Retrieval-Augmented Generation (RAG) Market – Global Forecast To 2030
검색 증강 생성(RAG) 시장 – 제공(솔루션(RAG 지원 플랫폼, 데이터 관리 및 인덱싱 계층, 검색 모델), 서비스), 유형, 애플리케이션, 최종 사용자 및 배포 유형 – 2030년까지의 세계 예측
Retrieval-augmented Generation (RAG) Market by Offering (Solution (RAG-enabled platforms, data management and indexing layers, retrieval & search models), Services), Type, Application, End User, and Deployment Type – Global Forecast to 2030
| 조사회사 | MarketsandMarkets |
| 출판년월 | 2025년10월 |
| 페이지 수 | 350 |
| 도표 수 | 286 |
| 라이센스 | Single User License |
| 가격 | USD 4,950 |
| 구성 | 영문조사보고서 |
MarketsandMarkets: 검색 증강 생성(RAG) 시장은 2025년에 19억 4천만 달러 규모로 추산되며, 2030년에는 연평균 성장률 38.4%로 98억 6천만 달러에 이를 것으로 예상됩니다.
Microsoft, AWS, Google, Anthropic, Cohere 등 주요 기술 기업들은 RAG 기반 솔루션, 통합 및 파트너십에 막대한 투자를 하고 있습니다. 클라우드 하이퍼스케일러들은 Azure OpenAI Service 및 AWS Bedrock과 같은 엔터프라이즈 AI 솔루션에 RAG를 내장하여 기업이 검색 기능을 생성 AI 애플리케이션에 더욱 쉽게 통합할 수 있도록 지원하고 있습니다. 이러한 생태계 확장은 RAG에 대한 인지도를 높일 뿐만 아니라 기업에 즉시 사용 가능하고 확장 가능한 솔루션을 제공함으로써 도입 장벽을 낮춥니다. RAG 스타트업에 대한 지속적인 벤처 투자와 모델 제공업체와 검색 인프라 공급업체 간의 파트너십은 시장의 성장 궤도를 더욱 가속화하고 있습니다.
이 시장 조사는 제품, 유형, 애플리케이션, 최종 사용자, 배포 유형 및 지역을 포함한 다양한 세그먼트에 걸친 검색 증강 생성(RAG) 시장 규모와 성장 잠재력을 다룹니다. 연구 대상 제품에는 솔루션(RAG 지원 플랫폼, 데이터 관리 및 인덱싱 계층, 검색 모델 및 기타 솔루션)과 서비스(관리형 및 전문형)가 포함됩니다. 유형 세그먼트에는 기본형 및 강화형 RAG, 에이전트형 및 적응형 RAG, 지식 구조화 및 메모리 기반 RAG, 개인 정보 보호 및 분산형 RAG 및 기타 유형이 포함됩니다. 애플리케이션 세그먼트에는 엔터프라이즈 검색, 도메인별 데이터 합성, 콘텐츠 요약 및 생성, 개인화된 추천 및 인사이트, 코드 및 개발자 생산성 및 기타 애플리케이션이 포함됩니다. 최종 사용자 세그먼트에는 의료 및 생명 과학, 소매 및 전자상거래, 금융 서비스, 통신, 교육, 미디어 및 엔터테인먼트, 소프트웨어 및 기술 제공업체 및 기타 최종 사용자가 포함됩니다. 배포 유형 세그먼트에는 온프레미스 및 클라우드가 포함됩니다. 검색 증강 생성(RAG) 시장의 지역별 분석은 북미, 유럽, 아시아 태평양, 중동 및 아프리카, 라틴 아메리카를 포함합니다.
본 보고서는 시장 선도 기업과 신규 진입 기업들에게 글로벌 검색 증강 생성(RAG) 시장 매출 규모 및 세부 세그먼트에 대한 가장 근접한 정보를 제공하는 데 도움이 될 것입니다. 또한 이해관계자들이 경쟁 환경을 이해하고, 통찰력을 얻고, 적절한 시장 진출 전략을 수립하는 데에도 도움이 될 것입니다. 나아가, 본 보고서는 이해관계자들에게 시장의 흐름을 이해하고 주요 시장 동인, 제약, 과제, 그리고 기회에 대한 정보를 제공할 수 있는 통찰력을 제공할 것입니다.
보고서는 다음과 같은 통찰력을 제공합니다.
검색 증강 생성(RAG) 시장 성장에 영향을 미치는 주요 동인(상황 인식 AI 응답을 통한 정확도 향상, 기업 디지털화 가속화), 제약(높은 인프라 비용 관리, 데이터 개인 정보 보호 및 보호 보장), 기회(도메인별 애플리케이션과 RAG 통합, 다국어 지원 확대), 과제(공급업체 분산 관리, AI 환각 위험 완화)에 대한 분석입니다.
- 제품 개발/혁신: 검색 증강 생성(RAG) 시장의 향후 기술, 연구 개발 활동, 신제품 및 서비스 출시에 대한 자세한 정보를 제공합니다.
- 시장 개발: 본 보고서는 다양한 지역의 검색 증강 생성(RAG) 시장을 분석하여 수익성이 높은 시장에 대한 포괄적인 정보를 제공합니다.
- 시장 다각화: 검색 증강 생성(RAG) 시장의 신제품 및 서비스, 미개척 지역, 최근 동향, 투자 등에 대한 포괄적인 정보를 제공합니다.
Report Description
MarketsandMarkets: The retrieval-augmented generation (RAG) market is estimated to be USD 1.94 billion in 2025 and is projected to reach USD 9.86 billion by 2030 at a CAGR of 38.4%.
Major technology companies, including Microsoft, AWS, Google, Anthropic, and Cohere, are heavily investing in RAG-powered solutions, integrations, and partnerships. Cloud hyperscalers are embedding RAG into their enterprise AI offerings, such as Azure OpenAI Service and AWS Bedrock, making it easier for businesses to integrate retrieval capabilities into their generative AI applications. This ecosystem expansion not only raises awareness of RAG but also lowers barriers to adoption by providing enterprises with ready-to-use, scalable solutions. Continued venture funding into RAG startups and partnerships between model providers and retrieval infrastructure vendors further accelerate the market’s growth trajectory.
“Data management and indexing layer solution segment to witness significant growth during forecast period.”
As enterprises continue to handle massive volumes of structured and unstructured data, robust indexing and efficient data management become critical for optimal RAG performance. Advances in vector databases, embeddings, and real-time data ingestion are driving rapid adoption of these solutions. With increasing demand for high-quality data retrieval, low-latency performance, and scalable architecture, the data management and indexing layer is projected to grow at the fastest rate, particularly in sectors with complex datasets like healthcare, financial services, and life sciences.
“By type, foundational and enhanced RAG segment to lead market during forecast period.”
Foundational and enhanced RAG is projected to account for the largest market share due to its early adoption across enterprises seeking reliable retrieval-augmented generative capabilities. This type combines large language models with robust retrieval architectures, enabling organizations to integrate structured and unstructured data sources for enhanced decision-making and knowledge generation. Foundational RAG solutions are widely deployed in enterprise search, content summarization, and domain-specific data synthesis, offering high accuracy, scalability, and operational efficiency. Enhanced RAG variants further improve the performance of foundational models by incorporating fine-tuned domain knowledge, relevance ranking, and advanced embedding mechanisms. Enterprises favor this type for its stability, established use cases, and proven ROI, making it the most prominent sub-segment in terms of market size. Additionally, technology vendors continue to enhance foundational RAG platforms with pre-trained models and plug-and-play integration capabilities, further reinforcing their market leadership.
“Asia Pacific to record highest growth rate during forecast period.”
Asia Pacific is becoming a key growth hub for the RAG market, driven by strong enterprise demand and a rapidly growing developer community. Companies in the region are using RAG to manage complex, data-heavy industries like healthcare, logistics, and energy. The rollout of cloud-based systems and 5G networks is opening up new opportunities for RAG-powered assistants and knowledge tools at the edge. Growth in the Asia Pacific comes from partnerships between governments, global tech giants, and local players, which ensures solutions meet local rules and cultural needs. Making Asia Pacific not just a fast adopter, but also a region that will influence the global future of RAG, especially in areas like multimodal and cross-domain AI.
