llm-cost-optimizer

Reduce LLM API spend by applying model routing to use cheaper models for simple tasks, prompt compression to lower token usage, and semantic caching to prevent redundant calls. These controls maintain user-facing quality while cutting costs.

Model routing lowers LLM costs by directing requests to smaller, cheaper models when task complexity allows. It ensures expensive models are only used for demanding tasks, reducing overall API spend while maintaining response quality.

Set up per-endpoint cost controls by configuring output length limits and max_tokens to prevent over-generation. Apply specific routing and caching rules per endpoint to meet defined cost and performance targets.

Prompt caching reduces token usage by storing and reusing prompt prefixes or semantically similar queries. Combined with prompt compression, it minimizes the total tokens processed per API request, lowering spend.

You can track LLM cost savings using observability dashboards to monitor per-request cost logging. This identifies savings opportunities across routing, caching, and prompt control implementations for cost-sensitive deployments.

Prompt compression limitations include the risk of losing critical context when reducing token counts. To mitigate this, apply output length control and per-endpoint max_tokens to prevent over-generation while monitoring quality through observability dashboards.