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

TL;DR

This paper develops a cost-benefit analysis framework to help organizations decide when deploying open-source LLMs on-premise is more economical than subscribing to commercial cloud services, considering costs and performance.

Contribution

It introduces a comprehensive framework for evaluating the economic viability of on-premise LLM deployment versus cloud services, including hardware, operational costs, and performance benchmarks.

Findings

Identifies breakeven points based on usage and performance needs.

Provides cost estimates for deploying open-source LLMs locally.

Offers practical guidance for LLM deployment strategy planning.

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.