llm-cost-optimization

To reduce LLM API costs without sacrificing quality, you can implement model routing to direct tasks to cost-effective models, apply caching and batching for token optimization, and establish budget governance with alerts to control spend.

Model routing is a cost optimization technique that balances cost and quality by directing diverse tasks like classification or coding to the most cost-effective LLM tier, ensuring you only pay for premium models when necessary.

You audit LLM spending by instrumenting all API calls to track features, models, input/output tokens, and estimated costs, creating a spend breakdown by feature and model to identify your top cost drivers.

Yes, you can establish cost governance and budgeting with tolerances for per-feature and per-user costs, setting up alerts to monitor daily LLM usage and prevent runaway spending during feature launches.

The best way to implement token usage optimization is by applying caching, batching, and prompt construction techniques to reduce input and output tokens across all LLM calls while preserving response quality.