List of Contents
What is the Machine Learning as a Service Market Size?
The global machine learning as a service market size is accounted at USD 61.57 billion in 2025 and predicted to increase from USD 85.77 billion in 2026 to approximately USD 1,599.07 billion by 2035, representing a CAGR of 38.15% from 2026 to 2035.
Machine Learning as a Service Market Key Takeaways
- The solution component segment accounted for 63% of revenue share in 2025.
- The large enterprises segment contributed 59% revenue share in 2025.
- North America region had the highest revenue share 40% in 2025.
- Based on end-user, the IT & telecom segment accounted highest market share in 2025.
Strategic Overview of the Global Machine Learning as a Service Industry
A group of services known as machine learning-as-a-service (MLaaS) offers machine-learning technologies as a component of cloud computing services. Tools including data visualisation, APIs, facial recognition, natural language processing, predictive analytics, and deep learning are available through these services from vendors. The provider's data centres handle the actual calculation. With consumers having the choice of many alternative solutions catered to various business needs, the MLaaS model is positioned to dominate the industry. Additionally, the market for machine learning as a service is anticipated to increase as a result of factors including the rising usage of cloud-based services, IoT, automation, and consumer behaviour research.
Deep learning techniques are utilised by machine learning as a service to improve decision-making through predictive analytics. The use of MLaaS does, however, provide security and data privacy problems for owners of ML models. Owners of data worry about the security and privacy of their data on MLaaS platforms. However, owners of MLaaS platforms are concerned about attackers impersonating clients and stealing their models.
The demand for effective data organisation increases as more firms move their data from on-premise to cloud storage. Since MLaaS platforms are essentially cloud providers, they make it simpler for data engineers to access and analyse the data by enabling solutions to manage it effectively for machine learning experiments and data pipelines. Data visualisation and predictive analytics are two features that MLaaS providers make available to businesses. Along with other things, they offer APIs for sentiment analysis, creditworthiness assessments, business intelligence, facial recognition, and healthcare. Data scientists don't need to worry about these computations because MLaaS companies have abstracted them away.
Artificial Intelligence: The Next Growth Catalyst in Machine Learning as a Service
AI is a core driver of the Machine Learning as a Service (MLaaS) market, fundamentally transforming it by increasing accessibility and efficiency. It provides pre-built models and algorithms through cloud platforms, allowing businesses of all sizes to implement advanced AI capabilities like natural language processing and predictive analytics without extensive in-house expertise or infrastructure investment.
The integration of AI technologies, such as Generative AI and automated machine learning (AutoML) tools, accelerates the development and deployment of solutions, drastically cutting down on development time and costs. Furthermore, AI enhances the sophistication of MLaaS applications in areas like real-time fraud detection and personalized customer experiences, while also enabling seamless integration with emerging technologies such as the Internet of Things (IoT) and edge computing.
Machine Learning as a Service Market Growth Factors
IoT and automation adoption will increase, driving the market. IoT operations guarantee that the hundreds or more devices operating securely and correctly on a corporate network, as well as that the data being gathered is reliable and timely. The hard lifting of processing the data stream is handled by sophisticated back-end analytics engines, but verifying the quality of the data is frequently left to dated approaches. Some IoT platform suppliers are using machine learning technologies to strengthen their operations management skills in order to secure the rein in vast IoT systems.
By analysing large amounts of data with powerful algorithms, machine learning may be able to decipher the hidden patterns in IoT data. Automated systems using statistically generated actions and ML inference may enhance or replace manual operations in crucial activities. The IoT data modelling process is automated by ML-based solutions, which eliminate the time-consuming and laborious model selection, coding, and validation steps.
IoT adoption by small enterprises may result in considerable time savings for the laborious machine learning process. Vendors of MLaaS may run more queries more rapidly and offer more sorts of analysis, delivering more types of information to extract more useful information from huge caches of data produced by numerous devices in the IoT network.
More businesses are utilising machine learning technology for data analytics as businesses adopt IoT-based technologies and solutions at an increasing rate. MLaaS is therefore anticipated to spur IoT innovation. Total IoT connections are anticipated to rise from 12.7 billion in 2021 to 32.5 billion in 2030, with a CAGR of 14%, according to Ericsson. MLaaS is currently connected with a number of sensors, but it is positioned to be a key component of the Internet of Things and automation.
AIOps issued research in 2019 titled "State of Automation, Artificial Intelligence, and Machine Learning in Network Management" in which 85% of respondents said that their company used more than one sort of automation. Only 27% of respondents said their company was well prepared for complete automation, though. However, according to roughly 65% of study participants, machine learning is essential for network management and will likely lead to more automation in the future.
Market Outlook
- Market Growth Overview: The machine learning as a service market is expected to grow significantly between 2025 and 2034, driven by growing adoption of cloud computing, rapid integration of generative AI, demand for no-code/low-code solutions, and high adoption in regulated industries.
- Sustainability Trends: Sustainability trends involve Green AI and computational efficiency, renewable energy and green data centers, and transparency and ethical accountability.
- Major Investors: Major investors in the market include Google Ventures (GV) / Alphabet Inc., Microsoft Venture (M12), Amazon (through AWS), and Intel Capital.
- Startup Economy: The startup economy is focused on generative AI and large language models, automated machine learning & no-code/low-code platforms, and MLOps (Machine Learning Operations) and governance.
Market Scope
| Report Coverage | Details |
| Market Size in 2025 | USD 61.57 Billion |
| Market Size in 2026 | USD 85.77 Billion |
| Market Size by 2035 | USD 1,599.07 Billion |
| Growth Rate from 2026 to 2035 | CAGR of 38.15% |
| Base Year | 2025 |
| Forecast Period | 2026 to 2035 |
| Segments Covered | Component, Organization Size, Application, Industry Vertical, Geography |
Machine Learning as a Service Market Segment Insights
Component Insights
Machine learning as asolutionmarket share was led by thesolution sector in 2025 and it is anticipated that this dominance would continue in the years to come. The Services segment has captured a revenue share of around 37% in 2025. This is attributable to elements including the expansion of application domains and the development of end-use industries in emerging countries, which are anticipated to propel the market for machine learning services. Industry participants are concentrating on putting technologically cutting-edge solutions in place to enhance the use of machine learning services.
The use of machine learning services in the healthcare sector to evaluate ECG and MRI, as well as to diagnose cancer, expands the market. Major market prospects are anticipated to be unlocked by the advantages provided by machine learning services, such as demand forecasting, cost reduction, real-time data analysis, and rise in the use of the cloud market. For example, Microsoft Corporation released an open dataset for health & genomics, transportation, labour & economics, supplementary, population & safety, and common datasets in April 2021. This dataset aims to increase the accuracy of machine learning models using publicly accessible datasets. This also enables businesses to use Azure Open Datasets with its machine learning and data analytics solutions to offer insights at hyperscale, which increases sales of these businesses' ML as a Service.
Application Insights
In 2025, the market for machine learning as a service had the biggest revenue share earned by the marketing and advertising category. A recommendation system's objective is to present clients with goods they are presently interested in. The marketing work algorithm is as follows: Marketing professionals create, test, judge, and analyse hypothese. This endeavour is time-consuming and labor-intensive, and the results are occasionally incorrect since information changes every second.
Marketers may use machine learning to quickly decide based on a lot of data. Businesses may now react to changes in the calibre of traffic brought about by advertising initiatives more swiftly thanks to machine learning. As a consequence, the company will have more time to focus on creating hypotheses than on executing menial activities.
Verticals Insights
In the market for machine learning as a service in 2025, the retail sector accounted for a sizeable revenue share. E-commerce has established itself as a major player in the retail trade sector. The retail industry is dynamic and demands both agility and improved client interactions. Retailers are utilising machine learning services as a result to give clients amazing shopping experiences.
Large retailers frequently employed analytical consulting firms to obtain information essential for marketing. Due to the availability of affordable cloud-based machine learning services, smaller retailers are able to use data to better understand their consumers, which is likely to open up potential for the growth of the machine learning as a service industry globally. Machine learning enables systems to automatically learn from each campaign given to a consumer and apply that knowledge in the subsequent iteration. The market for machine learning as a service is predicted to develop as machine learning driven marketing transforms the established mode of advertising. Machine learning integration in the retail industry lowers inventory costs, which raises consumer happiness. This creates a substantial opportunity for the market for machine learning as a service to expand.
Organization Size Insights
In 2025, the market for machine learning as a service saw a significant revenue share obtained by the small and medium business sector. SMBs use MLaaS because the machine learning application provides dynamic data. Machine learning algorithms are able to forecast future events with the use of predictive analytics, in addition to providing real-time data. By forecasting product demand and making recommendations on the time and volume of supplies needed to satisfy consumers' expectations, SMBs may utilise machine learning technologies to fine-tune their supply chain.
Machine Learning as a Service Market Regional Insights
The U.S. machine learning as a service market size is evaluated at USD 17.24 billion in 2025 and is predicted to be worth around USD 449.98 billion by 2035, rising at a CAGR of 38.57% from 2026 to 2035.
