5 Ways to Reduce Your LLM API Costs Today

If you're building with LLM APIs, you've probably noticed that costs can scale quickly. A single Claude or GPT call might cost fractions of a cent, but multiply that by thousands of users and requests per day, and you're looking at a serious line item.

Here are five things you can do right now to bring those costs down.

1. Stop Sending Unnecessary Context

The biggest source of wasted tokens is bloated prompts. Every token in your system prompt, every few-shot example, every piece of context — it all costs money on every single request.

Audit your prompts and ask: does this context actually improve the output? If you can't point to a measurable quality difference, cut it.

2. Use the Right Model for the Job

Not every task needs your most powerful model. Classification, extraction, and simple formatting tasks can usually be handled by a smaller, cheaper model like Haiku or GPT-4o-mini.

A simple routing layer that sends easy tasks to cheap models and hard tasks to expensive ones can cut costs by 40-60% with no quality loss on the tasks that matter.

3. Cache Aggressively

If you're sending the same (or very similar) prompts repeatedly, you're paying for the same computation over and over. Implement caching at multiple levels:

  • Exact match caching: Hash your prompt and cache the response
  • Semantic caching: Use embeddings to find similar previous queries
  • Prompt caching: Use API features like Anthropic's prompt caching to avoid reprocessing long system prompts

4. Compress Your Prompts

Many prompts are written in verbose, human-friendly prose. LLMs don't need that. They can understand compressed, structured formats just as well.

Instead of:

Please analyze the following customer review and determine whether
the sentiment is positive, negative, or neutral. Also identify the
main topics discussed in the review.

Try:

Classify sentiment (positive/negative/neutral) and extract topics.
Review:

Same output quality, 60% fewer tokens.

5. Set Sensible Max Token Limits

If you know your output should be a short classification label, don't leave max_tokens at 4096. Set it to 50. This prevents runaway responses and ensures you're not paying for tokens you'll throw away.

These are just the basics. For a comprehensive look at all the strategies that reduce LLM costs, see our complete guide to LLM token optimization. You can also dive deeper into context engineering techniques or designing for prompt cache hits.