Search papers, labs, and topics across Lattice.
This paper benchmarks the performance and cost-efficiency of deploying Point-of-Sale (POS) workloads on Google Cloud Platform (GCP) and Microsoft Azure using real-time API endpoints and open-source benchmarking code within free-tier resources. The study evaluates response latency, throughput, and scalability, estimating operational costs based on resource usage and public cloud pricing. Results indicate that GCP achieves 23.0% faster response times at baseline load, while Azure demonstrates 71.9% higher cost efficiency for steady-state operations, providing a framework for merchants considering cloud POS implementations.
Small retailers can now quantitatively assess the trade-off between response time and cost efficiency when choosing between Google Cloud and Azure for their Point-of-Sale systems, thanks to this open benchmark.
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.
