Anyone here using local models mainly to keep LLM costs under control?

Been noticing that once you use LLMs for real de work, the cost conversation gets messy fast. It is not just raw API spend. It is retries, long context, background evals, tool calls, embeddings, and all the little workflow decisions that look harmless until usage scales up. For some teams, local models seem like the obvious answer, but in practice it feels nuanced than just “run it yourself and save money.” You trade API costs for hardware, setup time, model routing decisions, and sometimes lower reliability depending on the task.