Architectural trade-offs: comparative analysis across K3s, serverless, and traditional server deployments

Abstract

In modern software architecture, combining serverless computing, microservices, and containers improves scalability, performance, observability, and resilience. However, choosing the right deployment strategy is crucial. Current individual deployment methods often limit productivity because of poor integration options. This study looks at three deployment approaches: Kubernetes cluster, AWS Lambda (serverless), and Traditional Java Server. We tested performance under different workloads using virtual machines and simulations. The results show that the K3s cluster provides high throughput and low latency because it manages resources directly. AWS Lambda’s pay-as-you-go model, along with its built-in cost optimization, works well for event-driven workloads. In contrast, Java Microservice is cost-effective but needs manual tuning to control latency and error rates. Bringing these scenarios together into a single service mesh architecture could help optimize costs, performance, and system resilience.