Containerization and application variety bring many challenges in automating evaluations for performance tuning and comparison among infrastructure choices. Due to the tightly-coupled design of benchmarks and evaluation tools, the present automated tools on Kubernetes are limited to trivial microbenchmarks and cannot be extended to complex cloud-native architectures such as microservices and serverless, which are usually managed by customized operators for setting up workload dependencies. In this paper, we propose AutoDECK, a performance evaluation framework with a fully declarative manner. The proposed framework automates configuring, deploying, evaluating, summarizing, and visualizing the benchmarking workload. It seamlessly integrates mature Kubernetes-native systems and extends multiple functionalities such as tracking the image-build pipeline, and auto-tuning. We present five use cases of evaluations and analysis through various kinds of benchmarks including microbenchmarks and HPC/AI benchmarks. The evaluation results can also differentiate characteristics such as resource usage behavior and parallelism effectiveness between different clusters. Furthermore, the results demonstrate the benefit of integrating an auto-tuning feature in the proposed framework, as shown by the 10% transferred memory bytes in the Sysbench benchmark.