A Cost-Benefit Analysis of On-Premise Large Language Model Deployment: Breaking Even with Commercial LLM Services

TL;DR

The paper tackles the problem of deciding when on-premise open-source LLM deployment is economically viable versus subscribing to commercial APIs. ItDevelops a total cost of ownership framework that combines CapEx, OpEx, and scaling costs with a performance benchmark-informed evaluation to compute break-even times $t^{*}$ under various workloads. The study surveys commercial pricing, analyzes open-weight model performance, and presents a 54-scenario break-even analysis that highlights distinct regimes: SMEs often reach break-even within months for sub-30B models, medium enterprises within roughly 6–24 months for balanced models, and large enterprises only after multi-year horizons for large models, frequently influenced by data-residency requirements and governance. The work provides a practical decision framework and an actionable planning tool to guideL collaboration between model performance, hardware economics, and business constraints, illustrating that on-premise open-source deployment is increasingly viable under specific cost and governance conditions.

Abstract

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.