Foundation Models Market Size, Share, Opportunities, And Trends By Component (Model-as-a-Service, APIs, Platforms), By Deployment Type (Cloud, On-Premise), By Application (Content Generation, Code Generation, Customer Support, Medical Research), By End-User Industry (Healthcare, BFSI, Retail And E-commerce, IT & Telecom, Government, Others), And By Geography – Forecasts From 2025 To 2030

Description

Foundation Models Market Size:

The foundation models market is expected to show steady growth in the forecasted timeframe.

The foundation models market is experiencing unprecedented growth, with funding flowing in and the rate of technological advancement doubling to previously unrecorded levels in history. What was once described as an emerging frontier in generative AI has evolved into a central infrastructure of enterprise computing - one in eight people globally engages with AI every month, and an almost limitless number of AI-native applications are generating billions in annual revenues. The leading players, Gemini 2.0 Flash, OpenAI's o-series, DeepSeek's R1, almost all are in perpetual review targets, allowing them to improve speed, efficiency and reasoning capability of their models. Confidence within institutions is equally impressive - Meta's $14 billion investment in Scale AI demonstrates not just a normal investment in the company, but also a fortification of existing infrastructure, a strategic acquisition of talent, and a commitment to vision over a long-term period. Investors can have confidence that the market continues to grow with further expanded usage, technical leap-frogging and commitment of capital to build upon their success.

Foundation Models Market Overview & Scope:

The foundation models market is segmented by:

• By Component: The foundation models market is segmented into Model-as-a-Service, APIs, and Platforms. Model-as-a-Service (MaaS) allows analysing, accessing, and using large-scale and extremely complex pre-trained models easily over cloud services without worrying about the infrastructure behind it. Companies are able to incorporate some of the world's most advanced AI models into their applications and services with little to no setup, which makes this exciting and provides less advanced AI capabilities at cheaper costs than having to manually run their models in the cloud. Examples of those companies include the tech giants who are actively pursuing the same subscription and pay-per-use models, allowing their enterprise customers to easily scale up and down in their enterprise strategy around AI. This is very advantageous for companies that want to benefit from machine learning models but may/or may not have sufficient resources to build or train, or fine-tune their large-scale models from scratch.
• By Deployment Type: The market is segmented into Cloud and On-Premise. Cloud deployment continues to outpace on-premises deployment in the next decade due to expandability, cost, and flexibility. The cloud provides a platform for businesses to utilise large-scale and powerful foundation models in pre-existing external infrastructure, therefore outsourcing otherwise a large amount of expenses. Cloud models allow for frequent updates and the option to function in multi-regions which is extremely beneficial in rapidly advancing industries like e-commerce, retail, and customer service.
• By Application: The market is segmented into Content Generation, Code Generation, Customer Support, Medical Research, and Others. Content Generation is a significant and developed application area. Foundation models can be used for generating text, images, and videos to be commercialised for marketing, publishing or entertainment. These models can then be applied across multiple platforms to create content in significant volume. Companies can apply well-trained and fine-tuned foundation models to generate contextually rich and diverse content while automating the creative process and decreasing their time to market. Actual material examples to include blog posts, product descriptions, social media content, commercials or infomercials, and scripted play or story writing.
• By End-User Industry: The market is segmented into Healthcare, BFSI, Retail and E-commerce, IT & Telecom, Government, and Others. Retail and E-commerce are among the most rapidly expanding industries benefiting from foundation models. Businesses are utilising foundation models for recommendation engines, triggered marketing collaterals, merchandising/optimised inventory management, and automated customer support. Companies are learning how to harness foundation models to massively reduce user effort and time spent engaging with companies' services or products. Foundation models are a strategy for enhancing user engagement, ultimately leading to increased conversion rates, and improved streamlined backend business operations, providing a competitive advantage for electronic commerce businesses, particularly in a digital-first economy.

