Deployment technologies like Kubernetes, Helm, and Terraform are almost used everywhere, each requiring its own, in-depth domain knowledge. Chained together, they create a complex and automated deployment process. The Essential Deployment Metamodel (EDMM) has the potential to unify the deployment models needed for such complex deployment scenarios. In combination with the Deployment Model Abstraction Framework (DeMAF), they provide a solution for the automated creation of technology-agnostic deployment models, yet the current DeMAF Kubernetes plugin supports only a limited set of Kubernetes resources which restricts its usability across various deployment scenarios. Additionally, the comprehensibility of EDMM-based deployment models, compared to native Kubernetes models, has not been evaluated. In this work, we aim at enhancing the Kubernetes plugin to support the most common Kubernetes resources and to assess the comprehensibility of EDMM-based deployment models compared to Kubernetes models. To achieve this, the plugin was extended to incorporate the most common Kubernetes resources and a user study was conducted, comparing participants’ comprehension and perception of deployment models in both formats. The extended plugin significantly broadens DeMAF’s applicability as it supports a wider range of deployment scenarios. The user study showed that in terms of comprehensibility, EDMM and Kubernetes are not significantly different. However, participants were slightly faster in answering fact-based questions when working with EDMM-based models and perceived EDMM-based models as less complex. The presented plugin extension provides insight into DeMAF’s capabilities for unifying deployment models and the thesis showed the advantages of EDMM-based models over Kubernetes models. Further work is needed to support less common Kubernetes resources.