How to Reduce LLM Costs in Production

LLM API costs can grow quickly at scale. Here are proven strategies to reduce costs while maintaining quality.

1. Semantic Caching

Cache responses for similar queries
Typical savings: 20-40% on repeated patterns
Monitor cache hit quality to avoid stale responses

2. Model Routing

Use GPT-5-nano or Claude Haiku for simple tasks
Route complex queries to larger models
Typical savings: 50-70% on mixed workloads

3. Prompt Optimization

Remove unnecessary context
Use concise system prompts
Request shorter outputs when appropriate

4. Monitor and Measure

Track cost per request and per user
Identify expensive query patterns
Monitor quality alongside costs
Set alerts for cost anomalies

How can I reduce costs?

Key strategies: 1) Use semantic caching for repeated queries, 2) Route simple queries to smaller/cheaper models, 3) Optimize prompts to reduce tokens, 4) Batch requests where possible, 5) Monitor usage to identify waste.

Does optimization affect quality?

It can if done poorly. Monitor quality metrics alongside costs. Use smaller models only for tasks they handle well. Track hallucination rates when switching models or optimizing prompts.