cost-aware-llm-pipeline

name	cost-aware-llm-pipeline
description	Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching.
origin	harness
workloads	["ai"]

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)

2. Immutable Cost Tracking

Track cumulative spend with frozen dataclasses. Each API call returns a new tracker — never mutates state.

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.

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.

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:

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
Haiku 4.5	$0.80	$4.00	1x
Sonnet 4.6	$3.00	$15.00	~4x
Opus 4.5	$15.00	$75.00	~19x
Opus 4.8	$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

When to Use

• Any application calling Claude, 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