Software-as-a-Service (SaaS) deliver value to users through a subscription model, offering convenient access to software without the need for direct installation or maintenance. The accessibility to different features varies according to the user’s subscription configuration (i.e. the set of options to which the user is subscribed). Finding the right infrastructure for a SaaS to be deployed is a challenging task that is usually solved by matching the capacity of the infrastructure to the demands of the service, with a plethora of solving approaches that stem from this premise. This work proposes a novel approach to manage demand and capacity that consists of understanding the demand of each possible subscription configuration of a service. This segmentation of demand management can then be used to carry out capacity management strategies that respond better to demand changes. Furthermore, as the capacity is tailored towards the subscriptions, our proposal addresses a way of tailoring the prices of the subscriptions to the cost of the capacity they demand. To do this, we propose a three-step approach: (1) demand management, (2) capacity management, (3) subscription price tailoring. The first step relies on machine learning solutions to obtain the demand of subscription configurations with minimal resource usage. The second reverse engineers the pricing calculators of cloud service providers to optimise capacity management. Finally, the third compares the results of the two previous steps to obtain a suitable price for each subscription.