From expensive tokens to intelligent compression: how we optimize LLM costs in production

We spend absurd amounts on AI tokens. And that number is only going up. At 498Advance we run multiple LLMs in production — Claude for development, Gemini for multimodal, DeepSeek and OpenAI models locally for routine tasks. Every model does something well and fails at something else. That is why they coexist. But this creates a problem: dependency and cost . What happens when a provider goes down? What happens when pricing changes overnight? Here is how we deal with it, and why a new Google Research paper caught our attention this week. Layer 1: Fallback policies If a model fails, the system automatically redirects to the next available model. No human intervention, no perceptible downtime. # Simplified fallback logic models = [ " claude-opus " , " gpt-4o " , " gemini-pro " , " deepseek-local " ] def inference ( prompt , task_type ): for model in get_ranked_models ( task_type ): try : return call_model ( model , prompt ) except ModelUnavailable : log . warning ( f " { model } unavailab