Large Language Model (LLM) Market Size, Share, Trends, Industry Analysis Report: By Offering (Software and Services), By Deployment, By Modality, By Model Size, By Application, By Industry Vertical, and By Region – Market Forecast, 2025–2034
Description
The large language model (LLM) market size is expected to reach USD 130.65 billion by 2034, according to a new study by Polaris Market Research. The report “Large Language Model (LLM) Market Size, Share, Trends, Industry Analysis Report: By Offering (Software and Services), By Deployment, By Modality, By Model Size, By Application, By Industry Vertical, and By Region – Market Forecast, 2025–2034” gives a detailed insight into current market dynamics and provides analysis on future market growth.
The incorporation of a zero human intervention feature within training systems stands out as a key catalyst propelling the large language model (LLM) market growth. This functionality significantly enhances efficiency by allowing models to autonomously learn and adapt without continuous manual oversight, thereby reducing both time and resource requirements. Techniques such as transfer learning and self-supervised learning have further refined LLM capabilities, enabling them to leverage pre-existing knowledge and seamlessly adapt to new tasks.
Advancements in hardware infrastructure, notably graphics processing units (GPUs) and tensor processing units (TPUs), have expedited training and inference processes, facilitating the handling of larger and more intricate models. These technological strides empower LLMs by bolstering their performance through enhanced contextual understanding, optimized memory management, and streamlined training methodologies. Consequently, this heightened efficacy makes LLMs increasingly appealing to enterprises seeking to optimize operational efficiency, gain competitive advantages, and ensure financial viability in the marketplace.
The copious availability of internet data has emerged as a significant driver propelling the LLM market expansion. This wealth of data serves as a fundamental resource for LLMs, enabling them to glean insights from diverse and extensive sources, thereby catalyzing substantial improvements in their performance and adaptability. Access to such a vast reservoir of information facilitates comprehensive and nuanced learning, empowering LLMs to grasp context more effectively, refine language comprehension, and expand proficiency in various language-related tasks. The abundance of internet data fuels ongoing enhancements in LLM technology, broadening their applicability across diverse industries and augmenting their appeal for a myriad of use cases. Furthermore, advances in machine learning algorithms, particularly in natural language processing and neural network architectures, play a pivotal role in shaping the capabilities of large language models. Coupled with the continuous influx of diverse and expansive datasets, these advancements contribute to more sophisticated learning processes, enabling models to refine their grasp of language nuances and contexts iteratively.
Large Language Model Market Report Highlights
Based on offering, in 2024, the service segment dominated the large language model market revenue share, driven by the increasing demand for customized AI solutions, integration services, and ongoing model training and support.
The BFSI segment, by industry vertical, is expected to witness significant growth during the forecast period as financial institutions are increasingly using LLMs to improve customer service through AI-powered chatbots, automate document processing, detect fraud, and provide personalized financial recommendations.
In 2024, North America dominated the market due to the strong presence of tech leaders, heavy R&D investments, and a supportive ecosystem of AI startups, accelerators, and venture capital driving innovation and growth.
Asia Pacific is projected to witness the fastest market growth during the forecast period, driven by digitalization, rising data generation, and increasing demand for multilingual AI solutions to support diverse, rapidly expanding digital economies.
A few global key market players are Alibaba Group Holding Limited; Amazon.com, Inc.; Baidu, Inc.; Google LLC; Huawei Technologies Co., Ltd.; Meta Platforms, Inc.; Microsoft Corporation; OpenAI LP; Tencent Holdings Limited; and Yandex NV.
