Inference Economics of Enterprise Coding Agents: A Case Study of Cloud vs. On-Premise LLMs
Autonomous coding agents force engineering organizations to choose between API-based frontier models -- strong reasoning at high token cost -- and on-premise quantized open-weights models, which promise low-marginal-cost scaling and data sovereignty at some loss of reasoning fidelity. We study this trade-off through a single-developer, non-randomized longitudinal case study over two contiguous 28-day periods on a production monorepo: an API-based Claude Opus 4.7/4.8 configuration using Claude Code versus an on-premise GLM-5.1/5.2 configuration using Opencode, quantized to NVFP4, on NVIDIA Blackwell hardware. Analyzing LLM telemetry and Git history, we find that prompt caching (99.3% hit r...
| Authors | Sheng-Wei Peng, Yi-Hsun Lin, Yi-Pei Lee |
|---|---|
| Primary category | cs.SE |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
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