A Cost-Benefit Analysis of On-Premise Large Language Model Deployment: Breaking Even with Commercial LLM Services
Published 11 Nov 2025 · arXiv · Guanzhong Pan
Overview
The paper examines the economic viability of deploying large language models (LLMs) on-premise compared to subscribing to commercial LLM services. It provides a cost-benefit analysis framework considering hardware, operational costs, and performance benchmarks.
Key Insights
- Cost-Benefit Framework: The study presents a framework to assess when on-premise LLM deployment becomes economically viable.
- Hardware and Operational Costs: It evaluates the hardware requirements and operational expenses for deploying open-source models like Qwen and Llama.
- Breakeven Analysis: The paper estimates the breakeven point based on usage levels and performance needs.
BFSI Relevance
- Why Relevant: Understanding the cost dynamics of LLM deployment is crucial for financial institutions considering AI integration.
- Primary Sector: Financial Services
- Subsectors: Asset Management, Corporate Banking
- Actionable Implications: BFSI professionals should evaluate their AI strategy, considering both cost and data privacy implications of on-premise versus cloud-based LLMs.
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