How can I reduce my LLM API costs?
Optimize Model Selection
Choose the smallest model that meets your quality needs. For many tasks, a model like GPT-3.5 Turbo or Claude Haiku costs a fraction of larger models and performs well enough.
Consider open-source models via providers like Together AI or Anyscale, which can be cheaper per token than proprietary APIs, especially for high-volume, simpler tasks.
- Use smaller models for simple tasks
- Reserve large models for complex reasoning
- Test open-source alternatives
Implement Caching and Batching
Cache frequent responses to avoid repeated API calls. Many providers offer prompt caching that reduces costs for repeated prefixes.
Batch multiple requests into a single API call when possible. Some APIs charge less for batched requests or offer batch processing discounts.
- Cache common queries and responses
- Use prompt caching if available
- Batch requests to reduce overhead
Monitor and Control Usage
Set spending limits and alerts in your API dashboard. Track token usage per feature to identify costly areas.
Optimize prompts to be concise and avoid unnecessary context. Trim conversation history and use system messages efficiently.
- Set budget alerts
- Monitor token usage per feature
- Shorten prompts and context
Common mistakes
- Assuming the largest model is always necessary; often a smaller model suffices.
- Ignoring caching opportunities; repeated identical requests waste money.
- Not setting spending limits, leading to unexpected bills.
