Democratizing AI Development: A Feature-Based Categorization of API Platforms, Development Frameworks, LCNC and AIaaS Platforms for LLM-Based Applications

In recent years, LLM-based AI development platforms have gained widespread adoption, enabling both IT professionals and citizen developers to create AI-powered applications. However, the landscape remains fragmented, with a variety of API-based platforms, AI development frameworks, Low Code/No Code (LCNC) platforms, and Domain-Specific AI as a Service (AIaaS) solution, each offering varying levels of accessibility and customization. Due to the recency of the interest in LLM-based AI development platforms, there is limited systematic research categorizing these tools based on their functionalities and intended user groups. This paper addresses this gap by proposing a structured, feature-based categorization framework, distinguishing between platforms based on criteria such as primary target group, degree of customization, and level of abstraction. Methodologically, we apply a feature-driven analysis grounded in documented capabilities and design affordances across a representative set of tools, and we operationalize the two core dimensions (customization and abstraction) through an anchored ordinal scoring rubric to produce a visual map of categories and overlaps. However, further empirical research is needed to validate the attitude of users towards the different tools in the categories. By providing a clearer understanding of AI development tools, this research supports more informed decision-making and contributes to the democratization of AI adoption across industries.