Market Overview:
Machine learning as a service (MLaaS) is a cloud service that provides machine learning models and APIs which can be used by applications without needing expertise in algorithms, data cleaning or feature engineering. MLaaS allows users to build, train and deploy machine learning models without the need to set up and maintain the infrastructure. Industries leverage MLaaS to implement AI capabilities without building AI operations from scratch. By tapping into cloud-based MLaaS, businesses can rapidly spin up models and APIs to enhance their applications and processes tasks like predictive analytics, image recognition and natural language processing.
Market Key Trends:
With the rise of artificial intelligence and machine learning, demand for MLaaS solutions is growing rapidly as companies adopt cloud-based strategies to leverage AI. One key trend driving the MLaaS market is the growing preference for pay-as-you-go pricing models. Most MLaaS providers offer flexible pay-as-you-go pricing based on usage with no upfront costs or long-term commitments. This pricing model allows businesses of any size to experiment with MLaaS on a small scale before scaling up. The pay-as-you-go model lowers the barriers for companies to adopt AI technologies by eliminating the need for complex contractual agreements or high upfront license fees. This pay-as-you go pricing is expected to boost adoption of MLaaS solutions over the forecast period.
The global Machine Learning as a Service (MLaaS) Market Size is estimated to be valued at US$ 10,072.55 Mn in 2023 and is expected to exhibit a CAGR of 38.% over the forecast period 2023 to 2030, as highlighted in a new report published by Coherent Market Insights.
Porter’s Analysis
Threat of new entrants: Low, as machine learning as a service market requires huge capital investments and technical expertise to start a MLaaS business. Existing players have significant brand advantage.
Bargaining power of buyers: Moderate to high. Buyers can negotiate on pricing and select between various service providers. Buyers also have option to build ML in house.
Bargaining power of suppliers: Low. ML industry has many suppliers of cloud computing and AI technologies. Suppliers do not have pricing power in this fragmented industry.
Threat of new substitutes: Low. Machine learning algorithms and cloud computing provide unique value and specialized solutions compared to traditional substitutes.
Competitive rivalry: High. Market has many global and regional players. Players compete based on technology expertise, pricing models, service quality and customized solutions.
Key Takeaways
The global Machine Learning as a Service (MLaaS) market is expected to witness high growth, exhibiting 38% CAGR over the forecast period 2023 to 2030, due to increasing demand for simplified AI solutions and business need to leverage ML capabilities without significant investments.
North America dominated the market in 2023 due to heavy investments in AI and presence of major tech companies providing MLaaS. Asia Pacific is expected to be the fastest growing regional market, exhibiting over 40% CAGR during the forecast period, led by growing tech adoption in China and India.
Key players operating in the Machine Learning as a Service (MLaaS) market are H2O.ai, Google Inc., Predictron Labs Ltd, IBM Corporation, Ersatz Labs Inc., Microsoft Corporation, Yottamine Analytics, Amazon Web Services Inc., FICO, and BigML Inc. Key players are focusing on expanding model catalog, building industry specific solutions and offering pay as you go pricing models to gain more customers.
*Note:
1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it
