Cloud LLMs Cheaper, On-Premise Increases Developer Debugging.

This research presents a detailed case study comparing the economic and operational implications of using cloud-based versus on-premise Large Language Models (LLMs) for enterprise coding agents. The study tracked a single developer working on a production monorepo over two 28-day periods, using Claude Opus via API in one period and an on-premise GLM/Opencode configuration on NVIDIA Blackwell hardware in the other. A significant finding was the dramatic impact of prompt caching on cloud API costs, reducing the effective cost per million tokens by nearly 89% to $0.57. This made the cloud API surprisingly competitive, even undercutting the amortized unit cost of a shared on-premise slice. However, when considering Total Cost of Ownership (TCO) under Taiwan-market parameters, shared on-premise deployment still offered a 40.1% saving, while dedicated on-premise was 43.8% more expensive than the cached API. Operationally, the on-premise configuration was associated with a substantially higher defect-repair burden, evidenced by a Fix Commit Ratio (FCR) of 74.9% compared to 45.9% for the cloud setup. Developers experienced more debugging spirals and a slower commit cadence with the local LLM. The study concludes that while on-premise solutions can offer TCO savings under shared allocation, this comes at the cost of developer experience and increased defect rates, suggesting a trade-off between infrastructure savings and code quality.