Cloud AI Market

Report Code TC 9251
Published in Dec, 2024, By MarketsandMarkets™
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Cloud AI Market by Cloud AI Infrastructure (Compute, Storage, Network), AI & ML Platforms (Auto ML), MLOps and Lifecycle Management (AI Workflow Orchestration), AIaaS, Technology (Generative AI and Other AI) - Global Forecast to 2029

 

Overview

The global cloud AI market is estimated to grow from USD 80.30 billion in 2024 to USD 327.15 billion by 2029 at a CAGR of 32.4 % from 2024 to 2029. The Cloud AI market marks a transformative integration of cloud computing and artificial intelligence (AI), enabling businesses to access powerful AI tools and infrastructure. Combining the scalability of cloud computing with AI's analytical and automation capabilities, organizations deploy machine learning (ML), natural language processing (NLP), computer vision, and other functionalities with minimal in-house resources. This integration enables enterprises to incorporate advanced AI into their operations effortlessly, driving innovation and streamlining workflows on a wide scale. Cloud AI boosts operational efficiency, such as in order processing, by automating routine tasks and enabling real-time decision-making. Organizations can enhance inventory management and order-tracking workflows by integrating AI with cloud platforms. By adopting Cloud AI, Cloud AI infrastructure provides scalable computing power, secure data storage, and seamless integration with enterprise applications, allowing systems to process large volumes of unstructured data quickly. Cloud AI reduces errors by ensuring consistent performance during peak demand, helping businesses optimize operations and improve customer satisfaction. This factor is expected to drive demand for Cloud AI in the end users, such as banking, healthcare, and e-commerce.

Cloud AI Market

Attractive Opportunities in the Cloud AI Market

ASIA PACIFIC

Asia Pacific is projected to grow at the highest CAGR during the forecast period. North America is estimated to account for the largest market share in 2024

The surge in cloud adoption and the growing need for intelligent automation are driving the Cloud AI market, as businesses seek to leverage AI capabilities to enhance operational workflows and scalability.

Acquisitions and new product launches featuring advanced AI-driven analytics, real-time language processing, and automated scalability are expected to generate significant opportunities for market players in the Cloud AI sector in the coming years.

The increasing adoption of AI-driven applications and the demand for scalable, real-time data processing have accelerated the growth of the Cloud AI market

The global demand for Cloud AI is driven by the need for advanced data analytics, seamless automation, enhanced cybersecurity, and scalable solutions, enabling organizations to innovate and focus on strategic growth.

Impact of AI on Cloud AI Market

ENHANCED CLOUD SERVICES

Generative AI elevates cloud services by enabling more intelligent, adaptive applications, boosting service quality, and providing businesses with powerful tools for customization and advanced analytics.

AUTOMATED DATA PROCESSING

Utilizes generative AI to streamline data processing, automating complex analyses for faster, more accurate insights, improving operational efficiency and reducing time spent on manual data management.

AI-DRIVEN CYBERSECURITY

Generative AI bolsters cybersecurity by detecting threats in real-time, learning from data patterns, and autonomously responding to anomalies to prevent security breaches and safeguard cloud environments.

REAL-TIME LANGUAGE SUPPORT

Enables cloud-based AI to deliver instant language translation and support, enhancing global communication by providing seamless, real-time multilingual assistance in customer service and enterprise settings.

FASTER INNOVATION CYCLES

Accelerates product and service development with AI-automated coding and testing, reducing time-to-market, and allowing businesses to iterate rapidly based on real-time customer feedback.

SCALABLE AI MODELS

Generative AI enables cloud providers to scale AI models efficiently, adapting to variable workloads and maintaining optimal performance during peak usage or growth phases.

Cloud AI Market Impact

Global Cloud AI Market Dynamics

Driver: Increasing advancements in Gen AI and intelligent automation to drive market growth

Advancements in Gen AI and intelligent automation are significantly driving the growth of the cloud AI market. Gen AI algorithms create new content, patterns, and solutions by analyzing and learning from enormous amounts of data, transforming businesses such as healthcare, banking, retail, and manufacturing by increasing creativity and innovation. For example, in healthcare, Gen AI creates personalized treatment plans by analyzing patient data and medical research, improving patient outcomes. As businesses increasingly automate complex tasks using Gen AI, the demand for cloud-based AI services rises because these services provide the necessary infrastructure and scalability to handle large datasets and perform complex computations. Gen AI enables enterprises to use powerful machine learning models and algorithms to evaluate real-time data, enhance accuracy, and offer insights more quickly.

