cost-aware-llm-pipeline

# 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. ```python MODEL_SONNET = "claude-sonnet-4-6" 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) ``` ### 2. Immutable Cost Tracking Track cumulative spend with frozen dataclasses. Each API call returns a new tracker — never mutates state. ```python from dataclasses import dataclass @dataclass(frozen=True, slots=True) class CostRecord: model: str input_tokens: int output_tokens: int cost_usd: float @dataclass(frozen=True, slots=True) class CostTracker: budget_limit: float = 1.00 records: tuple[CostRecord, ...] = () def add(self, record: CostRecord) -> "CostTracker": """Return new tracker with added record (never mutates self).""" return CostTracker( budget_limit=self.budget_limit, records=(*self.records, record), ) @property def total_cost(self) -> float: return sum(r.cost_usd for r in self.records) @property def over_budget(self) -> bool: return self.total_cost > self.budget_limit ``` ### 3. Narrow Retry Logic Retry only on transient errors. Fail fast on authentication or bad request errors. ```python from anthropic import ( APIConnectionError, InternalServerError, RateLimitError, ) _RETRYABLE_ERRORS = (APIConnectionError, RateLimitError, InternalServerError) _MAX_RETRIES = 3 def call_with_retry(func, *, max_retries: int = _MAX_RETRIES): """Retry only on transient errors, fail fast on others.""" for attempt in range(max_retries): try: return func() except _RETRYABLE_ERRORS: if attempt == max_retries - 1: raise time.sleep(2 ** attempt) # Exponential backoff # AuthenticationError, BadRequestError etc. → raise immediately ``` ### 4. Prompt Caching Cache long system prompts to avoid resending them on every request. ```python messages = [ { "role": "user", "content":