API pricing based on relative value of API for its consumers

A method for determining API pricing, the method comprising:

• identifying application programming interface (API) consumption parameters of an API using a supervised learning model, wherein said API consumption parameters comprise attributes, availability, quality of profile-specific information, functional dimensions, non-functional dimensions and topic, wherein said supervised learning model uses machine learning to infer a function based on training data;
• determining, by a processor, reference pricing using machine learning using said identified API consumption parameters, wherein said reference pricing comprises competitive pricing;
• determining, by said processor, an API price using said identified API consumption parameters and said determined reference pricing;
• deriving, by said processor, an API pricing score for said determined API price;
• dynamically adjusting said API price by dynamically adjusting said API consumption parameters using said supervised learning model in response to said API pricing score not exceeding a threshold value thereby improving computing capabilities of API pricing systems by lessening computing resources utilized by API pricing systems;
• and
• dynamically selecting, by said processor, said API price as a suggested price for said API in response to said API pricing score exceeding said threshold value, wherein said suggested price for said API establishes an API price that is commensurate with a value assessed by an API consumer.