한글목차

소규모 언어 모델(SLM) 시장 규모는 2025년 9억 3,000만 달러에서 2032년에는 54억 5,000만 달러로 성장하며, 예측 기간 중 CAGR은 28.7%에 달할 것으로 예측됩니다.

SLM은 낮은 컴퓨팅 성능을 필요로 하므로 금융, 헬스케어, 제조 등의 산업에서 대화형 AI, 부정행위 감지, 예지보전 등의 업무에 적합합니다. 또한 AI 기반 자동화 및 로보틱 프로세스 자동화(RPA)의 성장으로 SLM의 도입이 가속화되고 있으며, 기업은 워크플로우 자동화, 데이터 추출, 고객 지원을 위한 효율적이고 비용 효율적인 AI 솔루션을 찾고 있습니다. SLM은 적은 매개변수, 복잡한 추론, 뉘앙스가 풍부한 텍스트 생성, 깊은 문맥 이해에 대한 낮은 능력으로 인해 성능의 한계에 직면해 있습니다. 이는 광범위한 지식과 복잡한 의사결정이 필요한 작업의 정확도와 효율성에 영향을 미칠 수 있습니다. 또한 SLM은 제한된 훈련 데이터로 인해 특수한 용도에서 어려움을 겪는 경우가 많으며, 대규모 도메인별 전문 지식에 필요한 깊이가 부족할 수 있고, 법률 사례 분석, 의료 진단, 과학 연구와 같은 분야에서는 그다지 효과적이지 않을 수 있습니다.

시맨틱 검색 및 정보 검색은 업계 전반에서 더 빠르고 정확한 검색 결과에 대한 요구가 증가함에 따라 소규모 언어 모델 시장에서 가장 높은 CAGR을 나타낼 것으로 예상됩니다. 기존의 키워드 기반 검색과 달리 시맨틱 검색은 쿼리의 의도와 맥락을 이해하여 보다 관련성 높은 결과를 제공합니다. 기업은 고객 지원, 지식 관리, 데이터 분석을 개선하기 위해 SLM이 탑재된 검색 솔루션을 채택하고 있습니다. 의료, 법률, 금융 등의 산업에서는 방대한 양의 정보를 효율적으로 처리할 수 있는 SLM의 능력이 유용하게 활용되고 있습니다. 또한 AI 기반 챗봇, 가상 비서, 기업 검색 툴의 등장으로 시맨틱 검색 기능에 대한 수요가 증가하고 있으며, 이는 SLM 도입의 주요 성장 분야가 되고 있습니다.

다양한 산업에서 즉시 사용 가능한 AI 모델에 대한 수요가 증가함에 따라 소프트웨어 분야가 예측 기간 중 가장 큰 시장 점유율을 차지할 것으로 예상됩니다. 소프트웨어 기반 SLM 솔루션은 챗봇, 컨텐츠 생성, 시맨틱 검색, 자동화 등을 위해 비용 효율적이고 확장 가능하며 쉽게 도입할 수 있는 AI 기능을 제공하므로 기업이 선호하고 있습니다. 또한 모델 최적화 기술의 발전으로 SLM은 클라우드, 온프레미스, 엣지 디바이스에서 보다 효율적으로 사용할 수 있게 되었습니다. 기업은 생산성과 의사결정을 강화하기 위해 SLM을 기존 소프트웨어 생태계에 통합하는 사례가 증가하고 있으며, AI 알고리즘의 지속적인 개선과 BFSI, 헬스케어, 리테일 등의 분야에서 채택이 확대됨에 따라 소프트웨어 분야가 SLM 시장을 주도할 것으로 보입니다.

아시아태평양은 소형 언어 모델 시장에서 가장 빠른 CAGR로 성장할 것으로 예상되며, 북미는 가장 큰 시장 점유율을 차지할 것으로 예상됩니다. 싱가포르는 동남아시아의 다양한 언어에 적합한 AI 모델을 구축하기 위해 5,200만 달러의 자금을 투입하여 National Multimodal Language Model Programme을 출범시켰으며, 말레이시아의 Mesolitica는 16개 지역 언어를 지원하는 AI 모델 MaLLaM을 도입하여 고객 서비스와 데이터 분석을 강화했습니다. 이 지역 국가들은 고객 서비스, 재무 분석, E-Commerce 최적화 등의 용도로 SLM을 활용하며 수요를 촉진하고 있습니다. 또한 AI 스타트업 증가와 정부의 AI 연구 지원 정책은 시장 확대에 박차를 가하고 있습니다. 한편, 북미는 기업내 AI의 강력한 도입, 잘 구축된 기술 인프라, AI 연구개발에 대한 막대한 투자로 시장을 주도하고 있으며, OpenAI, Microsoft, Meta와 같은 기업은 성능과 접근성을 최적화하기 위해 더 작고 효율적인 AI 모델을 개발하고 있습니다. 또한 기업은 특정 요구사항에 맞는 자체적인 소규모 AI 모델 채택을 늘리고 있으며, 대규모 범용 AI 솔루션에 대한 의존도를 낮추고 있습니다.

세계의 소규모 언어 모델 시장에 대해 조사했으며, 오퍼링별, 배포 모드별, 용도별, 데이터 모달리티별, 모델 사이즈별, 최종사용자별, 지역별 동향 및 시장에 참여하는 기업의 개요 등을 정리하여 전해드립니다.

목차

제1장 서론

제2장 조사 방법

제3장 개요

제4장 주요 인사이트

제5장 시장 개요와 업계 동향

• 서론
• 시장 역학
• 소규모 언어 모델 시장 : 진화
• 에코시스템 분석
• 공급망 분석
• 투자 상황과 자금조달 시나리오
• 사례 연구 분석
• 기술 분석
• 규제 상황
• 특허 분석
• 가격 분석
• 2025-2026년의 주요 컨퍼런스와 이벤트
• Porter's Five Forces 분석
• 고객 비즈니스에 영향을 미치는 동향/혼란
• 주요 이해관계자와 구입 기준

제6장 소규모 언어 모델 시장, 오퍼링별

• 서론
• 소프트웨어
• 서비스

제7장 소규모 언어 모델 시장, 배포 모드별

• 서론
• 클라우드
• 온프레미스
• 엣지 디바이스

제8장 소규모 언어 모델 시장, 용도별

• 서론
• 컨텐츠 생성
• 감정 분석
• 시맨틱 검색·정보 검색
• 대화형 AI
• 번역과 로컬리제이션
• 데이터 추출과 문서 분석
• 기타

제9장 소규모 언어 모델 시장, 데이터 모달리티별

• 서론
• 텍스트
• 음성
• 영상
• 코드
• 멀티모달

제10장 소규모 언어 모델 시장, 모델 사이즈별

• 서론
• 20억 미만 파라미터
• 20억-80억 파라미터
• 80억-120억 파라미터
• 120억-200억 파라미터
• 파라미터수별로 본 주요 소규모 언어 모델

제11장 소규모 언어 모델 시장, 최종사용자별

• 서론
• 기업
• 개인 사용자

제12장 소규모 언어 모델 시장, 지역별

• 서론
• 북미
  • 북미 : 소규모 언어 모델 시장 성장 촉진요인
  • 북미 : 거시경제 전망
  • 미국
  • 캐나다
• 유럽
  • 유럽 : 소규모 언어 모델 시장 성장 촉진요인
  • 유럽 : 거시경제 전망
  • 영국
  • 독일
  • 프랑스
  • 이탈리아
  • 스페인
  • 기타
• 아시아태평양
  • 아시아태평양 : 소규모 언어 모델 시장 성장 촉진요인
  • 아시아태평양 : 거시경제 전망
  • 중국
  • 일본
  • 인도
  • 한국
  • 기타
• 중동 및 아프리카
  • 중동 및 아프리카 : 소규모 언어 모델 시장 성장 촉진요인
  • 중동 및 아프리카 : 거시경제 전망
  • 아랍에미리트
  • 사우디아라비아
  • 남아프리카공화국
  • 기타
• 라틴아메리카
  • 라틴아메리카 : 소규모 언어 모델 시장 성장 촉진요인
  • 라틴아메리카 : 거시경제 전망
  • 브라질
  • 멕시코
  • 기타

제13장 경쟁 구도

• 개요
• 주요 참여 기업의 전략/강점, 2022-2025년
• 매출 분석, 2020-2024년
• 시장 점유율 분석, 2024년
• 제품 비교 분석
• 기업 평가와 재무 지표
• 기업 평가 매트릭스 : 주요 참여 기업(소프트웨어 프로바이더), 2024년
• 기업 평가 매트릭스 : 주요 참여 기업(서비스 프로바이더), 2024년
• 경쟁 시나리오

제14장 기업 개요

• 서론
• 영리 SLM 프로바이더
  • INFOSYS
  • MICROSOFT
  • IBM
  • META
  • AMAZON WEB SERVICES(AWS)
  • MISTRAL AI
  • ARCEE AI
  • AI21 LABS
  • ANTHROPIC
  • OPENAI
  • COHERE
  • DEEPSEEK
  • KRUTRIM
  • STABILITY AI
  • UPSTAGE
  • ALIBABA GROUP
• SLM 서비스 프로바이더
  • TOGETHER AI
  • LAMINI
  • GROQ
  • MALTED AI
  • PREDIBASE
  • CEREBRAS SYSTEMS
  • OLLAMA
  • FIREWORKS AI
  • SNOWFLAKE
  • PREM AI
• 비영리 SLM 프로바이더
  • NVIDIA
  • GOOGLE
  • HUGGING FACE
  • APPLE
  • SALESFORCE
  • DATABRICKS
  • SARVAM AI
  • SAKANA AI
  • EVOLUTIONARYSCALE
  • EDGERUNNER AI
  • ALMAWAVE
  • LG
  • H20.AI
  • NOUS RESEARCH
  • RHYMES AI
  • REFUEL
  • ELEUTHERAI

제15장 인접 시장과 관련 시장

제16장 부록

영문목차

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