Althoughthereislittleempiricalresearchonplatform-specific performance for retail workloads, the digital transformation of the retail industry has accelerated the adoption of cloud-based Point-of-Sale (POS) systems. This paper presents a systematic, repeatable comparison of POS workload deployments on Google Cloud Platform (GCP) and Microsoft Azure using real-time API endpoints and open-source benchmarking code. Using free-tier cloud resources, we offer a transparent methodology for POS workload evaluation that small retailers and researchers can use. Our approach measures important performance metrics like response latency, throughput, and scalability while estimating operational costs based on actual resource usage and current public cloud pricing because there is no direct billing under free-tier usage. All the tables and figures in this study are generated directly from code outputs, ensuring that the experimental data and the reported results are consistent. Our analysis shows that GCP achieves 23.0% faster response times at baseline load, while Azure shows 71.9% higher cost efficiency for steady-state operations. We look at the architectural components that lead to these differences and provide a helpful framework for merchants considering cloud point-of-sale implementation. This study establishes a strong, open benchmarking methodology for retail cloud applications and offers the first comprehensive, code-driven comparison of workloads unique to point-of-sale systems across leading cloud platforms.

翻译：尽管针对零售工作负载的平台特定性能的实证研究较少，但零售业的数字化转型加速了基于云的销售点（POS）系统的采用。本文使用实时API端点和开源基准测试代码，对Google Cloud Platform（GCP）和Microsoft Azure上的POS工作负载部署进行了系统化、可重复的比较。利用免费层云资源，我们为小型零售商和研究人员提供了一种透明的POS工作负载评估方法。我们的方法测量了响应延迟、吞吐量和可扩展性等重要性能指标，同时基于实际资源使用情况和当前公有云定价估算运营成本（因为免费层使用不产生直接计费）。本研究中的所有表格和图表均直接从代码输出生成，确保了实验数据与报告结果的一致性。我们的分析表明，在基准负载下，GCP的响应时间快23.0%，而Azure在稳态运营中显示出高出71.9%的成本效益。我们研究了导致这些差异的架构组件，并为考虑实施云销售点的商家提供了一个实用的框架。本研究为零售云应用建立了一个强大、开放的基准测试方法，并首次对领先云平台上销售点系统特有的工作负载进行了全面的、代码驱动的比较。