As an increasing number of applications continue to migrate into the cloud, the implementation of automatic scaling for computing resources to meet service-level objectives in a dynamic load environment is becoming a common challenge for software developers. To research how this problem can be tackled in practice, a state-of-the-art auto-scaling solution was developed and implemented in cooperation with AEB SE as a part of their application migration to a new Kubernetes cluster. Requirement elicitation was done via interviews with their development and IT operations staff, who put a strong focus on fast response times for automated requests as the main performance goal, with CPU, memory and response times being the most commonly used performance indicators for their systems. Using the collected knowledge, a scaling architecture was developed using their existing performance monitoring tools and Kubernetes' own Horizontal Pod Autoscaler, with a special adapter used for communicating the metrics between the two components. The system was tested on a deployment of AEB's test product using three different scaling approaches, using CPU utilization, JVM Memory usage and response time quantiles respectively. Evaluation results show that scaling approaches based on CPU utilization and memory usage are highly dependent on the type of requests and the implementation of the tested application, while response time-based scaling provides a more aggregated view on system performance and also reflects the actions of the scaler in its metrics. Overall though, the resulting performance was mostly the same for all scaling approaches, showing that the described architecture works in practice, but a more elaborate evaluation on a larger scale in a more optimized cluster would be needed to clearly distinguish between performances of different scaling strategies in a production environment.