Cost-Aware LLM Pipeline
Patterns for controlling LLM API costs while maintaining quality. Combines model routing, budget tracking, retry logic, and prompt caching into a composable pipeline.
When to Activate
- Building applications that call LLM APIs (Claude, GPT, etc.)
- Processing batches of items with varying complexity
- Need to stay within a budget for API spend
- Optimizing cost without sacrificing quality on complex tasks
Core Concepts
1. Model Routing by Task Complexity
Automatically select cheaper models for simple tasks, reserving expensive models for complex ones.
MODEL_SONNET = "claude-sonnet-5"
MODEL_HAIKU = "claude-haiku-4-5-20251001"
_SONNET_TEXT_THRESHOLD = 10_000 # chars
_SONNET_ITEM_THRESHOLD = 30 # items
def select_model(
text_length: int,
item_count: int,
force_model: str | None = None,
) -> str:
"""Select model based on task complexity."""
if force_model is not None:
return force_model
if text_length >= _SONNET_TEXT_THRESHOLD or item_count >= _SONNET_ITEM_THRESHOLD:
return MODEL_SONNET # Complex task
return MODEL_HAIKU # Simple task (3-4x cheaper)
