How to reduce token usage: practical prompt optimization that saves money
Prompt optimization is the fastest way to cut LLM costs. Start by removing politeness language and moving instructions to the system prompt. Then enable prompt caching. These two changes alone typically reduce costs by 30% or more.
Prompt optimization is the practice of getting the same or better results from an LLM with fewer input tokens. Since most API pricing charges per input token, trimming your prompts directly reduces your bill. A 30% reduction in token usage means a 30% reduction in cost, with no change to the model or provider.
By TechCompare · Updated
How this is calculated
Effective optimization techniques: remove politeness padding ('please', 'could you', 'I would like you to'), which can add 10-15% to token counts. Use system prompts for persistent instructions rather than repeating them in every user message. Compress few-shot examples to the minimum that demonstrates the pattern. Use the prompt cache feature (available on OpenAI, Anthropic, and DeepSeek) for static content like system prompts and few-shot examples, where cached tokens cost 50-90% less. For structured output, use the model's native JSON mode or structured output feature rather than lengthy formatting instructions in the prompt. For multi-turn conversations, prune conversation history aggressively.
Verdict
A 30% token reduction maps directly to a 30% bill reduction with no model or provider change. Politeness padding like 'please' and 'could you' quietly adds 10-15% to counts, and moving persistent instructions to the system prompt (which can be cached) compounds the savings. Structured output via JSON mode beats long formatting prompts, and aggressive history pruning in multi-turn chats trims dead weight.
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Frequently asked questions
Does prompt length actually affect my API bill?
How do I cut tokens without losing output quality?
What is prompt caching and how much does it save?
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