Abstract

Multi-tenant architectures have become essential in cloud computing, allowing multiple clients to share a single software instance, and thus optimizing costs and resource utilization. However, challenges in data privacy, customization, and scalability limit the effectiveness of traditional multi-tenant systems. This study investigates the potential of AI-driven Natural Language Processing (NLP) to address these limitations by enhancing tenant-specific customizations, improving query handling, and ensuring real-time processing. Using transformer-based models such as BERT and GPT-3, the study implements advanced techniques like federated learning, differential privacy, and model compression in a microservices-based multi-tenant setup. Results indicate substantial improvements in accuracy, latency, data privacy, and tenant satisfaction, with statistically significant performance gains across all metrics. These findings highlight the transformative role of AI-driven NLP in delivering secure, responsive, and highly personalized multi-tenant applications, marking a step forward in scalable, intelligent cloud service architectures.