세계의 AIaaS(AI as a Service) 시장 : 제품 유형별, 조직 규모별, 비즈니스 기능별, 서비스 유형별, 최종사용자별, 지역별 - 예측(-2030년)
AI as a Service Market by Product Type (Chatbots & AI Agents, ML Framework, API, No Code/Low Code Tools, Data Labeling), Service Type (ML as a Service, NLP as a Service, Generative AI as a Service), Business Function, End User - Global Forecast to 2030
AIaaS(AI as a Service) 시장 규모는 2025년 202억 6,000만 달러에서 2030년 912억 달러로 성장하여 예측 기간 동안 35.1%의 CAGR을 기록할 것으로 예상됩니다.
AIaaS(AI as a Service) 시장의 성장은 주로 클라우드 기반 솔루션의 채택 증가, 지능형 가상 비서에 대한 수요 증가, 산업 전반의 의사결정을 강화하기 위한 고급 데이터 분석의 필요성에 의해 주도되고 있습니다. 기업들은 AIaaS를 활용하여 운영 비용 절감, 고객 경험 향상, 사내 AI 인프라 구축에 대한 부담 없이 경쟁 우위를 확보하고자 합니다. 그러나 데이터 프라이버시 및 보안 위험에 대한 우려, AI 기술을 효과적으로 관리하고 도입할 수 있는 숙련된 인력의 부족 등이 시장 성장을 저해하고 있습니다. 또한, 기존 시스템과의 통합 문제, 중소기업을 위한 고도의 AI 서비스의 높은 비용도 보급의 제약 요인으로 작용하고 있습니다.
| 조사 범위 |
| 조사 대상 연도 |
2020-2030년 |
| 기준 연도 |
2024년 |
| 예측 기간 |
2025-2030년 |
| 검토 단위 |
달러(10억 달러) |
| 부문 |
제품 유형별, 조직 규모별, 비즈니스 기능별, 서비스 유형별, 최종사용자별, 지역별 |
| 대상 지역 |
북미, 유럽, 아시아태평양, 중동 및 아프리카, 라틴아메리카 |
운영 및 공급망 부문은 실시간 인사이트, 수요 예측, 프로세스 최적화에 대한 요구가 증가함에 따라 예측 기간 동안 AIaaS 시장에서 가장 빠르게 성장할 것으로 예상됩니다. AI 기반 솔루션은 예측 분석과 자동화를 통해 기업이 공급망 가시성을 높이고, 혼란을 줄이며, 재고 관리를 개선할 수 있도록 돕습니다. 세계 공급망이 복잡해짐에 따라 기업들은 AIaaS를 도입하여 물류를 간소화하고, 경로를 최적화하며, 의사결정의 효율성을 높이고 있습니다. 또한, AI를 IoT 및 고급 분석과 통합함으로써 AI의 운영 적용을 더욱 촉진하여 디지털 혁신과 경쟁 우위를 위한 중요한 분야가 되고 있습니다.
대기업은 풍부한 자금력, 첨단 IT 인프라, 신흥 기술을 도입할 준비가 되어 있어 AIaaS 시장에서 가장 큰 시장 점유율을 차지할 것으로 예상됩니다. 이들 조직은 대량의 데이터를 다루는 경우가 많으며, 자동화, 고객 참여, 의사결정을 위한 확장 가능하고 효율적인 솔루션이 필요하며, AIaaS 플랫폼은 이를 효과적으로 제공할 수 있습니다. 또한, 대기업들은 업무 효율성을 높이고 경쟁력을 유지하기 위해 AI를 활용한 디지털 전환에 적극적으로 투자하고 있습니다. 맞춤형 솔루션을 위해 AI 공급업체와 협력하고 복잡한 도입을 관리할 수 있는 능력은 시장에서의 지배적 지위를 더욱 강화하고 있습니다.
아시아태평양은 급속한 디지털 혁신, AI 기술에 대한 투자 증가, 인도, 중국, 동남아시아 국가들의 클라우드 기반 서비스 채택 확대로 인해 AIaa 시장에서 가장 빠른 성장세를 보일 것으로 예상됩니다. 이 지역의 스타트업 생태계 확대와 AI 혁신을 지원하기 위한 정부의 노력은 이러한 성장을 촉진하고 있습니다. 한편, 북미는 첨단 기술의 조기 도입, 주요 AI 벤더의 존재감, 성숙한 클라우드 인프라에 힘입어 가장 큰 시장 점유율을 차지할 것으로 보입니다. 높은 R&D 투자, 탄탄한 디지털 생태계, 헬스케어, 금융, 소매 분야에서의 AI의 광범위한 통합이 북미 시장의 우위에 기여하고 있습니다.
세계의 AIaaS(AI as a Service) 시장에 대해 조사했으며, 제품 유형별, 조직 규모별, 비즈니스 기능별, 서비스 유형별, 최종사용자별, 지역별 동향, 시장 진입 기업 프로파일 등의 정보를 정리하여 전해드립니다.