Furthermore, intelligent automation, enabled by AI and machine learning, transforms company operations by speeding workflows and boosting decision-making processes. This is accomplished by automating regular processes like data input and report production, allowing the workforce to focus on more critical tasks. Cloud AI services provide real-time data processing and predictive analytics, enabling businesses to provide tailored customer experiences.

Restraint: Data privacy and security concerns

As more businesses move sensitive data to the cloud, there are growing concerns about cyber threats, illegal access, and meeting compliance with regulatory bodies such as GDPR (General Data Protection Regulation) and CCPA(California Consumer Privacy Act), making strong security critical for securing the vast amounts of private and personal data that AI systems process. These measures protect against cyber threats and ensure compliance with regulations.

However, many businesses hesitate to adopt cloud-based AI solutions because they have concerns about data ownership, encryption practices, and the potential misuse of AI-generated insights. Businesses also deal with complicated legal requirements to avoid penalties and protect user data, especially in regulated verticals such as healthcare, finance, and government. Even after adopting Cloud AI, businesses often struggle to fully secure data from cyberattacks. Increased attack surfaces, AI algorithm vulnerabilities, misconfigurations, human error, and insufficient security measures are vital factors restraining companies from adopting Cloud AI.

 

Opportunity: Integration with emerging technologies

Incorporating cloud AI with new technologies represents a substantial opportunity for growth in the cloud AI market. As technologies such as the Internet of Things (IoT), blockchain, and 5G continue to evolve, massive volumes of data are generated. This increases demand for AI-driven solutions. Cloud AI is essential because it allows for fast data processing and accurate forecasting by providing the computing power needed to quickly analyze large amounts of data. This capability is crucial for IoT applications such as smart cities, driverless cars, and industrial automation, where rapid, data-driven choices are required to manage systems effectively and respond to changes quickly. AI combined with blockchain strengthens data security and transparency by creating secure, tamper-proof records instrumental in sensitive sectors like finance and healthcare. At the same time, 5G's faster speeds enable AI to process data in real time and handle significant amounts of information, making these applications more efficient and scalable.

Challenge: High Costs of AI Implementation

High costs make adopting AI in the cloud tough for small and medium-sized enterprises (SMEs). While using cloud-based AI reduces the need for physical infrastructure, businesses still incur significant expenses for advanced software, powerful computing resources, data storage, and hiring skilled workers like data scientists and AI experts.

Ongoing expenditures such as subscription fees, model training, and maintenance increase SMEs financial challenges. Additionally, the complexity of AI projects and the need for specialized skills and resources make implementation even more difficult for cost-sensitive enterprise businesses. Many companies are concerned about seeing a good return on investment (ROI) from their AI efforts, making them reluctant to spend much money. This hesitation is extreme in industries where budgets are a significant constraint, causing a careful approach to adopting cloud-based AI solutions. Additionally, without enough financial backing or access to funding, SMEs struggle to compete with larger companies that can afford to invest in extensive AI strategies.

Global Cloud AI Market Ecosystem Analysis

The Cloud AI ecosystem consists of many vital components that work together to facilitate the deployment and upgrading of AI solutions. Cloud providers provide the necessary infrastructure at the core of AI operations. Next to them are AI platforms and business application providers, which provide tools for designing, implementing, and improving AI models across many sectors. AI service providers deliver customized solutions that fulfill the needs of individual businesses. Data providers supply high-quality datasets, which are required for training AI models. Finally, regulatory agencies develop rules and standards to guarantee that AI technology is utilized safely, ethically, and following applicable legislation.

Top Companies in Cloud AI Market

Source: MarketsandMarkets Analysis
© 2009 - 2024 MarketsandMarkets Research Private Ltd. All rights reserved

 

Based on the hosting type, the managed hosting segment is expected to dominate the market during the forecast period.

Managed hosting is becoming significant in the Cloud AI market as more businesses seek to adopt AI without the complexity of managing the underlying infrastructure. In this system, service providers take responsibility for everything from infrastructure setup and maintenance to security and performance optimization. This lets companies focus on deploying AI applications and driving innovation without getting caught up in technical details.

