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

The paper titled 'A Cost-Benefit Analysis of On-Premise Large Language Model Deployment' provides a framework for organizations to evaluate whether deploying large language models (LLMs) locally is more cost-effective than subscribing to commercial services from providers like OpenAI, Anthropic, and Google. As LLMs gain traction, organizations face critical decisions regarding productivity and data privacy. The analysis considers hardware requirements, operational expenses, and performance benchmarks of open-source models such as Qwen, Llama, and Mistral. It identifies a breakeven point based on usage levels, indicating when local deployment becomes economically viable. This research is significant as it addresses the growing interest in local deployments driven by concerns over data privacy and the long-term costs associated with cloud services.