End-to-End Observability in API-Driven Architecture using MuleSoft and Prometheus

Abstract

End-to-end observability gives full visibility into distributed environments which allows performance and reliability to be achieved while giving faster troubleshooting and for those using API-driven architecture.This study explores observability techniques in API architecture when using MuleSoft and Prometheus tools. Due to more businesses use APIs to improve their systems, it is now important to check how well these APIs are working. The aim is to manage API and integration are provided smoothly by MuleSoft, and by referring to Prometheus, organisations get quick monitoring and alerting services. It is shown in the research that using both of these tools improves the reliability of systems, helps catch mistakes early, and provides support for better decisions. Use cases with Siemens, BMW, and PayPal describe how AI is applied and what its results have been. API performance issues, problems with microservices, and their monitoring challenges are handled. The study ends by outlining recommendation and pointing out how to move ahead by using AI for observability and applying it to IoT systems.