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 Use - Building applications that call LLM APIs (Gemini, 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_FLASH = "gemini-2.5-flash" MODEL_FLASH_LITE = "gemini-2.5-flash-lite" _FLASH_TEXT_THRESHOLD = 10_000 # chars _FLASH_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 >= _FLASH_TEXT_THRESHOLD or item_count >= _FLASH_ITEM_THRESHOLD: return MODEL_FLASH # Complex task return MODEL_FLASH_LITE # 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 google.api_core.exceptions import ( ServiceUnavailable, ResourceExhausted, InternalServerError, ) _RETRYABLE_ERRORS = (ServiceUnavailable, ResourceExhausted, 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 # PermissionDenied, InvalidArgument etc. → raise immediately ``` ### 4. Prompt Caching Cache long system prompts to avoid resending them on every request. ```python messages = [ { "role": "user", "content": [ { "type": "text", "text": system_prompt, "cache_control": {"type": "ephemeral"}, # Cache this }, { "type": "text", "text": user_input, # Variable part }, ], } ] ``` ## Composition Combine all four techniques in a single pipeline function: ```python def process(text: str, config: Config, tracker: CostTracker) -> tuple[Result, CostTracker]: # 1. Route model model = select_model(len(text), estimated_items, config.force_model) # 2. Check budget if tracker.over_budget: raise BudgetExceededError(tracker.total_cost, tracker.budget_limit) # 3. Call with retry + caching response = call_with_retry(lambda: client.messages.create( model=model, messages=build_cached_messages(system_prompt, text), )) # 4. Track cost (immutable) record = CostRecord(model=model, input_tokens=..., output_tokens=..., cost_usd=...) tracker = tracker.add(record) return parse_result(response), tracker ``` ## Pricing Reference (2025-2026) | Model | Input ($/1M tokens) | Output ($/1M tokens) | Relative Cost | |-------|---------------------|----------------------|---------------| | Flash Lite 2.5 | $0.80 | $4.00 | 1x | | Flash 2.5 | $3.00 | $15.00 | ~4x | | Pro 2.5 | $15.00 | $75.00 | ~19x | ## Best Practices - **Start with the cheapest model** and only route to expensive models when complexity thresholds are met - **Set explicit budget limits** before processing batches — fail early rather than overspend - **Log model selection decisions** so you can tune thresholds based on real data - **Use prompt caching** for system prompts over 1024 tokens — saves both cost and latency - **Never retry on authentication or validation errors** — only transient failures (network, rate limit, server error) ## Anti-Patterns to Avoid - Using the most expensive model for all requests regardless of complexity - Retrying on all errors (wastes budget on permanent failures) - Mutating cost tracking state (makes debugging and auditing difficult) - Hardcoding model names throughout the codebase (use constants or config) - Ignoring prompt caching for repetitive system prompts ## Ideal For - Any application calling Gemini, OpenAI, or similar LLM APIs - Batch processing pipelines where cost adds up quickly - Multi-model architectures that need intelligent routing - Production systems that need budget guardrails