For instance, H2O.ai offers a fully managed cloud service that allows customers to create a secure, customized environment with minimal setup. This managed hosting also provides high availability by strategically positioned data centers in various places. Each data center operates independently, with a separate power supply, network, and disaster recovery plans to guard against interruptions such as natural calamities or regional outages.

To secure client data, managed hosting security utilizes a multi-layered strategy. Each client gets their dedicated environment, which keeps their data inside a specified zone and ensures encryption during storage and transit. Zero-trust access, two-factor authentication, and continuous monitoring are extra safety measures that help adhere to stringent security requirements.

Based on the technology type, the generative AI segment is expected to have the highest CAGR growth rate during the forecast period.

Generative AI in the Cloud AI market transforms business operations by offering enhanced tools for developing new content, increasing productivity, and customizing consumer experiences. For instance, in marketing and online shopping, generative AI helps make personalized content, write ads, and suggest products, which helps businesses connect better with their customers. These tools assist in code generation for software development, accelerating development cycles and reducing human error. The media and entertainment industries use generative AI to create virtual content, such as video editing and animation, allowing faster production and better customization.

Generative AI is also used in research and development, especially in healthcare, where it helps identify new drugs by creating molecular structures and predicting how different compounds will react. In customer service, AI chatbots and virtual assistants converse naturally, making it easier for customers to receive assistance.

According to The Pulse of Cloud (Quarterly Report, July 2024) by Wipro FullStride Cloud Services, while a majority of respondents (55%) report cloud adoption outpacing AI adoption, over one-third (35%) are advancing with both technologies together, driven by the need for scalable infrastructure to support generative AI workloads. This parallel adoption underscores how cloud capabilities such as high processing power and seamless data access are essential for deploying generative AI at scale. By leveraging the cloud's flexibility, businesses can quickly integrate and scale AI models, enabling real-time content creation, enhanced personalization, and rapid product innovation across sectors, ultimately accelerating business transformation.

The US market is expected to hold North America's largest cloud AI market share.

The US is expected to lead the cloud AI market in 2024. The country is seeing a significant increase in cloud AI adoption, fueled by advancements in AI technology and a growing awareness of its benefits. A supportive ecosystem, government initiatives, and specific industry applications also contribute to this trend. With improved AI algorithms and greater computing power, businesses use artificial intelligence to make data-driven decisions, automate processes, and boost efficiency. Leading tech companies, research institutions, and venture capital firms also support the growth of AI innovation in the region.

The government in this region is supporting AI growth through investments in research and development and establishing regulatory frameworks. For reference, an Information Services Group (ISG) report in October 2024 highlights that increasing AI and cloud adoption has significantly influenced how US companies approach application development and management (ADM) in the past year. The 2024 ISG Provider Lens Next-Gen ADM Services report shows that businesses prioritize cost optimization, accelerating AI usage throughout the application lifecycle and encouraging early adoption of generative AI.

Additionally, in February 2024, the US government launched the AI National Strategy to promote responsible AI development while ensuring national security and economic growth. This strategy outlines important areas for investment and regulation. Later, in September 2024, during the 79th Session of the United Nations General Assembly in New York, Secretary of State Antony Blinken announced the Partnership for Global Inclusivity on AI (PGIAI) to promote sustainable development through AI. The partnership, which included the US Department of State, Amazon, Anthropic, Google, IBM, Meta, Microsoft, Nvidia, and OpenAI, committed over USD100 million to enhance AI's role in developing countries, focusing on increasing access to AI tools, building technical capacity, and expanding local datasets.

HIGHEST CAGR MARKET IN 20XX
XX FASTEST GROWING MARKET IN THE REGION
Cloud AI Market Size and Share

