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検索拡張生成（RAG）市場規模、シェア、動向、2030年までの世界予測

Retrieval-Augmented Generation (RAG) Market - Global Forecast To 2030

検索拡張生成（RAG）市場 - 提供内容（ソリューション（RAG対応プラットフォーム、データ管理およびインデックスレイヤー、検索モデル）、サービス）、タイプ、アプリケーション、エンドユーザー、および導入タイプ - 2030年までの世界予測
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

商品番号 : SMB-89274

出版社MarketsandMarkets
出版年月2025年10月
ページ数350
図表数286
価格タイプシングルユーザライセンス
価格USD 4,950
種別英文調査報告書

検索拡張生成（RAG）市場は2025年に19億4,000万米ドル規模になることが見込まれ、CAGR 38.4%で成長し、2030年までに98億6,000万米ドルに達するとMarketsandMarketsでは予測しています。

MarketsandMarkets（マーケッツアンドマーケッツ）「検索拡張生成（RAG）市場 – 提供内容（ソリューション（RAG対応プラットフォーム、データ管理およびインデックスレイヤー、検索モデル）、サービス）、タイプ、アプリケーション、エンドユーザー、および導入タイプ – 2030年までの世界予測 – 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」は世界の検索拡張生成（RAG）市場を調査し、主要セグメント別に分析・予測を行っています。

調査対象セグメント

  • オファリング
    • ソリューション
    • サービス
  • タイプ
    • 基礎的RAGと強化型RAG
    • エージェント型RAGと適応型RAG
    • 構造化知識RAGとメモリーベースのRAG
    • プライバシー保護型RAGと分散型RAG
    • その他のタイプ
  • 用途
    • エンタープライズサーチ
    • 領域限定データ合成
    • コンテンツの要約と生成
    • パーソナライズされた提言とインサイト
    • コードと開発者の生産性
    • その他の用途
  • 展開タイプ
    • オンプレミス
    • クラウド
  • エンドユーザ
    • 医療＆ライフサイエンス
    • 小売業＆eコマース
    • 金融サービス
    • 電気通信
    • 教育
    • メディア＆エンタテインメント
    • その他のエンドユーザ
  • 地域
    • 北米
      • 米国
      • カナダ
    • 欧州
      • 英国
      • ドイツ
      • フランス
      • イタリア
      • その他の欧州
    • アジア太平洋地域
      • 中国
      • インド
      • 日本
      • オーストラリア＆ニュージーランド
      • 韓国
      • その他のアジア太平洋地域
    • 中東＆アフリカ
      • アラブ首長国連邦（UAE）
      • サウジアラビア王国
      • 南アフリカ
      • その他の中東＆アフリカ
    • ラテンアメリカ
      • ブラジル
      • メキシコ
      • その他のラテンアメリカ

Microsoft、AWS、Google、Anthropic、Cohereといった大手テクノロジー企業は、RAGを活用したソリューション、統合、そしてパートナーシップに多額の投資を行っています。クラウド・ハイパースケーラーは、Azure OpenAI ServiceやAWS BedrockといったエンタープライズAIサービスにRAGを組み込んでおり、企業が自社のジェネレーティブAIアプリケーションに検索機能を統合しやすくしています。こうしたエコシステムの拡大は、RAGの認知度を高めるだけでなく、すぐに使えるスケーラブルなソリューションを企業に提供することで、導入の障壁を下げています。RAGスタートアップ企業への継続的なベンチャー資金提供と、モデルプロバイダーと検索インフラベンダーとのパートナーシップは、市場の成長軌道をさらに加速させています。

この市場調査は、検索拡張生成（RAG）市場の規模と成長の可能性を、提供内容、タイプ、アプリケーション、エンドユーザー、導入タイプ、地域など、さまざまなセグメントにわたって調査しています。調査対象の提供内容には、ソリューション（RAG対応プラットフォーム、データ管理およびインデックス作成レイヤー、検索および検索モデル、その他のソリューション）とサービス（マネージドおよびプロフェッショナル）が含まれます。タイプセグメントには、基礎的および拡張RAG、エージェント型および適応型RAG、知識構造化およびメモリベースのRAG、プライバシー保護および分散型RAG、その他のタイプが含まれます。アプリケーションセグメントには、エンタープライズ検索、ドメイン固有のデータ合成、コンテンツの要約と生成、パーソナライズされた推奨事項と洞察、コードおよび開発者の生産性、その他のアプリケーションが含まれます。エンドユーザーセグメントには、ヘルスケアおよびライフサイエンス、小売およびeコマース、金融サービス、通信、教育、メディアおよびエンターテイメント、ソフトウェアおよびテクノロジープロバイダー、その他のエンドユーザーが含まれます。導入タイプセグメントには、オンプレミスおよびクラウドが含まれます。 RAG (Retrieval Augmented Generation) 市場の地域分析は、北米、ヨーロッパ、アジア太平洋、中東およびアフリカ、ラテンアメリカをカバーしています。

本レポートは、市場リーダーと新規参入企業にとって、世界の検索拡張生成（RAG）市場の収益数値とサブセグメントに関する近似値に関する情報を提供するのに役立ちます。また、ステークホルダーが競争環境を理解し、洞察を獲得し、適切な市場開拓戦略を策定する上でも役立ちます。さらに、本レポートは、ステークホルダーが市場の動向を把握し、主要な市場牽引要因、制約要因、課題、そして機会に関する情報を提供するための洞察を提供します。

このレポートでは、次のような洞察が示されています。

  • 検索拡張世代（RAG）市場の成長に影響を与える主要な推進要因（コンテキストアウェアAIによる精度向上、企業のデジタル化の加速）、制約要因（高額なインフラコストの管理、データのプライバシーと保護の確保）、機会（RAGとドメイン固有アプリケーションの統合、多言語サポートの拡大）、課題（ベンダーの断片化への対応、AI幻覚のリスク軽減）を分析します。
  • 製品開発／イノベーション：検索拡張世代（RAG）市場における今後の技術、研究開発活動、新製品・新サービスの発売に関する詳細な洞察を提供します。
  • 市場開発：本レポートは、様々な地域における検索拡張世代（RAG）市場を分析し、収益性の高い市場に関する包括的な情報を提供します。
  • 市場の多様化：検索拡張世代（RAG）市場における新製品・新サービス、未開拓地域、最近の動向、投資に関する包括的な情報を提供します。

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.

検索拡張生成（RAG）市場規模、シェア、動向、2030年までの世界予測
Retrieval-Augmented Generation (RAG) Market – Global Forecast To 2030

“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.

検索拡張生成（RAG）市場規模、シェア、動向、2030年までの世界予測 - by offering
Retrieval-Augmented Generation (RAG) Market – Global Forecast To 2030 – by offering

“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.

検索拡張生成（RAG）市場規模、シェア、動向、2030年までの世界予測 - 地域
Retrieval-Augmented Generation (RAG) Market – Global Forecast To 2030 – region

“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.

検索拡張生成（RAG）市場規模、シェア、動向、2030年までの世界予測 - 対象企業
Retrieval-Augmented Generation (RAG) Market – Global Forecast To 2030 – ecosystem

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

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