Building Resilient Software Platforms: API Design And Infrastructure Engineering For Scalable Systems

Abstract

In an era where digital services demand high availability and seamless performance, building resilient and scalable software platforms has become a strategic imperative. This study explores how API design and infrastructure engineering jointly influence the resilience, fault tolerance, and scalability of modern distributed systems. Through a combination of controlled experiments, performance benchmarking, and chaos simulations, three API models synchronous, RESTful, and event-driven and three deployment architectures monolithic, federated, and microservice mesh were evaluated under variable workloads and failure conditions. The results reveal that event-driven APIs deliver the lowest response times and highest resilience scores, while microservice mesh architectures achieve superior system availability, faster recovery, and efficient resource utilization. Statistical analyses, including ANOVA, regression, and correlation tests, confirm the significant impact of these design choices on key resilience metrics such as Mean Time to Recovery (MTTR), Mean Time Between Failures (MTBF), and availability percentage. Additionally, expert validation and high test reliability reinforce the credibility of the findings. The study concludes that resilience must be engineered as a full-stack principle, integrating asynchronous API strategies, observability, automated failover, and distributed infrastructure orchestration. These insights offer a data-driven framework for developers and platform architects aiming to build robust, scalable software systems that can thrive in unpredictable, high-demand environments.