How can I reduce the cost of running AI agents?
Optimize model selection and usage
Not every task needs a top-tier model. Route simple queries to cheaper, smaller models (like GPT-3.5 or Claude Haiku) and reserve expensive models for complex reasoning. This alone can cut costs by 50-80% in many cases.
Implement caching for repeated queries—if the same input comes up often, store and reuse the response. Also, use streaming to avoid paying for tokens you don't need, and set max token limits per request to prevent runaway generation.
- Use smaller models for routine tasks
- Cache frequent responses
- Set max token limits
- Batch multiple requests into one API call
- Trim prompts to remove unnecessary context
Monitor and control spending
Track token usage per user, session, or feature to identify cost drivers. Many platforms offer usage dashboards or APIs. Set budget alerts and hard caps to avoid surprises.
Consider fine-tuning a smaller model on your specific data—it can outperform a larger general model on narrow tasks at a fraction of the cost. Also, evaluate whether you can replace some agent calls with deterministic code or rule-based logic.
Common mistakes
- Assuming the most expensive model always gives the best results—often a smaller model is sufficient.
- Ignoring token usage in development; small inefficiencies scale quickly in production.
- Forgetting that retries and error handling can double or triple token consumption.
