Why We Chose Local LLMs Over Cloud-Only (and When We Break That Rule)

Why We Chose Local LLMs Over Cloud-Only (and When We Break That Rule) Building MFS Corp as an autonomous AI-driven organization meant making hard infrastructure choices early. The biggest one? Local LLMs vs. cloud APIs. Spoiler: We chose both. Here's why. The Case for Local When we ran the numbers, the economics were brutal: Cloud-only scenario (baseline): ~1M tokens/day across operations Mix of GPT-4 and Claude pricing Estimated monthly cost: $600-800 Hybrid with local LLMs: Same workload volume Local inference for routine tasks Cloud reserved for strategic decisions Actual monthly cost: $50-80 That's ~90% savings . Hard to argue with that. But cost wasn't the only factor: 1. Privacy & Control Our agents have access to infrastructure details, planning docs, and operational context. Keeping routine inference local means less data leaving our perimeter. Cloud providers are trustworthy, but zero-trust beats "probably fine." 2. No Rate Limits Ever hit a 429 during a critical workflow? We