기반 모델 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the foundation model market size is projected to expand from USD 21.72 billion in 2025 and USD 31.20 billion in 2026 to USD 119.29 billion by 2031, registering a CAGR of 30.76% from 2026 to 2031.

This report is Segmented by Model Type (Large Language Models, Multimodal Models, and More), Deployment Mode (Cloud-Based and On-Premise), Enterprise Size (Large Enterprises and Small and Medium Enterprises), Application (Content Generation, Customer Support and Virtual Assistants, and More), End User (BFSI, Healthcare, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Foundation Model Market Trends and Insights

Enterprise Demand for Multimodal and Reasoning Models Drives Architecture Upgrades

Enterprises are no longer buying models mainly for draft generation, because they now want systems that can process documents, images, audio, and structured records inside the same workflow. This is changing procurement standards across the foundation model market, where buyers increasingly expect models to support multi-step reasoning and dependable task execution. Multimodal capability matters more in sectors such as healthcare, defense, and media, where input data arrives in multiple formats and cannot be handled effectively by text-only systems. It also strengthens use cases that depend on connecting records, visuals, and instructions before producing an action or recommendation. Apple's third-generation foundation model family reflects this direction by combining on-device and server-based variants for language and image understanding in hardware-constrained environments. As these architectures mature, the foundation model market is shifting toward broader reasoning systems rather than standalone content tools.

Rapid Shift to Domain-Tuned Foundation Models Changes Buying Priorities in High-Stakes Verticals

General-purpose models trained on broad internet data are becoming less effective in workflows that need precision, traceability, and domain context. In finance, research presented through IEEE CSCloud showed that domain-adaptive post-training with modular LoRA on financial datasets enabled compact 7-billion-parameter models to outperform GPT-4 on selected financial benchmarks. In healthcare, EHR foundation models fine-tuned on HL7 FHIR-standardized clinical data have demonstrated progress across 6 major clinical forecasting tasks, underscoring why specialized architectures are gaining ground. This is pushing enterprises in regulated settings to prefer smaller, more targeted systems over broader models that require greater supervision. It also lowers total operating cost when a fine-tuned model can run inside controlled infrastructure instead of sending every task through a premium frontier API. In the foundation model market, that shift is moving value toward vendors that support fine-tuning, integration, and governance rather than only raw model access.

High GPU Dependency and Frontier Training Costs Compress the Competitive Field

High GPU dependency remains one of the clearest structural restraints on the foundation model market, as frontier model training still requires substantial capital commitments. Current-generation frontier training runs now regularly exceed USD 100 million, and the largest single run in 2024 reached nearly USD 390 million. This keeps true frontier development concentrated among a very small group of hyperscaler-backed organizations with the capital and infrastructure to absorb repeated training cycles. The effect is not limited to training, because access to advanced hardware also shapes inference scale, release timing, and long-term service economics. Export controls on advanced semiconductors add another layer of uneven access across jurisdictions, which affects who can scale at the leading edge. In the foundation model market, that combination narrows the field of firms that can sustain model-performance leadership over time.

Other drivers and restraints analyzed in the detailed report include:

1. Inference Cost Compression from Open-Weight Ecosystems Reshapes Deployment Economics
2. AI Agent Deployment Across Core Business Workflows Embeds Models in Revenue-Critical Systems
3. Hallucination Risk Slows Adoption in Regulated Workflows Despite Wider Deployment

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

Segment Analysis

Large language models accounted for 59.11% of the foundation model market share in 2025 by model type, maintaining text-centric deployments as the primary commercial base. Multimodal models are projected to expand at a 31.34% CAGR through 2031, as buyers increasingly seek a single system that can process text, images, audio, and structured data across connected workflows. Vision models remain a focused but important category in the foundation model market, especially in inspection, radiology triage, and visual search environments where image understanding is central. Other model types, including speech, audio, and domain-specific models, are also gaining traction where voice interfaces, latency, or technical vocabularies create a poor fit for broad architectures. The segment mix shows that the foundation model market is moving from single-modality tools toward broader reasoning systems that can operate across more complex enterprise contexts.

The boundary between large language models and multimodal models is already becoming less clear, as many leading releases now support documents, images, and code within the same workflow. Apple's third-generation foundation model family reflects this trend with on-device and server-based variants that combine language and image understanding for hardware-constrained environments. The foundation model industry is therefore likely to reward vendors that can combine model breadth with more efficient inference and simpler deployment.