목차
제1장 소개
제2장 조사 방법
제3장 주요 요약
제4장 주요 인사이트
제5장 시장 개요와 업계 동향
- 소개
- 시장 역학
- 2025년 미국 관세의 영향 - AIaaS 시장
- AIaaS 시장 : 진화
- 생태계 분석
- 공급망 분석
- 투자 상황과 자금 조달 시나리오
- 사례 연구 분석
- 기술 분석
- 규제 상황
- 아키텍처 : AIaaS(AI as a Service)
- 특허 분석
- 가격 분석
- 2025-2026년의 주요 회의와 이벤트
- Porter's Five Forces 분석
- 고객 비즈니스에 영향을 미치는 동향/혼란
- 주요 이해관계자와 구입 기준
제6장 AIaaS 시장(제품 유형별)
- 소개
- 챗봇과 AI 에이전트
- 머신러닝 프레임워크
- 애플리케이션 프로그래밍 인터페이스
- 노코드 또는 로우코드 ML 툴
- 데이터 라벨링과 전처리 툴
제7장 AIaaS 시장(조직 규모별)
제8장 AIaaS 시장(비즈니스 기능별)
- 소개
- 파이낸스
- 마케팅
- 판매
- 오퍼레이션과 공급망
- 인사
제9장 AIaaS 시장(서비스 유형별)
- 소개
- Machine Learning as a Service(MLaaS)
- Natural Language Processing as a Service(NLPaaS)
- Computer Vision as a Service
- Redictive Analytics And Data Science as a Service(DSaaS)
- Generative AI as a Service
제10장 AIaaS 시장(최종사용자별)
제11장 AIaaS 시장(지역별)
- 소개
- 북미
- 북미 : AIaaS 시장 성장 촉진요인
- 북미 : 거시경제 전망
- 미국
- 캐나다
- 유럽
- 유럽 : AIaaS 시장 성장 촉진요인
- 유럽 : 거시경제 전망
- 영국
- 독일
- 프랑스
- 이탈리아
- 스페인
- 기타
- 아시아태평양
- 아시아태평양 : AIaaS 시장 성장 촉진요인
- 아시아태평양 : 거시경제 전망
- 중국
- 인도
- 일본
- 한국
- 호주와 뉴질랜드
- 싱가포르
- 기타
- 중동 및 아프리카
- 중동 및 아프리카 : AIaaS 시장 성장 촉진요인
- 중동 및 아프리카 : 거시경제 전망
- 중동
- 아프리카
- 라틴아메리카
- 라틴아메리카 : AIaaS 시장 성장 촉진요인
- 라틴아메리카 : 거시경제 전망
- 브라질
- 멕시코
- 아르헨티나
- 기타
제12장 경쟁 구도
- 개요
- 주요 진출 기업 전략/강점, 2022-2025년
- 매출 분석, 2020-2024년
- 시장 점유율 분석, 2024년
- 제품 비교 분석
- 기업 평가와 재무 지표
- 기업 평가 매트릭스 : 주요 진출 기업, 2024년
- 기업 평가 매트릭스 : 스타트업/중소기업, 2024년
- 경쟁 시나리오와 동향
제13장 기업 개요
- 소개
- 주요 진출 기업
- AWS
- GOOGLE
- MICROSOFT
- IBM
- ORACLE
- SAP
- SALESFORCE
- NVIDIA
- ALIBABA CLOUD
- OPENAI
- RAINBIRD TECHNOLOGIES
- BIGML
- COHERE
- GLEAN
- SCALE AI
- LANDING AI
- YELLOW.AI
- ANYSCALE
- MISTRAL AI
- H20.AI
- SYNTHESIA
- CLARIFAI
- MONKEYLEARN
- 기타 기업
- FICO
- CLOUDERA
- SERVICENOW
- HPE
- ALTAIR
- SAS INSTITUTE
- DATAROBOT
- DATABRICKS
- C3 AI
- DOMO
- INTELLIAS
- YOTTAMINE ANALYTICS
- INFLECTION AI
- ABRIDGE
- CODEIUM
- ARTHUR
- LEVITY AI
- UNSTRUCTURED.IO
- KATONIC AI
- DEEPSEARCH
- MINDTITAN
- VISO.AI
- SOFTWEB SOLUTIONS
제14장 인접 시장과 관련 시장
제15장 부록
ksm
The AI as a Service market is projected to grow from USD 20.26 billion in 2025 to USD 91.20 billion by 2030, at a compound annual growth rate (CAGR) of 35.1% during the forecast period. The growth of the AI as a Service (AIaaS) market is primarily driven by the increasing adoption of cloud-based solutions, rising demand for intelligent virtual assistants, and the need for advanced data analytics to enhance decision-making across industries. Organizations are leveraging AIaaS to reduce operational costs, improve customer experience, and gain a competitive advantage without the burden of building in-house AI infrastructure. However, market growth is restrained by concerns related to data privacy, security risks, and the lack of skilled personnel to manage and implement AI technologies effectively. Additionally, integration challenges with existing systems and the high cost of advanced AI services for smaller businesses also pose limitations to widespread adoption.
| Scope of the Report |
|---|
| Years Considered for the Study | 2020-2030 |
| Base Year | 2024 |
| Forecast Period | 2025-2030 |
| Units Considered | USD (Billion) |
| Segments | Product Type, Organization Size, Business Function, Service Type, End User, and Region |
| Regions covered | North America, Europe, Asia Pacific, Middle East & Africa, and Latin America |
"Operations & supply chain business function segment is expected to have the fastest growth rate during the forecast period"
The operations and supply chain segment is expected to witness the fastest growth in the AI as a Service market during the forecast period due to the increasing need for real-time insights, demand forecasting, and process optimization. AI-powered solutions help businesses enhance supply chain visibility, reduce disruptions, and improve inventory management through predictive analytics and automation. As global supply chains become more complex, organizations adopt AIaaS to streamline logistics, optimize routes, and enhance decision-making efficiency. Additionally, integrating AI with IoT and advanced analytics further drives its application in operations, making it a critical area for digital transformation and competitive advantage.
"The large enterprises of the organization size segment will hold the largest market share during the forecast period"
Large enterprises are expected to hold the largest market share in the AI as a Service market due to their substantial financial resources, advanced IT infrastructure, and greater readiness to adopt emerging technologies. These organizations often deal with massive volumes of data and require scalable, efficient solutions for automation, customer engagement, and decision-making, which AIaaS platforms effectively provide. Additionally, large enterprises actively invest in AI-driven digital transformation initiatives to enhance operational efficiency and maintain a competitive edge. Their ability to collaborate with AI vendors for customized solutions and to manage complex deployments further strengthens their dominant position in the market.
"Asia Pacific will likely witness rapid AI as a Service growth fueled by innovation and emerging technologies, while North America leads in market size"
Asia Pacific is projected to experience the fastest growth in the AI as a Service (AIaaS) market due to rapid digital transformation, increasing investments in AI technologies, and growing adoption of cloud-based services across India, China, and Southeast Asian nations. The region's expanding startup ecosystem and government initiatives supporting AI innovation fuel this growth. In contrast, North America will hold the largest market share, driven by the early adoption of advanced technologies, a strong presence of major AI vendors, and a mature cloud infrastructure. High R&D investments, robust digital ecosystems, and the widespread integration of AI across healthcare, finance, and retail sectors contribute to North America's market dominance.
Breakdown of primaries
In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the AI as a Service market.
- By Company: Tier I - 35%, Tier II - 45%, and Tier III - 20%
- By Designation: C-Level Executives - 35%, D-Level Executives - 25%, and others - 40%
- By Region: North America - 40%, Europe - 25%, Asia Pacific - 20%, Middle East & Africa - 10%, and Latin America - 5%
The report includes a study of key players offering AI as a Service solution and service. It profiles major vendors in the AI as a Service market. These include Microsoft (US), IBM (US), Google (US), AWS (US), OpenAI (US), NVIDIA (US), Salesforce (US), Oracle (US), SAP (Germany), FICO (US), Cloudera (US), ServiceNow (US), HPE (US), Altair (US), SAS Institute (US), DataRobot (US), Databricks (US), C3 AI (US), H2O.ai (US), Alibaba Cloud (China), Domo (US), Intellias (US), Mistral AI (France), Rainbird Technologies (UK), BigML (US), Yottamine Analytics (US), Scale AI (US), Landing AI (US), Synthesia (UK), Yellow.ai (US), Cohere (Canada), Anyscale (US), Abridge (US), Inflection AI (US), Glean (US), Codeium (US), Arthur (US), Levty AI (US), Unstructured.io (US), Clarifai (US), DeepSearch (Austria), Katonic AI (Australia), MindTitan (Estonia), Viso.ai (Switzerland), MonkeyLearn (US), and Softweb Solutions (US).
Study Coverage
This research report covers the AI as a Service market and has been segmented based on product type, organization size, business function, service type, and end user. The product type segment comprises chatbots & AI agents, machine learning frameworks, application programming interface (API), no-code or low-code ML tools, and data labeling & pre-processing tools. The organization size segment contains small and medium-sized enterprises and large enterprises. The business function segment is classified into finance, marketing, sales, operations & supply chain, and human resources. The service type segment includes machine learning as a service (MLaaS), natural language processing as a service (NLPaaS), computer vision as a service, predictive analytics and data science as a service (DSaaS), and generative AI as a service. The end user segment is split into enterprises and individual users.
The enterprise end users consist of media & entertainment, BFSI, healthcare & life sciences, manufacturing, retail & e-commerce, transportation & logistics, energy & utilities, government & defense, IT & ITeS, telecommunications, and other enterprise end users (travel & hospitality, education, and construction & real estate). The regional analysis of the digital transformation market covers North America, Europe, Asia Pacific, the Middle East & Africa (MEA), and Latin America. The report also contains a detailed analysis of AI as a service architecture, pricing models, regulatory landscape, ecosystem analysis, supply chain analysis, technology roadmap, and technology analysis.
Key Benefits of Buying the Report
The report would provide the market leaders/new entrants with information on the closest approximations of the revenue numbers for the overall AI as a Service market and its subsegments. It would help stakeholders understand the competitive landscape and gain better insights to position their business and plan suitable go-to-market strategies. It also helps stakeholders understand the market's pulse and provides information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
- Analysis of key drivers (AIaaS democratizes access for small and medium enterprises, growing demand for AI-enhanced cybersecurity solutions to combat sophisticated threats, and the rise of pre-trained AI models that require minimal customization accelerates AIaaS adoption), restraints (integration issues with legacy systems create inefficiencies, managing the environmental impact of energy-intensive AI computations and data centers, and high dependency on cloud providers hampers trust and hinders adoption), opportunities (emergence of federated learning techniques for collaborative AI model training, increasing demand for explainable AI (XAI) to enhance trust and transparency, and rising interest in quantum computing-based AI services for complex problem-solving), and challenges (balancing innovation with regulatory compliance, mitigating risks associated with AI model drift and maintaining model accuracy over time, and managing cost of high-performance AI infrastructure).
- Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the AI as a Service market.
- Market Development: Comprehensive information about lucrative markets - the report analyses the AI as a Service market across varied regions.
- Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the AI as a Service market.
- Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of leading players like Microsoft (US), IBM (US), Google (US), AWS (US), OpenAI (US), NVIDIA (US), Salesforce (US), Oracle (US), SAP (Germany), FICO (US), Cloudera (US), ServiceNow (US), HPE (US), Altair (US), SAS Institute (US), DataRobot (US), Databricks (US), C3 AI (US), H2O.ai (US), Alibaba Cloud (China), Domo (US), Intellias (US), Mistral AI (France), Rainbird Technologies (UK), BigML (US), Yottamine Analytics (US), Scale AI (US), Landing AI (US), Synthesia (UK), Yellow.ai (US), Cohere (Canada), Anyscale (US), Abridge (US), Inflection AI (US), Glean (US), Codeium (US), Arthur (US), Levty AI (US), Unstructured.io (US), Clarifai (US), DeepSearch (Austria), Katonic AI (Australia), MindTitan (Estonia), Viso.ai (Switzerland), MonkeyLearn (US), and Softweb Solutions (US), among others in the AI as a Service market. The report also helps stakeholders understand the pulse of the AI as a Service market and provides them with information on key market drivers, restraints, challenges, and opportunities.
TABLE OF CONTENTS
1 INTRODUCTION
- 1.1 STUDY OBJECTIVES
- 1.2 MARKET DEFINITION
- 1.3 STUDY SCOPE
- 1.3.1 MARKET SEGMENTATION AND REGIONAL SCOPE
- 1.3.2 INCLUSIONS AND EXCLUSIONS
- 1.3.3 YEARS CONSIDERED
- 1.4 CURRENCY CONSIDERED
- 1.5 STAKEHOLDERS
- 1.6 SUMMARY OF CHANGES
2 RESEARCH METHODOLOGY
- 2.1 RESEARCH DATA
- 2.1.1 SECONDARY DATA
- 2.1.2 PRIMARY DATA
- 2.1.2.1 Breakup of primary profiles
- 2.1.2.2 Key industry insights
- 2.2 MARKET BREAKUP AND DATA TRIANGULATION
- 2.3 MARKET SIZE ESTIMATION
- 2.3.1 TOP-DOWN APPROACH
- 2.3.2 BOTTOM-UP APPROACH
- 2.4 MARKET FORECAST
- 2.5 RESEARCH ASSUMPTIONS
- 2.6 STUDY LIMITATIONS
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
- 4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN AI AS A SERVICE MARKET
- 4.2 AI AS A SERVICE MARKET: TOP THREE SERVICE TYPES
- 4.3 NORTH AMERICA: AI AS A SERVICE MARKET, BY PRODUCT TYPE AND ENTERPRISE END USER
- 4.4 AI AS A SERVICE MARKET, BY REGION
5 MARKET OVERVIEW AND INDUSTRY TRENDS
- 5.1 INTRODUCTION
- 5.2 MARKET DYNAMICS
- 5.2.1 DRIVERS
- 5.2.1.1 Democratization of advanced technologies
- 5.2.1.2 Growing demand for AI-enhanced cybersecurity solutions to combat sophisticated threats
- 5.2.1.3 Surge in pre-trained AI models requiring minimal customization
- 5.2.2 RESTRAINTS
- 5.2.2.1 Integration issues with legacy systems
- 5.2.2.2 Environmental impact of energy-intensive AI computations and data centers
- 5.2.2.3 High dependency on cloud providers
- 5.2.3 OPPORTUNITIES
- 5.2.3.1 Emergence of federated learning techniques for collaborative AI model training
- 5.2.3.2 Increasing demand for explainable AI
- 5.2.3.3 Rising interest in quantum computing-based AI services for complex problem-solving
- 5.2.4 CHALLENGES
- 5.2.4.1 Balancing innovation with regulatory compliance
- 5.2.4.2 Mitigating risks associated with AI model drift and maintaining model accuracy over time
- 5.2.4.3 Managing cost of high-performance AI infrastructure
- 5.3 IMPACT OF 2025 US TARIFF - AI AS A SERVICE MARKET
- 5.3.1 INTRODUCTION
- 5.3.2 KEY TARIFF RATES
- 5.3.3 PRICE IMPACT ANALYSIS
- 5.3.3.1 Strategic Shifts and Emerging Trends
- 5.3.4 IMPACT ON COUNTRY/REGION
- 5.3.4.1 The US
- 5.3.4.2 Strategic Shifts and Key Observations
- 5.3.4.3 China
- 5.3.4.4 Strategic Shifts and Key Observations
- 5.3.4.5 Europe
- 5.3.4.6 Strategic Shifts and Key Observations
- 5.3.4.7 India
- 5.3.4.8 Strategic Shifts and Key Observations
- 5.3.5 IMPACT ON END-USE INDUSTRIES
- 5.3.5.1 Healthcare
- 5.3.5.2 Automotive
- 5.3.5.3 Finance
- 5.3.5.4 Manufacturing
- 5.3.5.5 Retail
- 5.4 AI AS A SERVICE MARKET: EVOLUTION
- 5.5 ECOSYSTEM ANALYSIS
- 5.5.1 CHATBOT & AI AGENT PROVIDERS
- 5.5.2 MACHINE LEARNING FRAMEWORK PROVIDERS
- 5.5.3 NO-CODE/LOW-CODE TOOL PROVIDERS
- 5.5.4 DATA PRE-PROCESSING TOOL PROVIDERS
- 5.5.5 API PROVIDERS
- 5.5.6 PUBLIC & MANAGED CLOUD PROVIDERS
- 5.6 SUPPLY CHAIN ANALYSIS
- 5.7 INVESTMENT LANDSCAPE AND FUNDING SCENARIO
- 5.8 CASE STUDY ANALYSIS
- 5.8.1 CASE STUDY 1: ADVANCED ANALYTICS AND VISUAL AI FOR ACCELERATING ION CHANNEL DRUG DISCOVERY
- 5.8.2 CASE STUDY 2: ELULA'S AI SOLUTIONS HELPED BANKS IMPROVE CUSTOMER RETENTION
- 5.8.3 CASE STUDY 3: NAMA RELIES ON GOOGLE CLOUD TO FURTHER GENERATIVE AI AND BECOME MORE STRATEGIC BUSINESS
- 5.8.4 CASE STUDY 4: IMPROVING CUSTOMER SERVICE AND FRAUD DETECTION WITH IBM AIAAS
- 5.8.5 CASE STUDY 5: AUTOMATING SUPPORT REQUEST TRIAGE WITH SALESFORCE AIAAS
- 5.8.6 CASE STUDY 6: MICROSOFT AZURE AIAAS EMPOWERED ALASKA AIRLINES TO OPTIMIZE ON-TIME PERFORMANCE WITH PREDICTIVE MAINTENANCE
- 5.9 TECHNOLOGY ANALYSIS
- 5.9.1 KEY TECHNOLOGIES
- 5.9.1.1 Generative AI
- 5.9.1.2 Machine Learning
- 5.9.1.3 Conversational AI
- 5.9.1.4 Cloud Computing
- 5.9.1.5 Natural Language Processing (NLP)
- 5.9.2 COMPLEMENTARY TECHNOLOGIES
- 5.9.2.1 Cognitive Computing
- 5.9.2.2 Big Data Analytics
- 5.9.2.3 Robotic Process Automation (RPA)
- 5.9.3 ADJACENT TECHNOLOGIES
- 5.9.3.1 Quantum Computing
- 5.9.3.2 Internet of Things (IoT)
- 5.9.3.3 Cybersecurity
- 5.10 REGULATORY LANDSCAPE
- 5.10.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
- 5.10.2 REGULATIONS BY REGION
- 5.10.2.1 North America
- 5.10.2.1.1 SCR 17: Artificial Intelligence Bill (California)
- 5.10.2.1.2 SB 1103: Artificial Intelligence Automated Decision Bill (Connecticut)
- 5.10.2.1.3 National Artificial Intelligence Initiative Act (NAIIA)
- 5.10.2.1.4 The Artificial Intelligence and Data Act (AIDA) - Canada
- 5.10.2.2 Europe
- 5.10.2.2.1 The European Union (EU) - Artificial Intelligence Act (AIA)
- 5.10.2.2.2 General Data Protection Regulation (Europe)
- 5.10.2.3 Asia Pacific
- 5.10.2.3.1 Interim Administrative Measures for Generative Artificial Intelligence Services (China)
- 5.10.2.3.2 The National AI Strategy (Singapore)
- 5.10.2.3.3 The Hiroshima AI Process Comprehensive Policy Framework (Japan)
- 5.10.2.4 Middle East & Africa
- 5.10.2.4.1 The National Strategy for Artificial Intelligence (UAE)
- 5.10.2.4.2 The National Artificial Intelligence Strategy (Qatar)
- 5.10.2.4.3 The AI Ethics Principles and Guidelines (Dubai)
- 5.10.2.5 Latin America
- 5.10.2.5.1 The Santiago Declaration (Chile)
- 5.10.2.5.2 The Brazilian Artificial Intelligence Strategy (EBIA)
- 5.11 ARCHITECTURE: AI AS A SERVICE
- 5.11.1 AI INFRASTRUCTURE
- 5.11.2 AI SERVICES
- 5.11.3 AI TOOLS
- 5.12 PATENT ANALYSIS
- 5.12.1 METHODOLOGY
- 5.12.2 PATENTS FILED, BY DOCUMENT TYPE
- 5.12.3 INNOVATION AND PATENT APPLICATIONS
- 5.13 PRICING ANALYSIS
- 5.13.1 AVERAGE SELLING PRICE TREND OF KEY PLAYERS, BY SERVICE TYPE, 2025
- 5.13.2 AVERAGE SELLING PRICE TREND, BY PRODUCT TYPE, 2025
- 5.14 KEY CONFERENCES AND EVENTS, 2025-2026
- 5.15 PORTER'S FIVE FORCES ANALYSIS
- 5.15.1 THREAT OF NEW ENTRANTS
- 5.15.2 THREAT OF SUBSTITUTES
- 5.15.3 BARGAINING POWER OF SUPPLIERS
- 5.15.4 BARGAINING POWER OF BUYERS
- 5.15.5 INTENSITY OF COMPETITIVE RIVALRY
- 5.16 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
- 5.16.1 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
- 5.17 KEY STAKEHOLDERS & BUYING CRITERIA
- 5.17.1 KEY STAKEHOLDERS IN BUYING PROCESS
- 5.17.2 BUYING CRITERIA
6 AI AS A SERVICE MARKET, BY PRODUCT TYPE
- 6.1 INTRODUCTION
- 6.1.1 PRODUCT TYPE: AI AS A SERVICE MARKET DRIVERS
- 6.2 CHATBOTS & AI AGENTS
- 6.2.1 GROWING INTEGRATION OF AI TOOLS INTO CRM SYSTEMS TO ADOPT AI-DRIVEN CONVERSATIONAL SOLUTIONS
- 6.3 MACHINE LEARNING FRAMEWORKS
- 6.3.1 WIDE USE OF OPEN-SOURCE FRAMEWORKS SUCH AS TENSORFLOW, PYTORCH, AND SCIKIT-LEARN TO DRIVE MARKET
- 6.4 APPLICATION PROGRAMMING INTERFACE
- 6.4.1 RISING NEED FOR EFFICIENT METHODS THAT INTERACT WITH AI SERVICES TO INCORPORATE ADVANCED AI TECHNOLOGIES
- 6.5 NO-CODE OR LOW-CODE ML TOOLS
- 6.5.1 RISING DEMAND FOR EASY-TO-USE INTERFACES AND VISUAL WORKFLOWS TO PROMOTE USE OF AI IN DIFFERENT VERTICALS
- 6.6 DATA LABELING & PRE-PROCESSING TOOLS
- 6.6.1 NEED FOR TRANSFORMING RAW DATA INTO ANNOTATED DATASETS TO BOOST DEMAND FOR DATA LABELING TOOLS
7 AI AS A SERVICE MARKET, BY ORGANIZATION SIZE
- 7.1 INTRODUCTION
- 7.1.1 ORGANIZATION SIZE: AI AS A SERVICE MARKET DRIVERS
- 7.2 SMALL & MEDIUM-SIZED ENTERPRISES
- 7.2.1 USE OF GENERATIVE AIAAS IN AUTOMATING CUSTOMER SERVICE OR ANALYZING HUGE DATASETS TO DRIVE MARKET
- 7.3 LARGE ENTERPRISES
- 7.3.1 QUICK DEPLOYMENT AND INTEGRATION OF AI CAPABILITIES FOR LARGE ENTERPRISES TO DRIVE MARKET
8 AI AS A SERVICE MARKET, BY BUSINESS FUNCTION
- 8.1 INTRODUCTION
- 8.1.1 BUSINESS FUNCTION: AI AS A SERVICE MARKET DRIVERS
- 8.2 FINANCE
- 8.2.1 AI TO RESHAPE FINANCIAL SECTOR BY AUTOMATING TASKS AND ENHANCING COMPLIANCE WITH ADVANCED DATA ANALYSIS
- 8.3 MARKETING
- 8.3.1 AI TO REVOLUTIONIZE MARKETING TRENDS THROUGH HYPER-PERSONALIZATION AND PREDICTIVE ANALYTICS
- 8.4 SALES
- 8.4.1 AIAAS PLATFORMS TO OFFER IMMEDIATE UNDERSTANDING OF CUSTOMER ACTIONS TO CUSTOMIZE SALES
- 8.5 OPERATIONS & SUPPLY CHAIN
- 8.5.1 AI-DRIVEN PREDICTIVE ANALYSIS TO RECOGNIZE POSSIBLE INTERRUPTIONS AND RESTRICTIONS IN SUPPLY NETWORK
- 8.6 HUMAN RESOURCES
- 8.6.1 AI PROGRAMS ANTICIPATE SKILL DEFICIENCIES AND DETECT POSSIBLE TURNOVER CONCERNS
9 AI AS A SERVICE MARKET, BY SERVICE TYPE
- 9.1 INTRODUCTION
- 9.1.1 SERVICE TYPE: AI AS A SERVICE MARKET DRIVERS
- 9.2 MACHINE LEARNING AS A SERVICE (MLAAS)
- 9.2.1 USERS CAN LEVERAGE MLAAS PLATFORMS TO CREATE PREDICTIVE MODELS, TAKING ADVANTAGE OF SCALABILITY AND FLEXIBILITY
- 9.2.2 DATA PREPARATION AND PREPROCESSING
- 9.2.3 MODEL DEVELOPMENT AND TRAINING
- 9.2.4 MODEL DEPLOYMENT AND MANAGEMENT
- 9.2.5 MODEL EVALUATION AND TESTING
- 9.2.6 RECOMMENDATION SERVICES
- 9.2.7 OTHERS IN MACHINE LEARNING AS A SERVICE
- 9.3 NATURAL LANGUAGE PROCESSING AS A SERVICE (NLPAAS)
- 9.3.1 GROWING DEPENDENCE ON DATA-BASED DECISION-MAKING AND REQUIREMENT FOR EFFECTIVE COMMUNICATION TO FUEL DEMAND FOR NLPAAS
- 9.3.2 SPEECH RECOGNITION
- 9.3.3 SEMANTIC SEARCH
- 9.3.4 SENTIMENT ANALYSIS
- 9.3.5 VOICE RECOGNITION
- 9.3.6 TEXT-TO-SPEECH (TTS)
- 9.3.7 OTHERS IN NATURAL LANGUAGE PROCESSING AS A SERVICE
- 9.4 COMPUTER VISION AS A SERVICE
- 9.4.1 COMPUTER VISION TO USE COMPLEX ALGORITHMS AND ML FRAMEWORKS WITHOUT IN-HOUSE INFRASTRUCTURE OR EXPERTISE
- 9.4.2 IMAGE RECOGNITION
- 9.4.3 FACE RECOGNITION
- 9.4.4 VIDEO ANALYTICS
- 9.4.5 OBJECT DETECTION
- 9.4.6 OTHERS IN COMPUTER VISION AS A SERVICE
- 9.5 PREDICTIVE ANALYTICS AND DATA SCIENCE AS A SERVICE (DSAAS)
- 9.5.1 DSAAS TO SUPPORT PREDICTIVE ANALYTICS BY PROVIDING ADVANCED ANALYTICAL CAPABILITIES THAT DO NOT REQUIRE INTERNAL EXPERTISE
- 9.5.2 OPERATIONAL INTELLIGENCE
- 9.5.3 SUPPLY CHAIN ANALYTICS
- 9.5.4 PREDICTIVE MAINTENANCE
- 9.5.5 RISK MANAGEMENT
- 9.5.6 OTHERS IN PREDICTIVE ANALYTICS AND DATA SCIENCE AS A SERVICE
- 9.6 GENERATIVE AI AS A SERVICE
- 9.6.1 USE OF DATA AUGMENTATION, UTILIZING AI-CREATED SAMPLES TO IMPROVE TRAINING DATASETS FOR ML MODELS
- 9.6.2 CODE GENERATION & SOFTWARE DEVELOPMENT
- 9.6.3 CONTENT CREATION
- 9.6.4 FRAUD DETECTION
- 9.6.5 CONTENT MODERATION
- 9.6.6 DATA EXTRACTION
- 9.6.7 OTHERS IN GENERATIVE AI AS A SERVICE
10 AI AS A SERVICE MARKET, BY END USER
- 10.1 INTRODUCTION
- 10.1.1 END USER: AI AS A SERVICE MARKET DRIVERS
- 10.2 ENTERPRISES
- 10.2.1 BFSI
- 10.2.1.1 AIaaS and blockchain to create secure and transparent transactions
- 10.2.2 RETAIL & E-COMMERCE
- 10.2.2.1 Advancements in machine learning and natural language processing to drive retail & e-commerce market
- 10.2.3 TECHNOLOGY & SOFTWARE
- 10.2.3.1 AIaaS to enable technology firms to rapidly test new concepts and applications by offering pre-built algorithms and models
- 10.2.3.2 IT & ITeS
- 10.2.3.3 Software development companies
- 10.2.3.4 Other technology & software
- 10.2.4 MEDIA & ENTERTAINMENT
- 10.2.4.1 Use of ML algorithms to analyze viewer preferences and behaviors to provide personalized content suggestions
- 10.2.5 MANUFACTURING
- 10.2.5.1 Predictive maintenance capability to significantly reduce downtime and maintenance costs
- 10.2.6 HEALTHCARE & LIFE SCIENCES
- 10.2.6.1 AIaaS to help address critical challenges in patient care, diagnostics, and drug development
- 10.2.7 ENERGY & UTILITIES
- 10.2.7.1 Data obtained from sensors and smart meters to allow energy suppliers determine system inefficiencies
- 10.2.8 GOVERNMENT & DEFENSE
- 10.2.8.1 AI algorithms to detect potential threats and emerging patterns using large amounts of data from different sources
- 10.2.9 TELECOMMUNICATIONS
- 10.2.9.1 AI-powered analysis to help understand customer preferences and behaviors by using advanced ML models
- 10.2.10 TRANSPORTATION & LOGISTICS
- 10.2.10.1 Examining traffic patterns, weather conditions, and delivery windows to enhance fleet management
- 10.2.11 OTHER ENTERPRISE END USERS
- 10.3 INDIVIDUAL USERS
11 AI AS A SERVICE MARKET, BY REGION
- 11.1 INTRODUCTION
- 11.2 NORTH AMERICA
- 11.2.1 NORTH AMERICA: AI AS A SERVICE MARKET DRIVERS
- 11.2.2 NORTH AMERICA: MACROECONOMIC OUTLOOK
- 11.2.3 US
- 11.2.3.1 US AIaaS market continues to grow with strong institutional backing and technical advancement
- 11.2.4 CANADA
- 11.2.4.1 Canada's strategic growth in AIaaS market: Innovation, investment, and ethical leadership
- 11.3 EUROPE
- 11.3.1 EUROPE: AI AS A SERVICE MARKET DRIVERS
- 11.3.2 EUROPE: MACROECONOMIC OUTLOOK
- 11.3.3 UK
- 11.3.3.1 UK's leadership in AIaaS Market: Innovation, safety, and sustainable growth
- 11.3.4 GERMANY
- 11.3.4.1 Germany's focus on ethical AI practices positions it well for continued growth in AIaaS market
- 11.3.5 FRANCE
- 11.3.5.1 France's emphasis on ethical AI practices and regulatory frameworks to foster trust among businesses and consumers
- 11.3.6 ITALY
- 11.3.6.1 Comprehensive AI strategy to balance opportunities presented by AI technologies
- 11.3.7 SPAIN
- 11.3.7.1 Transformative potential of AI to drive market
- 11.3.8 REST OF EUROPE
- 11.4 ASIA PACIFIC
- 11.4.1 ASIA PACIFIC: AI AS A SERVICE MARKET DRIVERS
- 11.4.2 ASIA PACIFIC: MACROECONOMIC OUTLOOK
- 11.4.3 CHINA
- 11.4.3.1 Use of Nvidia chips via Azure and Google Cloud highlights its ability to leverage global resources
- 11.4.4 INDIA
- 11.4.4.1 Growth of AIaaS market in India driven by combination of government initiatives and technological innovation
- 11.4.5 JAPAN
- 11.4.5.1 Incorporation of cutting-edge technologies and solid government backing for modernization to drive market
- 11.4.6 SOUTH KOREA
- 11.4.6.1 Development and distribution of AI outlining guidelines for safe and ethical use of AI technologies to drive market
- 11.4.7 AUSTRALIA & NEW ZEALAND
- 11.4.7.1 Initiatives promoting innovation and ethical practices to foster environment conducive to sustainable AI development
- 11.4.8 SINGAPORE
- 11.4.8.1 Singapore government investment to support initiatives that uplift various sectors by integrating AI
- 11.4.9 REST OF ASIA PACIFIC
- 11.5 MIDDLE EAST & AFRICA
- 11.5.1 MIDDLE EAST & AFRICA: AI AS A SERVICE MARKET DRIVERS
- 11.5.2 MIDDLE EAST & AFRICA: MACROECONOMIC OUTLOOK
- 11.5.3 MIDDLE EAST
- 11.5.3.1 Saudi Arabia
- 11.5.3.1.1 Need for fostering innovation, attracting international talent, and creating robust regulatory framework to drive market
- 11.5.3.2 UAE
- 11.5.3.2.1 Growing demand for AIaaS by driving economic growth to address societal challenges through innovative AI applications
- 11.5.3.3 QATAR
- 11.5.3.3.1 Integration of AI technologies across various sectors to boost market
- 11.5.3.4 Turkey
- 11.5.3.4.1 International collaborations to enhance economic growth
- 11.5.3.5 Rest of Middle East
- 11.5.4 AFRICA
- 11.6 LATIN AMERICA
- 11.6.1 LATIN AMERICA: AI AS A SERVICE MARKET DRIVERS
- 11.6.2 LATIN AMERICA: MACROECONOMIC OUTLOOK
- 11.6.3 BRAZIL
- 11.6.3.1 Brazilian government to unveil various initiatives to accelerate AI development
- 11.6.4 MEXICO
- 11.6.4.1 Vibrant startup ecosystem and increasing collaboration between government and private enterprises to drive market
- 11.6.5 ARGENTINA
- 11.6.5.1 Rising investments to promote AI and technological innovation to drive market
- 11.6.6 REST OF LATIN AMERICA
12 COMPETITIVE LANDSCAPE
- 12.1 OVERVIEW
- 12.2 KEY PLAYER STRATEGIES/RIGHT TO WIN, 2022-2025
- 12.3 REVENUE ANALYSIS, 2020-2024
- 12.4 MARKET SHARE ANALYSIS, 2024
- 12.5 PRODUCT COMPARATIVE ANALYSIS
- 12.5.1 PRODUCT COMPARATIVE ANALYSIS, BY AI AS A SERVICE MARKET
- 12.6 COMPANY VALUATION AND FINANCIAL METRICS
- 12.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2024
- 12.7.1 STARS
- 12.7.2 EMERGING LEADERS
- 12.7.3 PERVASIVE PLAYERS
- 12.7.4 PARTICIPANTS
- 12.7.5 COMPANY FOOTPRINT: KEY PLAYERS, 2024
- 12.7.5.1 Company footprint
- 12.7.5.2 Region footprint
- 12.7.5.3 Business function footprint
- 12.7.5.4 Product type footprint
- 12.7.5.5 End user footprint
- 12.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2024
- 12.8.1 PROGRESSIVE COMPANIES
- 12.8.2 RESPONSIVE COMPANIES
- 12.8.3 DYNAMIC COMPANIES
- 12.8.4 STARTING BLOCKS
- 12.8.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2024
- 12.8.5.1 Detailed list of key startups/SMEs
- 12.8.5.2 Competitive benchmarking of key startups/SMEs
- 12.9 COMPETITIVE SCENARIO AND TRENDS
- 12.9.1 PRODUCT LAUNCHES/ENHANCEMENTS
- 12.9.2 DEALS
13 COMPANY PROFILES
- 13.1 INTRODUCTION
- 13.2 KEY PLAYERS
- 13.2.1 AWS
- 13.2.1.1 Business overview
- 13.2.1.2 Products/Solutions/Services offered
- 13.2.1.3 Recent developments
- 13.2.1.3.1 Product launches and enhancements
- 13.2.1.3.2 Deals
- 13.2.1.4 MnM view
- 13.2.1.4.1 Right to win
- 13.2.1.4.2 Strategic choices made
- 13.2.1.4.3 Weaknesses and competitive threats
- 13.2.2 GOOGLE
- 13.2.2.1 Business overview
- 13.2.2.2 Products/Solutions/Services offered
- 13.2.2.3 Recent developments
- 13.2.2.3.1 Product enhancements
- 13.2.2.3.2 Deals
- 13.2.2.4 MnM view
- 13.2.2.4.1 Right to win
- 13.2.2.4.2 Strategic choices made
- 13.2.2.4.3 Weaknesses and competitive threats
- 13.2.3 MICROSOFT
- 13.2.3.1 Business overview
- 13.2.3.2 Products/Solutions/Services offered
- 13.2.3.3 Recent developments
- 13.2.3.3.1 Product enhancements
- 13.2.3.3.2 Deals
- 13.2.3.4 MnM view
- 13.2.3.4.1 Right to win
- 13.2.3.4.2 Strategic choices made
- 13.2.3.4.3 Weaknesses and competitive threats
- 13.2.4 IBM
- 13.2.4.1 Business overview
- 13.2.4.2 Products/Solutions/Services offered
- 13.2.4.3 Recent developments
- 13.2.4.3.1 Product enhancements
- 13.2.4.3.2 Deals
- 13.2.4.4 MnM view
- 13.2.4.4.1 Right to win
- 13.2.4.4.2 Strategic choices made
- 13.2.4.4.3 Weaknesses and competitive threats
- 13.2.5 ORACLE
- 13.2.5.1 Business overview
- 13.2.5.2 Products/Solutions/Services offered
- 13.2.5.3 Recent developments
- 13.2.5.3.1 Product enhancements
- 13.2.5.3.2 Deals
- 13.2.5.4 MnM view
- 13.2.5.4.1 Right to win
- 13.2.5.4.2 Strategic choices made
- 13.2.5.4.3 Weaknesses and competitive threats
- 13.2.6 SAP
- 13.2.6.1 Business overview
- 13.2.6.2 Products/Solutions/Services offered
- 13.2.6.3 Recent developments
- 13.2.6.3.1 Product Enhancements
- 13.2.6.3.2 Deals
- 13.2.7 SALESFORCE
- 13.2.7.1 Business overview
- 13.2.7.2 Products/Solutions/Services offered
- 13.2.7.3 Recent developments
- 13.2.7.3.1 Product enhancements
- 13.2.7.3.2 Deals
- 13.2.8 NVIDIA
- 13.2.8.1 Business overview
- 13.2.8.2 Products/Solutions/Services offered
- 13.2.8.3 Recent developments
- 13.2.8.3.1 Product enhancements
- 13.2.8.3.2 Deals
- 13.2.9 ALIBABA CLOUD
- 13.2.10 OPENAI
- 13.2.11 RAINBIRD TECHNOLOGIES
- 13.2.12 BIGML
- 13.2.13 COHERE
- 13.2.14 GLEAN
- 13.2.15 SCALE AI
- 13.2.16 LANDING AI
- 13.2.17 YELLOW.AI
- 13.2.18 ANYSCALE
- 13.2.19 MISTRAL AI
- 13.2.20 H20.AI
- 13.2.21 SYNTHESIA
- 13.2.22 CLARIFAI
- 13.2.23 MONKEYLEARN
- 13.3 OTHER PLAYERS
- 13.3.1 FICO
- 13.3.1.1 Business overview
- 13.3.1.2 Products/Solutions/Services Offered
- 13.3.1.3 Recent developments
- 13.3.1.3.1 Product Enhancements
- 13.3.1.3.2 Deals
- 13.3.2 CLOUDERA
- 13.3.2.1 Business overview
- 13.3.2.2 Products/Solutions/Services offered
- 13.3.2.3 Recent developments
- 13.3.2.3.1 Product Enhancements
- 13.3.2.3.2 Deals
- 13.3.3 SERVICENOW
- 13.3.4 HPE
- 13.3.5 ALTAIR
- 13.3.6 SAS INSTITUTE
- 13.3.7 DATAROBOT
- 13.3.8 DATABRICKS
- 13.3.9 C3 AI
- 13.3.10 DOMO
- 13.3.11 INTELLIAS
- 13.3.12 YOTTAMINE ANALYTICS
- 13.3.13 INFLECTION AI
- 13.3.14 ABRIDGE
- 13.3.15 CODEIUM
- 13.3.16 ARTHUR
- 13.3.17 LEVITY AI
- 13.3.18 UNSTRUCTURED.IO
- 13.3.19 KATONIC AI
- 13.3.20 DEEPSEARCH
- 13.3.21 MINDTITAN
- 13.3.22 VISO.AI
- 13.3.23 SOFTWEB SOLUTIONS
14 ADJACENT AND RELATED MARKETS
- 14.1 INTRODUCTION
- 14.2 ARTIFICIAL INTELLIGENCE (AI) MARKET - GLOBAL FORECAST TO 2030
- 14.2.1 MARKET DEFINITION
- 14.2.2 MARKET OVERVIEW
- 14.2.2.1 Artificial Intelligence Market, by Offering
- 14.2.2.2 Artificial Intelligence Market, by Technology
- 14.2.2.3 Artificial Intelligence Market, by Business Function
- 14.2.2.4 Artificial Intelligence Market, by Vertical
- 14.2.2.5 Artificial Intelligence Market, by Region
- 14.3 GENERATIVE AI MARKET- GLOBAL FORECAST TO 2030
- 14.3.1 MARKET DEFINITION
- 14.3.2 MARKET OVERVIEW
- 14.3.2.1 Generative AI Market, by Offering
- 14.3.2.2 Generative AI Market, by Application
- 14.3.2.3 Generative AI Market, by Vertical
- 14.3.2.4 Generative AI Market, by Region
15 APPENDIX
- 15.1 DISCUSSION GUIDE
- 15.2 KNOWLEDGESTORE: MARKETSANDMARKETS' SUBSCRIPTION PORTAL
- 15.3 CUSTOMIZATION OPTIONS
- 15.4 RELATED REPORTS
- 15.5 AUTHOR DETAILS