Region: Geographically, the market is expanding at varying rates depending on the location. North America is expected to lead due to early adoption of AI technologies, the presence of major players like OpenAI, Google, and Meta, and strong investment in research and development. The U.S. government and private sector heavily fund foundation model innovation across industries.Europe is mobilising enormous amounts of public-private investment in AI to build AI infrastructure and lessen dependence on foreign technology.

Top Trends Shaping the Foundation Models Market:

1. Rise of Multimodal Foundation Models

• Artificial intelligence systems increasingly integrate multiple modalities (e.g., text, image, audio and video) into single models. This can lead to more robust and contextual outputs and is the basis for new applications such as autonomous driving, diagnostics and creative content generation. The move towards multimodal AI poses significant risks to industry and will dramatically expand the likely use case of foundation models. The growth trajectory of multimodal models should be exponential.

2. Efficiency Through Model Distillation & Small Models

• There has been an explosion in the creation of small "student" models, distilled from larger "teacher" models, that are capable of achieving similar performance for considerably less money. Smaller models can be deployed at the edge and allow for wider access in general to AI systems by reducing paradigm and infrastructure requirements. The rapid application of various distillation techniques will amplify enterprise uptake of foundation models and exponentially increase competitive intensity by dramatically lowering operating costs for companies deploying AI.

Foundation Models Market Growth Drivers vs. Challenges:

Drivers:

• Explosive Enterprise AI Adoption: Companies across finance, healthcare, media and software are rapidly adopting generative AI and foundation models to create efficiencies, automate tasks and launch new products. UBS and Citi identify enterprise adoption of AI (alongside enterprise tech and consumer adoption) as one of three key pillars of demand for AI, which is creating sustained investment in infrastructure (i.e., GPUs and cloud services).
• Explosive Enterprise AI Adoption: Businesses like Anthropic are achieving extraordinary enterprise traction: revenue growth sustained from $1 billion in December 2024 to $3 billion annualised revenue in May 2025. This is evidence of considerable enterprise investment in not only LLMs, but also AI-powered code generation, both of which are confirming AI as an enterprise model, and the foundation model market as an attractive software-as-a-service enterprise segment.

 Challenges:

• Data Privacy & Security Risks: Concerns about security and privacy are among the top deployment concerns; around 39% of enterprises are concerned about the improper handling of sensitive or personally identifiable data in AI training pipelines. Mitigation requires strong data governance, anonymisation, and protection against malicious inputs to the model.
• High Infrastructure Costs & Complexity: Training and deploying large, complex models require substantial compute power, GPUs, compute clusters, and specialised hardware that many organisations find prohibitively high cost for and complex to manage. These barriers are raising costs and slowing adoption.

Foundation Models Market Regional Analysis:

• Europe: Europe is mobilising enormous amounts of public-private investment in AI to build AI infrastructure and lessen dependence on foreign technology. For example, French President Emmanuel Macron announced over €109?billion in funding for AI, and along with initiatives across the European Union, including InvestAI (€200?billion overall with €20?billion for AI “gigafactories”), large-scale projects are underway. By way of example, the Netherlands is building a €70?million AI facility in Groningen to establish Europe as a leader for applied AI in agriculture, healthcare, energy, and defence. European homegrown companies Mistral, Aleph Alpha, and Axelera are gaining momentum as Europe ventures toward sovereign models that are tailored to fit the region's language, regulations, and data privacy requirements.

Foundation Models Market Competitive Landscape:

The foundation models market is competitive, with a mix of established players and specialised innovators driving its growth.

• OpenAI highlights Chinese rival Zhipu AI’s expansion: Zhipu AI from China has been able to access over $1.4 billion in state funding and is now winning government contracts in countries including Malaysia, Singapore, the UAE, Saudi Arabia, and Kenya, a strategic move to influence AI in other parts of the world without being reliant on US technology.