Cloud-based deployment accounted for 66.39% of the foundation model market in 2025, as managed services from AWS, Azure, and Google Cloud reduce the operational burden of model hosting. On-premise deployment is projected to expand at a 39.90% CAGR through 2031, reflecting stronger demand for security, control, and locally managed infrastructure in sensitive environments. This pattern shows that the foundation model market is not simply favoring one mode over another, because buyer priorities now differ by data sensitivity, workload type, and internal governance needs. The open-weight ecosystem supports that shift by giving enterprises more freedom to deploy models without tight vendor lock-in or fixed cloud-only operating models. In practice, cloud remains the default for many organizations, but local deployment has become a strategic requirement for an increasing share of high-value use cases.

Cloud and on-premise setups are also not replacing each other in a clean line, because many large organizations now use hybrid architectures that split workloads by risk and data class. Sensitive inference often runs on internal infrastructure, while non-sensitive, high-volume tasks continue to run through external APIs. Apple's third-generation foundation model family, spanning on-device and server-based variants, shows that hybrid deployment is becoming a practical design choice rather than an edge case. This means reported cloud leadership can understate the importance of internal deployment capability in the foundation model market. Government and defense adoption reinforces that point, because secure, air-gapped environments often require hardware-resident models and tailored support for them.

Complete Report Scope:

• By Model Type
  • Large Language Models
  • Multimodal Models
  • Vision Models
  • Other Model Types (Speech and Audio Models, Domain-Specific Models, etc.)
• By Deployment Mode
  • Cloud-Based
  • On-Premise
• By Enterprise Size
  • Large Enterprises
  • Small and Medium Enterprises
• By Application
  • Content Generation
  • Customer Support and Virtual Assistants
  • Knowledge Management
  • Cybersecurity and Fraud Detection
  • Business Intelligence and Analytics
  • Other Applications (Software Development, Drug Discovery, etc.)
• By End User
  • BFSI
  • Healthcare
  • IT and Telecommunications
  • Manufacturing
  • Government and Defense
  • Other End Users (Retail and E-Commerce, Media and Entertainment, Education, etc.)
• By Geography
  • North America
    • United States
    • Canada
  • South America
    • Brazil
    • Argentina
    • Rest of South America
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Spain
    • Rest of Europe
  • Asia-Pacific
    • China
    • India
    • Japan
    • South Korea
    • Rest of Asia-Pacific
  • Middle East
    • Saudi Arabia
    • United Arab Emirates
    • Rest of the Middle East
  • Africa
    • South Africa
    • Rest of Africa

Geography Analysis

North America accounted for 39.37% of the foundation model market in 2025, making it the largest regional revenue pool. The region benefits from the co-location of frontier AI labs, hyperscaler headquarters, and a deep enterprise software base that helps commercialize new models quickly. The United States remains the main anchor of this position because it combines model development leadership with strong cloud distribution and enterprise procurement activity. Canada adds depth through research strength linked to the Toronto and Montreal AI ecosystems, which continue to support talent supply and academic influence. In the foundation model market, South America remains earlier in adoption and more dependent on cloud APIs from U.S. and European providers than on local frontier model development.

Europe presents the most compliance-heavy operating environment in the foundation model market, because documentation, transparency, and testing obligations shape how providers launch and maintain models. That does not stop demand, as financial services and industrial manufacturing remain important buying centers across Germany, the United Kingdom, France, Italy, and Spain. The result is a two-track regional pattern in which deployment moves ahead, while governance spending also rises to meet new operating rules. The Middle East is also gaining relevance, as sovereign AI infrastructure plans and local hosting ambitions create a clearer role for regional deployment hubs.

Asia-Pacific is projected to expand at a 32.89% CAGR through 2031, making it the fastest-growing regional block in the foundation model market. China's open-weight ecosystem is scaling quickly, and Alibaba reported that the Qwen series had exceeded 300 model versions, 300 million downloads, and 100,000 derivative fine-tuned models by April 2025. China National Petroleum's Kunlun foundation model had reached 152 deployment scenarios by May 2026, which shows how the foundation model market in Asia-Pacific is linking model development with large industrial use cases. South Korea's Framework Act on Artificial Intelligence Development took effect in January 2026 and added a formal compliance layer for foreign AI companies operating in the country. India and Japan are also scaling quickly, while Africa, led by South Africa, remains at an earlier stage where multilingual design and mobile-first delivery are important for broader deployment.

1. OpenAI LLC
2. Microsoft Corporation
3. Google LLC
4. Amazon Web Services, Inc.
5. Meta Platforms, Inc.
6. Anthropic PBC
7. NVIDIA Corporation
8. IBM Corporation
9. Oracle Corporation
10. Salesforce, Inc.
11. Hugging Face, Inc.
12. Mistral AI SAS
13. Cohere Inc.
14. Databricks, Inc.
15. Baidu, Inc.
16. Alibaba Cloud (Alibaba Group Holding Limited)
17. Tencent Holdings Limited
18. Huawei Technologies Co., Ltd.
19. AI21 Labs Ltd.
20. xAI Corp.
21. DeepSeek (Hangzhou DeepSeek Artificial Intelligence Co., Ltd.)

