Cutting your LLM API bill means paying only for the tokens that change the answer. Most teams pay full price for the same system prompt thousands of times a day, run flagship models on trivial tasks, and let reasoning tokens run unchecked. Five techniques, prompt caching, batch APIs, model routing, semantic caching and output discipline, can cut a typical bill by half or more. The maths below uses one real workload throughout, so you can see exactly where the money goes.

Key takeaways

Prompt caching is the biggest win for repeated context: Google discounts cached input by 95% on Gemini 4 Argon, turning a $4,800 monthly input bill into $525.

Batch APIs cost half the standard rate at Google and Anthropic, so anything that can wait a few hours should never run synchronously.

Routing 70% of simple requests to Claude Haiku 5.5 cuts the bill by about two thirds on its own.

Combined, the five techniques take our workload from $6,600 a month to about $580, a 91% reduction.

Every introductory price here doubles…