Recent Developments of Cloud AI Market

  • In April 2024, NVIDIA acquired Run: ai, an Israeli firm, highlighting the significance of Kubernetes in AI infrastructure. Run: AI's technology improves GPU utilization for AI workloads, benefiting NVIDIA's ecosystem, broadening market reach, and advancing Kubernetes in cloud-native AI architecture.
  • In May 2024, IBM launched the AI Gateway for IBM API Connect, which became available in June. This new functionality enables users to access AI services from a single control point, enabling secure interaction between internal applications and external AI APIs. It also monitors AI API usage and gives insights for faster decisions about which large language models (LLMs) to utilize.
  • In July 2024, AWS launched AWS GenAI Lofts, a global initiative to foster innovation and engagement in the generative AI area. This program will set up temporary spaces in key AI hubs worldwide, allowing developers, companies, and AI enthusiasts to learn, create, and interact. Visitors can expect engaging experiences featuring exciting generative AI projects, workshops, informal talks, and hands-on sessions led by AI experts, community groups, and AWS partners like Anthropic, Cerebral Valley, Weights & Biases, and venture capital investors.
  • IBM and Intel announced a cooperation in August 2024 to deploy Intel Gaudi 3 AI accelerators as a service on IBM Cloud, with a launch date of early 2025. This collaboration intends to help enterprises expand their AI initiatives more effectively while maintaining high reliability and security. Gaudi 3 will be integrated into IBM's Watsonx AI and data platform, making IBM Cloud the first provider to provide Gaudi 3 for both hybrid and on-premises settings.
  • Salesforce strengthened its partnership with Google Cloud in September 2024, launching Agentforce Agents that enable safe collaboration between Salesforce Customer 360 and Google Workspace apps. This release improves previous connections, allowing mutual customers to deploy autonomous agents that work smoothly within their regular apps while benefiting from solid privacy and user data protection from Salesforce and Google Workspace.
  • In September 2024, Salesforce expanded its partnership with Google Cloud to create Agentforce Agents, which enable secure collaboration across Salesforce Customer 360 and Google Workspace apps. This launch enhances existing integrations, allowing mutual customers to deploy autonomous agents that work seamlessly within their daily apps while maintaining robust privacy and user data protections from both Salesforce and Google Workspace.

Key Market Players

List of Top Cloud AI Market Companies

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

  • Google (US)
  • IBM (US)
  • AWS (US)
  • Microsoft (US)
  • Oracle (US)
  • Nvidia (US)
  • Salesforce (US)
  • SAP (Germany)
  • Alibaba Cloud (China)
  • HPE (US)
  • Intel (US)
  • Tencent Cloud (China)
  • H2O.ai (US)
  • OpenAI (US)
  • Baidu (China)
  • DataRobot (US)
  • Huawei (China)
  • C3 AI (US)
  • Cloudera (US)
  • Altair Engineering (US)
  • Cohere (Canada)
  • Glean (US)
  • Scale AI (US)
  • CloudMinds (China)
  • Inflection AI (US)
  • Any scale (US)
  • Frame AI (US)
  • Dataiku (US)
  • InfraCloud Technologies (India)
  • Yellow.ai (US)
  • Viso.ai (Switzerland

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Scope of the Report

Report Attribute Details
Market size available for years 2019–2029
Base year considered 2023
Forecast period 2024–2029
Forecast units Value (USD Million/Billion)
Segments Covered Offering, Technology Type, Hosting Type, Organization Size, Business Function, Verticals
Regions covered North America, Europe, Asia Pacific, Middle East Africa, and Latin America

 

Key Questions Addressed by the Report

What is cloud AI?
Cloud AI refers to artificial intelligence services and capabilities hosted on the cloud rather than local servers or devices. This means organizations access powerful AI tools and applications over the internet without investing in expensive hardware or software.
What are the benefits of implementing cloud AI?
Implementing Cloud AI offers several benefits, including cost savings by reducing the need for expensive hardware, easy access to powerful AI tools, quick scalability to meet changing needs, and the ability to leverage the latest technology without worrying about maintenance or updates.
Which are the key vendors exploring cloud AI?
Some of the significant vendors offering cloud AI worldwide include Google (US), IBM (US), AWS (US), Microsoft (US), Oracle (US), Nvidia (US), Salesforce (US), SAP (Germany), Alibaba Cloud (China), HPE (US), and Intel (US).
What is the total CAGR projected for the cloud AI market from 2024 to 2029?
The cloud AI market will record a CAGR of 32.4% from 2024 to 2029.
Who are the stakeholders in the cloud AI market?
Stakeholders in the cloud AI market include the following:
  • Research organizations
  • Third-party service providers
  • Technology providers
  • Cloud services providers
  • AI consulting companies
  • Independent software vendors (ISVs)
  • Service providers and distributors
  • Application development vendors
  • System integrators
  • Consultants/consultancy/advisory firms
  • Training and education service providers
  • Support and maintenance service providers
  • Managed service providers

 