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 Enterprise Demand for Multimodal and Reasoning Models
  • 4.2.2 Rapid Shift to Domain-Tuned Foundation Models
  • 4.2.3 Inference Cost Compression from Open-Weight Ecosystems
  • 4.2.4 AI Agent Deployment Across Core Business Workflows
  • 4.2.5 Cloud-Native Model Hosting and Managed AI Platforms
  • 4.2.6 Demand for Model Fine-Tuning, Guardrails, and Governance Layers
• 4.3 Market Restraints
  • 4.3.1 High GPU Dependency and Frontier Training Costs
  • 4.3.2 Hallucination Risk in Regulated Workflows
  • 4.3.3 Data Sovereignty and Cross-Border Model Hosting Constraints
  • 4.3.4 Fragmented Compliance Burden Across Model, Data, and Deployment Layers
• 4.4 Industry Value Chain Analysis
• 4.5 Technological Outlook
• 4.6 Regulatory Landscape
• 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 Competitive Rivalry
• 4.8 Pricing Analysis
• 4.9 Impact of Macroeconomic Factors on the Market

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Model Type
  • 5.1.1 Large Language Models
  • 5.1.2 Multimodal Models
  • 5.1.3 Vision Models
  • 5.1.4 Other Model Types (Speech and Audio Models, Domain-Specific Models, etc.)
• 5.2 By Deployment Mode
  • 5.2.1 Cloud-Based
  • 5.2.2 On-Premise
• 5.3 By Enterprise Size
  • 5.3.1 Large Enterprises
  • 5.3.2 Small and Medium Enterprises
• 5.4 By Application
  • 5.4.1 Content Generation
  • 5.4.2 Customer Support and Virtual Assistants
  • 5.4.3 Knowledge Management
  • 5.4.4 Cybersecurity and Fraud Detection
  • 5.4.5 Business Intelligence and Analytics
  • 5.4.6 Other Applications (Software Development, Drug Discovery, etc.)
• 5.5 By End User
  • 5.5.1 BFSI
  • 5.5.2 Healthcare
  • 5.5.3 IT and Telecommunications
  • 5.5.4 Manufacturing
  • 5.5.5 Government and Defense
  • 5.5.6 Other End Users (Retail and E-Commerce, Media and Entertainment, Education, etc.)
• 5.6 By Geography
  • 5.6.1 North America
    • 5.6.1.1 United States
    • 5.6.1.2 Canada
  • 5.6.2 South America
    • 5.6.2.1 Brazil
    • 5.6.2.2 Argentina
    • 5.6.2.3 Rest of South America
  • 5.6.3 Europe
    • 5.6.3.1 Germany
    • 5.6.3.2 United Kingdom
    • 5.6.3.3 France
    • 5.6.3.4 Italy
    • 5.6.3.5 Spain
    • 5.6.3.6 Rest of Europe
  • 5.6.4 Asia-Pacific
    • 5.6.4.1 China
    • 5.6.4.2 India
    • 5.6.4.3 Japan
    • 5.6.4.4 South Korea
    • 5.6.4.5 Rest of Asia-Pacific
  • 5.6.5 Middle East
    • 5.6.5.1 Saudi Arabia
    • 5.6.5.2 United Arab Emirates
    • 5.6.5.3 Rest of the Middle East
  • 5.6.6 Africa
    • 5.6.6.1 South Africa
    • 5.6.6.2 Rest of Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves (Strategic Partnerships, Model Launches, Open-Weight Releases, Cloud Distribution, and M&A)
• 6.3 Market 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 OpenAI LLC
  • 6.4.2 Microsoft Corporation
  • 6.4.3 Google LLC
  • 6.4.4 Amazon Web Services, Inc.
  • 6.4.5 Meta Platforms, Inc.
  • 6.4.6 Anthropic PBC
  • 6.4.7 NVIDIA Corporation
  • 6.4.8 IBM Corporation
  • 6.4.9 Oracle Corporation
  • 6.4.10 Salesforce, Inc.
  • 6.4.11 Hugging Face, Inc.
  • 6.4.12 Mistral AI SAS
  • 6.4.13 Cohere Inc.
  • 6.4.14 Databricks, Inc.
  • 6.4.15 Baidu, Inc.
  • 6.4.16 Alibaba Cloud (Alibaba Group Holding Limited)
  • 6.4.17 Tencent Holdings Limited
  • 6.4.18 Huawei Technologies Co., Ltd.
  • 6.4.19 AI21 Labs Ltd.
  • 6.4.20 xAI Corp.
  • 6.4.21 DeepSeek (Hangzhou DeepSeek Artificial Intelligence Co., Ltd.)

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment