The ever-increasing complexity of HW/SW systems presents a persistent challenge, particularly in safety-critical domains like automotive, where extensive testing is imperative. However, the availability of hardware often lags behind, hindering early-stage software development. To address this, Virtual Platforms (VPs) based on the SystemC TLM-2.0 standard have emerged as a pivotal solution, enabling pre-silicon execution and testing of unmodified target software. In this study, we propose an approach leveraging containerization to encapsulate VPs in order to reduce environment dependencies and enable cloud deployment for fast, parallelized test execution, as well as open-source VP technologies such as QEMU and VCML to obviate the need for seat licenses. To demonstrate the efficacy of our approach, we present an Artificial Intelligence (AI) accelerator VP case study. Through our research, we offer a robust solution to address the challenges posed by the complexity of HW/SW systems, with practical implications for accelerating HW/SW co-development.