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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 & EXCLUSIONS
  • 1.3 MARKET SCOPE
    MARKET SEGMENTATION
    REGIONS COVERED
    YEARS CONSIDERED FOR THE STUDY
  • 1.4 CURRENCY CONSIDERED
  • 1.5 STAKEHOLDERS
RESEARCH METHODOLOGY
2
  • 2.1 RESEARCH APPROACH
    SECONDARY DATA
    PRIMARY DATA
    - Breakup of Primary Profiles
    - Key Industry Insights
  • 2.2 MARKET BREAKUP & DATA TRIANGULATION
  • 2.3 MARKET SIZE ESTIMATION
  • 2.4 MARKET FORECAST
  • 2.5 RESEARCH ASSUMPTIONS
  • 2.6 LIMITATIONS OF THE STUDY
EXECUTIVE SUMMARY
3
PREMIUM INSIGHTS
4
  • 4.1 BRIEF OVERVIEW OF THE CLOUD AI MARKET
  • 4.2 MARKET, BY OFFERING, 2024 VS 2029
  • 4.3 MARKET, BY BUSINESS FUNCTION, 2024 VS 2029
  • 4.4 MARKET, BY ORGANIZATION SIZE, 2024 VS 2029
  • 4.5 MARKET, BY HOSTING TYPE, 2024 VS 2029
  • 4.6 MARKET, BY TECHNOLOGY TYPE, 2024 VS 2029
  • 4.7 MARKET, BY VERTICAL, 2024 VS 2029
  • 4.8 CLOUD AI MARKET: REGIONAL SCENARIO, 2024 VS 2029
MARKET OVERVIEW AND INDUSTRY TRENDS
5
  • 5.1 INTRODUCTION
  • 5.2 MARKET DYNAMICS
    DRIVERS
    RESTRAINTS
    OPPORTUNITIES
    CHALLENGES
  • 5.3 CASE STUDY ANALYSIS
    CASE STUDY 1
    CASE STUDY 2
    CASE STUDY 3
    CASE STUDY 4
    CASE STUDY 5
  • 5.4 VALUE CHAIN ANALYSIS
  • 5.5 ECOSYSTEM ANALYSIS
  • 5.6 PORTER'S FIVE FORCES ANALYSIS
  • 5.7 PRICING ANALYSIS
    AVERAGE SELLING PRICE TREND OF KEY PLAYERS, BY OFFERING
    AVERAGE SELLING PRICE TREND, BY REGION
  • 5.8 TECHNOLOGY ANALYSIS
    KEY TECHNOLOGIES
    - AutoML
    - Cloud Computing
    COMPLEMENTARY TECHNOLOGIES
    - Edge Computing
    - Data Lakes
    - AI Development Frameworks
    ADJACENT TECHNOLOGIES
    - Blockchain
    - IoT
  • 5.9 PATENT ANALYSIS
  • 5.10 TRENDS/DISRUPTIONS IMPACTING BUYERS
  • 5.11 REGULATORY LANDSCAPE
    REGULATORY BODIES, GOVERNMENT AGENCIES, & OTHER ORGANIZATIONS
    REGULATIONS BY REGION
  • 5.12 KEY STAKEHOLDERS & BUYING CRITERIA
    KEY STAKEHOLDERS IN BUYING PROCESS
    BUYING CRITERIA
  • 5.13 KEY CONFERENCES & EVENTS (2024-2025)
  • 5.14 BUSINESS MODEL ANALYSIS
  • 5.15 INVESTMENT & FUNDING SCENARIO
  • 5.16 FUTURE OF CLOUD AI
  • 5.17 USE CASES OF AI CLOUD
  • 5.18 IMPACT OF AI/GEN AI ON CLOUD AI MARKET
CLOUD AI MARKET, BY OFFERING
6
  • 6.1 INTRODUCTION
    OFFERING: CLOUD AI MARKET DRIVERS
  • 6.2 INFRASTRUCTURE
    CLOUD AI INFRASTRUCTURE
    - Compute
    - Storage
    - Networking
    AI AND MACHINE LEARNING PLATFORMS
    - Machine Learning Platforms
    - Automated Machine Learning (AutoML)
    - Data Preparation and Management
    MLOPS AND LIFECYCLE MANAGEMENT
    - Model Monitoring and Version Control
    - AI Workflow Orchestration
  • 6.3 AI AS A SERVICE (AIAAS)
CLOUD AI MARKET, BY TECHNOLOGY TYPE
7
  • 7.1 INTRODUCTION
    TECHNOLOGY TYPE: MARKET DRIVERS
  • 7.2 GENERATIVE AI
  • 7.3 OTHER AI
CLOUD AI MARKET, BY HOSTING TYPE
8
  • 8.1 INTRODUCTION
    HOSTING TYPE: MARKET DRIVERS
  • 8.2 MANAGED HOSTING
  • 8.3 SELF-HOSTING
CLOUD AI MARKET, BY ORGANIZATION SIZE
9
  • 9.1 INTRODUCTION
    ORGANIZATION SIZE: MARKET DRIVERS
  • 9.2 LARGE ENTERPRISES
  • 9.3 SMALL & MEDIUM ENTERPRISES
    MARKET, BY BUSINESS FUNCTION
CLOUD AI MARKET, BY BUSINESS FUNCTION
10
  • 10.1 INTRODUCTION
    BUSINESS FUNCTION: MARKET DRIVERS
  • 10.2 MARKETING
    MARKETING: USE CASES
  • 10.3 SALES
    SALES : USE CASES
  • 10.4 HUMAN RESOURCES
    HUMAN RESOURCES: USE CASES
  • 10.5 FINANCE & ACCOUNTING
