AI 인프라 서비스(IaaS) 시장 : 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the AI infrastructure-as-a-Service market size is expected to grow from USD 38.72 billion in 2025 to USD 70.91 billion in 2026 and is forecast to reach USD 279.94 billion by 2031 at 31.60% CAGR over 2026-2031.

This report is Segmented by Infrastructure Type (AI Compute Infrastructure, AI Storage Infrastructure, and More), Workload Type (Model Training and Fine-Tuning, and More), Deployment Mode (Public Cloud, Managed Private Cloud, and More), Customer Organization Size (Large Enterprises, and More), End-Use (BFSI, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global AI Infrastructure-as-a-Service Market Trends and Insights

Demand For Elastic GPU Capacity Fuels AI IaaS Expansion

The strongest draw of the AI Infrastructure-as-a-Service market is the ability to scale GPU access up or down as workload volumes change. Many enterprises cannot accurately predict training runs, fine-tuning cycles, or inference peaks to justify fixed hardware ownership. That uncertainty makes burst access more valuable than static reservations, especially when model releases, application launches, or customer traffic can shift sharply within days. Akamai reported in 2026 that 64% of organizations required end-to-end AI response times below 250 milliseconds for critical use cases, while 50% of current deployments failed to meet that standard at peak load, which reinforces the value of elastic infrastructure tuned for scale and responsiveness. The AI Infrastructure-as-a-Service market is also seeing a shift in utilization, as inference now consumes a larger share of GPU hours than it did during the earlier training-led phase. This pattern favors providers that can quickly release capacity, move workloads across clusters, and support low-latency serving in production.

Shift From Capex-Heavy AI Buildouts to Usage-Based Consumption Models

The AI Infrastructure-as-a-Service market is benefiting from a clear shift away from capital-intensive GPU ownership toward operating-cost models. Enterprise AI programs are scaling quickly, but the hardware needed to support them can take many months to procure, install, and optimize. That timing mismatch creates stranded capital risk, especially as newer GPU generations deliver higher performance and shorten the useful life of earlier systems. Usage-based procurement reduces that risk by letting firms align compute spending with active models, business units, and deployment schedules. It also makes AI budgets easier to trace, because teams can map costs to specific applications rather than amortize them across broad infrastructure pools. As the AI Infrastructure-as-a-Service market matures, this spending model is likely to remain one of the main reasons enterprises choose shared or dedicated cloud capacity over owned GPU estates.

Chronic GPU And HBM Scarcity Limits Capacity Expansion

The largest near-term constraint on the AI Infrastructure-as-a-Service market is not demand, but the limited supply of advanced GPUs and the memory packages required to build them. High-bandwidth memory remains the critical upstream bottleneck because GPU assembly cannot scale without it. Industry reporting in 2026 showed that SK Hynix had pre-sold its HBM3e output through 2026 and substantially into 2027, while Micron projected that the HBM market would rise from USD 35 billion in 2025 to USD 100 billion by 2028. The same report noted that SK Hynix, Samsung, and Micron accounted for close to 95% of global HBM output, leaving little room for rapid supply diversification. Long lead times for advanced NVIDIA systems mean providers often have customer demand in hand before the required hardware is available. As a result, the AI Infrastructure-as-a-Service market faces a short-term revenue ceiling driven by supply availability rather than weak customer interest.

Other drivers and restraints analyzed in the detailed report include:

1. Rapid Enterprise Adoption of Managed AI Workload Stacks
2. Growing Need for Edge-Ready Low-Latency AI Infrastructure
3. High Power Density and Cooling Challenges Slow Data Center Build-Out

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

Segment Analysis

AI Compute Infrastructure held 72.53% of the AI Infrastructure-as-a-Service market share in 2025, which reflects the very high value of GPU cluster rentals and AI accelerator provisioning at scale. This part of the AI Infrastructure-as-a-Service market remains the economic core of the stack because nearly every training and inference workload depends on access to expensive accelerated compute. The dollar intensity of NVIDIA H100 and Blackwell systems keeps compute at the center of customer spending, even when software and orchestration become more important for buying decisions. Storage and networking remain essential supporting layers, because training performance depends on fast dataset retrieval and low-latency communication across multiple nodes. AI Storage Infrastructure supports sustained throughput for large datasets, while AI Networking Infrastructure enables distributed jobs that would otherwise suffer from bottlenecks between servers.

AI Infrastructure Management and Orchestration is projected to grow at a 32.78% CAGR through 2031, making it the fastest-rising subsegment of the AI Infrastructure-as-a-Service market. This growth reflects the move from single-cluster experimentation to multi-cloud, multi-model, and multi-region production environments. As inference takes a larger share of GPU utilization, enterprises need tools that can route jobs to the right hardware tier while meeting latency and cost targets. That makes orchestration software more central to customer value, because it connects scheduling, utilization monitoring, workload placement, and policy control. The AI Infrastructure-as-a-Service industry is therefore shifting from a hardware-centered purchase decision to a platform-centered one, where the ability to manage heterogeneous fleets becomes a key source of differentiation. Providers that combine compute access with strong management tooling are likely to retain accounts longer, because migration becomes harder once customers depend on those operating layers. This also gives smaller specialists room to compete, because they can add value in software even when they cannot match hyperscaler infrastructure breadth. Over time, orchestration is likely to capture a larger share of revenue as buyers prioritize workload efficiency and operating control alongside raw capacity. The result is a more balanced segment structure within the AI Infrastructure-as-a-Service market, even though compute remains the largest revenue pool today.

Model Training and Fine-Tuning accounted for 49.34% of revenue in 2025, making it the largest workload category in the AI Infrastructure-as-a-Service market. This leadership came from the high cost of dense GPU clusters, advanced interconnects, and long-duration jobs required for large-scale model development. Training workloads still matter because many enterprises and model developers continue to fine-tune frontier or domain-specific systems for production use. They also create demand for managed data preparation, storage throughput, and performance monitoring across large distributed environments. Even as the workload mix broadens, training remains a large revenue anchor because it consumes the most premium hardware and often runs on reserved or highly specialized cluster setups.

Model Inference and Serving is projected to grow at 32.45% CAGR through 2031, making it the fastest-growing workload in the AI Infrastructure-as-a-Service market. Vast.AI stated in 2026 that inference workloads accounted for roughly two-thirds of AI compute, supporting the view that production usage now outweighs experimentation in many environments. This shift changes infrastructure economics because inference is continuous, latency-sensitive, and closely tied to customer-facing demand. It also increases interest in model routing, autoscaling, caching, and co-located services such as vector databases for retrieval-augmented generation pipelines. AI Data Processing and Analytics remains important because enterprises need managed pipelines to prepare training data, ground responses, and support retrieval workflows after deployment. Other AI workloads, including synthetic data generation, reinforcement learning from human feedback, and simulation for drug discovery or autonomous systems, add burst demand that broadens utilization patterns.

Complete Report Scope:

• By Infrastructure Type
  • AI Compute Infrastructure
  • AI Storage Infrastructure
  • AI Networking Infrastructure
  • AI Infrastructure Management and Orchestration
• By Workload Type
  • Model Training and Fine-Tuning
  • Model Inference and Serving
  • AI Data Processing and Analytics
  • Other AI Workloads
• By Deployment Mode
  • Public Cloud
  • Managed Private Cloud
  • Hybrid Cloud
• By Customer Organization Size
  • Large Enterprises
  • Small and Medium Enterprises
  • Government, Research, and Educational Organizations
• By End-Use Industry
  • IT, Cloud, SaaS, and Digital Services
  • Telecommunications
  • BFSI
  • Healthcare and Life Sciences
  • Automotive and Mobility
  • Other End-Use Industries
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • South Korea
    • India
    • Southeast Asia
    • Rest of Asia-Pacific
  • South America
  • Middle East and Africa

Geography Analysis

North America held 56.12% of the AI Infrastructure-as-a-Service market in 2025, which kept it as the largest regional contributor. The region's lead rests on hyperscaler scale, early enterprise AI adoption, and deep access to capital for data center and GPU expansion. The United States remains the core market because most of the largest cloud platforms, AI software ecosystems, and high-value enterprise contracts are concentrated there. Canada adds strategic weight through renewable-energy-linked data center development and its proximity to major United States cloud corridors. Mexico supports the regional picture through a growing nearshore digital services base that can benefit from lower-latency access to North American AI infrastructure.

Europe held a meaningful share of the AI Infrastructure-as-a-Service market in 2025, with Germany, the United Kingdom, and France serving as the main national demand centers. Regional demand is being shaped by sovereign cloud priorities, procurement rules, and the need for clearer control over training data, infrastructure location, and operational governance. These conditions are giving local and sovereignty-aligned providers more room to compete in regulated workloads than they had before 2024. The AI Infrastructure-as-a-Service market in Europe is therefore evolving with a stronger policy layer than in North America, especially in finance, healthcare, and government use cases. That makes compliance features, audit trails, and regional operating models more important for winning enterprise contracts.

Asia-Pacific is projected to expand at 32.84% CAGR through 2031, making it the fastest-growing regional segment in the AI Infrastructure-as-a-Service market. China remains the largest single market in the region, while Japan, South Korea, and India each support different demand patterns tied to industrial policy, semiconductors, software services, and regulated enterprise adoption. Southeast Asia, led by Malaysia, Singapore, and Thailand, is gaining importance as a regional deployment hub because of its land, tax, and power advantages. South America and the Middle East and Africa are smaller today, but both are becoming more relevant as sovereign AI programs, telecom demand, and greenfield data center projects expand the future footprint of the AI Infrastructure-as-a-Service market.

1. CoreWeave, Inc.
2. Nebius Group N.V.
3. Lambda, Inc.
4. Crusoe Energy Systems LLC
5. Vultr Holdings Corporation
6. TensorWave Inc.
7. Together Computer, Inc.
8. Fireworks AI, Inc.
9. Baseten, Inc.
10. DataCrunch Oy
11. Genesis Cloud Ltd.
12. FluidStack Limited
13. DigitalOcean Holdings, Inc.
14. Oracle Corporation
15. Amazon Web Services, Inc.
16. Microsoft Corporation
17. Google LLC
18. NVIDIA Corporation
19. Hewlett Packard Enterprise Company

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 Market Drivers
  • 4.2.1 Demand for Elastic GPU Capacity for Model Training and Inference
  • 4.2.2 Shift From Capex Heavy AI Buildouts to Usage Based Infrastructure
  • 4.2.3 Rapid Enterprise Adoption of Managed AI Workload Stacks
  • 4.2.4 Growing Need for Edge Ready Low Latency AI Infrastructure
  • 4.2.5 Expansion of Sovereign and Data Residency Compliant AI Clouds
  • 4.2.6 Rising Demand for Multi Model and Multi Tenant AI Delivery Platforms
• 4.3 Market Restraints
  • 4.3.1 Chronic GPU and High Bandwidth Memory Supply Constraints
  • 4.3.2 High Power Density and Data Center Cooling Requirements
  • 4.3.3 Interoperability and Vendor Lock In Across AI Stack Layers
  • 4.3.4 Compliance Burden From Cross Border Data and AI Governance Rules
• 4.4 Industry Value Chain Analysis
• 4.5 Impact of Macroeconomic Factors on the Market
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Bargaining Power of Suppliers
  • 4.7.2 Bargaining Power of Buyers
  • 4.7.3 Threat of New Entrants
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Industry Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Infrastructure Type
  • 5.1.1 AI Compute Infrastructure
  • 5.1.2 AI Storage Infrastructure
  • 5.1.3 AI Networking Infrastructure
  • 5.1.4 AI Infrastructure Management and Orchestration
• 5.2 By Workload Type
  • 5.2.1 Model Training and Fine-Tuning
  • 5.2.2 Model Inference and Serving
  • 5.2.3 AI Data Processing and Analytics
  • 5.2.4 Other AI Workloads
• 5.3 By Deployment Mode
  • 5.3.1 Public Cloud
  • 5.3.2 Managed Private Cloud
  • 5.3.3 Hybrid Cloud
• 5.4 By Customer Organization Size
  • 5.4.1 Large Enterprises
  • 5.4.2 Small and Medium Enterprises
  • 5.4.3 Government, Research, and Educational Organizations
• 5.5 By End-Use Industry
  • 5.5.1 IT, Cloud, SaaS, and Digital Services
  • 5.5.2 Telecommunications
  • 5.5.3 BFSI
  • 5.5.4 Healthcare and Life Sciences
  • 5.5.5 Automotive and Mobility
  • 5.5.6 Other End-Use Industries
• 5.6 By Geography
  • 5.6.1 North America
    • 5.6.1.1 United States
    • 5.6.1.2 Canada
    • 5.6.1.3 Mexico
  • 5.6.2 Europe
    • 5.6.2.1 Germany
    • 5.6.2.2 United Kingdom
    • 5.6.2.3 France
    • 5.6.2.4 Italy
    • 5.6.2.5 Rest of Europe
  • 5.6.3 Asia-Pacific
    • 5.6.3.1 China
    • 5.6.3.2 Japan
    • 5.6.3.3 South Korea
    • 5.6.3.4 India
    • 5.6.3.5 Southeast Asia
    • 5.6.3.6 Rest of Asia-Pacific
  • 5.6.4 South America
  • 5.6.5 Middle East and Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Vendor Positioning Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.4.1 CoreWeave, Inc.
  • 6.4.2 Nebius Group N.V.
  • 6.4.3 Lambda, Inc.
  • 6.4.4 Crusoe Energy Systems LLC
  • 6.4.5 Vultr Holdings Corporation
  • 6.4.6 TensorWave Inc.
  • 6.4.7 Together Computer, Inc.
  • 6.4.8 Fireworks AI, Inc.
  • 6.4.9 Baseten, Inc.
  • 6.4.10 DataCrunch Oy
  • 6.4.11 Genesis Cloud Ltd.
  • 6.4.12 FluidStack Limited
  • 6.4.13 DigitalOcean Holdings, Inc.
  • 6.4.14 Oracle Corporation
  • 6.4.15 Amazon Web Services, Inc.
  • 6.4.16 Microsoft Corporation
  • 6.4.17 Google LLC
  • 6.4.18 NVIDIA Corporation
  • 6.4.19 Hewlett Packard Enterprise Company

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment