Kubernetes (K8s) has emerged as the de facto standard for orchestrating containerized applications, offering extensive configurability to meet diverse deployment needs. However, this flexibility introduces significant complexity, leading to steep learning curves and a high propensity for configuration errors due to the manual navigation of vast documentation. While variability modeling techniques has proven successful in software product lines (SPLs) to manage such configuration complexity, its application in infrastructure systems such as K8s remains limited. This paper presents an automated approach for synthesizing a comprehensive K8s variability model directly from its official OpenAPI schemas. We demonstrate that this automatically generated model offers broader coverage of the configuration space than a manually constructed model previously developed from the API documentation. Furthermore, we evaluate the model’s effectiveness in configuration validation against 250,000 real-world K8s configurations, comparing it with existing K8s validation tools. Our approach achieves 94.8% configuration validity, offering comprehensive structural and semantic checks that go beyond schema compliance or policy enforcement provided by other tools.