A Cost-Benefit Analysis of On-Premise Large Language Model Deployment: Breaking Even with Commercial LLM Services
Authors’ abstract
Anthropic, and Google are attractive because they provide easy access to state-of-the-art models and are easy to scale. However, concerns about data privacy, the difficulty of switching service providers, and long-term operating costs have driven interest in local deployment of open-source models. This paper presents a cost-benefit analysis framework to help organizations determine when on-premise LLM deployment becomes economically viable compared to commercial subscription services. We consider the hardware requirements, operational expenses, and performance benchmarks of the latest open-source models, including Qwen, Llama, Magistral, and etc. Then we compare the total cost of deploying these models locally with the major cloud providers subscription fee. Our findings provide an estimated breakeven point based on usage levels and performance needs. These results give organizations a practical framework for planning their LLM strategies. Index Terms—Large Language Models, On-Premise Deploy- ment, Cost-Benefit Analysis, Total Cost of Ownership. I.
Paper
- Authors
- G. Pan, V. Chodnekar, A. Roy, H. Wang
- Publication date
- 2025
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- Source license
- Creative Commons Attribution 4.0 International
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- CC-BY-4.0
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