Foundation Models Market Segmentation:

By Component

• Model-as-a-Service
• APIs
• Platforms
  

By Deployment Type

• Cloud
• On-Premise
  

By Application

• Content Generation
• Code Generation
• Customer Support
• Medical Research

By End-User Industry

• Healthcare
• BFSI
• Retail and E-commerce
• IT & Telecom
• Government
• Others
  

By Geography

• North America
• Europe
• Asia Pacific
• South America
• Middle East & Africa

Table Of Contents

1. EXECUTIVE SUMMARY 

2. MARKET SNAPSHOT

2.1. Market Overview

2.2. Market Definition

2.3. Scope of the Study

2.4. Market Segmentation

3. BUSINESS LANDSCAPE 

3.1. Market Drivers

3.2. Market Restraints

3.3. Market Opportunities 

3.4. Porter’s Five Forces Analysis

3.5. Industry Value Chain Analysis

3.6. Policies and Regulations 

3.7. Strategic Recommendations 

4. TECHNOLOGICAL OUTLOOK

5. FOUNDATION MODELS MARKET BY COMPONENT

5.1. Introduction 

5.2. Model-as-a-Service

5.3. APIs

5.4. Platforms

6. FOUNDATION MODELS MARKET BY DEPLOYMENT TYPE

6.1. Introduction 

6.2. Cloud

6.3. On-Premise

7. FOUNDATION MODELS MARKET BY APPLICATION

7.1. Introduction 

7.2. Content Generation

7.3. Code Generation

7.4. Customer Support

7.5. Medical Research

7.6. Others

8. FOUNDATION MODELS MARKET BY END?USER INDUSTRY

8.1. Introduction 

8.2. Healthcare

8.3. BFSI

8.4. Retail and E-commerce

8.5. IT & Telecom

8.6. Government

8.7. Others

9. FOUNDATION MODELS MARKET BY GEOGRAPHY

9.1. Introduction

9.2. North America

9.2.1. By Component

9.2.2. By Deployment

9.2.3. By Application

9.2.4. By End-User Industry

9.2.5. By Country

9.2.5.1. USA

9.2.5.2. Canada

9.2.5.3. Mexico

9.3. South America

9.3.1. By Component

9.3.2. By Deployment

9.3.3. By Application

9.3.4. By End-User Industry 

9.3.5. By Country

9.3.5.1. Brazil

9.3.5.2. Argentina

9.3.5.3. Others

9.4. Europe

9.4.1. By Component

9.4.2. By Deployment

9.4.3. By Application

9.4.4. By End-User Industry 

9.4.5. By Country

9.4.5.1. United Kingdom

9.4.5.2. Germany

9.4.5.3. France

9.4.5.4. Spain

9.4.5.5. Others

9.5. Middle East and Africa

9.5.1. By Component

9.5.2. By Deployment

9.5.3. By Application

9.5.4. By End-User Industry 

9.5.5. By Country

9.5.5.1. Saudi Arabia

9.5.5.2. UAE

9.5.5.3. Others

9.6. Asia Pacific

9.6.1. By Component

9.6.2. By Deployment

9.6.3. By Application

9.6.4. By End-User Industry 

9.6.5. By Country

9.6.5.1. China

9.6.5.2. Japan

9.6.5.3. India

9.6.5.4. South Korea

9.6.5.5. Taiwan

9.6.5.6. Others

10. COMPETITIVE ENVIRONMENT AND ANALYSIS

10.1. Major Players and Strategy Analysis

10.2. Market Share Analysis

10.3. Mergers, Acquisitions, Agreements, and Collaborations

10.4. Competitive Dashboard

11. COMPANY PROFILES

11.1. OpenAI

11.2. Anthropic

11.3. Google DeepMind

11.4. Meta

11.5. Microsoft

11.6. Mistral

11.7. Cohere

11.8. xAI

11.9. Amazon Web Services (AWS)

11.10. IBM

12. APPENDIX

12.1. Currency 

12.2. Assumptions

12.3. Base and Forecast Years Timeline

12.4. Key benefits for the stakeholders

12.5. Research Methodology 

12.6. Abbreviations 

Companies Profiled

OpenAI

Anthropic

Google DeepMind

Meta

Microsoft

Mistral

Cohere

xAI

Amazon Web Services (AWS)

IBM

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