Breakdown of primaries
The study contains insights from various industry experts, from solution vendors to Tier 1 companies. The break-up of the primaries is as follows:
- By Company Type: Tier 1 – 35%, Tier 2 – 45%, and Tier 3 – 20%
- By Designation: C-level –35%, D-level – 30%, and Others – 35%
- By Region: North America – 40%, Europe – 20%, Asia Pacific – 25%, Middle East & Africa – 9%, Latin America – 6%
The major players in the retrieval-augmented generation (RAG) market include Microsoft (US), Amazon Web Services, Inc. (US), Anthropic (US), Google (US), IBM (US), Cohere (Canada), NVIDIA (US), Pinecone (US), Elastic N.V. (US), Progress Software Corporation (US), Vectra AI, Inc. (US), Ragie.ai (US), Clarifai (US), Chatbees (US), Zilliz (US), Weaviate (Netherlands), Qdrant (Berlin), and MongoDB (US). These players have adopted various growth strategies, such as partnerships, agreements, collaborations, new product launches, enhancements, and acquisitions, to expand their market footprint.
Research Coverage
The market study covers the retrieval-augmented generation (RAG) market size and growth potential across different segments, including offering, type, application, end user, deployment type, and region. The offerings studied include solutions (RAG-enabled platforms, data management and indexing layers, retrieval & search models, and other solutions), and services (managed and professional). The type segment includes foundational & enhanced RAG, agentic & adaptive RAG, knowledge-structured & memory-based RAG, privacy-preserving & distributed RAG, and other types. The application segment includes enterprise search, domain-specific data synthesis, content summarization & generation, personalized recommendations & insights, code & developer productivity, and other applications. The end user segment includes healthcare & life sciences, retail & e-commerce, financial services, telecommunications, education, media & entertainment, software & technology providers, and other end users. The deployment type segment includes on-premises and cloud. The regional analysis of the retrieval-augmented generation (RAG) market covers North America, Europe, Asia Pacific, the Middle East & Africa, and Latin America.
Key Benefits of Buying the Report
The report will help market leaders and new entrants with information on the closest approximations of the global retrieval-augmented generation (RAG) market’s revenue numbers and subsegments. It will also help stakeholders understand the competitive landscape, gain insights, and plan suitable go-to-market strategies. Moreover, the report will provide insights for stakeholders to understand the market’s pulse and provide them with information on key market drivers, restraints, challenges, and opportunities.
The report provides the following insights.
Analysis of key drivers (Enhancing accuracy with context-aware AI responses, accelerating enterprise digitization), restraints (Managing high infrastructure costs, ensuring data privacy and protection), opportunities (Integrating RAG with domain-specific applications, expanding multilingual support), and challenges (Managing vendor fragmentation, mitigating risks of AI hallucinations) that are influencing the growth of the retrieval-augmented generation (RAG) market.
- Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the retrieval-augmented generation (RAG) market
- Market Development: The report provides comprehensive information about lucrative markets, analyzing the retrieval-augmented generation (RAG) market across various regions.
- Market Diversification: Comprehensive information about new products and services, untapped geographies, recent developments, and investments in the retrieval-augmented generation (RAG) market.
Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of leading players such as Microsoft (US), Amazon Web Services, Inc. (US), Anthropic (US), Google (US), IBM (US), Cohere (Canada), NVIDIA (US), Pinecone (US), Elastic N.V. (US), Progress Software Corporation (US), Vectra AI, Inc. (US), Ragie.ai (US), Clarifai (US), Chatbees (US), Zilliz (US), Weaviate (Netherlands), Qdrant (Berlin), and MongoDB (US).
Table of Contents
1 INTRODUCTION 29
1.1 STUDY OBJECTIVES 29
1.2 MARKET DEFINITION 29
1.3 STUDY SCOPE 30
1.3.1 MARKET SEGMENTATION AND REGIONS COVERED 30
1.3.2 INCLUSIONS AND EXCLUSIONS 31
1.4 YEARS CONSIDERED 31
1.5 CURRENCY CONSIDERED 32
1.6 STAKEHOLDERS 32
2 RESEARCH METHODOLOGY 33
2.1 RESEARCH DATA 33
2.1.1 SECONDARY DATA 34
2.1.2 PRIMARY DATA 34
2.1.2.1 Breakdown of primary profiles 35
2.2 MARKET SIZE ESTIMATION 35
2.2.1 TOP-DOWN APPROACH 36
2.2.2 BOTTOM-UP APPROACH 37
2.2.3 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET ESTIMATION: DEMAND-SIDE ANALYSIS 38
2.3 DATA TRIANGULATION 39
2.4 RISK ASSESSMENT 40
2.5 RESEARCH ASSUMPTIONS 40
2.6 RESEARCH LIMITATIONS 41
3 EXECUTIVE SUMMARY 42
4 PREMIUM INSIGHTS 45
4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET 45
4.2 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY OFFERING 45
4.3 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SOLUTION 46
4.4 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE 46
4.5 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY APPLICATION 47
4.6 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY DEPLOYMENT TYPE 47
4.7 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER 48
4.8 NORTH AMERICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER AND REGION 48
5 MARKET OVERVIEW AND INDUSTRY TRENDS 49
5.1 INTRODUCTION 49
5.2 MARKET DYNAMICS 49
5.2.1 DRIVERS 50
5.2.1.1 Enhancing Accuracy with Context-aware AI Responses 50
5.2.1.2 Accelerating Enterprise Digitalization 51
5.2.2 RESTRAINTS 51
5.2.2.1 Managing High Infrastructure Costs 51
5.2.2.2 Ensuring Data Privacy and Protection 51
5.2.3 OPPORTUNITIES 52
5.2.3.1 Integrating RAG with Domain-specific Applications 52
5.2.3.2 Expanding Multilingual Support 52
5.2.4 CHALLENGES 52
5.2.4.1 Mitigating Risks of AI Hallucinations 52
5.2.4.2 Managing Vendor Fragmentation 52
5.3 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET: BRIEF HISTORY 53
5.4 SUPPLY CHAIN ANALYSIS 54
5.5 ECOSYSTEM 56
5.6 CASE STUDIES 57
5.6.1 FILEVINE AND ZILLIZ CLOUD REVOLUTIONIZED CASE MANAGEMENT WITH VECTOR SEARCH 57
5.6.2 NEOPLE ASSISTANTS TRANSFORMING CUSTOMER SERVICE WITH WEAVIATE 58
5.6.3 DUST ADDRESSED COMPLEXITIES FACED BY QDRANT BY DEPLOYING LLMS 58
5.7 PORTER’S FIVE FORCES MODEL 59
5.7.1 THREAT OF NEW ENTRANTS 60
5.7.2 THREAT OF SUBSTITUTES 60
5.7.3 BARGAINING POWER OF BUYERS 60
5.7.4 BARGAINING POWER OF SUPPLIERS 60
5.7.5 INTENSITY OF COMPETITIVE RIVALRY 60
5.8 PATENT ANALYSIS 60
5.8.1 METHODOLOGY 60
5.8.2 LIST OF PATENTS IN RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, 2020–2024 61
5.9 DISRUPTIONS IMPACTING BUYERS/CLIENTS IN RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET 62
5.10 PRICING ANALYSIS 63
5.10.1 AVERAGE SELLING PRICE OF KEY PLAYERS, 2024 63
5.10.2 INDICATIVE PRICING ANALYSIS OF KEY PLAYERS, BY SOLUTION, 2024 63
5.11 KEY STAKEHOLDERS AND BUYING CRITERIA 65
5.11.1 KEY STAKEHOLDERS IN BUYING PROCESS 65
5.11.2 BUYING CRITERIA 66
5.12 TECHNOLOGY ANALYSIS 66
5.12.1 KEY TECHNOLOGIES 66
5.12.1.1 Large Language Models (LLMs) and Transformer-based Generators 66
5.12.1.2 Embedding Models 67
5.12.1.3 Dense Retrieval Mechanisms 67
5.12.1.4 Vector Databases 67
5.12.2 COMPLEMENTARY TECHNOLOGIES 68
5.12.2.1 Reranking Models 68
5.12.2.2 Knowledge Graphs 68
5.12.2.3 Semantic Search and NLP Techniques 68
5.12.2.4 Reasoning and Memory Modules 68
5.12.3 ADJACENT TECHNOLOGIES 69
5.12.3.1 Multimodal AI Processing 69
5.12.3.2 Data Privacy and Security Tools 69
5.12.3.3 AI/ML Frameworks and Orchestration Tools 69
5.13 REGULATORY LANDSCAPE 70
5.13.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 70
5.13.2 KEY REGULATIONS 73
5.13.2.1 North America 73
5.13.2.1.1 California Consumer Privacy Act (CCPA) 73
5.13.2.1.2 Canada’s Directive on Automated Decision-making 73
5.13.2.1.3 AI and Automated Decision Systems (AADS) Ordinance (New York City) 73
5.13.2.2 Europe 73
5.13.2.2.1 General Data Protection Regulation (GDPR) 73
5.13.2.2.2 European Union’s Artificial Intelligence Act (AIA) 73
5.13.2.2.3 Ethical Guidelines for Trustworthy AI by the European Commission 73
5.13.2.3 Asia Pacific 73
5.13.2.3.1 Personal Information Protection Law (PIPL) – China 73
5.13.2.3.2 Artificial Intelligence Ethics Guidelines – Japan 74
5.13.2.3.3 AI Strategy and Governance Framework – Australia 74
5.13.2.4 Middle East & Africa 74
5.13.2.4.1 UAE AI Regulation and Ethics Guidelines 74
5.13.2.4.2 South Africa’s Protection of Personal Information Act (POPIA) 74
5.13.2.4.3 Egypt’s Data Protection Law 74
5.13.2.5 Latin America 74
5.13.2.5.1 Brazil – General Data Protection Law (LGPD) 74
5.13.2.5.2 Mexico – Federal Law on the Protection of Personal Data Held by Private Parties (LFPDPPP) 75
5.13.2.5.3 Argentina – Personal Data Protection Law (PDPL) 75
5.14 KEY CONFERENCES & EVENTS 75
5.15 TECHNOLOGY ROADMAP FOR RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET 75
5.15.1 SHORT-TERM ROADMAP (2025-2026) 76
5.15.2 MID-TERM ROADMAP (2027–2028) 76
5.15.3 LONG-TERM ROADMAP (2029–2030) 76
5.16 BEST PRACTICES IN RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET 76
5.16.1 ENSURE HIGH-QUALITY KNOWLEDGE BASES 76
5.16.2 IMPLEMENT HYBRID SEARCH TECHNIQUES 76
5.16.3 ADOPT EXPLAINABLE AI PRACTICES 76
5.16.4 HUMAN-IN-THE-LOOP MECHANISMS 77
5.16.5 EMBED SECURITY AND COMPLIANCE FROM THE START 77
5.16.6 OPTIMIZE FOR LATENCY AND SCALE 77
5.16.7 MAINTAIN CONTINUOUS FEEDBACK LOOPS 77
5.17 CURRENT AND EMERGING BUSINESS MODELS 77
5.18 TOOLS, FRAMEWORKS, AND TECHNIQUES USED IN RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET 78
5.19 INVESTMENT AND FUNDING SCENARIO 78
5.20 IMPACT OF AI/GENERATIVE AI ON RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET 78
5.20.1 USE CASES OF GENERATIVE AI IN RETRIEVAL-AUGMENTED GENERATION (RAG) 79
5.21 IMPACT OF 2025 US TARIFF – RAG MARKET 80
5.21.1 INTRODUCTION 80
5.21.2 KEY TARIFF RATES 80
5.21.3 PRICE IMPACT ANALYSIS 81
5.21.3.1 Strategic Shifts and Emerging Trends 81
5.21.4 IMPACT ON COUNTRY/REGION 82
5.21.4.1 US 82
5.21.4.2 Asia Pacific 82
5.21.4.3 Europe 82
5.21.5 IMPACT ON END-USE INDUSTRIES 83
5.21.5.1 Healthcare & Life Sciences 83
5.21.5.2 Retail & E-commerce 83
5.21.5.3 Media & Entertainment 83
5.21.5.4 Financial Services 83
6 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY OFFERING 84
6.1 INTRODUCTION 85
6.1.1 OFFERING: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET DRIVERS 85
6.2 SOLUTIONS 86
6.2.1 RAG SOLUTIONS TO EVOLVE TOWARD MORE AUTONOMOUS AND ADAPTIVE FRAMEWORKS 86
6.2.2 RAG-ENABLED PLATFORMS 87
6.2.3 DATA MANAGEMENT AND INDEXING LAYER 88
6.2.3.1 Need for scalable and intelligent indexing drives solution growth 88
6.2.4 RETRIEVAL AND SEARCH MODELS 89
6.2.4.1 Growing enterprise needs for contextual intelligence 89
6.2.5 OTHER SOLUTIONS 89
6.3 SERVICES 90
6.3.1 STREAMLINING ACADEMIC AND ADMINISTRATIVE OPERATIONS VIA INTEGRATED DIGITAL SYSTEMS 90
6.3.2 MANAGED SERVICES 91
6.3.2.1 Simplifying RAG Operations and Enhancing Scalability 91
6.3.3 PROFESSIONAL SERVICES 92
6.3.3.1 Driving Tailored Implementation and Performance Optimization 92
6.3.3.2 Support and Maintenance 93
6.3.3.3 Consulting and Customization 94
6.3.3.4 Training and Development 94
7 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE 96
7.1 INTRODUCTION 97
7.1.1 TYPE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET DRIVERS 97
7.2 FOUNDATIONAL AND ENHANCED RAG 98
7.2.1 FOUNDATIONAL AND ENHANCED RAG BUILDING BLOCK FOR ADVANCED AI SYSTEMS 98
7.3 AGENTIC AND ADAPTIVE RAG 99
7.3.1 ENABLING DYNAMIC AND AUTONOMOUS INTELLIGENCE 99
7.4 KNOWLEDGE-STRUCTURED AND MEMORY-BASED RAG 99
7.4.1 KNOWLEDGE-STRUCTURED & MEMORY-BASED RAG ENHANCING CONTEXTUAL REASONING AND LONG-TERM RECALL 99
7.5 PRIVACY-PRESERVING AND DISTRIBUTED RAG 100
7.5.1 PRIVACY-PRESERVING & DISTRIBUTED RAG SECURING KNOWLEDGE RETRIEVAL IN ERA OF DATA COMPLIANCE 100
7.6 OTHER TYPES 101
8 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY APPLICATION 102
8.1 INTRODUCTION 103
8.1.1 APPLICATION: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET DRIVERS 103
8.2 ENTERPRISE SEARCH 104
8.2.1 ENTERPRISE SEARCH FUELED BY EXPONENTIAL GROWTH OF INTERNAL DATA 104
8.3 DOMAIN-SPECIFIC DATA SYNTHESIS 105
8.3.1 GROWING COMPLEXITY OF DOMAIN DATA DRIVES ADOPTION 105
8.4 CONTENT SUMMARIZATION AND GENERATION 105
8.4.1 AUTOMATE NARRATIVE CREATION TO BOOST KNOWLEDGE THROUGHPUT 105
8.5 PERSONALIZED RECOMMENDATIONS AND INSIGHTS 106
8.5.1 FOCUS ON USER-CENTRIC EXPERIENCES DRIVES ITS GROWTH 106
8.6 CODE AND DEVELOPER PRODUCTIVITY 107
8.6.1 AI-DRIVEN DEVELOPMENT TOOLS FUEL ADOPTION 107
8.7 OTHER APPLICATIONS 107
9 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY DEPLOYMENT TYPE 109
9.1 INTRODUCTION 110
9.1.1 DEPLOYMENT TYPE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET DRIVERS 110
9.2 ON-PREMISES 111
9.2.1 LOCALIZED AI-DRIVEN RETRIEVAL AND REASONING TO INCREASE AS REGULATORY SCRUTINY AROUND DATA USAGE INTENSIFIES 111
9.3 CLOUD 111
9.3.1 ACCELERATING SCALABILITY AND REAL-TIME INTELLIGENCE 111
10 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER 113
10.1 INTRODUCTION 114
10.1.1 END USER: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET DRIVERS 114
10.2 HEALTHCARE AND LIFE SCIENCES 115
10.2.1 ENHANCING CLINICAL INTELLIGENCE AND PATIENT OUTCOMES 115
10.3 RETAIL & E-COMMERCE 116
10.3.1 DRIVING PERSONALIZED AND CONTEXTUAL SHOPPING EXPERIENCES 116
10.4 FINANCIAL SERVICES 116
10.4.1 FINANCIAL SERVICES REINFORCING COMPLIANCE AND KNOWLEDGE AUTOMATION 116
10.5 TELECOMMUNICATIONS 117
10.5.1 POWERING INTELLIGENT NETWORK AND SERVICE AUTOMATION 117
10.6 EDUCATION 118
10.6.1 ADVANCING ADAPTIVE AND KNOWLEDGE-RICH LEARNING 118
10.7 MEDIA & ENTERTAINMENT 118
10.7.1 ACCELERATING CREATIVE AND CONTEXTUAL CONTENT GENERATION 118
10.8 OTHER END USERS 119
11 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION 120
11.1 INTRODUCTION 121
11.2 NORTH AMERICA 121
11.2.1 NORTH AMERICA: MACROECONOMIC OUTLOOK 121
11.2.2 US 125
11.2.2.1 Supportive regulatory environment and ecosystem-led commercialization of RAG 125
11.2.3 CANADA 128
11.2.3.1 Leveraging RAG technologies to enhance transparency and sectoral innovation 128
11.3 EUROPE 131
11.3.1 EUROPE: MACROECONOMIC OUTLOOK 131
11.3.2 UK 134
11.3.2.1 Driving enterprise adoption of RAG under strong regulatory frameworks 134
11.3.3 GERMANY 137
11.3.3.1 Industrial applications and compliance-driven RAG adoption 137
11.3.4 FRANCE 140
11.3.4.1 Strengthening multilingual RAG solutions through public–private collaboration 140
11.3.5 ITALY 143
11.3.5.1 Adoption of RAG to modernize knowledge-intensive industries 143
11.3.6 REST OF EUROPE 146
11.4 ASIA PACIFIC 146
11.4.1 ASIA PACIFIC: MACROECONOMIC OUTLOOK 147
11.4.2 CHINA 150
11.4.2.1 Domestic Vector & Knowledge-enhanced Models Power Large-scale RAG 150
11.4.3 INDIA 153
11.4.3.1 Public Pilots and SI Packages Convert RAG Trials into Production 153
11.4.4 JAPAN 156
11.4.4.1 SI-led, Language-aware RAG for Manufacturing and Service Sectors 156
11.4.5 AUSTRALIA & NEW ZEALAND 159
11.4.5.1 Government Pilots Driving Trusted RAG Use Cases 159
11.4.6 SOUTH KOREA 162
11.4.6.1 Telcos and Domestic Clouds Anchoring Sovereign RAG 162
11.4.7 REST OF ASIA PACIFIC 165
11.5 MIDDLE EAST & AFRICA 165
11.5.1 MIDDLE EAST & AFRICA: MACROECONOMIC OUTLOOK 166
11.5.2 UNITED ARAB EMIRATES 169
11.5.2.1 National AI Programs Anchoring RAG Commercialization 169
11.5.3 KINGDOM OF SAUDI ARABIA 172
11.5.3.1 Vision 2030 Investments Scaling Knowledge-centric AI 172
11.5.4 SOUTH AFRICA 174
11.5.4.1 Academic and Startup Ecosystem Piloting RAG 174
11.5.5 REST OF MIDDLE EAST & AFRICA 177
11.6 LATIN AMERICA 177
11.6.1 LATIN AMERICA: MACROECONOMIC OUTLOOK 178
11.6.2 BRAZIL 181
11.6.2.1 Legislative Pilots Driving Public-Sector RAG 181
11.6.3 MEXICO 184
11.6.3.1 SI adaptation of Spanish-language RAG for enterprise support 184
11.6.4 REST OF LATIN AMERICA 186
12 COMPETITIVE LANDSCAPE 187
12.1 INTRODUCTION 187
12.2 KEY PLAYER STRATEGIES/RIGHT TO WIN, 2022–2025 187
12.3 REVENUE ANALYSIS, 2024 188
12.4 MARKET SHARE ANALYSIS, 2024 188
12.5 BRAND/PRODUCT COMPARISON 191
12.6 COMPANY VALUATION AND FINANCIAL METRICS 192
12.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2024 193
12.7.1 STARS 193
12.7.2 EMERGING LEADERS 193
12.7.3 PERVASIVE PLAYERS 193
12.7.4 PARTICIPANTS 194
12.7.5 COMPANY FOOTPRINT: KEY PLAYERS, 2024 195
12.7.5.1 Company footprint 195
12.7.5.2 Region footprint 195
12.7.5.3 Deployment type footprint 196
12.7.5.4 End user footprint 196
12.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2024 197
12.8.1 PROGRESSIVE COMPANIES 197
12.8.2 RESPONSIVE COMPANIES 197
12.8.3 DYNAMIC COMPANIES 197
12.8.4 STARTING BLOCKS 197
12.8.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2024 199
12.8.5.1 Detailed list of key startups/SMEs 199
12.8.5.2 Competitive benchmarking of key startups/SMEs 199
12.9 COMPETITIVE SCENARIO 200
12.9.1 PRODUCT LAUNCHES 200
12.9.2 DEALS 201
13 COMPANY PROFILES 203
13.1 INTRODUCTION 203
13.2 KEY PLAYERS 203
13.2.1 MICROSOFT 203
13.2.1.1 Business overview 203
13.2.1.2 Products/Solutions/Services offered 204
13.2.1.3 Recent developments 205
13.2.1.3.1 Product launches 205
13.2.1.3.2 Deals 205
13.2.1.4 MnM view 206
13.2.1.4.1 Key strengths 206
13.2.1.4.2 Strategic choices 206
13.2.1.4.3 Weaknesses and competitive threats 206
13.2.2 AWS 207
13.2.2.1 Business overview 207
13.2.2.2 Products/Solutions/Services offered 208
13.2.2.3 Recent developments 208
13.2.2.3.1 Deals 208
13.2.2.4 MnM view 209
13.2.2.4.1 Key strengths 209
13.2.2.4.2 Strategic choices 209
13.2.2.4.3 Weaknesses and competitive threats 209
13.2.3 GOOGLE 210
13.2.3.1 Business overview 210
13.2.3.2 Products/Solutions/Services offered 211
13.2.3.3 Recent developments 212
13.2.3.3.1 Deals 212
13.2.3.4 MnM view 212
13.2.3.4.1 Key strengths 212
13.2.3.4.2 Strategic choices 212
13.2.3.4.3 Weaknesses and competitive threats 213
13.2.4 ANTHROPIC 214
13.2.4.1 Business overview 214
13.2.4.2 Products/Solutions/Services offered 214
13.2.4.3 Recent developments 214
13.2.4.3.1 Deals 214
13.2.5 IBM 215
13.2.5.1 Business overview 215
13.2.5.2 Products/Solutions/Services offered 216
13.2.5.3 Recent developments 217
13.2.5.3.1 Deals 217
13.2.6 NVIDIA 218
13.2.6.1 Business overview 218
13.2.6.2 Products/Solutions/Services offered 219
13.2.6.3 Recent developments 220
13.2.6.3.1 Deals 220
13.2.7 COHERE 221
13.2.7.1 Business overview 221
13.2.7.2 Products/Solutions/Services offered 221
13.2.7.3 Recent developments 222
13.2.7.3.1 Deals 222
13.2.8 PINECONE 223
13.2.8.1 Business overview 223
13.2.8.2 Products/Solutions/Services offered 223
13.2.8.3 Recent developments 223
13.2.8.3.1 Deals 223
13.2.9 ELASTIC 225
13.2.9.1 Business overview 225
13.2.9.2 Products/Solutions/Services offered 226
13.2.9.3 Recent developments 227
13.2.9.3.1 Deals 227
13.2.10 MONGODB 228
13.2.10.1 Business overview 228
13.2.10.2 Products/Solutions/Services offered 229
13.2.10.3 Recent developments 229
13.2.10.3.1 Product launches 229
13.2.10.3.2 Deals 229
13.3 OTHER PLAYERS 230
13.3.1 PROGRESS SOFTWARE 230
13.3.2 RAGIE.AI 230
13.3.3 CLARIFAI 231
13.3.4 VECTARA 231
13.3.5 WEAVIATE 232
13.3.6 CHATBEES 232
13.3.7 ZILLIZ 233
13.3.8 QDRANT 234
14 ADJACENT/RELATED MARKETS 235
14.1 INTRODUCTION 235
14.2 GENERATIVE AI MARKET 235
14.2.1 MARKET DEFINITION 235
14.2.2 MARKET OVERVIEW 235
14.2.3 GENERATIVE AI MARKET, BY OFFERING 235
14.2.4 GENERATIVE AI MARKET, BY DATA MODALITY 236
14.2.5 GENERATIVE AI MARKET, BY APPLICATION 237
14.2.6 GENERATIVE AI MARKET, BY END USER 238
14.2.7 GENERATIVE AI MARKET, BY REGION 239
14.3 LARGE LANGUAGE MODEL (LLM) MARKET 240
14.3.1 MARKET DEFINITION 240
14.3.2 MARKET OVERVIEW 240
14.3.3 LARGE LANGUAGE MODEL (LLM) MARKET, BY OFFERING 241
14.3.4 LARGE LANGUAGE MODEL (LLM) MARKET, BY ARCHITECTURE 242
14.3.5 LARGE LANGUAGE MODEL (LLM) MARKET, BY MODALITY 243
14.3.6 LARGE LANGUAGE MODEL (LLM) MARKET, BY MODEL SIZE 244
14.3.7 LARGE LANGUAGE MODEL (LLM) MARKET, BY APPLICATION 245
14.3.8 LARGE LANGUAGE MODEL (LLM) MARKET, BY END USER 247
14.3.9 LARGE LANGUAGE MODEL (LLM) MARKET, BY REGION 248
15 APPENDIX 250
15.1 DISCUSSION GUIDE 250
15.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 254
15.3 CUSTOMIZATION OPTIONS 256
15.4 RELATED REPORTS 256
15.5 AUTHOR DETAILS 257
LIST OF TABLES
TABLE 1 USD EXCHANGE RATES, 2020–2024 32
TABLE 2 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET: ECOSYSTEM 56
TABLE 3 IMPACT OF PORTER’S FORCES ON RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET 59
TABLE 4 INDICATIVE PRICING ANALYSIS OF KEY RETRIEVAL-AUGMENTED GENERATION (RAG), BY SOLUTION, 2024 64
TABLE 5 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR KEY END USERS (%) 65
TABLE 6 KEY BUYING CRITERIA FOR TOP THREE END USERS 66
TABLE 7 NORTH AMERICA: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 70
TABLE 8 EUROPE: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 71
TABLE 9 ASIA PACIFIC: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 71
TABLE 10 MIDDLE EAST & AFRICA: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 72
TABLE 11 LATIN AMERICA: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 72
TABLE 12 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET: KEY CONFERENCES & EVENTS, 2025–2026 75
TABLE 13 US ADJUSTED RECIPROCAL TARIFF RATES 80
TABLE 14 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY OFFERING,
2024–2030 (USD MILLION) 86
TABLE 15 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SOLUTION,
2024–2030 (USD MILLION) 87
TABLE 16 SOLUTION: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 87
TABLE 17 RAG-ENABLED PLATFORMS: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 88
TABLE 18 DATA MANAGEMENT AND INDEXING LAYER: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 88
TABLE 19 RETRIEVAL AND SEARCH MODELS: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 89
TABLE 20 OTHER SOLUTIONS: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY REGION, 2024–2030 (USD MILLION) 90
TABLE 21 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SERVICE,
2024–2030 (USD MILLION) 91
TABLE 22 SERVICES: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 91
TABLE 23 MANAGED SERVICES: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY REGION, 2024–2030 (USD MILLION) 92
TABLE 24 PROFESSIONAL SERVICES: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 93
TABLE 25 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 93
TABLE 26 SUPPORT AND MAINTENANCE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 94
TABLE 27 CONSULTING AND CUSTOMIZATION: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 94
TABLE 28 TRAINING AND DEVELOPMENT: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 95
TABLE 29 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE,
2024–2030 (USD MILLION) 98
TABLE 30 FOUNDATIONAL AND ENHANCED RAG: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 98
TABLE 31 AGENTIC AND ADAPTIVE RAG: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 99
TABLE 32 KNOWLEDGE-STRUCTURE AND MEMORY-BASED RAG: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 100
TABLE 33 PRIVACY-PRESERVING AND DISTRIBUTED RAG: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 100
TABLE 34 OTHER TYPES: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 101
TABLE 35 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY APPLICATION,
2024–2030 (USD MILLION) 104
TABLE 36 ENTERPRISE SEARCH: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY REGION, 2024–2030 (USD MILLION) 104
TABLE 37 DOMAIN-SPECIFIC DATA SYNTHESIS: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 105
TABLE 38 CONTENT SUMMARIZATION AND GENERATION: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 106
TABLE 39 PERSONALIZED RECOMMENDATIONS AND INSIGHTS: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 106
TABLE 40 CODE AND DEVELOPER PRODUCTIVITY: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 107
TABLE 41 OTHER APPLICATIONS: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY REGION, 2024–2030 (USD MILLION) 108
TABLE 42 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY DEPLOYMENT TYPE, 2024–2030 (USD MILLION) 110
TABLE 43 ON-PREMISES: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 111
TABLE 44 CLOUD: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION,
2024–2030 (USD MILLION) 112
TABLE 45 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER,
2024–2030 (USD MILLION) 115
TABLE 46 HEALTHCARE & LIFE SCIENCES: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 115
TABLE 47 RETAIL & E-COMMERCE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY REGION, 2024–2030 (USD MILLION) 116
TABLE 48 FINANCIAL SERVICES: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY REGION, 2024–2030 (USD MILLION) 117
TABLE 49 TELECOMMUNICATIONS: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 117
TABLE 50 EDUCATION: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 118
TABLE 51 MEDIA & ENTERTAINMENT: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION, 2024–2030 (USD MILLION) 119
TABLE 52 OTHER END USERS: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY REGION, 2024–2030 (USD MILLION) 119
TABLE 53 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY REGION,
2024–2030 (USD MILLION) 121
TABLE 54 NORTH AMERICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY 0FFERING, 2024–2030 (USD MILLION) 122
TABLE 55 NORTH AMERICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY SOLUTION, 2024–2030 (USD MILLION) 122
TABLE 56 NORTH AMERICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY SERVICE, 2024–2030 (USD MILLION) 123
TABLE 57 NORTH AMERICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 123
TABLE 58 NORTH AMERICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE, 2024–2030 (USD MILLION) 123
TABLE 59 NORTH AMERICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY APPLICATION, 2024–2030 (USD MILLION) 124
TABLE 60 NORTH AMERICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY DEPLOYMENT TYPE, 2024–2030 (USD MILLION) 124
TABLE 61 NORTH AMERICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER, 2024–2030 (USD MILLION) 124
TABLE 62 NORTH AMERICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY COUNTRY, 2024–2030 (USD MILLION) 125
TABLE 63 US: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY OFFERING,
2024–2030 (USD MILLION) 125
TABLE 64 US: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SOLUTION,
2024–2030 (USD MILLION) 126
TABLE 65 US: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SERVICE,
2024–2030 (USD MILLION) 126
TABLE 66 US: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 126
TABLE 67 US: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE,
2024–2030 (USD MILLION) 127
TABLE 68 US: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 127
TABLE 69 US: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY DEPLOYMENT TYPE, 2024–2030 (USD MILLION) 127
TABLE 70 US: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER,
2024–2030 (USD MILLION) 128
TABLE 71 CANADA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY OFFERING, 2024–2030 (USD MILLION) 128
TABLE 72 CANADA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 129
TABLE 73 CANADA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SERVICE, 2024–2030 (USD MILLION) 129
TABLE 74 CANADA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 129
TABLE 75 CANADA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE,
2024–2030 (USD MILLION) 130
TABLE 76 CANADA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 130
TABLE 77 CANADA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY DEPLOYMENT TYPE, 2024–2030 (USD MILLION) 130
TABLE 78 CANADA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER, 2024–2030 (USD MILLION) 131
TABLE 79 EUROPE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY OFFERING, 2024–2030 (USD MILLION) 132
TABLE 80 EUROPE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 132
TABLE 81 EUROPE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SERVICE, 2024–2030 (USD MILLION) 132
TABLE 82 EUROPE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 132
TABLE 83 EUROPE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE,
2024–2030 (USD MILLION) 133
TABLE 84 EUROPE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 133
TABLE 85 EUROPE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY DEPLOYMENT TYPE, 2024–2030 (USD MILLION) 133
TABLE 86 EUROPE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER, 2024–2030 (USD MILLION) 134
TABLE 87 EUROPE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY COUNTRY, 2024–2030 (USD MILLION) 134
TABLE 88 UK: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY OFFERING,
2024–2030 (USD MILLION) 135
TABLE 89 UK: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SOLUTION,
2024–2030 (USD MILLION) 135
TABLE 90 UK: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SERVICE,
2024–2030 (USD MILLION) 135
TABLE 91 UK: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 135
TABLE 92 UK: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE,
2024–2030 (USD MILLION) 136
TABLE 93 UK: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 136
TABLE 94 UK: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY DEPLOYMENT TYPE, 2024–2030 (USD MILLION) 136
TABLE 95 UK: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER,
2024–2030 (USD MILLION) 137
TABLE 96 GERMANY: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY OFFERING, 2024–2030 (USD MILLION) 137
TABLE 97 GERMANY: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 138
TABLE 98 GERMANY: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SERVICE, 2024–2030 (USD MILLION) 138
TABLE 99 GERMANY: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 138
TABLE 100 GERMANY: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE, 2024–2030 (USD MILLION) 139
TABLE 101 GERMANY: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY APPLICATION, 2024–2030 (USD MILLION) 139
TABLE 102 GERMANY: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY DEPLOYMENT TYPE, 2024–2030 (USD MILLION) 139
TABLE 103 GERMANY: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER, 2024–2030 (USD MILLION) 140
TABLE 104 FRANCE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY OFFERING, 2024–2030 (USD MILLION) 140
TABLE 105 FRANCE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 141
TABLE 106 FRANCE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SERVICE, 2024–2030 (USD MILLION) 141
TABLE 107 FRANCE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 141
TABLE 108 FRANCE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE,
2024–2030 (USD MILLION) 142
TABLE 109 FRANCE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 142
TABLE 110 FRANCE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY DEPLOYMENT TYPE, 2024–2030 (USD MILLION) 142
TABLE 111 FRANCE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER, 2024–2030 (USD MILLION) 143
TABLE 112 ITALY: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY OFFERING, 2024–2030 (USD MILLION) 143
TABLE 113 ITALY: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 144
TABLE 114 ITALY: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SERVICE,
2024–2030 (USD MILLION) 144
TABLE 115 ITALY: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 144
TABLE 116 ITALY: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE,
2024–2030 (USD MILLION) 145
TABLE 117 ITALY: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 145
TABLE 118 ITALY: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY DEPLOYMENT TYPE, 2024–2030 (USD MILLION) 145
TABLE 119 ITALY: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER, 2024–2030 (USD MILLION) 146
TABLE 120 ASIA PACIFIC: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY 0FFERING, 2024–2030 (USD MILLION) 148
TABLE 121 ASIA PACIFIC: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 148
TABLE 122 ASIA PACIFIC: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SERVICE, 2024–2030 (USD MILLION) 148
TABLE 123 ASIA PACIFIC: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 148
TABLE 124 ASIA PACIFIC: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE, 2024–2030 (USD MILLION) 149
TABLE 125 ASIA PACIFIC: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY APPLICATION, 2024–2030 (USD MILLION) 149
TABLE 126 ASIA PACIFIC: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY DEPLOYMENT TYPE, 2024–2030 (USD MILLION) 149
TABLE 127 ASIA PACIFIC: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER, 2024–2030 (USD MILLION) 150
TABLE 128 ASIA PACIFIC: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY COUNTRY, 2024–2030 (USD MILLION) 150
TABLE 129 CHINA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY OFFERING, 2024–2030 (USD MILLION) 151
TABLE 130 CHINA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 151
TABLE 131 CHINA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SERVICE,
2024–2030 (USD MILLION) 151
TABLE 132 CHINA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 151
TABLE 133 CHINA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE,
2024–2030 (USD MILLION) 152
TABLE 134 CHINA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 152
TABLE 135 CHINA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY DEPLOYMENT TYPE, 2024–2030 (USD MILLION) 152
TABLE 136 CHINA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER, 2024–2030 (USD MILLION) 153
TABLE 137 INDIA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY OFFERING, 2024–2030 (USD MILLION) 153
TABLE 138 INDIA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 154
TABLE 139 INDIA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SERVICE,
2024–2030 (USD MILLION) 154
TABLE 140 INDIA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 154
TABLE 141 INDIA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE,
2024–2030 (USD MILLION) 155
TABLE 142 INDIA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 155
TABLE 143 INDIA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY DEPLOYMENT TYPE, 2024–2030 (USD MILLION) 155
TABLE 144 INDIA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER, 2024–2030 (USD MILLION) 156
TABLE 145 JAPAN: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY OFFERING, 2024–2030 (USD MILLION) 156
TABLE 146 JAPAN: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 157
TABLE 147 JAPAN: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SERVICE,
2024–2030 (USD MILLION) 157
TABLE 148 JAPAN: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 157
TABLE 149 JAPAN: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE,
2024–2030 (USD MILLION) 158
TABLE 150 JAPAN: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 158
TABLE 151 JAPAN: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY DEPLOYMENT TYPE, 2024–2030 (USD MILLION) 158
TABLE 152 JAPAN: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER, 2024–2030 (USD MILLION) 159
TABLE 153 AUSTRALIA AND NEW ZEALAND: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY OFFERING, 2024–2030 (USD MILLION) 159
TABLE 154 AUSTRALIA AND NEW ZEALAND: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 160
TABLE 155 AUSTRALIA AND NEW ZEALAND: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SERVICE, 2024–2030 (USD MILLION) 160
TABLE 156 AUSTRALIA AND NEW ZEALAND: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 160
TABLE 157 AUSTRALIA AND NEW ZEALAND: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE, 2024–2030 (USD MILLION) 161
TABLE 158 AUSTRALIA AND NEW ZEALAND: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 161
TABLE 159 AUSTRALIA AND NEW ZEALAND: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY DEPLOYMENT TYPE, 2024–2030 (USD MILLION) 161
TABLE 160 AUSTRALIA AND NEW ZEALAND: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER, 2024–2030 (USD MILLION) 162
TABLE 161 SOUTH KOREA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY OFFERING, 2024–2030 (USD MILLION) 162
TABLE 162 SOUTH KOREA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY SOLUTION, 2024–2030 (USD MILLION) 163
TABLE 163 SOUTH KOREA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY SERVICE, 2024–2030 (USD MILLION) 163
TABLE 164 SOUTH KOREA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 163
TABLE 165 SOUTH KOREA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE, 2024–2030 (USD MILLION) 164
TABLE 166 SOUTH KOREA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY APPLICATION, 2024–2030 (USD MILLION) 164
TABLE 167 SOUTH KOREA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY DEPLOYMENT TYPE, 2024–2030 (USD MILLION) 164
TABLE 168 SOUTH KOREA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER, 2024–2030 (USD MILLION) 165
TABLE 169 MIDDLE EAST & AFRICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY OFFERING, 2024–2030 (USD MILLION) 166
TABLE 170 MIDDLE EAST & AFRICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY SOLUTION, 2024–2030 (USD MILLION) 166
TABLE 171 MIDDLE EAST & AFRICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY SERVICE, 2024–2030 (USD MILLION) 167
TABLE 172 MIDDLE EAST & AFRICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 167
TABLE 173 MIDDLE EAST & AFRICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY TYPE, 2024–2030 (USD MILLION) 167
TABLE 174 MIDDLE EAST & AFRICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY APPLICATION, 2024–2030 (USD MILLION) 168
TABLE 175 MIDDLE EAST & AFRICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY DEPLOYMENT TYPE, 2024–2030 (USD MILLION) 168
TABLE 176 MIDDLE EAST & AFRICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY END USER, 2024–2030 (USD MILLION) 168
TABLE 177 MIDDLE EAST & AFRICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY COUNTRY, 2024–2030 (USD MILLION) 169
TABLE 178 UAE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY OFFERING,
2024–2030 (USD MILLION) 169
TABLE 179 UAE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SOLUTION,
2024–2030 (USD MILLION) 169
TABLE 180 UAE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SERVICE,
2024–2030 (USD MILLION) 170
TABLE 181 UAE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 170
TABLE 182 UAE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE,
2024–2030 (USD MILLION) 170
TABLE 183 UAE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 171
TABLE 184 UAE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY DEPLOYMENT TYPE, 2024–2030 (USD MILLION) 171
TABLE 185 UAE: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER,
2024–2030 (USD MILLION) 171
TABLE 186 KSA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY OFFERING,
2024–2030 (USD MILLION) 172
TABLE 187 KSA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SOLUTION,
2024–2030 (USD MILLION) 172
TABLE 188 KSA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SERVICE,
2024–2030 (USD MILLION) 172
TABLE 189 KSA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 173
TABLE 190 KSA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE,
2024–2030 (USD MILLION) 173
TABLE 191 KSA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 173
TABLE 192 KSA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY DEPLOYMENT TYPE, 2024–2030 (USD MILLION) 174
TABLE 193 KSA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER,
2024–2030 (USD MILLION) 174
TABLE 194 SOUTH AFRICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY OFFERING, 2024–2030 (USD MILLION) 174
TABLE 195 SOUTH AFRICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY SOLUTION, 2024–2030 (USD MILLION) 175
TABLE 196 SOUTH AFRICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY SERVICE, 2024–2030 (USD MILLION) 175
TABLE 197 SOUTH AFRICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 175
TABLE 198 SOUTH AFRICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE, 2024–2030 (USD MILLION) 176
TABLE 199 SOUTH AFRICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY APPLICATION, 2024–2030 (USD MILLION) 176
TABLE 200 SOUTH AFRICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY DEPLOYMENT TYPE, 2024–2030 (USD MILLION) 176
TABLE 201 SOUTH AFRICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER, 2024–2030 (USD MILLION) 177
TABLE 202 LATIN AMERICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY OFFERING, 2024–2030 (USD MILLION) 178
TABLE 203 LATIN AMERICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY SOLUTION, 2024–2030 (USD MILLION) 178
TABLE 204 LATIN AMERICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY SERVICE, 2024–2030 (USD MILLION) 179
TABLE 205 LATIN AMERICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 179
TABLE 206 LATIN AMERICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE, 2024–2030 (USD MILLION) 179
TABLE 207 LATIN AMERICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY APPLICATION, 2024–2030 (USD MILLION) 180
TABLE 208 LATIN AMERICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY DEPLOYMENT TYPE, 2024–2030 (USD MILLION) 180
TABLE 209 LATIN AMERICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER, 2024–2030 (USD MILLION) 180
TABLE 210 LATIN AMERICA: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
BY COUNTRY, 2024–2030 (USD MILLION) 181
TABLE 211 BRAZIL: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY OFFERING, 2024–2030 (USD MILLION) 181
TABLE 212 BRAZIL: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 181
TABLE 213 BRAZIL: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SERVICE, 2024–2030 (USD MILLION) 182
TABLE 214 BRAZIL: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 182
TABLE 215 BRAZIL: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE,
2024–2030 (USD MILLION) 182
TABLE 216 BRAZIL: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 183
TABLE 217 BRAZIL: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY DEPLOYMENT TYPE, 2024–2030 (USD MILLION) 183
TABLE 218 BRAZIL: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER, 2024–2030 (USD MILLION) 183
TABLE 219 MEXICO: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY OFFERING, 2024–2030 (USD MILLION) 184
TABLE 220 MEXICO: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 184
TABLE 221 MEXICO: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY SERVICE, 2024–2030 (USD MILLION) 184
TABLE 222 MEXICO: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 185
TABLE 223 MEXICO: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY TYPE,
2024–2030 (USD MILLION) 185
TABLE 224 MEXICO: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 185
TABLE 225 MEXICO: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY DEPLOYMENT TYPE, 2024–2030 (USD MILLION) 186
TABLE 226 MEXICO: RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, BY END USER, 2024–2030 (USD MILLION) 186
TABLE 227 OVERVIEW OF STRATEGIES ADOPTED BY KEY RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET PLAYERS, 2022–2025 187
TABLE 228 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET: DEGREE OF COMPETITION 189
TABLE 229 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET: REGION FOOTPRINT 195
TABLE 230 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET: DEPLOYMENT TYPE FOOTPRINT 196
TABLE 231 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET: END USER FOOTPRINT 196
TABLE 232 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET: LIST OF KEY STARTUPS/SMES 199
TABLE 233 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET: COMPETITIVE BENCHMARKING OF KEY STARTUPS/SMES 199
TABLE 234 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET: PRODUCT LAUNCHES, JANUARY 2022–APRIL 2025 200
TABLE 235 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET: DEALS,
JANUARY 2022–APRIL 2025 201
TABLE 236 MICROSOFT: COMPANY OVERVIEW 203
TABLE 237 MICROSOFT: PRODUCTS/SOLUTIONS/SERVICES OFFERED 204
TABLE 238 MICROSOFT: PRODUCT LAUNCHES 205
TABLE 239 MICROSOFT: DEALS 205
TABLE 240 AWS: COMPANY OVERVIEW 207
TABLE 241 AWS: PRODUCTS/SOLUTIONS/SERVICES OFFERED 208
TABLE 242 AWS: DEALS 208
TABLE 243 GOOGLE: COMPANY OVERVIEW 210
TABLE 244 GOOGLE: PRODUCTS/SOLUTIONS/SERVICES OFFERED 211
TABLE 245 GOOGLE: DEALS 212
TABLE 246 ANTHROPIC: COMPANY OVERVIEW 214
TABLE 247 ANTHROPIC: PRODUCTS/SOLUTIONS/SERVICES OFFERED 214
TABLE 248 ANTHROPIC: DEALS 214
TABLE 249 IBM: COMPANY OVERVIEW 215
TABLE 250 IBM: PRODUCTS/SOLUTIONS/SERVICES OFFERED 216
TABLE 251 IBM: DEALS 217
TABLE 252 NVIDIA: COMPANY OVERVIEW 218
TABLE 253 NVIDIA: PRODUCTS/SOLUTIONS/SERVICES OFFERED 219
TABLE 254 NVIDIA: DEALS 220
TABLE 255 COHERE: COMPANY OVERVIEW 221
TABLE 256 COHERE: PRODUCTS/SOLUTIONS/SERVICES OFFERED 221
TABLE 257 COHERE: DEALS 222
TABLE 258 PINECONE: COMPANY OVERVIEW 223
TABLE 259 PINECONE: PRODUCTS/SOLUTIONS/SERVICES OFFERED 223
TABLE 260 PINECONE: DEALS 223
TABLE 261 ELASTIC: COMPANY OVERVIEW 225
TABLE 262 ELASTIC: PRODUCTS/SOLUTIONS/SERVICES OFFERED 226
TABLE 263 ELASTIC: DEALS 227
TABLE 264 MONGODB: COMPANY OVERVIEW 228
TABLE 265 MONGODB: PRODUCTS/SOLUTIONS/SERVICES OFFERED 229
TABLE 266 MONGODB: PRODUCT LAUNCHES 229
TABLE 267 MONGODB: DEALS 229
TABLE 268 GENERATIVE AI MARKET, BY OFFERING, 2020–2024 (USD MILLION) 236
TABLE 269 GENERATIVE AI MARKET, BY OFFERING, 2025–2032 (USD MILLION) 236
TABLE 270 GENERATIVE AI MARKET, BY DATA MODALITY, 2020–2024 (USD MILLION) 237
TABLE 271 GENERATIVE AI MARKET, BY DATA MODALITY, 2025–2032 (USD MILLION) 237
TABLE 272 GENERATIVE AI MARKET, BY APPLICATION, 2020–2024 (USD MILLION) 238
TABLE 273 GENERATIVE AI MARKET, BY APPLICATION, 2025–2032 (USD MILLION) 238
TABLE 274 GENERATIVE AI MARKET, BY END USER, 2020–2024 (USD MILLION) 239
TABLE 275 GENERATIVE AI MARKET, BY END USER, 2025–2032 (USD MILLION) 239
TABLE 276 GENERATIVE AI MARKET, BY REGION, 2020–2024 (USD MILLION) 240
TABLE 277 GENERATIVE AI MARKET, BY REGION, 2025–2032 (USD MILLION) 240
TABLE 278 LARGE LANGUAGE MODEL MARKET, BY OFFERING, 2020–2023 (USD MILLION) 241
TABLE 279 LARGE LANGUAGE MODEL MARKET, BY OFFERING, 2024–2030 (USD MILLION) 241
TABLE 280 LARGE LANGUAGE MODEL MARKET, BY ARCHITECTURE,
2020–2023 (USD MILLION) 242
TABLE 281 LARGE LANGUAGE MODEL MARKET, BY ARCHITECTURE,
2024–2030 (USD MILLION) 243
TABLE 282 LARGE LANGUAGE MODEL MARKET, BY MODALITY, 2020–2023 (USD MILLION) 243
TABLE 283 LARGE LANGUAGE MODEL MARKET, BY MODALITY, 2024–2030 (USD MILLION) 244
TABLE 284 LARGE LANGUAGE MODEL MARKET, BY MODEL SIZE, 2020–2023 (USD MILLION) 245
TABLE 285 LARGE LANGUAGE MODEL MARKET, BY MODEL SIZE, 2024–2030 (USD MILLION) 245
TABLE 286 LARGE LANGUAGE MODEL MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 246
TABLE 287 LARGE LANGUAGE MODEL MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 246
TABLE 288 LARGE LANGUAGE MODEL MARKET, BY END USER, 2020–2023 (USD MILLION) 247
TABLE 289 LARGE LANGUAGE MODEL MARKET, BY END USER, 2024–2030 (USD MILLION) 248
TABLE 290 LARGE LANGUAGE MODEL MARKET, BY REGION, 2020–2023 (USD MILLION) 249
TABLE 291 LARGE LANGUAGE MODEL MARKET, BY REGION, 2024–2030 (USD MILLION) 249
LIST OF FIGURES
FIGURE 1 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET: RESEARCH DESIGN 33
FIGURE 2 BREAKDOWN OF PRIMARY INTERVIEWS, BY COMPANY TYPE, DESIGNATION, AND REGION 35
FIGURE 3 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET: TOP-DOWN AND BOTTOM-UP APPROACHES 36
FIGURE 4 MARKET SIZE ESTIMATION METHODOLOGY—APPROACH 1 (SUPPLY SIDE): REVENUE OF VENDORS IN RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET 37
FIGURE 5 MARKET SIZE ESTIMATION METHODOLOGY—APPROACH 2 (DEMAND SIDE): RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET 37
FIGURE 6 MARKET SIZE ESTIMATION METHODOLOGY: DEMAND-SIDE ANALYSIS 38
FIGURE 7 MARKET SIZE ESTIMATION USING BOTTOM-UP APPROACH 38
FIGURE 8 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET: DATA TRIANGULATION 39
FIGURE 9 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET,
2024–2030 (USD MILLION) 43
FIGURE 10 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET: REGIONAL AND COUNTRY-WISE SHARE, 2025 44
FIGURE 11 RAPID DIGITAL TRANSFORMATION AND GROWING ENTERPRISE AI ADOPTION TO DRIVE MARKET 45
FIGURE 12 SOLUTIONS SEGMENT TO HOLD LARGER MARKET SHARE IN 2025 45
FIGURE 13 RAG-ENABLED PLATFORMS SEGMENT TO HOLD LARGEST MARKET SHARE IN 2025 46
FIGURE 14 FOUNDATIONAL & ENHANCED RAG SEGMENT TO HOLD LARGEST MARKET SHARE IN 2025 46
FIGURE 15 ENTERPRISE SEARCH SEGMENT TO HOLD LARGEST MARKET SHARE IN 2025 47
FIGURE 16 CLOUD SEGMENT TO HOLD LARGER MARKET SHARE IN 2025 47
FIGURE 17 HEALTHCARE & LIFE SCIENCES SEGMENT TO LEAD MARKET IN 2025 48
FIGURE 18 HEALTHCARE & LIFE SCIENCES SEGMENT AND US TO ACCOUNT FOR SIGNIFICANT MARKET SHARES IN 2025 48
FIGURE 19 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES 50
FIGURE 20 BRIEF HISTORY OF RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET 53
FIGURE 21 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET: SUPPLY CHAIN ANALYSIS 54
FIGURE 22 KEY PLAYERS IN RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET ECOSYSTEM 56
FIGURE 23 PORTER’S FIVE FORCES ANALYSIS 59
FIGURE 24 MAJOR PATENTS FOR RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET 61
FIGURE 25 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET: DISRUPTIONS IMPACTING BUYERS/CLIENTS 62
FIGURE 26 AVERAGE SELLING PRICE OF KEY PLAYERS, USD PER MONTH, 2024 63
FIGURE 27 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR KEY END USERS 65
FIGURE 28 KEY BUYING CRITERIA FOR TOP THREE END USERS 66
FIGURE 29 TOOLS, FRAMEWORKS, AND TECHNIQUES USED IN RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET 78
FIGURE 30 INVESTMENT AND FUNDING SCENARIO 78
FIGURE 31 USE CASES OF GENERATIVE AI IN RETRIEVAL-AUGMENTED GENERATION (RAG) 79
FIGURE 32 SERVICES SEGMENT TO GROW AT HIGHER CAGR DURING FORECAST PERIOD 85
FIGURE 33 DATA MANAGEMENT & INDEXING LAYER SEGMENT TO GROW AT HIGHEST CAGR DURING FORECAST PERIOD 86
FIGURE 34 MANAGED SERVICES SEGMENT TO GROW AT HIGHER CAGR DURING FORECAST PERIOD 90
FIGURE 35 TRAINING AND DEVELOPMENT TO GROW AT HIGHEST CAGR DURING FORECAST PERIOD 92
FIGURE 36 FOUNDATIONAL & ENHANCED RAG SEGMENT TO HOLD THE LARGEST MARKET SHARE DURING FORECAST PERIOD 97
FIGURE 37 ENTERPRISE SEARCH SEGMENT TO HOLD THE LARGEST MARKET SHARE DURING FORECAST PERIOD 103
FIGURE 38 CLOUD SEGMENT TO GROW AT HIGHER CAGR DURING FORECAST PERIOD 110
FIGURE 39 HEALTHCARE & LIFE SCIENCES SEGMENT TO HOLD LARGEST MARKET SHARE DURING FORECAST PERIOD 114
FIGURE 40 NORTH AMERICA: MARKET SNAPSHOT 122
FIGURE 41 ASIA PACIFIC: MARKET SNAPSHOT 147
FIGURE 42 REVENUE ANALYSIS OF KEY PLAYERS IN RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, 2022 TO 2024 (USD BILLION) 188
FIGURE 43 SHARES OF LEADING COMPANIES IN RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET, 2024 189
FIGURE 44 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET: BRAND/PRODUCT COMPARISON 191
FIGURE 45 COMPANY VALUATION OF KEY VENDORS, 2025 192
FIGURE 46 FINANCIAL METRICS OF KEY VENDORS, 2025 193
FIGURE 47 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET: COMPANY EVALUATION MATRIX (KEY PLAYERS), 2024 194
FIGURE 48 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET: COMPANY FOOTPRINT 195
FIGURE 49 RETRIEVAL-AUGMENTED GENERATION (RAG) MARKET: COMPANY EVALUATION MATRIX (STARTUPS/SMES), 2024 198
FIGURE 50 MICROSOFT: COMPANY SNAPSHOT 204
FIGURE 51 AWS: COMPANY SNAPSHOT 207
FIGURE 52 GOOGLE: COMPANY SNAPSHOT 211
FIGURE 53 IBM: COMPANY SNAPSHOT 216
FIGURE 54 NVIDIA: COMPANY SNAPSHOT 219
FIGURE 55 ELASTIC: COMPANY SNAPSHOT 226
FIGURE 56 MONGODB: COMPANY SNAPSHOT 228
