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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.18101 |
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| _version_ | 1866908643141615616 |
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| author | Pan, Guanzhong Chodnekar, Vishal Roy, Abinas Wang, Haibo |
| author_facet | Pan, Guanzhong Chodnekar, Vishal Roy, Abinas Wang, Haibo |
| contents | Large language models (LLMs) are becoming increasingly widespread. Organizations that want to use AI for productivity now face an important decision. They can subscribe to commercial LLM services or deploy models on their own infrastructure. Cloud services from providers such as OpenAI, 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, Mistral, 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18101 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Cost-Benefit Analysis of On-Premise Large Language Model Deployment: Breaking Even with Commercial LLM Services Pan, Guanzhong Chodnekar, Vishal Roy, Abinas Wang, Haibo Artificial Intelligence Machine Learning Large language models (LLMs) are becoming increasingly widespread. Organizations that want to use AI for productivity now face an important decision. They can subscribe to commercial LLM services or deploy models on their own infrastructure. Cloud services from providers such as OpenAI, 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, Mistral, 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. |
| title | A Cost-Benefit Analysis of On-Premise Large Language Model Deployment: Breaking Even with Commercial LLM Services |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2509.18101 |