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Xin, Qi
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Computer Science & IT

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Hybrid Cloud Architecture for Efficient and Cost-Effective Large Language Model Deployment Xin, Qi
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1170

Abstract

Large Language Models (LLMs) have achieved remarkable success across natural language tasks, but their enormous computational requirements pose challenges for practical deployment. This paper proposes a hybrid cloud–edge architecture to deploy LLMs in a cost-effective and efficient manner. The proposed system employs a lightweight on-premise LLM to handle the bulk of user requests, and dynamically offloads complex queries to a powerful cloud-hosted LLM only when necessary. We implement a confidence-based routing mechanism to decide when to invoke the cloud model. Experiments on a question-answering use case demonstrate that our hybrid approach can match the accuracy of a state-of-the-art LLM while reducing cloud API usage by over 60%, resulting in significant cost savings and a ~40% reduction in average latency. We also discuss how the hybrid strategy enhances data privacy by keeping sensitive queries on-premise. These results highlight a promising direction for organizations to leverage advanced LLM capabilities without prohibitive expense or risk, by intelligently combining local and cloud resources.
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