Stop Burning Money on AI: Cost Tracking & Rate Limiting for Local LLMs

Running Large Language Models (LLMs) locally offers incredible privacy and control, but it’s easy to spin up costs you didn’t anticipate. Just like a cloud API bills per token, your local LLM consumes valuable resources – CPU, GPU, memory, and even electricity. Without careful management, you risk system instability, poor user experience, and ultimately, wasted hardware. This post dives into the operational economics of local AI, showing you how to track costs and implement rate limiting to keep your LLM applications running smoothly and efficiently. The Economics of Local AI: From Compute to Cash We’ve all been there: a functional prototype that works beautifully… until multiple users hit it simultaneously. Integrating LLMs into your Node.js applications (using tools like Ollama and Transformers.js) is just the first step. To move to production, you need to treat inference as a finite resource with tangible costs. Think of it like a database. A query consumes CPU cycles and I/O. An LL