Cost Optimization: Token Budgets, Caching and Model Routing
~13 min read
LLM cost scales with tokens and model size. The three biggest levers are budgeting tokens, caching repeated work, and routing each request to the cheapest model that can handle it.
Cost Optimization: Token Budgets, Caching and Model Routing is a Pro topic
Sign in, then upgrade to Pro or Power to unlock this topic and the full AI Engineering curriculum.
Key points
- •LLM cost scales with total tokens (input + output) times per-token model price — attack both levers
- •Token budgeting: cap output length, compress histories, retrieve only top chunks, trim system prompts — small savings multiply across millions of calls
- •Caching: exact-match response cache for repeats, semantic cache for paraphrases, prefix/KV cache for shared prompt prefixes
- •Model routing sends easy requests to a small cheap model and escalates only hard ones to a large model, cutting average cost sharply
- •Combined, these routinely reduce LLM bills by large multiples with little quality loss — cost engineering is a first-class production concern