulating latency budgets, rather than just space and power, demonstrating how performance metrics are reshaping leasing norms. The Artificial Intelligence Data Center market benefits from this distributed build-out, which complements rather than replaces hyperscale regions.

Software dominated the market with a 45.12% share in 2025, as organizations experimented with frameworks and orchestration stacks. However, hardware is slated to expand at a 26.95% CAGR, driven by the procurement of accelerators and high-efficiency power infrastructure. Power distribution units are upgraded from 208 V to 415 V to reduce amperage losses, while AI-class UPS systems incorporate silicon-carbide inverters for improved part-load efficiency.

Cooling spend increases as operators replace CRAH units with rear-door heat exchangers and immersion tanks, which enable higher rack densities. These retrofits elevate the Artificial Intelligence Data Center market size for hardware wallets. Services revenue follows, as integrators design, commission, and maintain liquid loops that enterprise facilities teams lack the skill to support. Platform vendors bundle turnkey racks with integrated cooling manifolds, compressing deployment timelines and reinforcing ecosystems around specific accelerator types.

Complete Report Scope:

• By Data Center Type
  • Cloud Service Providers
  • Colocation Data Centers
  • Enterprise / On-Premises / Edge
• By Component
  • Hardware
    • Power Infrastructure
    • Cooling Infrastructure
    • IT Equipment
    • Racks and Other Hardware
  • Software
    • Technology
    • Machine Learning
    • Deep Learning
    • Natural Language Processing
    • Computer Vision
  • Services
    • Managed Services
    • Professional Services
• By Tier Standard
  • Tier III
  • Tier IV
• By End-user Industry
  • IT and ITES
  • Internet and Digital Media
  • Telecom Operators
  • Banking, Financial Services and Insurance (BFSI)
  • Healthcare and Life Sciences
  • Manufacturing and Industrial IoT
  • Government and Defense
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • South America
    • Brazil
    • Argentina
    • Chile
    • Rest of South America
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Spain
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • South Korea
    • India
    • Australia
    • Singapore
    • Malaysia
    • Rest of Asia-Pacific
  • Middle East and Africa
    • Middle East
      • Saudi Arabia
      • United Arab Emirates
      • Turkey
      • Rest of Middle East
    • Africa
      • South Africa
      • Nigeria
      • Rest of Africa

Geography Analysis

North America controlled a 37.35% share in 2025 owing to mature hyperscale campuses, strong venture funding, and a supportive policy backdrop that includes USD 65 billion for grid upgrades. Constraints surface in Northern Virginia and Silicon Valley, where transmission capacity lags compute demand, steering new builds toward Texas and the Pacific Northwest. Canada's hydro-rich provinces attract training clusters seeking low-carbon power, while Mexico benefits from near-shoring strategies that reduce supply-chain risk.

Asia-Pacific is on course for a 26.12% CAGR through 2031 as China, Japan, and India embed AI capacity targets in national digital agendas. Beijing funnels more than USD 50 billion annually into domestic GPU development and purpose-built campuses that secure data sovereignty. Tokyo incentivizes liquid-cooled facilities connected to district heating loops, thereby lowering total energy costs and greenhouse gas intensity. India's Digital India 2.0 program funds regional AI zones in tier-2 cities, widening addressable demand beyond legacy IT hubs.

Europe experiences slower but steady expansion. High electricity prices and water constraints pose challenges for operators, yet GDPR compliance requires AI workloads to process sensitive personal data. Germany and the United Kingdom spearhead investments in carbon-neutral campuses powered by wind and district heat reuse, while France leverages sovereign cloud mandates to localize public-sector AI. The Nordic region distinguishes itself through low ambient temperatures and abundant hydropower, keeping PUE below 1.2, even for 70 kW racks.

1. Amazon Web Services Inc.
2. Microsoft Corporation
3. Alphabet Inc. (Google Cloud)
4. NVIDIA Corporation
5. Meta Platforms Inc.
6. International Business Machines Corporation
7. Oracle Corporation
8. Alibaba Group Holding Limited (Alibaba Cloud)
9. Tencent Holdings Limited (Tencent Cloud)
10. Equinix Inc.
11. Digital Realty Trust Inc.
12. CyrusOne LLC
13. Switch Inc.
14. GDS Holdings Limited
15. NTT Global Data Centers
16. Huawei Technologies Co. Ltd.
17. Baidu Inc. (Baidu AI Cloud)
18. KDDI Corporation (Telehouse)
19. OVH Groupe SA
20. Iron Mountain Incorporated

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 PESTEL Analysis
• 4.3 Market Drivers
  • 4.3.1 Explosive growth in AI model-training compute requirements
  • 4.3.2 Rising adoption of hyperscale cloud services integrating AI accelerators
  • 4.3.3 Government incentives for green and energy-efficient data centers
  • 4.3.4 Emergence of AI-specific liquid cooling shaping facility design
  • 4.3.5 Substation-level grid-edge AI forecasting enabling higher DC power density
  • 4.3.6 Monetization of waste heat from AI clusters in district energy systems
• 4.4 Market Restraints
  • 4.4.1 Soaring electricity costs and grid congestion
  • 4.4.2 Supply-chain constraints for high-end GPUs and power electronics
  • 4.4.3 Water-usage restrictions in drought-prone regions impacting cooling choices
  • 4.4.4 Local community resistance to land and noise footprint in edge deployments
• 4.5 Impact on Sustainability and Carbon-Neutral Energy Goals
  • 4.5.1 Sustainable Power Source and Management
    • 4.5.1.1 Renewable vs Non-Renewable Sources of Power (Green DCs and AI Innovations)
    • 4.5.1.2 Carbon-Footprint Reduction (Heat Pumps, District Cooling and Heating, others)
  • 4.5.2 Sustainable Cooling Solutions and Management
    • 4.5.2.1 Efficient Cooling Solutions for AI-Optimized DCs
    • 4.5.2.2 PUE Ratio, WUE Ratio - Analysis
• 4.6 Industry Ecosystem Analysis
• 4.7 Regulatory or Technological Outlook
• 4.8 Porter's Five Forces Analysis
  • 4.8.1 Threat of New Entrants
  • 4.8.2 Bargaining Power of Suppliers
  • 4.8.3 Bargaining Power of Buyers
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Intensity of Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Data Center Type
  • 5.1.1 Cloud Service Providers
  • 5.1.2 Colocation Data Centers
  • 5.1.3 Enterprise / On-Premises / Edge
• 5.2 By Component
  • 5.2.1 Hardware
    • 5.2.1.1 Power Infrastructure
    • 5.2.1.2 Cooling Infrastructure
    • 5.2.1.3 IT Equipment
    • 5.2.1.4 Racks and Other Hardware
  • 5.2.2 Software
    • 5.2.2.1 Technology
    • 5.2.2.2 Machine Learning
    • 5.2.2.3 Deep Learning
    • 5.2.2.4 Natural Language Processing
    • 5.2.2.5 Computer Vision
  • 5.2.3 Services
    • 5.2.3.1 Managed Services
    • 5.2.3.2 Professional Services
• 5.3 By Tier Standard
  • 5.3.1 Tier III
  • 5.3.2 Tier IV
• 5.4 By End-user Industry
  • 5.4.1 IT and ITES
  • 5.4.2 Internet and Digital Media
  • 5.4.3 Telecom Operators
  • 5.4.4 Banking, Financial Services and Insurance (BFSI)
  • 5.4.5 Healthcare and Life Sciences
  • 5.4.6 Manufacturing and Industrial IoT
  • 5.4.7 Government and Defense
• 5.5 By Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Canada
    • 5.5.1.3 Mexico
  • 5.5.2 South America
    • 5.5.2.1 Brazil
    • 5.5.2.2 Argentina
    • 5.5.2.3 Chile
    • 5.5.2.4 Rest of South America
  • 5.5.3 Europe
    • 5.5.3.1 Germany
    • 5.5.3.2 United Kingdom
    • 5.5.3.3 France
    • 5.5.3.4 Italy
    • 5.5.3.5 Spain
    • 5.5.3.6 Rest of Europe
  • 5.5.4 Asia-Pacific
    • 5.5.4.1 China
    • 5.5.4.2 Japan
    • 5.5.4.3 South Korea
    • 5.5.4.4 India
    • 5.5.4.5 Australia
    • 5.5.4.6 Singapore
    • 5.5.4.7 Malaysia
    • 5.5.4.8 Rest of Asia-Pacific
  • 5.5.5 Middle East and Africa
    • 5.5.5.1 Middle East
      • 5.5.5.1.1 Saudi Arabia
      • 5.5.5.1.2 United Arab Emirates
      • 5.5.5.1.3 Turkey
      • 5.5.5.1.4 Rest of Middle East
    • 5.5.5.2 Africa
      • 5.5.5.2.1 South Africa
      • 5.5.5.2.2 Nigeria
      • 5.5.5.2.3 Rest of Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Share Analysis
• 6.4 Company Profiles (includes Global level Overview, Market level overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share for key companies, Products and Services, and Recent Developments)
  • 6.4.1 Amazon Web Services Inc.
  • 6.4.2 Microsoft Corporation
  • 6.4.3 Alphabet Inc. (Google Cloud)
  • 6.4.4 NVIDIA Corporation
  • 6.4.5 Meta Platforms Inc.
  • 6.4.6 International Business Machines Corporation
  • 6.4.7 Oracle Corporation
  • 6.4.8 Alibaba Group Holding Limited (Alibaba Cloud)
  • 6.4.9 Tencent Holdings Limited (Tencent Cloud)
  • 6.4.10 Equinix Inc.
  • 6.4.11 Digital Realty Trust Inc.
  • 6.4.12 CyrusOne LLC
  • 6.4.13 Switch Inc.
  • 6.4.14 GDS Holdings Limited
  • 6.4.15 NTT Global Data Centers
  • 6.4.16 Huawei Technologies Co. Ltd.
  • 6.4.17 Baidu Inc. (Baidu AI Cloud)
  • 6.4.18 KDDI Corporation (Telehouse)
  • 6.4.19 OVH Groupe SA
  • 6.4.20 Iron Mountain Incorporated

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment