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AI as a Service Market

Report Code TC 6185
Published in Apr, 2025, By MarketsandMarkets™
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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

US Tariff Impact on AI as a Service Market

Trump Tariffs Are Reshaping Global Business

 

Overview

The AI as a service (AIaaS) market is expanding rapidly, with a projected market size anticipated to rise from about USD 20.26 billion in 2025 to USD 91.20 billion by 2030, featuring a CAGR of 35.1%. The AIaaS market is swiftly growing, revolutionizing industries with affordable and scalable AI offerings. Regulatory organizations such as the European Commission and the US FTC influence the market by advancing ethical standards and fostering innovation. Over 30% of organizations utilize AI services to tackle workforce gaps, whereas over 75% prefer low-code platforms to liberate developer time. AI-supported systems have enhanced case resolution rates by 30–40%, showcasing significant efficiency improvements. The product type segment accelerates AI adoption by making advanced capabilities more accessible across industries. This variety enables businesses of all sizes to integrate AI seamlessly, driving growth and innovation in the AI as a Service market.

AI as a Service Market

Attractive Opportunities in the AI as a Service Market

ASIA PACIFIC

Asia Pacific is projected to have the fastest growth rate in the AI as a Service market due to rapid digital transformation, increasing government support for AI initiatives, and surging demand from e-commerce and smart cities. The region's expanding IT infrastructure and growing investment in AI technologies further drive adoption across industries.

Technological advancements in the AI as a Service market include enhanced cloud-based platforms, improved machine learning algorithms, and AI-driven automation tools, enabling faster data processing, real-time analytics, and more scalable, cost-effective solutions for businesses across industries.

Partnerships, collaborations, and product launches would offer lucrative opportunities for market players in the next five years.

Machine learning (ML) frameworks are expected to see healthy growth in the AI as a Service market due to increasing demand for customizable, scalable AI solutions. Businesses rely on ML frameworks for data-driven insights, automation, and improved decision-making.

The global AI as a Service market is experiencing rapid growth, driven by increasing adoption across industries like healthcare, finance, and retail. Factors such as digital transformation and cloud infrastructure expansion contributes to its widespread market expansion.

Global AI as a Service Market Dynamics

Driver: Increasing need for pre-trained AI models requiring minimal customization

The AI as a Service (AIaaS) market is experiencing significant growth, fueled primarily by the growing availability and complexity of pre-trained AI models. These models, which are optimized to perform tasks such as natural language processing, image recognition, and predictive analytics, are dramatically reducing the entry barriers that have long been associated with AI adoption. Pre-trained AI models provide integrated features that demand little customization, allowing organizations to adopt AI-driven solutions without needing extensive technical knowledge or prolonged development periods. This plug-and-play method improves accessibility, enabling businesses of any size to leverage advanced AI features with lower investment and quicker returns.

One of the most significant strengths of AIaaS is that it can be rapidly deployed at a low cost, which is particularly useful in today’s dynamic digital landscape where agility and efficiency are paramount. The lower training and fine-tuning requirements also help to facilitate quicker implementation and a greater return on investment (ROI). Consequently, the appeal of AIaaS is growing in healthcare, finance, and retail industries, where businesses are identifying the revolutionary capability of these intelligent solutions to transform operational enhancements, customer interactions, and data-based decision-making.

Restraint: Limited AI model explainability & transparency

Despite the growing adoption of AI as a Service, a significant barrier hindering its wider implementation is the limited explainability and transparency of many AI models, particularly in high-stakes and heavily regulated industries such as finance, healthcare, and legal services. Many AIaaS offerings function as black-box systems, where the internal decision-making logic is too complex or insufficiently documented to be easily understood by end users. This opacity generates serious concerns around algorithmic bias, accountability, and regulatory compliance, with the increasing stringency of global frameworks such as the EU AI Act, which emphasizes transparency and ethical AI use.

For organizations that depend on AI-generated insights for critical decision-making, a lack of transparency in the reasoning behind these recommendations poses a significant barrier to trust and wider adoption. As a result, AIaaS companies focus on explainable AI (XAI), model interpretability tools, and ethical transparency frameworks. Hence, striking a balance between high-performing and accurate models while also being easy for users to interpret continues to be a difficult challenge. Consequently, restricted transparency remains a significant hindrance to the growth and broad confidence in AIaaS offerings within sensitive industry sectors.

 

Opportunity: Expansion of AI marketplaces & Plug-and-Play AI models

The rise and swift growth of AI marketplaces, along with the increasing accessibility of off-the-shelf AI models, is transforming the AIaaS market by making advanced AI functionalities available to companies of all sizes. These marketplaces act as centralized hubs where businesses can effortlessly explore, tailor, and implement pre-trained AI models without requiring internal data science teams or extensive technical expertise. This change significantly affects small and medium-sized enterprises, as they frequently do not have the financial and human resources needed for comprehensive AI development.

AI marketplaces are slashing deployment times and enhancing operational efficiency by offering plug-and-play solutions for applications such as natural language processing, predictive analytics, and computer vision. As industries increasingly seek AI tools tailored to their specific needs, vendors are responding by creating modular, domain-specific AI models that integrate seamlessly into existing systems and workflows. Additionally, AIaaS suppliers focus on user-friendliness and compatibility, facilitating seamless integration across various IT ecosystems. This trend hastened the widespread acceptance of AI, making AI marketplaces an essential facilitator of AI democratization and a catalyst for the digital transformation of today’s businesses..

Challenge: Managing costs associated with maintaining and scaling high-performance AI infrastructure for service delivery

AI as a Service (AIaaS) providers face serious challenges when managing the hefty costs of maintaining and scaling the complex infrastructure needed for advanced AI capabilities. Running high-performance AI models requires powerful GPUs, specialized hardware, and a ton of computational resources, all driving up operational expenses. Energy consumption is another big issue, as handling large-scale AI workloads demands a constant power supply, leading to high electricity bills and environmental concerns. With clients expecting quicker, more accurate, and scalable AI solutions, providers are pressured to invest in cutting-edge computing infrastructure, robust cloud environments, and energy-efficient technologies.

Adding to these challenges is the relentless pace of innovation in the AI sector, which requires regular upgrades and reinvestments in infrastructure just to stay competitive. This dynamic increases capital expenditures, making it a constant juggling act between profitability and sustainability. To tackle these challenges, AIaaS vendors seek cost-effective strategies, like optimizing resource use, adopting scalable cloud architectures, and integrating renewable energy sources to lessen their carbon footprints. These initiatives are crucial for managing costs and aligning with the broader movement toward sustainable and efficient AI service delivery.

Global AI as a Service Market Ecosystem Analysis

The AI as a service market ecosystem comprises a diverse range of stakeholders. Key players include chatbot & AI agent providers, machine learning framework providers, no-code/low-code tool providers, data labeling & pre-processing tool providers, API providers, and public and managed cloud providers. These entities collaborate to develop, deliver, and utilize AI solutions, driving innovation and growth in the AI industry.

Top Companies in AI as a Service Market

Note: The above diagram only shows the representation of the AI as a Service Market ecosystem; it is not limited to the companies represented above.
Source: Secondary Research and MarketsandMarkets Analysis

 

Machine learning framework product type segment to hold largest market share during forecast period

Machine learning frameworks dominate the AIaaS market since they make AI model development and deployment processes streamlined and less cumbersome. Frameworks such as TensorFlow and PyTorch provide pre-made libraries that eliminate the necessity for extensive knowledge in AI for businesses in various sectors. The ability of the ML framework to scale and handle different tasks, such as data analysis and natural language processing, makes it a flexible option. Being open-source increases innovation and collaboration, which is a factor that ensures speedy adoption and further development, thus leading to market share in the AIaaS landscape. These frameworks act as the backbone for numerous AI-as-a-Service (AIaaS) solutions, empowering service providers to craft highly flexible and efficient offerings that address various business needs.

Developers can swiftly experiment, test, and launch models due to their modular design, which significantly cuts down the time it takes to get AI services out into the market. Additionally, the strong community backing and ongoing updates enhance their value even further. As businesses aim to tap into AI without building everything from the ground up, these frameworks provide a budget-friendly and trustworthy alternative. The influence of machine learning frameworks in the AIaaS space is set to keep growing due to their role in speeding up innovation, lowering technical barriers, and improving the entire AI development lifecycle.

Generative AI as a service segment to account for fastest growth rate during forecast period

Generative AI as a service is anticipated to see the most rapid growth due to its ability to generate content, designs, and simulations with minimal human involvement. This type of service is growing in various sectors, such as media, entertainment, and marketing, to produce text, images, music, and videos, improving creativity and customization. The increasing need for automation in content creation, customer engagement, and product development is fueling its implementation. Moreover, generative AI helps with fast prototyping and product innovation, giving companies a competitive advantage and driving their rapid growth in the AIaaS industry. Generative AI presents a powerful solution by automating tasks that once required a lot of manual effort.

Its capacity to personalize content on a large scale makes it particularly appealing for customer-facing roles, where customized interactions can significantly improve brand engagement and conversion rates. Additionally, generative AI tools aid in iterative design processes, enabling teams to explore various creative options quickly. With ongoing advancements in technology and broader integration across platforms, generative AI is poised to be a transformative force in the AIaaS landscape, speeding up digital innovation and changing how businesses approach creative production and customer engagement.

Asia Pacific set to experience fastest growth during forecast period

The Asia Pacific region is set to achieve the highest growth rate in the AIaaS market due to swift digital evolution throughout various sectors, as China, India, and Japan make significant investments in AI infrastructure and cloud technology. The emergence of smart cities and AI-driven manufacturing in China, alongside India’s flourishing IT industry and increasing use of AI in healthcare and finance, drives the demand for AIaaS solutions. Furthermore, government efforts such as Japan’s “Society 5.0” and India’s AI strategy are hastening AI research and its commercialization.

Additionally, the extensive tech-literate workforce in the area and the increasing integration of AI in SMEs encourage the use of plug-and-play AI models and marketplace solutions. With the progression of internet connectivity and the growth of cloud infrastructure, the Asia Pacific region is set for swift AIaaS development. These dynamics are bolstered by regional collaboration efforts, public-private partnerships, and supportive regulatory frameworks encouraging innovation and cross-border technological advancement. As the region increasingly focuses on automation, digital services, and scalable AI solutions, Asia Pacific is positioning itself as a strategic center for AIaaS providers.

HIGHEST CAGR MARKET TILL 2030
INDIA FASTEST GROWING MARKET IN THE REGION
AI as a Service Market by region

Recent Developments of AI as a Service Market

  • In April 2025, IBM announced the acquisition of Hakkoda Inc., a leading global data and AI consultancy. Hakkoda will expand IBM Consulting’s data transformation services portfolio, adding specialized data platform expertise to help clients get their data ready to fuel AI-powered business operations.
  • In March 2025, Publicis Sapient announced a global strategic collaboration agreement with Amazon Web Services to accelerate enterprise IT modernization. Publicis Sapient will leverage AWS’s advanced generative AI services to help enterprises accelerate their digital business transformation journeys and more easily build personalized campaigns and experiences to reduce churn and enhance customer loyalty.
  • In January 2025, Pearson and Microsoft announced a multiyear partnership to transform learning and workforce development using AI. The partnership will integrate AI-powered products and services into Pearson’s offerings to enhance skilling and learning for employers, workers, and learners.
  • In January 2025, AWS and General Catalyst announced a collaboration to transform healthcare using AI. This collaboration aims to develop and deploy AI-powered solutions addressing critical needs in predictive and personalized care, interoperability, operational and clinical efficiency, diagnostics, and patient engagement.

Key Market Players

List of Top AI as a Service Market Companies

The AI as a Service Market is dominated by a few major players that have a wide regional presence. The major players in the AI as a Service Market are

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Scope of the Report

Report Attribute Details
Market size available for years 2020–2030
Base year considered 2024
Forecast period 2025–2030
Forecast units USD (Million)
Segments Covered 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

Key Questions Addressed by the Report

What is AI as a service?
AI as a service (AIaaS) is a cloud-based solution offered by third-party providers. It enables businesses and individual users to integrate AI-powered capabilities, such as machine learning, natural language processing, and computer vision, into their systems without significant upfront investments in infrastructure or expertise. These AI tools, hosted in the cloud and accessible over the internet, allow for on-demand scalability and flexibility, making AI more accessible to many users.
What is the total CAGR expected for the AI as a service market during 2025-2030?
The AI as a service market is expected to record a CAGR of 35.1% from 2025 to 2030.
What are the key benefits of AI as a service?
AI as a service offers end users cost-effective access to advanced AI technologies, scalability for varying workloads, ease of integration with existing systems, rapid deployment, and the ability to leverage expertise from leading AI providers without heavy upfront investments.
What are the challenges involved in AI as a service?
Challenges in AI as a service include managing the costs of high-performance infrastructure, ensuring data privacy and security, addressing integration complexities, maintaining compliance with regulations, and mitigating biases in AI models to ensure fairness.
Which are the top 3 service types in the AI as a service market?
The top three service types in AI as a service include Machine Learning as a Service, which provides accessible ML tools; Natural Language Processing as a Service, enabling language understanding applications; and Predictive Analytics & Data Science as a Service, which empowers data-driven decision-making through advanced analytics.
Who are the key vendors in the AI as a service market?
Some major players in the AI as a Service market include Microsoft (US), IBM (US), SAP (Germany), Oracle (US), Google (US), Salesforce (US), HPE (US), OpenAI (US), AWS (US), Cloudera (US), NVIDIA (US), ServiceNow (US), Alibaba Cloud (China), and Intellias (US), and DOMO (US).

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Table of Contents

Exclusive indicates content/data unique to MarketsandMarkets and not available with any competitors.

TITLE
PAGE NO
INTRODUCTION
1
  • 1.1 OBJECTIVES OF THE STUDY
  • 1.2 MARKET DEFINITION
    INCLUSIONS AND EXCLUSIONS
  • 1.3 MARKET SCOPE
    MARKET SEGMENTATION
    REGIONS COVERED
    YEARS CONSIDERED FOR THE STUDY
  • 1.4 CURRENCY CONSIDERED
  • 1.5 STAKEHOLDERS
  • 1.6 SUMMARY OF CHANGES
RESEARCH METHODOLOGY
2
  • 2.1 RESEARCH DATA
    SECONDARY DATA
    PRIMARY DATA
    - BREAKUP OF PRIMARY PROFILES
    - KEY INDUSTRY INSIGHTS
  • 2.2 MARKET BREAKUP AND DATA TRIANGULATION
  • 2.3 MARKET SIZE ESTIMATION
    TOP-DOWN APPROACH
    BOTTOM-UP APPROACH
  • 2.4 MARKET FORECAST
  • 2.5 ASSUMPTIONS FOR THE STUDY
  • 2.6 LIMITATIONS OF THE STUDY
EXECUTIVE SUMMARY
3
PREMIUM INSIGHTS
4
  • 4.1 ATTRACTIVE OPPORTUNITIES IN THE GLOBAL AI AS A SERVICE MARKET
  • 4.2 MARKET, BY PRODUCT TYPE, 2025 VS. 2030
  • 4.3 MARKET, BY ORGANIZATION SIZE, 2025 VS. 2030
  • 4.4 MARKET, BY BUSINESS FUNCTION, 2025 VS. 2030
  • 4.5 MARKET, BY SERVICE TYPE, 2025 VS. 2030
  • 4.6 MARKET, BY END USER, 2025 VS. 2030
  • 4.7 AI AS A SERVICE MARKET, BY REGION, 2025
MARKET OVERVIEW
5
  • 5.1 INTRODUCTION
  • 5.2 MARKET DYNAMICS
    DRIVERS
    RESTRAINTS
    OPPORTUNITIES
    CHALLENGES
  • 5.3 EVOLUTION OF AI AS A SERVICE
  • 5.4 SUPPLY CHAIN ANALYSIS
  • 5.5 ECOSYSTEM ANALYSIS
  • 5.6 INVESTMENT LANDSCAPE AND FUNDING SCENARIO
  • 5.7 CASE STUDY ANALYSIS
    CASE STUDY 1
    CASE STUDY 2
    CASE STUDY 3
  • 5.8 TECHNOLOGY ANALYSIS
    KEY TECHNOLOGIES
    - GENERATIVE AI
    - MACHINE LEARNING
    - CONVERSATIONAL AI
    - CLOUD COMPUTING
    - NATURAL LANGUAGE PROCESSING (NLP)
    COMPLEMENTARY TECHNOLOGIES
    - COGNITIVE COMPUTING
    - BIG DATA ANALYTICS
    - ROBOTIC PROCESS AUTOMATION (RPA)
    ADJACENT TECHNOLOGIES
    - QUANTUM COMPUTING
    - INTERNET OF THINGS (IOT)
    - CYBERSECURITY
  • 5.9 REGULATORY LANDSCAPE
    REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
    KEY REGULATIONS
    - NORTH AMERICA
    - EUROPE
    - ASIA PACIFIC
    - MIDDLE EAST AND AFRICA
    - LATIN AMERICA
    PATENT ANALYSIS
    - METHODOLOGY
    - PATENTS FILED, BY DOCUMENT TYPE, 2016–2025
    - INNOVATION AND PATENT APPLICATIONS
    PRICING ANALYSIS
    - AVERAGE SELLING PRICE OF SERVICE TYPE, BY KEY PLAYERS, 2025
    - AVERAGE SELLING PRICE, BY PRODUCT TYPE, 2025
    KEY CONFERENCES AND EVENTS, 2025-2026
    PORTER FIVE FORCES ANALYSIS
    - THREAT FROM NEW ENTRANTS
    - THREAT OF SUBSTITUTES
    - BARGAINING POWER OF SUPPLIERS
    - BARGAINING POWER OF BUYERS
    - INTENSITY OF COMPETITION RIVALRY
    TRENDS/DISRUPTIONS IMPACTING BUYER/CLIENTS OF AI AS A SERVICE MARKET
    KEY STAKEHOLDERS AND BUYING CRITERIA
    - KEY STAKEHOLDERS IN BUYING PROCESS
    - BUYING CRITERIA
    AIAAS ARCHITECTURE
AI AS A SERVICE MARKET, BY PRODUCT TYPE
6
  • 6.1 INTRODUCTION
    PRODUCT TYPE: MARKET DRIVERS
  • 6.2 CHATBOTS & AI AGENTS
  • 6.3 MACHINE LEARNING FRAMEWORKS
  • 6.4 APPLICATION PROGRAMMING INTERFACE (API)
  • 6.5 NO-CODE OR LOW-CODE ML TOOLS
  • 6.6 DATA LABELING & PRE-PROCESSING TOOLS
AI AS A SERVICE MARKET, BY ORGANIZATION SIZE
7
  • 7.1 INTRODUCTION
    ORGANIZATION SIZE: MARKET DRIVERS
  • 7.2 SMALL & MEDIUM-SIZED ENTERPRISES
  • 7.3 LARGE ENTERPRISES
AI AS A SERVICE MARKET, BY BUSINESS FUNCTION
8
  • 8.1 INTRODUCTION
    BUSINESS FUNCTION: MARKET DRIVERS
  • 8.2 FINANCE
  • 8.3 MARKETING
  • 8.4 SALES
  • 8.5 HUMAN RESOURCES
  • 8.6 OPERATIONS & SUPPLY CHAIN
AI AS A SERVICE MARKET, BY SERVICE TYPE
9
  • 9.1 INTRODUCTION
    SERVICE TYPE: MARKET DRIVERS
  • 9.2 MACHINE LEARNING AS A SERVICE (MLAAS)
    DATA PREPARATION AND PREPROCESSING
    MODEL DEVELOPMENT AND TRAINING
    MODEL DEPLOYMENT AND MANAGEMENT
    MODEL EVALUATION AND TESTING
    RECOMMENDATION SERVICES
    OTHERS (PRE-TRAINED MODELS AND QUALITY CONTROL)
  • 9.3 NATURAL LANGUAGE PROCESSING AS A SERVICE (NLPAAS)
    SPEECH RECOGNITION
    SEMANTIC SEARCH
    SENTIMENT ANALYSIS
    VOICE RECOGNITION
    TEXT-TO-SPEECH (TTS)
    OTHERS (MACHINE TRANSLATION AND EMOTION DETECTION)
  • 9.4 COMPUTER VISION AS A SERVICE
    IMAGE RECOGNITION
    FACE RECOGNITION
    VIDEO ANALYTICS
    OBJECT DETECTION
    OTHERS (OBJECT TRACKING AND OPTICAL CHARACTER RECOGNITION)
  • 9.5 PREDICTIVE ANALYTICS AND DATA SCIENCE AS A SERVICE (DSAAS)
    OPERATIONAL INTELLIGENCE
    SUPPLY CHAIN ANALYTICS
    PREDICTIVE MAINTENANCE
    RISK MANAGEMENT
    OTHERS (OBJECT TRACKING AND OPTICAL CHARACTER RECOGNITION)
  • 9.6 GENERATIVE AI AS A SERVICE
    CODE GENERATION & SOFTWARE DEVELOPMENT
    CONTENT CREATION
    FRAUD DETECTION
    CONTENT MODERATION
    DATA EXTRACTION
    OTHERS (TEXT SUMMARIZATION AND DATA QUERY & ANALYTICS)
    AI AS A SERVICE MARKET, BY END USER
AI AS A SERVICE MARKET, BY END USER
10
  • 10.1 INTRODUCTION
    END USER: MARKET DRIVERS
  • 10.2 ENTERPRISES
    BFSI
    RETAIL & E-COMMERCE
    TECHNOLOGY & SOFTWARE
    - IT & ITES
    - SOFTWARE DEVELOPMENT COMPANIES
    - OTHER TECHNOLOGIES & SOFTWARE (CLOUD HYPERSCALERS AND FOUNDATION MODEL/LLM PROVIDERS)
    MEDIA & ENTERTAINMENT
    MANUFACTURING
    HEALTHCARE & LIFE SCIENCES
    ENERGY & UTILITIES
    GOVERNMENT & DEFENSE
    TELECOMMUNICATIONS
    - TRANSPORTATION & LOGISTICS
    - OTHER ENTERPRISE END USERS (TRAVEL & HOSPITALITY, EDUCATION, AND CONSTRUCTION & REAL ESTATE)
  • 10.3 INDIVIDUAL USERS
AI AS A SERVICE MARKET, BY REGION
11
  • 11.1 INTRODUCTION
  • 11.2 NORTH AMERICA
    NORTH AMERICA: MARKET DRIVERS
    MACROECONOMIC OUTLOOK FOR NORTH AMERICA
    UNITED STATES
    CANADA
  • 11.3 EUROPE
    EUROPE: AI AS A SERVICE MARKET DRIVERS
    MACROECONOMIC OUTLOOK FOR EUROPE
    UK
    GERMANY
    FRANCE
    ITALY
    SPAIN
    REST OF EUROPE
  • 11.4 ASIA PACIFIC
    ASIA PACIFIC:MARKET DRIVERS
    MACROECONOMIC OUTLOOK FOR ASIA PACIFIC
    CHINA
    JAPAN
    INDIA
    SOUTH KOREA
    AUSTRALIA & NEW ZEALAND
    SINGAPORE
    REST OF ASIA PACIFIC
  • 11.5 MIDDLE EAST & AFRICA
    MIDDLE EAST & AFRICA: AI AS A SERVICE MARKET DRIVERS
    MACROECONOMIC OUTLOOK FOR MIDDLE EAST & AFRICA
    MIDDLE EAST
    - KSA
    - UAE
    - QATAR
    - TURKEY
    - REST OF MIDDLE EAST
    AFRICA
  • 11.6 LATIN AMERICA
    LATIN AMERICA: AI AS A SERVICE MARKET DRIVERS
    MACROECONOMIC OUTLOOK FOR LATIN AMERICA
    BRAZIL
    MEXICO
    ARGENTINA
    REST OF LATIN AMERICA
COMPETITIVE LANDSCAPE
12
  • 12.1 OVERVIEW
  • 12.2 STRATEGIES ADOPTED BY KEY PLAYERS
    OVERVIEW OF STRATEGIES ADOPTED BY KEY AIAAS VENDORS
  • 12.3 REVENUE ANALYSIS OF KEY PLAYERS, 2020 - 2024
    MARKET SPECIFIC REVENUE ANALYSIS
  • 12.4 MARKET SHARE ANALYSIS, 2024
    MARKET RANKING ANALYSIS
  • 12.5 PRODUCT COMPARATIVE ANALYSIS
    PRODUCT COMPARATIVE ANALYSIS, BY PRODUCT TYPE
    - Product Name (Company Name)
    - Product Name (Company Name)
    - Product Name (Company Name)
  • 12.6 COMPANY VALUATION AND FINANCIAL METRICS OF KEY AIAAS VENDORS
  • 12.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2024
    STARS
    EMERGING LEADERS
    PERVASIVE PLAYERS
    PARTICIPANTS
    COMPANY FOOTPRINT: KEY PLAYERS, 2024
    - COMPANY FOOTPRINT
    - REGION FOOTPRINT
    - PRODUCT TYPE FOOTPRINT
    - SERVICE TYPE FOOTPRINT
    - END USER FOOTPRINT
  • 12.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2024
    PROGRESSIVE COMPANIES
    RESPONSIVE COMPANIES
    DYNAMIC COMPANIES
    STARTING BLOCKS
    COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2024
    - DETAILED LIST OF KEY STARTUPS/SMES
    - COMPETITIVE BENCHMARKING OF KEY STARTUPS/SMES
  • 12.9 COMPETITIVE SCENARIO
    PRODUCT LAUNCHES AND ENHANCEMENTS
    DEALS
    OTHERS
COMPANY PROFILES
13
  • 13.1 INTRODUCTION
  • 13.2 KEY PLAYERS
    MICROSOFT
    IBM
    SAP
    AWS
    GOOGLE
    SALESFORCE
    ORACLE
    NVIDIA
    FICO
    - CLOUDERA
    - SERVICENOW
    - HPE
    - ALTAIR
    - OPENAI
    - SAS INSTITUTE
    - DATAROBOT
    - DATABRICKS
    - C3 AI
    - H20.AI
    - ALIBABA CLOUD
    - DOMO
    - INTELLIAS
  • 13.3 SMES/START-UPS
    RAINBIRD TECHNOLOGIES
    BIGML
    COHERE
    GLEAN
    YOTTAMINE ANALYTICS
    SCALE AI
    LANDING AI
    YELLOW.AI
    INFLECTION AI
    - ANYSCALE
    - ABRIDGE
    - MISTRAL AI
    - CODEIUM
    - ARTHUR
    - LEVITY AI
    - UNSTRUCTURED.IO
    - CLARIFAI
    - SYNTHESIA
    - KATONIC AI
    - DEEPSEARCH
    - MINDTITAN
    - VISO.AI
    - SOFTWEB SOLUTIONS
    - MONKEYLEARN
ADJACENT AND RELATED MARKETS
14
  • 14.1 INTRODUCTION
  • 14.2 ARTIFICIAL INTELLIGENCE MARKET – GLOBAL FORECAST TO 2030
    MARKET DEFINITION
    MARKET OVERVIEW
  • 14.3 GENERATIVE AI MARKET – GLOBAL FORECAST TO 2030
    MARKET DEFINITION
    MARKET OVERVIEW
APPENDIX
15
  • 15.1 DISCUSSION GUIDE
  • 15.2 KNOWLEDGE STORE: MARKETANDMARKETS’ SUBSCRIPTION PORTAL
  • 15.3 AVAILABLE CUSTOMIZATIONS
  • 15.4 RELATED REPORTS
  • 15.5 AUTHOR DETAILS

 

The research study for AI as a Service market involved extensive secondary sources, directories, journals, and paid databases. Primary sources were mainly industry experts from the core and related industries, preferred AI as a Service providers, third-party service providers, consulting service providers, end users, and other commercial enterprises. In-depth interviews were conducted with various primary respondents, including key industry participants and subject matter experts, to obtain and verify critical qualitative and quantitative information and assess the market’s prospects.

Secondary Research

In the secondary research process, various sources were referred to identify and collect information for the study. The secondary sources included annual reports, press releases, and investor presentations of companies; white papers, journals, and certified publications; and articles from recognized authors, directories, and databases. The data was also collected from other secondary sources, such as IoT conferences and related magazines. Additionally, the AI as a Service spending of various countries was extracted from respective sources. Secondary research was used to obtain key information about the industry’s value chain and supply chain to identify key players by solution, service, market classification, and segmentation according to the offerings of major players and industry trends related to solutions, applications, verticals, and regions, and key developments from both market and technology-oriented perspectives.

Primary Research

In the primary research process, various primary sources from supply and demand sides were interviewed to obtain qualitative and quantitative information on the market. The primary sources from the supply side included various industry experts, including Chief Experience Officers (CXOs); Vice Presidents (VPs); directors from business development, marketing, and AI as a Service expertise; related key executives from AI as a Service solution vendors, SIs, managed service providers, and industry associations; and key opinion leaders.

Primary interviews were conducted to gather insights, such as market statistics, revenue data collected from solutions and services, market breakups, market size estimations, market forecasts, and data triangulation. Primary research also helped understand various trends related to technologies, applications, service types, product types, end users, and regions. Stakeholders from the demand side, such as Chief Information Officers (CIOs), Chief Technology Officers (CTOs), Chief Strategy Officers (CSOs), and end users using AI as a Service solution, were interviewed to understand the buyer’s perspective on suppliers, products, service providers, and their current usage of AI as a Service, which would impact the overall AaaS market.

AI as a Service Market Size, and Share

Note: Tier 1 companies account for annual revenue of >USD 10 billion; tier 2 companies’ revenue ranges
between USD 1 and 10 billion; and tier 3 companies’ revenue ranges between USD 500 million and USD 1 billion
Source: MarketsandMarkets Analysis

To know about the assumptions considered for the study, download the pdf brochure

Market Size Estimation

Multiple approaches were adopted to estimate and forecast the AI as a service market. The first approach estimates market size by summating companies’ revenue generated by selling solutions and services.

Market Size Estimation Methodology: Top-down approach

In the top-down approach, an exhaustive list of all the vendors offering solutions and services in the AI as a Service market was prepared. The revenue contribution of the market vendors was estimated through annual reports, press releases, funding, investor presentations, paid databases, and primary interviews. Each vendor’s offerings were evaluated based on the breadth of solutions according to product type, business functions, organization size, service type, and end user. The aggregate of all the companies’ revenue was extrapolated to reach the overall market size. Each subsegment was studied and analyzed for its global market size and regional penetration. The markets were triangulated through primary and secondary research. The primary procedure included extensive interviews for key insights from industry leaders, such as CIOs, CEOs, VPs, directors, and marketing executives. The market numbers were further triangulated with the existing MarketsandMarkets’ repository for validation.

Market Size Estimation Methodology-Bottom-up approach

The bottom-up approach identified the adoption rate of AI as a Service solutions and services among different end users in key countries, concerning their regions contributing the most to the market share. For cross-validation, the adoption of AI as a Service solution among industries, along with different use cases concerning their regions, was identified and extrapolated. Weightage was given to use cases identified in different regions for the market size calculation.

Based on the market numbers, the regional split was determined by primary and secondary sources. The procedure included the analysis of the AI as a Service market’s regional penetration. Based on secondary research, the regional spending on Information and Communications Technology (ICT), socio-economic analysis of each country, strategic vendor analysis of major AI as a Service providers, and organic and inorganic business development activities of regional and global players were estimated. With the data triangulation procedure and data validation through primary interviews, the exact values of the overall AI as a Service market size and the segments’ size were determined and confirmed using the study.

AI as a Service Market : Top-Down and Bottom-Up Approach

AI as a Service Market Top Down and Bottom Up Approach

Data Triangulation

After determining the overall market size using the market size estimation processes as explained above, the market was split into several segments and subsegments. To complete the overall market engineering process and arrive at the exact statistics of each market segment and subsegment, data triangulation and market breakup procedures were employed, wherever applicable. The overall market size was then used in the top-down procedure to estimate the size of other individual markets via percentage splits of the market segmentation.

Market Definition

AI as a Service (AIaaS) is a cloud-based solution offered by third-party providers, enabling businesses and individual users to integrate AI-powered capabilities, such as machine learning, natural language processing, and computer vision, into their systems without significant upfront investments in infrastructure or expertise. These AI tools, hosted in the cloud and accessible over the internet, allow for on-demand scalability and flexibility, making AI more accessible to many users. AIaaS provides a low-risk, cost-effective approach for enterprises to automate processes, enhance decision-making, and drive operational efficiency while catering to individual users for personal or smaller-scale AI applications.

Stakeholders

  • AI as a Service software providers
  • Cybersecurity firms
  • Business analysts
  • Cloud service providers
  • Consulting service providers
  • Enterprise end users
  • Distributors and value-added resellers (VARs)
  • Government agencies
  • Independent software vendors (ISV)
  • Managed service providers
  • Market research and consulting firms
  • Support & maintenance service providers
  • System integrators (SIs)/migration service providers
  • Technology providers

Report Objectives

  • To define, describe, and forecast the AI as a Service market, by product type, organization size, business function, service type, and end user
  • To provide detailed information related to major factors (drivers, restraints, opportunities, and industry-specific challenges) influencing the market growth
  • To analyze the micro markets with respect to individual growth trends, prospects, and their contribution to the total market
  • To analyze the opportunities in the market for stakeholders by identifying the high-growth segments of the AI as a Service market
  • To analyze opportunities in the market and provide details of the competitive landscape for stakeholders and market leaders
  • To forecast the market size of segments for five main regions: North America, Europe, Asia Pacific, the Middle East & Africa, and Latin America
  • To profile the key players and comprehensively analyze their market ranking and core competencies
  • To analyze competitive developments, such as partnerships, product launches, and mergers and acquisitions, in the AI as a Service market

Available Customizations

With the given market data, MarketsandMarkets offers customizations per your company’s specific needs. The following customization options are available for the report:

Product Analysis

  • The product quadrant gives a detailed comparison of each company’s product portfolio.

Geographic Analysis as per Feasibility

  • Further breakup of the North American AI as a Service market
  • Further breakup of the European market
  • Further breakup of the Asia Pacific market
  • Further breakup of the Middle Eastern & African market
  • Further breakup of the Latin American AI as a Service market

Company Information

  • Detailed analysis and profiling of additional market players (up to five)

 

Previous Versions of this Report

AI as a Service Market by Offering (SaaS, PaaS, IaaS), Technology (Machine Learning, Natural Language Processing, Context Awareness, Computer Vision), Cloud Type (Public, Private, Hybrid), Organization Size, Vertical and Region - Global Forecast to 2028

Report Code TC 6185
Published in Apr, 2023, By MarketsandMarkets™

AI as a Service Market by Offering (SaaS, PaaS, IaaS), Technology (Machine Learning, Natural Language Processing, Context Awareness, Computer Vision), Cloud Type (Public, Private, Hybrid), Organization Size, Vertical and Region - Global Forecast to 2028

Report Code TC 6185
Published in Apr, 2018, By MarketsandMarkets™
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Growth opportunities and latent adjacency in AI as a Service Market

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