Polaris Market Research has segmented the market report on the basis of offering, deployment, modality, model size, application, industry vertical, and region:
By Offering Outlook (Revenue USD Billion, 2020–2034)
Software
Services
By Deployment Outlook (Revenue USD Billion, 2020–2034)
Cloud
On-premises
By Modality Outlook (Revenue USD Billion, 2020–2034)
Code
Image
Text
Video
By Model Size Outlook (Revenue USD Billion, 2020–2034)
Below 1 Billion Parameters
1 Billion to 10 Billion Parameters
10 Billion to 50 Billion Parameters
50 Billion to 100 Billion Parameters
100 Billion to 200 Billion Parameters
200 Billion to 500 Billion Parameters
Above 500 Billion Parameters
By Application Outlook (Revenue USD Billion, 2020–2034)
Information Retrieval
Language Translation
Localization
Content Generation
Code Generation
Customer Service Automation
Other
By Industry Vertical Outlook (Revenue USD Billion, 2020–2034)
BFSI
Education
Healthcare & Life Sciences
IT/ITeS
Law Firms
Manufacturing
Media & Entertainment
Retail
Other
By Regional Outlook (Revenue USD Billion, 2020–2034)
North America
US
Canada
Europe
Germany
France
UK
Italy
Spain
Netherlands
Russia
Rest of Europe
Asia Pacific
China
Japan
India
Malaysia
South Korea
Indonesia
Australia
Vietnam
Rest of Asia Pacific
Middle East & Africa
Saudi Arabia
UAE
Israel
South Africa
Rest of Middle East & Africa
Latin America
Mexico
Brazil
Argentina
Rest of Latin America
The incorporation of a zero human intervention feature within training systems stands out as a key catalyst propelling the large language model (LLM) market growth. This functionality significantly enhances efficiency by allowing models to autonomously learn and adapt without continuous manual oversight, thereby reducing both time and resource requirements. Techniques such as transfer learning and self-supervised learning have further refined LLM capabilities, enabling them to leverage pre-existing knowledge and seamlessly adapt to new tasks.
Advancements in hardware infrastructure, notably graphics processing units (GPUs) and tensor processing units (TPUs), have expedited training and inference processes, facilitating the handling of larger and more intricate models. These technological strides empower LLMs by bolstering their performance through enhanced contextual understanding, optimized memory management, and streamlined training methodologies. Consequently, this heightened efficacy makes LLMs increasingly appealing to enterprises seeking to optimize operational efficiency, gain competitive advantages, and ensure financial viability in the marketplace.
The copious availability of internet data has emerged as a significant driver propelling the LLM market expansion. This wealth of data serves as a fundamental resource for LLMs, enabling them to glean insights from diverse and extensive sources, thereby catalyzing substantial improvements in their performance and adaptability. Access to such a vast reservoir of information facilitates comprehensive and nuanced learning, empowering LLMs to grasp context more effectively, refine language comprehension, and expand proficiency in various language-related tasks. The abundance of internet data fuels ongoing enhancements in LLM technology, broadening their applicability across diverse industries and augmenting their appeal for a myriad of use cases. Furthermore, advances in machine learning algorithms, particularly in natural language processing and neural network architectures, play a pivotal role in shaping the capabilities of large language models. Coupled with the continuous influx of diverse and expansive datasets, these advancements contribute to more sophisticated learning processes, enabling models to refine their grasp of language nuances and contexts iteratively.
Large Language Model Market Report Highlights
Based on offering, in 2024, the service segment dominated the large language model market revenue share, driven by the increasing demand for customized AI solutions, integration services, and ongoing model training and support.
The BFSI segment, by industry vertical, is expected to witness significant growth during the forecast period as financial institutions are increasingly using LLMs to improve customer service through AI-powered chatbots, automate document processing, detect fraud, and provide personalized financial recommendations.
In 2024, North America dominated the market due to the strong presence of tech leaders, heavy R&D investments, and a supportive ecosystem of AI startups, accelerators, and venture capital driving innovation and growth.
Asia Pacific is projected to witness the fastest market growth during the forecast period, driven by digitalization, rising data generation, and increasing demand for multilingual AI solutions to support diverse, rapidly expanding digital economies.
A few global key market players are Alibaba Group Holding Limited; Amazon.com, Inc.; Baidu, Inc.; Google LLC; Huawei Technologies Co., Ltd.; Meta Platforms, Inc.; Microsoft Corporation; OpenAI LP; Tencent Holdings Limited; and Yandex NV.
Polaris Market Research has segmented the market report on the basis of offering, deployment, modality, model size, application, industry vertical, and region:
By Offering Outlook (Revenue USD Billion, 2020–2034)
Software
Services
By Deployment Outlook (Revenue USD Billion, 2020–2034)
Cloud
On-premises
By Modality Outlook (Revenue USD Billion, 2020–2034)
Code
Image
Text
Video
By Model Size Outlook (Revenue USD Billion, 2020–2034)
Below 1 Billion Parameters
1 Billion to 10 Billion Parameters
10 Billion to 50 Billion Parameters
50 Billion to 100 Billion Parameters
100 Billion to 200 Billion Parameters
200 Billion to 500 Billion Parameters
Above 500 Billion Parameters
By Application Outlook (Revenue USD Billion, 2020–2034)
Information Retrieval
Language Translation
Localization
Content Generation
Code Generation
Customer Service Automation
Other
By Industry Vertical Outlook (Revenue USD Billion, 2020–2034)
BFSI
Education
Healthcare & Life Sciences
IT/ITeS
Law Firms
Manufacturing
Media & Entertainment
Retail
Other
By Regional Outlook (Revenue USD Billion, 2020–2034)
North America
US
Canada
Europe
Germany
France
UK
Italy
Spain
Netherlands
Russia
Rest of Europe
Asia Pacific
China
Japan
India
Malaysia
South Korea
Indonesia
Australia
Vietnam
Rest of Asia Pacific
Middle East & Africa
Saudi Arabia
UAE
Israel
South Africa
Rest of Middle East & Africa
Latin America
Mexico
Brazil
Argentina
Rest of Latin America
Table of Contents
125 Pages
- Chapter 1. Introduction
- 1.1 Report Description
- 1.1.1 Objective of the Study
- 1.1.2 Market Scope
- 1.1.3 Assumptions
- 1.2 Stakeholders
- Chapter 2. Research Methodology
- 2.1 Research Methodology
- 2.2 Research Scope and Assumptions
- 2.3 Information Procurement
- 2.3.1 Purchased Database
- 2.3.2 Internal Database
- 2.3.3 Secondary Sources
- 2.3.4 Third Party Perspective
- 2.3.5 Primary Research
- 2.4 Information Analysis
- 2.4.1 Data Analysis Models
- 2.5 Market Formulation and Data Visualization
- 2.6 Data Validation and Publishing (Secondary Sources)
- Chapter 3. Executive Summary
- Chapter 4. Market Insights
- 4.1 Kimchi – Industry snapshot
- 4.2 Kimchi Market - Value Chain Analysis
- 4.2.1 Kimchi Market Dynamics
- 4.2.2 Drivers and Opportunities
- 4.2.2.1 Rising Consumer Awareness of Health Benefits of Kimchi
- 4.2.2.2 Rising Popularity of Korean Cuisine
- 4.2.2.3 Health Consciousness and Demand for Fermented Foods
- 4.2.3 Restraints and Challenges
- 4.2.3.1 Growing number of distribution issues along with lack of consumer preferences
- 4.3 Kimchi Market – Porter’s Five Forces
- 4.3.1 Threat of Substitutes: (Low to Moderate)
- 4.3.2 Threat of New Entrants: (Moderate)
- 4.3.3 Bargaining power of buyers: (Moderate)
- 4.3.4 Bargaining power of suppliers: (Moderate)
- 4.3.5 Competitive Rivalry: (Moderate to High)
- 4.4 Kimchi Market – PESTLE Analysis
- 4.5 Covid-19 Impact Analysis & Industry Trends
- 4.6 Recent Advancement in Kimchi Industry
- 4.7 Production Data
- 4.7.1 Production Data and Forecast for Kimchi in the U.S. (2022 – 2032)
- 4.7.2 Production Data for Kimchi in China (2019 – 2024)
- Chapter 5. Kimchi Market Assessment by Type
- 5.1 Introduction
- 5.2 Baechu Kimchi
- 5.3 Dongchimi
- 5.4 Kkakdugi
- 5.5 Pa Kimchi
- 5.6 Oi Sobagi
- 5.7 Others
- Chapter 6. Kimchi Market Assessment by Distribution Channel
- 6.1 Introduction
- 6.2 Departmental stores
- 6.3 Supermarkets/ Hypermarkets
- 6.4 Online Retail
- 6.5 Others
- Chapter 7. Kimchi Market Assessment by Region
- 7.1 Introduction
- 7.2 Kimchi Market – North America
- 7.2.1 North America Kimchi, By Type
- 7.2.2 North America Kimchi, By Distribution Channel
- 7.2.3 Kimchi Market – U.S.
- 7.2.3.1 US Kimchi, By Type
- 7.2.3.2 US Kimchi, By Distribution Channel
- 7.2.4 Kimchi Market – Canada
- 7.2.4.1 Canada Kimchi, By Type
- 7.2.4.2 Canada Kimchi, By Distribution Channel
- 7.3 Kimchi Market – Europe
- 7.3.1 Europe Kimchi, By Type
- 7.3.2 Europe Kimchi, By Distribution Channel
- 7.3.3 Kimchi Market – France
- 7.3.3.1 France Kimchi, By Type
- 7.3.3.2 France Kimchi, By Distribution Channel
- 7.3.4 Kimchi Market – Germany
- 7.3.4.1 Germany Kimchi, By Type
- 7.3.4.2 Germany Kimchi, By Distribution Channel
- 7.3.5 Kimchi Market – UK
- 7.3.5.1 UK Kimchi, By Type
- 7.3.5.2 UK Kimchi, By Distribution Channel
- 7.3.6 Kimchi Market – Italy
- 7.3.6.1 Italy Kimchi, By Type
- 7.3.6.2 Italy Kimchi, By Distribution Channel
- 7.3.7 Kimchi Market – Netherlands
- 7.3.7.1 Netherlands Kimchi, By Type
- 7.3.7.2 Netherlands Kimchi, By Distribution Channel
- 7.3.8 Kimchi Market – Spain
- 7.3.8.1 Spain Kimchi, By Type
- 7.3.8.2 Spain Kimchi, By Distribution Channel
- 7.3.9 Kimchi Market – Russia
- 7.3.9.1 Russia Kimchi, By Type
- 7.3.9.2 Russia Kimchi, By Distribution Channel
- 7.3.10 Kimchi Market – Rest of Europe
- 7.3.10.1 Rest of Europe Kimchi, By Type
- 7.3.10.2 Rest of Europe Kimchi, By Distribution Channel
- 7.4 Kimchi Market – Asia Pacific
- 7.4.1 Asia Pacific Kimchi, By Type
- 7.4.2 Asia Pacific Kimchi, By Distribution Channel
- 7.4.3 Kimchi Market – Japan
- 7.4.3.1 Japan Kimchi, By Type
- 7.4.3.2 Japan Kimchi, By Distribution Channel
- 7.4.4 Kimchi Market – China
- 7.4.4.1 China Kimchi, By Type
- 7.4.4.2 China Kimchi, By Distribution Channel
- 7.4.5 Kimchi Market – India
- 7.4.5.1 India Kimchi, By Type
- 7.4.5.2 India Kimchi, By Distribution Channel
- 7.4.6 Kimchi Market – Malaysia
- 7.4.6.1 Malaysia Kimchi, By Type
- 7.4.6.2 Malaysia Kimchi, By Distribution Channel
- 7.4.7 Kimchi Market – Indonesia
- 7.4.7.1 Indonesia Kimchi, By Type
- 7.4.7.2 Indonesia Kimchi, By Distribution Channel
- 7.4.8 Kimchi Market – South Korea
- 7.4.8.1 South Korea Kimchi, By Type
- 7.4.8.2 South Korea Kimchi, By Distribution Channel
- 7.4.9 Kimchi Market – Rest of APAC
- 7.4.9.1 Rest of APAC Kimchi, By Type
- 7.4.9.2 Rest of APAC Kimchi, By Distribution Channel
- 7.5 Kimchi Market – Latin America
- 7.5.1 Latin America Kimchi, By Type
- 7.5.2 Latin America Kimchi, By Distribution Channel
- 7.5.3 Kimchi Market – Mexico
- 7.5.3.1 Mexico Kimchi, By Type
- 7.5.3.2 Mexico Kimchi, By Distribution Channel
- 7.5.4 Kimchi Market – Brazil
- 7.5.4.1 Brazil Kimchi, By Type
- 7.5.4.2 Brazil Kimchi, By Distribution Channel
- 7.5.5 Kimchi Market – Argentina
- 7.5.5.1 Argentina Kimchi, By Type
- 7.5.5.2 Argentina Kimchi, By Distribution Channel
- 7.5.6 Kimchi Market – Rest of Latin America
- 7.5.6.1 Rest of LATAM Kimchi, By Type
- 7.5.6.2 Rest of LATAM Kimchi, By Distribution Channel
- 7.6 Kimchi Market – Middle East Africa
- 7.6.1 Middle East & Africa Kimchi, By Type
- 7.6.2 Middle East & Africa Kimchi, By Distribution Channel
- 7.6.3 Kimchi Market – Saudi Arabia
- 7.6.3.1 Saudi Arabia Kimchi, By Type
- 7.6.3.2 Saudi Arabia Kimchi, By Distribution Channel
- 7.6.4 Kimchi Market – UAE
- 7.6.4.1 UAE Kimchi, By Type
- 7.6.4.2 UAE Kimchi, By Distribution Channel
- 7.6.5 Kimchi Market – Israel
- 7.6.5.1 Israel Kimchi, By Type
- 7.6.5.2 Israel Kimchi, By Distribution Channel
- 7.6.6 Kimchi Market – South Africa
- 7.6.6.1 South Africa Kimchi, By Type
- 7.6.6.2 South Africa Kimchi, By Distribution Channel
- 7.6.7 Kimchi Market – Rest of MEA
- 7.6.7.1 Rest of MEA Kimchi, By Type
- 7.6.7.2 Rest of MEA Kimchi, By Distribution Channel
- Chapter 8. Competitive Landscape
- 8.1 Key Market Players: Categorization
- 8.2 Strategy Framework
- 8.3 Vendor Landscape
- 8.4 Strategies Categorization
- 8.4.1 Product Launch
- 8.4.2 Partnership
- 8.4.3 Expansions/Acquisition
- Chapter 9. Company Profiles
- 9.1 Choi's Kimchi
- 9.1.1 Business Overview
- 9.1.2 Products and Services
- 9.2 Sinto Gourmet
- 9.2.1 Business Overview
- 9.2.2 Products and Services
- 9.3 Cosmos Food Co.
- 9.3.1 Business Overview
- 9.3.2 Products and Services
- 9.4 Real Pickles
- 9.4.1 Business Overview
- 9.4.2 Products and Services
- 9.5 Mama O'S Premium Kimchi, Inc.
- 9.5.1 Business Overview
- 9.5.2 Products and Services
- 9.6 Dongwon F&B Co Ltd
- 9.6.1 Business Overview
- 9.6.2 Financial Snapshot
- 9.6.3 Products and Services
- 9.6.4 Recent Developments
- 9.7 CJ CheilJedang Corp
- 9.7.1 Business Overview
- 9.7.2 Financial Snapshot
- 9.7.3 Products and Services
- 9.7.4 Recent Developments
- 9.8 Daesang Corporation
- 9.8.1 Business Overview
- 9.8.2 Financial Snapshot
- 9.8.3 Products and Services
- 9.8.4 Recent Developments
- 9.9 Sunja’s Kimchi
- 9.9.1 Business Overview
- 9.9.2 Products and Services
- 9.10 Pulmuone Foods
- 9.10.1 Business Overview
- 9.10.2 Financial Snapshot
- 9.10.3 Products and Services
- 9.10.4 Recent Developments
- 9.11 Eden Foods
- 9.11.1 Business Overview
- 9.11.2 Products and Services
- 9.12 Tazaki Foods
- 9.12.1 Business Overview
- 9.12.2 Products and Services
- 9.13 Narichan
- 9.13.1 Business Overview
- 9.13.2 Products and Services
- 9.13.3 Recent Developments
- 9.14 Cleveland Kitchen
- 9.14.1 Business Overview
- 9.14.2 Products and Services
- 9.15 Bombucha
- 9.15.1 Business Overview
- 9.15.2 Products and Services
Pricing
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