On-premise systems have started to shift from investing in their own resources to renting systems from cloud providers. Nowadays, there are a variety of services from many service providers that organizations can choose according to their needs. The main purpose of using the cloud is to reduce costs. Most enterprise systems run web applications and therefore choose Kubernetes, an open source platform to implement robust systems. But organizations may face the problem of selecting server resources suitable for their Kubernetes deployment. A common method is to use human analysis to select server properties or may use individual experience to evaluate. The purpose of this research is to design and develop a modeling tool on the Kubernetes platform. To help organizations select the most cost-effective resources for their web applications. This research consists of data analysis and processing steps to find the best value for the resource. The built-in tool uses factors such as server resource traffic over time and individual experience data to evaluate the eligibility for a web application. The results of this research will help organizations to select the most appropriate resources and effectively use them in the resource scope in line with their existing budget.