Why North America dominates the machine learning as a service market?
In terms of technical breakthroughs and acceptance, North America is the area that is developing the fastest within the worldwide market for machine learning as a service. It has an infrastructure that is well-equipped and has the financial means to pay for machine learning as a service solution. Additionally, the market is anticipated to grow during the forecast period due to an increase in spending in the defence sector and technical developments in the telecommunications sector. The market for machine learning services is anticipated to be strongly influenced by government rules on data security. The market is anticipated to be driven by services like cloud applications and security information. The significant industry presence of companies like Google, IBM, Microsoft, and Amazon Web Services as well as their wide range of product offerings have also contributed to the increase in demand for machine learning in this area. Additionally, the development of cognitive computing and artificial intelligence is anticipated to provide up lucrative prospects for market participants to capitalise on several industrial applications, including fraud management, fraud detection, and natural language processing.
Why Asia-Pacific region is growing faster in the machine learning as a service market?
However, over the projected period, Asia-Pacific is anticipated to have the highest CAGR and be the area with the quickest growth. Leading companies are concentrating on the Asia-Pacific area in order to build their businesses since the BFSI (banking, financial services, and insurance) industry is predicted to have significant growth in the deployment of security services in this region. During the projected period, the CAGR for Asia-Pacific is anticipated to be the highest, making it the region with the quickest growth. Industry players are becoming aware of how crucial it is to offer multimodal platforms in order to guarantee excellent customer service. The main trend anticipated to have an impact on the market growth in this area is the rise in the penetration of AI applications. Additionally, government entities have made significant attempts to accelerate machine learning and related technologies' penetration in the region.
The U.S.'s machine learning as a service market is experiencing significant growth, driven by the integration of advanced Generative AI and AutoML models, increasing accessibility for businesses with limited data science expertise. High adoption rates in regulated industries like healthcare and finance highlight the market's practical applications and value proposition. Ultimately, the U.S. market is characterized by a strong focus on automation, efficiency, and robust governance as MLaaS solutions become mainstream across the economy.
China's machine learning as a service market is rapidly evolving, primarily driven by aggressive national AI strategies and high domestic adoption of Generative AI tools. The market is dominated by local tech giants, which provide solutions that comply with strict data localization and security mandates.
While high adoption is evident, the industry is navigating challenges related to talent shortages and the transition from experimental use to full implementation. This top-down, government-supported ecosystem positions China as a formidable and fast-moving player in the global MLaaS landscape.
Europe's stringent regulatory environment mandates privacy and ethical compliance through frameworks like the GDPR and AI Act. The market benefits from substantial public investment into R&D and specialized, vertical-specific AI solutions across key industries. A preference for interoperable hybrid cloud strategies also defines this region's strategic, trust-focused approach to adopting machine learning services.
Germany's machine learning as a service market is experiencing robust government funding and a strategic focus on Industry 4.0 applications, particularly within its dominant automotive sector. The market's development is heavily influenced by strict data sovereignty and GDPR compliance requirements, favoring hybrid and multi-cloud solutions. Ultimately, Germany's strength in MLaaS lies in leveraging a strong research base and talent pool for practical, compliant, and industrially relevant AI applications. More information is available online.
Value Chain Analysis of the Machine Learning as a Service Market
- Infrastructure & Platform Development:
This foundational stage involves building and maintaining the scalable cloud infrastructure, hardware (GPUs/TPUs), and core software platforms that host ML services.
Key Players: Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), Alibaba Cloud. - Algorithm & Model Development:
This stage focuses on R&D for developing and refining the ML models and algorithms that are offered as services, including pre-built models for specific tasks like NLP, computer vision, and predictive analytics.
Key Players: Google DeepMind, OpenAI, Anthropic, Baidu, IBM Research. - Data Preparation & Management:
This crucial stage involves collecting, cleaning, labeling, and managing the vast datasets required to train effective ML models. Specialized companies provide tools and services for this labor-intensive process, ensuring the data is high-quality, compliant with regulations (like GDPR), and ready for ingestion by MLaaS platforms.
Key Players: Scale AI, Snowflake Inc., Palantir Technologies, Informatica, SambaNova Systems. - Service Provision & User Experience (UX) Platforms:
This stage delivers the actual MLaaS platforms and tools to the end-user, focusing on accessibility, ease of use (e.g., AutoML, no-code interfaces), and seamless integration into existing business workflows.
Key Players: Dataiku, DataRobot, C3.ai, H2O.ai, TIBCO Software. - Integration, Consulting, and Professional Services:
This final stage involves helping enterprises integrate MLaaS solutions into their specific operations, customizing models, and ensuring proper MLOps (Machine Learning Operations) and governance are in place.
Key Players: Accenture plc, Deloitte Touche Tohmatsu Limited, IBM Global Services, Cognizant, Wipro Limited.
Top Companies in the Machine Learning as a Service Market & Their Offerings:
- Google Inc.: Google is a market leader with its Google Cloud AI and Vertex AI platforms, which provide access to state-of-the-art machine learning models and infrastructure for businesses of all sizes.
- SAS Institute Inc.: SAS provides a powerful MLaaS platform that integrates advanced analytics, data management, and AI capabilities to help enterprises turn data into actionable insights.
- FICO: FICO specializes in applied intelligence solutions, using MLaaS to provide predictive analytics and decision management technology primarily for the financial services industry.
- Hewlett Packard Enterprise (HPE): HPE contributes to the MLaaS market by providing the essential hardware infrastructure, servers, and high-performance computing solutions required to run demanding ML workloads. They also offer software platforms like HPE GreenLake for AI, enabling hybrid cloud and on-premises MLaaS solutions with data sovereignty in mind.
- Yottamine Analytics: Yottamine provides an MLaaS platform that focuses on delivering predictive analytics and optimization solutions through a user-friendly interface. Their services help businesses generate accurate forecasts and identify actionable opportunities without extensive in-house data science expertise.
- Amazon Web Services (AWS): AWS is a dominant force in the MLaaS market through its comprehensive Amazon SageMaker platform, which provides an end-to-end suite of tools for building, training, and deploying ML models at scale. Their vast cloud infrastructure and extensive ecosystem of services make AI accessible to a broad range of industries and use cases.
- BigML, Inc: BigML offers a user-friendly, no-code MLaaS platform that simplifies the creation and deployment of predictive models. Their focus on simplicity and accessibility allows users to easily visualize and automate machine learning tasks for various business needs.
- Microsoft Corporation: Microsoft is a key player with its Azure Machine Learning platform, which offers a robust, enterprise-ready environment for AI development and deployment. They focus on hybrid cloud flexibility, strong MLOps tools, and seamless integration with the broader Microsoft software ecosystem.
- Predictron Labs Ltd: Predictron Labs provides specialized MLaaS solutions with a focus on delivering bespoke predictive analytics and data science consulting services. They help clients in specific verticals, such as engineering and manufacturing, leverage AI to solve complex operational challenges.
- IBM Corporation: IBM offers its MLaaS solutions via the IBM Watson platform, focusing on enterprise AI applications with a strong emphasis on trust, transparency, and data governance.
Machine Learning as a Service Market Companies
- GOOGLE INC
- SAS INSTITUTE INC
- FICO
- HEWLETT PACKARD ENTERPRISE
- YOTTAMINE ANALYTICS
- AMAZON WEB SERVICES
- BIGML, INC
- MICROSOFT CORPORATION
- PREDICTRON LABS LTD
- IBM CORPORATION
Key market developments
- In order to strengthen the capabilities of organisations employing analytics and machine learning platforms to make decisions, Cognizant, a major participant in the machine learning as a service industry, purchased Inawisdom in December 2021.
- In order to support digital business transformation by concentrating on new business outcomes, Kyndryl, another significant competitor in the market for machine learning as a service, engaged into a relationship with Google Cloud in December 2021.
- Using the brand-new machine learning platform "Azure," Microsoft, a major participant in the market for machine learning as a service, unveiled new machine learning services in May 2019. These services range from hosted notebooks to no-code tools.
Machine Learning as a Service Market Segments Covered in the Report
By Component
- Solution
- Services
By Organization Size
- Small and Medium-Sized Enterprises
- Large Enterprises
By Application
- Marketing & Advertising
- Fraud Detection & Risk Management
- Computer vision
- Security & Surveillance
- Predictive analytics
- Natural Language Processing
- Augmented & Virtual Reality
- Others
By Industry Vertical
- BFSI
- IT & Telecom
- Automotive
- Healthcare
- Aerospace & Defense
- Retail
- Government
- Others
By Geography
- North America
- U.S.
- Canada
- Europe
- U.K.
- Germany
- France
- Asia-Pacific
- China
- India
- Japan
- South Korea
- Malaysia
- Philippines
- Latin America
- Brazil
- Rest of Latin America
- Middle East & Africa (MEA)
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