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-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.

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

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