    FINANCE & ACCOUNTING: USE CASES
  • 10.6 OPERATIONS & SUPPLY CHAIN
    FINANCE & ACCOUNTING: USE CASES
CLOUD AI MARKET, BY VERTICAL
11
  • 11.1 INTRODUCTION
    VERTICAL: MARKET DRIVERS
  • 11.2 BFSI
    BFSI: USE CASES
  • 11.3 RETAIL & E-COMMERCE
    RETAIL & E-COMMERCE: USE CASES
  • 11.4 MANUFACTURING
    MANUFACTURING: USE CASES
  • 11.5 GOVERNMENT & DEFENSE
    GOVERNMENT & DEFENSE: USE CASES
  • 11.6 HEALTHCARE & LIFE SCIENCES
    HEALTHCARE & LIFE SCIENCES: USE CASES
  • 11.7 TECHNOLOGY & SOFTWARE PROVIDER
    TECHNOLOGY & SOFTWARE PROVIDER: USE CASES
  • 11.8 IT & TELECOM
    IT & ITES: USE CASES
  • 11.9 ENERGY & UTILITIES
    ENERGY & UTILITIES: USE CASES
  • 11.10 MEDIA & ENTERTAINMENT
    MEDIA & ENTERTAINMENT: USE CASES
  • 11.11 AUTOMOTIVE, TRANSPORTATION & LOGISTICS
    AUTOMOTIVE, TRANSPORTATION & LOGISTICS: USE CASES
  • 11.12 OTHER VERTICALS (AGRICULTURE, CONSTRUCTION, AND EDUCATION)
CLOUD AI MARKET, BY REGION
12
  • 12.1 INTRODUCTION
  • 12.2 NORTH AMERICA
    NORTH AMERICA: MARKET DRIVERS
    NORTH AMERICA: MACROECONOMIC
    US
    CANADA
  • 12.3 EUROPE
    EUROPE: MARKET DRIVERS
    EUROPE:MACROECONMOIC FACTOR
    UK
    GERMANY
    FRANCE
    ITALY
    NORDIC
    SPAIN
    REST OF EUROPE
  • 12.4 ASIA PACIFIC
    ASIA PACIFIC: MARKET DRIVERS
    ASIA PACIFIC: MACROECONOMIC FACTOR
    CHINA
    JAPAN
    SOUTH KOREA
    AUSTRALIA & NEW ZEALAND
    INDIA
    REST OF ASIA PACIFIC
  • 12.5 MIDDLE EAST & AFRICA
    MIDDLE EAST & AFRICA: MARKET DRIVERS
    MIDDLE EAST & AFRICA : MACROECONOMIC FACTOR
    GCC
    - Saudi Arabia
    - UAE
    - Qatar
    - Rest of GCC
    SOUTH AFRICA
    TURKEY
    REST OF MIDDLE EAST & AFRICA
  • 12.6 LATIN AMERICA
    LATIN AMERICA: MARKET DRIVERS
    LATIN AMERICA: MACROECONOMIC
    BRAZIL
    ARGENTINA
    MEXICO
    REST OF LATIN AMERICA
COMPETITIVE LANDSCAPE
13
  • 13.1 INTRODUCTION
  • 13.2 KEY PLAYER STRATEGIES/RIGHT TO WIN
  • 13.3 REVENUE ANALYSIS
  • 13.4 COMPANY VALUATION AND FINANCIAL METRICS
  • 13.5 MARKET SHARE ANALYSIS
  • 13.6 BRAND/PRODUCT COMPARISON
  • 13.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023
    STARS
    EMERGING LEADERS
    PERVASIVE PLAYERS
    PARTICIPANTS
    COMPANY FOOTPRINT: KEY PLAYERS, 2023
    - COMPANY FOOTPRINT
    - REGION FOOTPRINT
    - OFFERING FOOTPRINT
    - BUSINESS FUNCTION FOOTPRINT
    - VERTICAL FOOTPRINT
  • 13.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023
    PROGRESSIVE COMPANIES
    RESPONSIVE COMPANIES
    DYNAMIC COMPANIES
    STARTING BLOCKS
    COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023
    - DETAILED LIST OF KEY STARTUPS/SMES
    - COMPETITIVE BENCHMARKING OF KEY STARTUPS/SMES
  • 13.9 KEY MARKET DEVELOPMENTS
    NEW LAUNCHES
    DEALS
    OTHERS
    AWS
COMPANY PROFILES
14
GOOGLE
IBM
INTEL
MICROSOFT
NVIDIA
ORACLE
SALESFORCE
SAP
META
ALIBABA CLOUD
OPENAI
HPE
SIEMENS
BAIDU
ALTAIR ENGINEERING
MONGODB
ZTE CORP
TENCENT
SAS INSTITUTE
INFOSYS
DATAIKU
CIRRASCALE CLOUD SERVICES
H2O
CLOUDMINDS TECHNOLOGY
AIBRAIN LLC
SOUNDHOUND
CEREBRAS SYSTEMS
HAILO
AERA TECHNOLOGY
METROPOLIS TECHOLOGIES
ADJACENT MARKETS
15
  • 15.1 INTRODUCTION TO ADJACENT MARKETS
  • 15.2 LIMITATIONS
  • 15.3 CLOUD AI MARKET ECOSYSTEM & ADJACENT MARKETS
  • 15.4 ADJACENT MARKET 1
  • 15.5 ADJACENT MARKET 2
APPENDIX
16
  • 16.1 DISCUSSION GUIDE
  • 16.2 KNOWLEDGE STORE: MARKETSANDMARKETS' SUBSCRIPTION PORTAL
  • 16.3 AVAILABLE CUSTOMIZATIONS
  • 16.4 RELATED REPORTS
  • 16.5 AUTHOR DETAILS

 

The study comprised four main activities to estimate the cloud AI market size. We conducted significant secondary research to gather data on the market, the competing market, and the parent market. The following stage involved conducting primary research to confirm these conclusions and hypotheses and sizing with industry experts throughout the value chain. The overall market size was evaluated using a blend of top-down and bottom-up approach methodologies. After that, we estimated the market sizes of the various cloud AI market segments using the market breakup and data triangulation techniques.

Secondary Research

We determined the size of companies offering cloud AI based on secondary data from paid and unpaid sources. We also analyzed major companies' product portfolios and rated them based on their performance and quality.

In the secondary research process, various sources were referred to identify and collect information for this study. 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 journals, government websites, blogs, and vendors' websites. Additionally, the spending of various countries on the cloud AI market was extracted from the respective sources. We used secondary research to obtain the critical information related to the industry's value chain and supply chain to identify the key players based on offering, market classification, and segmentation according to components of the major players, industry trends related to components, users, and regions, and the key developments from both market- and technology-oriented perspectives.

Primary Research

In the primary research process, we interviewed various primary sources from the supply and demand sides of the cloud AI market to obtain qualitative and quantitative information. The primary sources from the supply side included industry experts, such as Chief Executive Officers (CEOs), Vice Presidents (VPs), marketing directors, technology and innovation directors, and related key executives from vendors providing offerings, associated service providers, and operating in the targeted countries. All possible parameters that affect the market covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to arrive at the final quantitative and qualitative data.

After the complete market engineering process (including calculations for market statistics, market breakup, market size estimations, market forecasting, and data triangulation), we conducted extensive primary research to gather information and verify and validate the critical numbers arrived at. The primary research also helped identify and validate the segmentation, industry trends, key players, competitive landscape, and market dynamics, such as drivers, restraints, opportunities, challenges, and key strategies. In the complete market engineering process, the bottom-up approach and several data triangulation methods were extensively used to perform market estimation and market forecasting for the overall market segments and subsegments listed in this report. We conducted an extensive qualitative and quantitative analysis of the complete market engineering process to list the key information/insights throughout the report.

Cloud AI Market Size, and Share

Note 1: Tier 1 companies have revenues greater than USD 10 billion; tier 2 companies' revenues range
between USD 1 and 10 billion; and tier 3 companies' revenues range between USD 500 million and 1 billion
Note 2: Others include sales, marketing, and product managers.
Source: Secondary Literature, Interviews with Experts, and MarketsandMarkets Analysis

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Market Size Estimation

The cloud AI market and related submarkets were estimated and forecasted using top-down and bottom-up methodologies. We used the bottom-up method to determine the market's overall size, using the revenues and product offerings of the major market players. This research ascertained and validated the precise value of the total parent market size through data triangulation techniques and primary interview validation. Next, using percentage splits of the market segments, we utilized the overall market size in the top-down approach to estimate the size of other individual markets.

The research methodology used to estimate the market size included the following:

  • We used primary and secondary research to determine the revenue contributions of the major market participants in each country after secondary research helped identify them.
  • Throughout the process, we obtained critical insights by conducting in-depth interviews with industry professionals, including directors, CEOs, VPs, and marketing executives, and by reading the annual and financial reports of the top firms in the market.
  • We used primary sources to verify all percentage splits and breakups, which we calculated using secondary sources.

Cloud AI Market : Top-Down and Bottom-Up Approach

Cloud AI Market Top Down and Bottom Up Approach

Data Triangulation

Once the overall market size was determined, we divided the market into segments and subsegments using the previously described market size estimation procedures. When required, market breakdown and data triangulation procedures were employed to complete the market engineering process and specify the exact figures for every market segment and subsegment. The data was triangulated by examining several variables and patterns from government entities' supply and demand sides.

Market Definition

Considering the views of various sources and associations, MarketsandMarkets defines cloud AI as combining cloud computing with artificial intelligence to provide businesses and individuals easy access to advanced AI tools and services. Organizations can streamline their operations and enhance their capabilities by integrating technologies like machine learning, natural language processing, and computer vision into cloud platforms such as Google Cloud AI, Amazon Web Services (AWS), and Microsoft Azure. Cloud AI enables companies to harness the full potential of AI without heavy upfront investments, making it scalable and cost-effective. As a result, cloud AI empowers businesses to innovate, improve decision-making, and gain a competitive edge in their industries.

Stakeholders

  • Research organizations
  • Third-party service providers
  • Technology providers
  • Cloud services providers
  • AI consulting companies
  • Independent software vendors (ISVs)
  • Service providers and distributors
  • Application development vendors
  • System integrators
  • Consultants/consultancy/advisory firms
  • Training and education service providers
  • Support and maintenance service providers
  • Managed service providers

Report Objectives

  • To define, describe, and forecast the cloud AI market based on offering (infrastructure [Cloud AI Infrastructure, AI and ML Platforms, MLOPS, and Lifecycle Management], AI-as-a-service), technology type, hosting type, organization size, business function, vertical, and region
  • To forecast the market size of the five major regional segments: North America, Europe, Asia Pacific, the Middle East & Africa, and Latin America
  • To strategically analyze the market subsegments concerning individual growth trends, prospects, and contributions to the total market
  • To provide detailed information related to the significant factors influencing the growth of the market (drivers, restraints, opportunities, and challenges)
  • To strategically analyze macro and micro markets for growth trends, prospects, and their contributions to the overall market
  • To analyze industry trends, patents and innovations, and pricing data related to the market
  • To analyze the opportunities in the market for stakeholders and provide details of the competitive landscape for prominent players
  • To analyze the impact of AI/GenAI on the cloud AI market
  • To profile key players in the market and comprehensively analyze their market share/ranking and core competencies.
  • To track and analyze competitive developments, such as mergers & acquisitions, product launches and enhancements, and partnerships & collaborations in the market.

Available Customizations

MarketsandMarkets provides customizations based on the company's unique requirements using market data. The following customization options are available for the report:

Product Analysis

  • The product matrix provides a detailed comparison of each company's portfolio.

Geographic Analysis

  • Further breakup of the cloud AI market

Company Information

  • Detailed analysis and profiling of five additional market players

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Growth opportunities and latent adjacency in Cloud AI Market

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