AI-as-a-Service (AIaaS)

By Abhivardhan

Definition 

AIaaS refers to a cloud-based service model in which third-party providers host AI infrastructure, computational resources or pretrained models to enable users to build, deploy and utilise AI applications without maintaining their own AI infrastructure. It enables organisations to integrate AI into existing business operations through external service providers, thereby significantly reducing the financial, technical and operational barriers traditionally associated with AI development.¹ 

Commentary

Origin of the term 

The concept of AI as a Service emerged from the evolution of the cloud computing service model, as an extension of the ‘as-a-service' paradigm beyond IaaS, PaaS and SaaS to the delivery of artificial intelligence capabilities.² As AI development became increasingly dependent on sophisticated computing infrastructure and resources, the cloud providers have begun to offer AI functionalities as remotely accessible services, which has enabled organisations to access advanced AI capabilities without investing in dedicated infrastructure to develop proprietary AI systems.³ 

Operation in Practice

In practice, AIaaS providers host AI models and supporting infrastructure on cloud, edge, or fog computing environments and make them available through APIs or cloud interfaces. Businesses subscribe to services such as machine learning, natural language processing, predictive analytics etc. integrating them directly into their products and workflows without independently developing AI models.⁴ 

Such services find application across sectors including healthcare, telecommunications, manufacturing, industrial automation, etc.

Relevance vis-a-vis Competition Law 

AIaaS has significant implications in the antitrust domain because it has the potential to promote both market access and market concentration. By eliminating the need for substantial investment in AI infrastructure, computational resources and expertise, it lowers entry barriers and can enable the startups and downstream firms to access sophisticated AI capabilities thereby promoting innovation and dynamic competition. However, increasing dependence on a limited number of AI and cloud service providers may create vendor lock-in, raise switching costs and raise concerns related to interoperability and vertical integration.⁵ 

Related nomenclature for this concept includes Artificial Intelligence as a Product (AI-Pro), Artificial Intelligence as a Component (AI-Com), Artificial Intelligence as a System (AI-S), Artificial Intelligence-enabled Infrastructure as a Service (AI-IaaS). 

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   1. Abishai Christopher Singh D., ‘AIaaS and Market Manipulation: Opportunities for Growth and the Need for Regulation in India (2024) 6(5) IJLSI <https://ijlsi.com/wp-content/uploads/AIaaS-and-Market-Manipulation.pdf> accessed 12 August 2026.↩︎

   2. N. Syed et al., ‘Artificial Intelligence as a Service (AIaaS) for Cloud, Fog and the Edge: State-of-the-Art Practices’ (2025) 57(8) ACM <https://dl.acm.org/doi/epdf/10.1145/3712016> accessed 12 August 2026.↩︎

   3. ‘AI-as-a-Service: Types, Top Vendors, Benefits & Challenges’ (Total eBiz Solutions) <https://www.totalebizsolutions.com/blogs/ai-as-a-service-types-top-vendors-benefits-challenges/> accessed 12 August 2026.↩︎

   4. ‘Section 6 – Commercial Methods of Classification’ (Artificial Intelligence Act) accessed 12 August 2026.↩︎

   5. ibid 3.↩︎