How to Cut Your LLM Bill by 60% Without Sacrificing Quality
Six concrete tactics — from prompt caching to model cascading — that real teams use to slash LLM spend.
If your AI bill is growing faster than your traffic, you have a margin problem. Here are six tactics that compound.
1. Cascade your models
Send 80% of requests to a cheap model (GPT-4o mini, Haiku 4.5) and only escalate to a frontier model when the cheaper one is uncertain.
2. Cache aggressively
OpenAI and Anthropic both offer prompt caching for repeated system prompts. Savings: up to 90% on input tokens.
3. Compress system prompts
Most system prompts have 30%+ fat. Cut examples, collapse markdown, remove redundancy.
4. Limit output tokens
Output tokens cost 4–5x more than input. Set max_tokens aggressively.
5. Batch where you can
Anthropic and OpenAI batch APIs offer 50% discounts for non-realtime workloads.
6. Measure first
Use our cost calculator and token estimator to find the biggest line item, then optimize that.