소규모 언어 모델 시장 예측(-2032년) : 오퍼링별, 배포 모드별, 용도별, 데이터 모달리티별, 모델 사이즈별, 최종사용자별, 지역별

The Small language models market is projected to grow from USD 0.93 billion in 2025 to USD 5.45 billion by 2032, at a compound annual growth rate (CAGR) of 28.7% during the forecast period. SLMs require lower computational power, making them ideal for tasks like conversational AI, fraud detection, and predictive maintenance in industries such as finance, healthcare, and manufacturing. Additionally, the growth of AI-powered automation and robotic process automation (RPA) is driving SLM adoption, as businesses seek efficient, cost-effective AI solutions for automating workflows, data extraction, and customer support. SLMs enable on-device processing, reducing reliance on cloud infrastructure and enhancing privacy. SLMs face performance limitations, as they have fewer parameters and reduced capacity for complex reasoning, nuanced text generation, and deep contextual understanding. This can impact their accuracy and effectiveness in tasks requiring extensive knowledge or intricate decision-making. Additionally, SLMs often struggle with specialized applications due to limited training data. SLMs may lack the depth needed for large domain-specific expertise, making them less effective in areas like legal case analysis, medical diagnostics, or scientific research.

"Semantic Search & Information Retrieval Application to Have Highest CAGR During Forecast Period"

The semantic search & information retrieval is expected to have highest CAGR in the small language models market due to the increasing need for faster and more accurate search results across industries. Unlike traditional keyword-based search, semantic search understands the intent and context behind queries, delivering more relevant results. Businesses are adopting SLM-powered search solutions to improve customer support, knowledge management, and data analysis. Industries such as healthcare, legal, and finance benefit from SLMs ability to process vast amounts of information efficiently. Additionally, the rise of AI-powered chatbots, virtual assistants, and enterprise search tools is driving demand for semantic search capabilities, making it a key growth area for SLM adoption.

"Software Offerings to Hold Largest Market Share During Forecast Period"

The software segment is expected to hold the largest market share during the forecast period due to the growing demand for ready-to-use AI models across various industries. Businesses prefer software-based SLM solutions as they offer cost-effective, scalable, and easily deployable AI capabilities for applications like chatbots, content generation, semantic search, and automation. Additionally, advancements in model optimization techniques have made SLMs more efficient, enabling their use on cloud, on-premises, and edge devices. Companies are increasingly integrating SLMs into their existing software ecosystems to enhance productivity and decision-making. With continuous improvements in AI algorithms and increasing adoption across sectors such as BFSI, healthcare, and retail, the software segment is set to dominate the SLM market.

"Asia Pacific's rapid small language models market growth fueled by funding and emerging technologies, while North America leads in market size"

The Asia Pacific region is expected to grow at the fastest CAGR in the small language models market, while North America is projected to hold the largest market share. Singapore launched the National Multimodal Language Model Programme with USD 52 million in funding to build AI models suited for Southeast Asia's diverse languages, while Malaysia's Mesolitica introduced MaLLaM, an AI model supporting 16 regional languages, enhancing customer service and data analysis. Countries in this region are leveraging SLMs for applications like customer service, financial analysis, and e-commerce optimization, driving demand. Additionally, the growing number of AI startups and government initiatives supporting AI research are fueling market expansion. Meanwhile, North America dominates the market driven by strong AI adoption across enterprises, well-established technology infrastructure, and significant investments in AI research and development. Companies such as OpenAI, Microsoft, and Meta are developing smaller yet efficient AI models to optimize performance and accessibility. Additionally, enterprises are increasingly adopting proprietary small-scale AI models tailored to their specific needs, reducing reliance on large, generalized AI solutions.

Breakdown of primaries

In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the small language models market.

• By Company: Tier I - 27%, Tier II - 40%, and Tier III - 33%
• By Designation: Directors - 30%, Managers - 44%, and others - 26%
• By Region: North America - 48%, Europe - 24%, Asia Pacific - 18%, Middle East & Africa - 4%, and Latin America - 6%

The report includes the study of vendors offering small language models market. It profiles major vendors in the small language models market. The major players in the small language models market include Microsoft (US), IBM (US), Infosys (India), Mistral AI (France), AWS (US), Meta (US), Anthropic (US), Cohere (Canada), OpenAI (US), Alibaba (China), Arcee AI (US), Deepseek (China), Upstage AI (US), AI21 Labs (Israel), Krutrim (India), Stability AI (UK), Together AI (US), Lamini AI (US), Groq (US), Malted.ai (UK), Predibase (US), Cerebras (US), Ollama (US), Fireworks AI (US), Snowflake (US), and Prem AI (Switzerland).

Research coverage

This research report categorizes the small language models market by offering, deployment mode, application, data modality, model size, and end user. The offering segment is split into software and services. The services segment include custom model development services, model training & fine-tuning services, integration & deployment services, consulting & advisory services, and other services (prompt engineering and support & maintenance services). The deployment mode segment includes cloud, edge devices, and on-premise deployment modes. The application segment is split into content generation, sentiment analysis, semantic search & information retrieval, conversational AI, translation & localization, data extraction & document analysis, and other applications (behavioral analytics, anomaly detection and code generation & debugging). Data modality segment is split into text, voice, video, code, and multimodal. Model size segment includes small language models less than 2 billion parameters, 2 billion to less than 8 billion parameters, 8 billion to, less than 12 billion parameters, and 12 billion to 20 billion parameters. The end user segment includes individual users, and enterprise users. Enterprise end-users are further split into BFSI, healthcare & life sciences, retail & e-commerce, technology & software providers, media & entertainment, telecommunications, automotive, manufacturing, law firms, and others (education, and transportation & logistics). The scope of the report covers detailed information regarding the major factors, such as drivers, restraints, challenges, and opportunities, influencing the growth of the small language models market. A detailed analysis of the key industry players has been done to provide insights into their business overview, solutions, and services; key strategies; contracts, partnerships, agreements, new product & service launches, mergers and acquisitions, and recent developments associated with the small language models market.

Key Benefits of Buying the Report

The report would provide the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall small language models market and its subsegments. It would help stakeholders understand the competitive landscape and gain more insights better to position their business and plan suitable go-to-market strategies. It also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.

The report provides insights on the following pointers:

• Analysis of key drivers (regulatory compliance driving local AI adoption, affordable AI solutions expanding market reach, advancements in model compression enabling efficiency and industry-specific AI models enhancing performance), restraints (shallow contextual understanding limits accuracy, lack of multimodal processing restricts functionality and fragmented development tools slowing standardization), opportunities (self-optimizing AI models enabling continuous improvement, automated AI model optimization via meta-learning and specialized AI infrastructure enhancing SLM efficiency), and challenges (combating AI-generated misinformation and deepfakes and limited scalability restricting generalized AI applications).
• Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the small language models market.
• Market Development: Comprehensive information about lucrative markets - the report analyses the small language models market across varied regions.
• Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the small language models market.
• Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of leading players like Microsoft (US), IBM (US), Infosys (India), Mistral AI (France), AWS (US), Meta (US), Anthropic (US), Cohere (Canada), OpenAI (US), Alibaba (China), Arcee AI (US), Deepseek (China), Upstage AI (US), AI21 Labs (Israel), Krutrim (India), Stability AI (UK), Together AI (US), Lamini AI (US), Groq (US), Malted.ai (UK), Predibase (US), Cerebras (US), Ollama (US), Fireworks AI (US), Snowflake (US), and Prem AI (Switzerland), among others in the small language models market. The report also helps stakeholders understand the pulse of the small language models market and provides them with information on key market drivers, restraints, challenges, and opportunities.

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 STUDY OBJECTIVES
• 1.2 MARKET DEFINITION
  • 1.2.1 INCLUSIONS AND EXCLUSIONS
• 1.3 MARKET SCOPE
  • 1.3.1 MARKET SEGMENTATION
  • 1.3.2 YEARS CONSIDERED
• 1.4 CURRENCY CONSIDERED
• 1.5 STAKEHOLDERS

2 RESEARCH METHODOLOGY

• 2.1 RESEARCH DATA
  • 2.1.1 SECONDARY DATA
  • 2.1.2 PRIMARY DATA
    • 2.1.2.1 Breakup of primary profiles
    • 2.1.2.2 Key industry insights
• 2.2 MARKET BREAKUP AND DATA TRIANGULATION
• 2.3 MARKET SIZE ESTIMATION
  • 2.3.1 TOP-DOWN APPROACH
  • 2.3.2 BOTTOM-UP APPROACH
• 2.4 MARKET FORECAST
• 2.5 RESEARCH ASSUMPTIONS
• 2.6 RESEARCH LIMITATIONS

3 EXECUTIVE SUMMARY

4 PREMIUM INSIGHTS

• 4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN SMALL LANGUAGE MODELS MARKET
• 4.2 SMALL LANGUAGE MODELS MARKET: TOP THREE APPLICATIONS
• 4.3 NORTH AMERICA: SMALL LANGUAGE MODELS MARKET, BY MODEL SIZE AND DATA MODALITY
• 4.4 SMALL LANGUAGE MODELS MARKET, BY REGION

5 MARKET OVERVIEW AND INDUSTRY TRENDS

• 5.1 INTRODUCTION
• 5.2 MARKET DYNAMICS
  • 5.2.1 DRIVERS
    • 5.2.1.1 Regulatory compliance driving local AI adoption
    • 5.2.1.2 Affordable AI solutions expanding market reach
    • 5.2.1.3 Advancements in model compression enabling efficiency
    • 5.2.1.4 Industry-specific AI models enhancing performance
  • 5.2.2 RESTRAINTS
    • 5.2.2.1 Shallow contextual understanding limits accuracy
    • 5.2.2.2 Lack of multimodal processing restricts functionality
    • 5.2.2.3 Fragmented development tools slowing standardization
  • 5.2.3 OPPORTUNITIES
    • 5.2.3.1 Self-optimizing AI models enabling continuous improvement
    • 5.2.3.2 Automated AI model optimization via meta-learning
    • 5.2.3.3 Specialized AI infrastructure enhancing SLM efficiency
  • 5.2.4 CHALLENGES
    • 5.2.4.1 Combating AI-generated misinformation and deepfakes
    • 5.2.4.2 Limited scalability restricting generalized AI applications
• 5.3 SMALL LANGUAGE MODELS MARKET: EVOLUTION
• 5.4 ECOSYSTEM ANALYSIS
  • 5.4.1 SOFTWARE PROVIDERS, BY PARAMETER COUNT
  • 5.4.2 COMMERCIAL (PAID) SLM PROVIDERS
  • 5.4.3 SLM SERVICE PROVIDERS
  • 5.4.4 FREE-TO-USE SLM PROVIDERS
• 5.5 SUPPLY CHAIN ANALYSIS
• 5.6 INVESTMENT LANDSCAPE AND FUNDING SCENARIO
• 5.7 CASE STUDY ANALYSIS
  • 5.7.1 CASE STUDY 1: GUILD EDUCATION ENHANCES CAREER GUIDANCE WITH DOMAIN-ADAPTED SLMS
  • 5.7.2 CASE STUDY 2: LAW&COMPANY REVOLUTIONIZES SOUTH KOREAN LEGAL SERVICES
  • 5.7.3 CASE STUDY 3: AT&T OPTIMIZES CALL CENTER OPERATIONS WITH H2O.AI
  • 5.7.4 CASE STUDY 4: ACTIVELOOP STREAMLINES PATENT SEARCH & GENERATION WITH PATENTPT
  • 5.7.5 CASE STUDY 5: UPSTAGE REVOLUTIONIZES MEDIA PROOFREADING WITH SOLAR-PROOFREAD ON PREDIBASE
• 5.8 TECHNOLOGY ANALYSIS
  • 5.8.1 KEY TECHNOLOGIES
    • 5.8.1.1 Model quantization & pruning
    • 5.8.1.2 Knowledge distillation
    • 5.8.1.3 Transformer & efficient architectures
    • 5.8.1.4 Federated learning
    • 5.8.1.5 Sparse & low-rank adaptation
  • 5.8.2 COMPLEMENTARY TECHNOLOGIES
    • 5.8.2.1 Edge AI & neuromorphic computing
    • 5.8.2.2 Few-shot & zero-shot learning
    • 5.8.2.3 Adversarial training & security mechanisms
    • 5.8.2.4 Continual learning & adaptive AI
  • 5.8.3 ADJACENT TECHNOLOGIES
    • 5.8.3.1 Multimodal AI
    • 5.8.3.2 Digital twins & simulation AI
    • 5.8.3.3 AI-powered code generation & AutoML
    • 5.8.3.4 Blockchain & decentralized AI
• 5.9 REGULATORY LANDSCAPE
  • 5.9.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
  • 5.9.2 KEY REGULATIONS, BY REGION
    • 5.9.2.1 North America
      • 5.9.2.1.1 SCR 17: Artificial Intelligence Bill (California)
      • 5.9.2.1.2 S1103: Artificial Intelligence Automated Decision Bill (Connecticut)
      • 5.9.2.1.3 National Artificial Intelligence Initiative Act (NAIIA)
      • 5.9.2.1.4 Artificial Intelligence and Data Act (AIDA) - Canada
    • 5.9.2.2 Europe
      • 5.9.2.2.1 European Union (EU) - Artificial Intelligence Act (AIA)
      • 5.9.2.2.2 General Data Protection Regulation (Europe)
    • 5.9.2.3 Asia Pacific
      • 5.9.2.3.1 Interim Administrative Measures for Generative Artificial Intelligence Services (China)
      • 5.9.2.3.2 National AI Strategy (Singapore)
      • 5.9.2.3.3 Hiroshima AI Process Comprehensive Policy Framework (Japan)
    • 5.9.2.4 Middle East & Africa
      • 5.9.2.4.1 National Strategy for Artificial Intelligence (UAE)
      • 5.9.2.4.2 National Artificial Intelligence Strategy (Qatar)
      • 5.9.2.4.3 AI Ethics Principles and Guidelines (Dubai)
  • 5.9.3 LATIN AMERICA
    • 5.9.3.1 Santiago Declaration (Chile)
    • 5.9.3.2 Brazilian Artificial Intelligence Strategy (EBIA)
• 5.10 PATENT ANALYSIS
  • 5.10.1 METHODOLOGY
  • 5.10.2 PATENTS FILED, BY DOCUMENT TYPE
  • 5.10.3 INNOVATION AND PATENT APPLICATIONS
• 5.11 PRICING ANALYSIS
  • 5.11.1 AVERAGE SELLING PRICE OF KEY PLAYERS, BY OFFERING, 2024
  • 5.11.2 AVERAGE SELLING PRICE OF KEY PLAYERS, BY PARAMETER SIZE, 2024
• 5.12 KEY CONFERENCES AND EVENTS, 2025-2026
• 5.13 PORTER'S FIVE FORCES ANALYSIS
  • 5.13.1 THREAT OF NEW ENTRANTS
  • 5.13.2 THREAT OF SUBSTITUTES
  • 5.13.3 BARGAINING POWER OF SUPPLIERS
  • 5.13.4 BARGAINING POWER OF BUYERS
  • 5.13.5 INTENSITY OF COMPETITIVE RIVALRY
• 5.14 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
  • 5.14.1 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
• 5.15 KEY STAKEHOLDERS & BUYING CRITERIA
  • 5.15.1 KEY STAKEHOLDERS IN BUYING PROCESS
  • 5.15.2 BUYING CRITERIA

6 SMALL LANGUAGE MODELS MARKET, BY OFFERING

• 6.1 INTRODUCTION
  • 6.1.1 DRIVERS: SMALL LANGUAGE MODELS MARKET, BY OFFERING
• 6.2 SOFTWARE
  • 6.2.1 OPTIMIZING SLM ARCHITECTURE FOR EFFICIENCY AND SCALABILITY
• 6.3 SERVICES
  • 6.3.1 HELPING BUSINESSES DEVELOP, DEPLOY, AND OPTIMIZE AI SOLUTIONS
  • 6.3.2 CUSTOM MODEL DEVELOPMENT
  • 6.3.3 MODEL TRAINING AND FINE-TUNING SERVICES
  • 6.3.4 INTEGRATION & DEPLOYMENT
  • 6.3.5 CONSULTING & ADVISORY SERVICES
  • 6.3.6 OTHER SERVICES

7 SMALL LANGUAGE MODELS MARKET, BY DEPLOYMENT MODE

• 7.1 INTRODUCTION
  • 7.1.1 DEPLOYMENT MODE: SMALL LANGUAGE MODELS MARKET DRIVERS
• 7.2 CLOUD
  • 7.2.1 AUTOMATIC MAINTENANCE, SECURITY UPDATES, AND PERFORMANCE OPTIMIZATIONS
• 7.3 ON-PREMISES
  • 7.3.1 CUSTOMIZE MODELS BASED ON SPECIFIC REQUIREMENTS
• 7.4 EDGE DEVICES
  • 7.4.1 REAL-TIME RESPONSES, LOW LATENCY, AND MINIMAL RELIANCE ON CLOUD INFRASTRUCTURE

8 SMALL LANGUAGE MODELS MARKET, BY APPLICATION

• 8.1 INTRODUCTION
  • 8.1.1 APPLICATION: SMALL LANGUAGE MODELS MARKET DRIVERS
• 8.2 CONTENT GENERATION
  • 8.2.1 AUTOMATES MARKETING COPY AND SOCIAL MEDIA CONTENT
• 8.3 SENTIMENT ANALYSIS
  • 8.3.1 INTEGRATES SLMS TO TRACK BRAND SENTIMENT
• 8.4 SEMANTIC SEARCH & INFORMATION RETRIEVAL
  • 8.4.1 IMPROVES INFORMATION RETRIEVAL EFFICIENCY IN KNOWLEDGE-INTENSIVE DOMAINS
• 8.5 CONVERSATIONAL AI
  • 8.5.1 ENABLES MORE NATURAL, REAL-TIME INTERACTIONS
• 8.6 TRANSLATION & LOCALIZATION
  • 8.6.1 ENSURES ACCURACY IN SPECIALIZED FIELDS
• 8.7 DATA EXTRACTION & DOCUMENT ANALYSIS
  • 8.7.1 FACILITATES AUTOMATED EXTRACTION OF KEY INSIGHTS FROM CONTRACTS, INVOICES, AND COMPLIANCE DOCUMENTS
• 8.8 OTHER APPLICATIONS

9 SMALL LANGUAGE MODELS MARKET, BY DATA MODALITY

• 9.1 INTRODUCTION
  • 9.1.1 DATA MODALITY: SMALL LANGUAGE MODELS MARKET DRIVERS
• 9.2 TEXT
  • 9.2.1 ENHANCES NATURAL LANGUAGE PROCESSING
• 9.3 VOICE
  • 9.3.1 ENABLES EFFICIENT SPEECH RECOGNITION, VOICE ASSISTANTS, TRANSCRIPTION, AND REAL-TIME LANGUAGE TRANSLATION
• 9.4 VIDEO
  • 9.4.1 USED FOR AUTOMATED VIDEO INDEXING, INTERACTIVE CONTENT GENERATION, AND ACCESSIBILITY SOLUTIONS
• 9.5 CODE
  • 9.5.1 INDUSTRY-WIDE ADOPTION FOR EFFICIENT DEVELOPMENT
• 9.6 MULTIMODAL
  • 9.6.1 INTEGRATES DIFFERENT DATA MODALITIES TO ENHANCE AI CAPABILITIES

10 SMALL LANGUAGE MODELS MARKET, BY MODEL SIZE

• 10.1 INTRODUCTION
  • 10.1.1 MODEL SIZE: SMALL LANGUAGE MODELS MARKET DRIVERS
• 10.2 LESS THAN 2 BILLION PARAMETERS
  • 10.2.1 PREFERRED BY COMPANIES IN REGULATED INDUSTRIES FOR ON-PREMISES AI DEPLOYMENT
• 10.3 2 BILLION TO LESS THAN 8 BILLION PARAMETERS
  • 10.3.1 PREFERRED BY ENTERPRISES FOR INTELLIGENT AUTOMATION, SEMANTIC SEARCH, FRAUD DETECTION, AND REAL-TIME CUSTOMER ENGAGEMENT
• 10.4 8 BILLION TO LESS THAN 12 BILLION PARAMETERS
  • 10.4.1 PREFERRED BY ORGANIZATIONS REQUIRING ADAPTABLE AI SYSTEMS
• 10.5 12 BILLION TO 20 BILLION PARAMETERS
  • 10.5.1 PREFERRED BY ORGANIZATIONS FOR HIGH-CONTEXT UNDERSTANDING, LONG-FORM CONTENT GENERATION, AND DECISION-SUPPORT SYSTEMS
• 10.6 PROMINENT SMALL LANGUAGE MODELS, BY PARAMETER COUNT

11 SMALL LANGUAGE MODELS MARKET, BY END USER

• 11.1 INTRODUCTION
  • 11.1.1 END USERS: SMALL LANGUAGE MODELS MARKET DRIVERS
• 11.2 ENTERPRISES
  • 11.2.1 BFSI
    • 11.2.1.1 Cost reduction, enhanced customer experiences, and strengthened security measures
  • 11.2.2 HEALTHCARE & LIFE SCIENCES
    • 11.2.2.1 Enhanced patient care and advanced medical research
  • 11.2.3 RETAIL & E-COMMERCE
    • 11.2.3.1 Tailored product recommendations enhancing shopping experience
  • 11.2.4 TECHNOLOGY & SOFTWARE PROVIDERS
    • 11.2.4.1 Maintain competitive edge and meet dynamic needs
  • 11.2.5 MEDIA & ENTERTAINMENT
    • 11.2.5.1 Transform media workflows, making advanced AI capabilities accessible
  • 11.2.6 TELECOMMUNICATIONS
    • 11.2.6.1 More personalized and efficient solutions through SLMs
  • 11.2.7 AUTOMOTIVE
    • 11.2.7.1 Transform automotive functionalities, making advanced AI capabilities
  • 11.2.8 MANUFACTURING
    • 11.2.8.1 Enhanced risk management, automation of complex processes, and improved operational efficiency
  • 11.2.9 LAW FIRMS
    • 11.2.9.1 Enhanced document analysis, improved risk assessment, and streamlined administrative processes
  • 11.2.10 OTHER ENTERPRISES
• 11.3 BY INDIVIDUAL USERS

12 SMALL LANGUAGE MODELS MARKET, BY REGION

• 12.1 INTRODUCTION
• 12.2 NORTH AMERICA
  • 12.2.1 NORTH AMERICA: SMALL LANGUAGE MODELS MARKET DRIVERS
  • 12.2.2 NORTH AMERICA: MACROECONOMIC OUTLOOK
  • 12.2.3 US
    • 12.2.3.1 Advancements in SLMs and broader AI technologies align with national interests
  • 12.2.4 CANADA
    • 12.2.4.1 Canada's small language models market driven by key initiatives
• 12.3 EUROPE
  • 12.3.1 EUROPE: SMALL LANGUAGE MODELS MARKET DRIVERS
  • 12.3.2 EUROPE: MACROECONOMIC OUTLOOK
  • 12.3.3 UK
    • 12.3.3.1 UK government's research and innovation ecosystem focused on responsible and trustworthy AI
  • 12.3.4 GERMANY
    • 12.3.4.1 Industry demand and government support drive market
  • 12.3.5 FRANCE
    • 12.3.5.1 AI demand and fundings drive market growth
  • 12.3.6 ITALY
    • 12.3.6.1 Growth of market driven by regulations and AI incorporation
  • 12.3.7 SPAIN
    • 12.3.7.1 Market growth fueled by strategic initiatives and industry innovation
  • 12.3.8 REST OF EUROPE
• 12.4 ASIA PACIFIC
  • 12.4.1 ASIA PACIFIC: SMALL LANGUAGE MODELS MARKET DRIVERS
  • 12.4.2 ASIA PACIFIC: MACROECONOMIC OUTLOOK
  • 12.4.3 CHINA
    • 12.4.3.1 Market driven by government policies, grants, research programs, and public-private partnerships
  • 12.4.4 JAPAN
    • 12.4.4.1 Government's focus on research and development drives growth
  • 12.4.5 INDIA
    • 12.4.5.1 Market driven by significant developments from key industry players, substantial funding activities, and notable technological advancements
  • 12.4.6 SOUTH KOREA
    • 12.4.6.1 Increase in AI adoption and innovation drives growth
  • 12.4.7 REST OF ASIA PACIFIC
• 12.5 MIDDLE EAST & AFRICA
  • 12.5.1 MIDDLE EAST & AFRICA: SMALL LANGUAGE MODELS MARKET DRIVERS
  • 12.5.2 MIDDLE EAST & AFRICA: MACROECONOMIC OUTLOOK
  • 12.5.3 UAE
    • 12.5.3.1 Development and deployment of SLMs drive growth
  • 12.5.4 SAUDI ARABIA
    • 12.5.4.1 Saudi Arabia established SDAIA to spearhead AI strategies in line with Vision 2030
  • 12.5.5 SOUTH AFRICA
    • 12.5.5.1 Integration of SLMs presents significant opportunities across various sectors
  • 12.5.6 REST OF MIDDLE EAST & AFRICA
• 12.6 LATIN AMERICA
  • 12.6.1 LATIN AMERICA: SMALL LANGUAGE MODELS MARKET DRIVERS
  • 12.6.2 LATIN AMERICA: MACROECONOMIC OUTLOOK
  • 12.6.3 BRAZIL
    • 12.6.3.1 Rapid market growth driven by government initiatives
  • 12.6.4 MEXICO
    • 12.6.4.1 ANIA to strengthen Mexico's AI ecosystem and lay groundwork for future AI regulations
  • 12.6.5 REST OF LATIN AMERICA

13 COMPETITIVE LANDSCAPE

• 13.1 OVERVIEW
• 13.2 KEY PLAYER STRATEGIES/RIGHT TO WIN, 2022-2025
• 13.3 REVENUE ANALYSIS, 2020-2024
• 13.4 MARKET SHARE ANALYSIS, 2024
  • 13.4.1 MARKET SHARE OF KEY PLAYERS OFFERING SMALL LANGUAGE MODELS
  • 13.4.2 MARKET RANKING ANALYSIS
• 13.5 PRODUCT COMPARATIVE ANALYSIS
• 13.6 COMPANY VALUATION AND FINANCIAL METRICS
• 13.7 COMPANY EVALUATION MATRIX: KEY PLAYERS (SOFTWARE PROVIDERS), 2024
  • 13.7.1 STARS
  • 13.7.2 EMERGING LEADERS
  • 13.7.3 PERVASIVE PLAYERS
  • 13.7.4 PARTICIPANTS
  • 13.7.5 COMPANY FOOTPRINT: KEY PLAYERS (SOFTWARE PROVIDERS), 2024
    • 13.7.5.1 Company footprint
    • 13.7.5.2 Regional footprint
    • 13.7.5.3 Application footprint
    • 13.7.5.4 Data modality footprint
    • 13.7.5.5 End user footprint
• 13.8 COMPANY EVALUATION MATRIX: KEY PLAYERS (SERVICE PROVIDERS), 2024
  • 13.8.1 STARS
  • 13.8.2 EMERGING LEADERS
  • 13.8.3 PERVASIVE PLAYERS
  • 13.8.4 PARTICIPANTS
  • 13.8.5 COMPANY FOOTPRINT: KEY PLAYERS (SERVICE PROVIDERS), 2024
    • 13.8.5.1 Company footprint
    • 13.8.5.2 Regional footprint
    • 13.8.5.3 Offering footprint
    • 13.8.5.4 Deployment mode footprint
    • 13.8.5.5 End user footprint
• 13.9 COMPETITIVE SCENARIO
  • 13.9.1 PRODUCT LAUNCHES AND ENHANCEMENTS
  • 13.9.2 DEALS

14 COMPANY PROFILES

• 14.1 INTRODUCTION
• 14.2 COMMERCIAL SLM PROVIDERS
  • 14.2.1 INFOSYS
    • 14.2.1.1 Business overview
    • 14.2.1.2 Products/Solutions/Services offered
    • 14.2.1.3 Recent developments
      • 14.2.1.3.1 Product launches and enhancements
      • 14.2.1.3.2 Deals
    • 14.2.1.4 MnM view
      • 14.2.1.4.1 Right to win
      • 14.2.1.4.2 Strategic choices
      • 14.2.1.4.3 Weaknesses and competitive threats
  • 14.2.2 MICROSOFT
    • 14.2.2.1 Business overview
    • 14.2.2.2 Products/Solutions/Services offered
    • 14.2.2.3 Recent developments
      • 14.2.2.3.1 Product launches and enhancements
      • 14.2.2.3.2 Deals
    • 14.2.2.4 MnM view
      • 14.2.2.4.1 Right to win
      • 14.2.2.4.2 Strategic choices
      • 14.2.2.4.3 Weaknesses and competitive threats
  • 14.2.3 IBM
    • 14.2.3.1 Business overview
    • 14.2.3.2 Products/Solutions/Services offered
    • 14.2.3.3 Recent developments
      • 14.2.3.3.1 Product launches and enhancements
      • 14.2.3.3.2 Deals
    • 14.2.3.4 MnM view
      • 14.2.3.4.1 Right to win
      • 14.2.3.4.2 Strategic choices
      • 14.2.3.4.3 Weaknesses and competitive threats
  • 14.2.4 META
    • 14.2.4.1 Business overview
    • 14.2.4.2 Products/Solutions/Services offered
    • 14.2.4.3 Recent developments
      • 14.2.4.3.1 Product launches and enhancements
      • 14.2.4.3.2 Deals
    • 14.2.4.4 MnM view
      • 14.2.4.4.1 Right to win
      • 14.2.4.4.2 Strategic choices
      • 14.2.4.4.3 Weaknesses and competitive threats
  • 14.2.5 AMAZON WEB SERVICES (AWS)
    • 14.2.5.1 Business overview
    • 14.2.5.2 Products/Solutions/Services offered
    • 14.2.5.3 Recent developments
      • 14.2.5.3.1 Deals
    • 14.2.5.4 MnM view
      • 14.2.5.4.1 Right to win
      • 14.2.5.4.2 Strategic choices
      • 14.2.5.4.3 Weaknesses and competitive threats
  • 14.2.6 MISTRAL AI
    • 14.2.6.1 Business overview
    • 14.2.6.2 Products/Solutions/Services offered
    • 14.2.6.3 Recent developments
      • 14.2.6.3.1 Product launches and enhancements
      • 14.2.6.3.2 Deals
  • 14.2.7 ARCEE AI
    • 14.2.7.1 Business overview
    • 14.2.7.2 Products/Solutions/Services offered
    • 14.2.7.3 Recent developments
      • 14.2.7.3.1 Product launches and enhancements
      • 14.2.7.3.2 Deals
  • 14.2.8 AI21 LABS
    • 14.2.8.1 Business overview
    • 14.2.8.2 Products/Solutions/Services offered
    • 14.2.8.3 Recent developments
      • 14.2.8.3.1 Product launches and enhancements
      • 14.2.8.3.2 Deals
  • 14.2.9 ANTHROPIC
    • 14.2.9.1 Business overview
    • 14.2.9.2 Products/Solutions/Services offered
    • 14.2.9.3 Recent developments
      • 14.2.9.3.1 Product launches and enhancements
      • 14.2.9.3.2 Deals
  • 14.2.10 OPENAI
    • 14.2.10.1 Business overview
    • 14.2.10.2 Products/Solutions/Services offered
    • 14.2.10.3 Recent developments
      • 14.2.10.3.1 Product launches and enhancements
      • 14.2.10.3.2 Deals
  • 14.2.11 COHERE
  • 14.2.12 DEEPSEEK
  • 14.2.13 KRUTRIM
  • 14.2.14 STABILITY AI
  • 14.2.15 UPSTAGE
  • 14.2.16 ALIBABA GROUP
• 14.3 SLM SERVICE PROVIDERS
  • 14.3.1 TOGETHER AI
  • 14.3.2 LAMINI
  • 14.3.3 GROQ
  • 14.3.4 MALTED AI
  • 14.3.5 PREDIBASE
  • 14.3.6 CEREBRAS SYSTEMS
  • 14.3.7 OLLAMA
  • 14.3.8 FIREWORKS AI
  • 14.3.9 SNOWFLAKE
  • 14.3.10 PREM AI
• 14.4 NON-COMMERCIAL SLM PROVIDERS
  • 14.4.1 NVIDIA
  • 14.4.2 GOOGLE
  • 14.4.3 HUGGING FACE
  • 14.4.4 APPLE
  • 14.4.5 SALESFORCE
  • 14.4.6 DATABRICKS
  • 14.4.7 SARVAM AI
  • 14.4.8 SAKANA AI
  • 14.4.9 EVOLUTIONARYSCALE
  • 14.4.10 EDGERUNNER AI
  • 14.4.11 ALMAWAVE
  • 14.4.12 LG
  • 14.4.13 H20.AI
  • 14.4.14 NOUS RESEARCH
  • 14.4.15 RHYMES AI
  • 14.4.16 REFUEL
  • 14.4.17 ELEUTHERAI

15 ADJACENT AND RELATED MARKETS

• 15.1 INTRODUCTION
• 15.2 LARGE LANGUAGE MODEL MARKET - GLOBAL FORECAST TO 2030
  • 15.2.1 MARKET DEFINITION
  • 15.2.2 MARKET OVERVIEW
    • 15.2.2.1 Large language model market, by offering
    • 15.2.2.2 Large language model market, by architecture
    • 15.2.2.3 Large language model market, by modality
    • 15.2.2.4 Large language model market, by model size
    • 15.2.2.5 Large language model market, by application
    • 15.2.2.6 Large language model market, by end user
    • 15.2.2.7 Large language model market, by region
• 15.3 GENERATIVE AI MARKET - GLOBAL FORECAST TO 2030
  • 15.3.1 MARKET DEFINITION
  • 15.3.2 MARKET OVERVIEW
    • 15.3.2.1 Generative AI market, by offering
    • 15.3.2.2 Generative AI market, by data modality
    • 15.3.2.3 Generative AI market, by application
    • 15.3.2.4 Generative AI market, by end user
    • 15.3.2.5 Generative AI market, by region

16 APPENDIX

• 16.1 DISCUSSION GUIDE
• 16.2 KNOWLEDGESTORE: MARKETSANDMARKETS' SUBSCRIPTION PORTAL
• 16.3 CUSTOMIZATION OPTIONS
• 16.4 RELATED REPORTS
• 16.5 AUTHOR DETAILS