Can you use local and cloud AI together?

You do not have to pick one side. The pattern most real systems settle on uses both, with a piece in front called a router that decides, per request, where each prompt should go.

The idea is simple. Many prompts are easy, and a small local model or a cheap cloud model answers them fine. A few prompts are hard, and those are worth sending to a strong, expensive model. The router looks at each incoming prompt, guesses how hard it is, and sends it to the cheaper option when it can. You only pay for the strong model on the prompts that actually need it.

Two published results show why this is attractive. The RouteLLM team reported that their routers could cut cost by up to 85% while keeping 95% of GPT-4’s performance on the MT Bench benchmark.^[1] A separate study, Hybrid LLM, found that routing by predicted difficulty let it make up to 40% fewer calls to the large model with no drop in response quality.^[2] Both numbers are specific to their setups, not guarantees for every workload, but they point the same way: a lot of traffic can go to the cheaper model without users noticing.

The router itself is typically a small trained model, not a simple rule of thumb like counting words in the prompt. RouteLLM’s own toolkit ships several options, a matrix-factorization model, a BERT classifier, and an LLM-based classifier, all trained on preference data: records of which of two models a human or automated judge preferred on a given prompt.^[1] The router learns to predict when the cheap model would have been judged good enough, without ever running the expensive model just to check. That is also why a trained router can generalize to a different pair of models later without retraining: it learned a pattern in what makes a prompt hard, not a fixed rule tied to one specific pairing.^[1]