cost-aware-llm-pipeline

To reduce LLM API spend, implement automatic model routing that directs tasks to cheaper models based on complexity thresholds, minimizing cost while maintaining quality across batch processing workflows.

Prompt caching lowers LLM costs by storing system prompts locally, reducing latency and avoiding the need to re-send identical context data across multiple API calls.

Track LLM API costs across workflows using immutable cost tracking, which logs spend data for each call to enable accurate budgeting, auditing, and rollback capabilities.

Narrow retry logic helps cost-constrained workflows by limiting automated retry attempts to specific failure conditions, preventing excessive token consumption and controlling API spend.

Yes, automatic model routing for multi-model deployments uses task complexity thresholds to route calls appropriately, ensuring high quality is maintained while minimizing cost.