Comparative Analysis of Large Language Models: On-Premise Architectures vs. Cloud-Based Deployments

Abstract

Large Language Models (LLMs) are transforming industries through their applications in automation, natural language processing, and decision-making. This paper provides a comparative analysis of on premise and cloud-based deployment strategies for LLMs, using key criteria such as scalability, cost efficiency, performance, energy consumption, data security, and customization. Cloud solutions offer dynamic scaling and rapid integration but raise concerns regarding data sovereignty, vendor lock-in, and regulatory compliance. On-premise deployments provide tighter control and regulatory alignment but demand significant capital investment and ongoing maintenance. The paper also explores emerging hybrid approaches and emphasizes the environmental and operational implications of infrastructure choices. By offering a structured evaluation and real-world case studies, this study equips stakeholders with actionable insights to align LLM deployment strategies with their technical, legal, and strategic goals.