A Scalable Cloud-Based CRM System Using Serverless Microservices and Containerise AI

Abstract

CRM systems now use cloud-native technologies, and there has been a shift in the architecture to modular and highly scalable, with many supporting backends and integrations. The given paper will introduce a scalable cloud-based CRM solution that involves serverless microservices and containerized AI-based recommendation engines. The suggested architecture enables the CRM to be highly available, cost-efficient, real-time, and scalable, which resolves the performance bottlenecks of traditional CRM systems. Our technology enables us to decouple traditional CRM features into fully deployable serverless services, such as customer data ingestors, analytics, and marketing automation. These functions connect with each other through event-based approaches and API gateways, rendering them loosely coupled and simpler to maintain. To provide personalized recommendations, we package the AI models in Docker containers and can easily manage them by using Kubernetes, so that the optimal usage of resources and portability can be ensured. We conducted extensive trials with the top cloud providers, including AWS and GCP, to compare their performance standards in terms of latency, throughput, cost-effectiveness, and AI prediction accuracy. The results led to a 40 percent lower cost of operation and a 50 percent faster response rate of our system than the monolithic ones. The paper also addresses the issues of design trade-offs, implementation problems, future work, along with key Privacy and Security Risks to be considered. As our evidence shows, using serverless microservices and containerized AI engines would provide an attractive paradigm for next-generation CRM platforms.