cost-aware-llm-pipeline

You can optimize LLM API costs by routing tasks to appropriate models based on complexity, caching system prompts, and tracking cumulative spend immutably to stay within budget constraints. This pipeline automatically selects cheaper models for simple tasks and expensive models for complex ones.

Model routing for LLM cost optimization automatically selects the appropriate model for each task based on its complexity. It routes simple queries to cheaper models and complex queries to expensive ones, ensuring efficient API usage without compromising output quality.

You can track LLM API spend immutably by using frozen dataclasses to record cumulative costs. This approach ensures the cost tracking state never mutates, providing a reliable and tamper-proof record of your cumulative budget consumption across API calls.

Yes, prompt caching reduces LLM API costs by caching long system prompts to avoid resending them on every request. This minimizes token usage and lowers expenses, especially in applications with frequent, repetitive system-level instructions.

You should use narrow retry logic for LLM API error handling to retry only on transient errors while failing fast on permanent issues like authentication errors. This prevents wasting API calls and budget on unrecoverable failures during batch processing.