Cost Aware Llm Pipeline

affaan-m/ECC/docs/zh-CN/skills/cost-aware-llm-pipeline

by affaan-mef648e01899ba3e8dc6371642deaaf64b4477775No license275K starsListed Oct 9, 2026Updated Oct 9, 2026Repository updated 4 days ago

LLM API 使用成本优化模式 —— 基于任务复杂度的模型路由、预算跟踪、重试逻辑和提示缓存。

Instructions onlyAI & Agents
AI-generated overview

Patterns for controlling LLM API costs through model routing, budget tracking, retry logic and prompt caching.

What it does
This skill documents cost-control patterns for applications that call LLM APIs. It covers routing simple tasks to cheaper models and complex ones to expensive models, immutable cost tracking against a budget limit, retrying only transient errors with exponential backoff, and caching long system prompts. It also includes a 2026 price reference table, best practices and anti-patterns, and a composed pipeline example.
When to use it
Use it when building applications that call LLM APIs such as Claude or GPT, when processing batches of items of varying complexity, or when API spending must stay within a budget without sacrificing quality on complex tasks.
Requirements
No scripts are shipped; the skill is instructions and code examples only. The examples reference the Anthropic Python client and its error types, and assume an LLM API key and network access at runtime.

成本感知型 LLM 流水线

在保持质量的同时控制 LLM API 成本的模式。将模型路由、预算跟踪、重试逻辑和提示词缓存组合成一个可组合的流水线。

何时激活

  • 构建调用 LLM API(Claude、GPT 等)的应用程序时
  • 处理具有不同复杂度的批量项目时
  • 需要将 API 支出控制在预算范围内时
  • 需要在复杂任务上优化成本而不牺牲质量时

核心概念

1. 根据任务复杂度进行模型路由

自动为简单任务选择更便宜的模型,为复杂任务保留昂贵的模型。

python
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. 不可变的成本跟踪

使用冻结的数据类跟踪累计支出。每个 API 调用都会返回一个新的跟踪器 —— 永不改变状态。

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. 窄范围重试逻辑

仅在暂时性错误时重试。对于认证或错误请求错误,快速失败。

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. 提示词缓存

缓存长的系统提示词,以避免在每个请求上重新发送它们。

python
messages = [    {        "role": "user",        "content": [            {                "type": "text",                "text": system_prompt,                "cache_control": {"type": "ephemeral"},  # Cache this            },            {                "type": "text",                "text": user_input,  # Variable part            },        ],    }]

组合

将所有四种技术组合到一个流水线函数中:

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

价格参考(2026)

模型输入(美元/百万令牌)输出(美元/百万令牌)相对成本
Haiku 3.5 (legacy)$0.80$4.000.8x
Haiku 4.5$1.00$5.001x
Sonnet 5$2.00$10.002x
Sonnet 4.6$3.00$15.003x
Opus 4.8$5.00$25.005x
Fable 5 / Mythos 5$10.00$50.0010x
Opus 4.0 / 4.1 (legacy)$15.00$75.0015x

最佳实践

  • 从最便宜的模型开始,仅在达到复杂度阈值时才路由到昂贵的模型
  • 在处理批次之前设置明确的预算限制 —— 尽早失败而不是超支
  • 记录模型选择决策,以便您可以根据实际数据调整阈值
  • 对于超过 1024 个令牌的系统提示词,使用提示词缓存 —— 既能节省成本,又能降低延迟
  • 切勿在认证或验证错误时重试 —— 仅针对暂时性故障(网络、速率限制、服务器错误)重试

应避免的反模式

  • 无论复杂度如何,对所有请求都使用最昂贵的模型
  • 对所有错误都进行重试(在永久性故障上浪费预算)
  • 改变成本跟踪状态(使调试和审计变得困难)
  • 在整个代码库中硬编码模型名称(使用常量或配置)
  • 对重复的系统提示词忽略提示词缓存

适用场景

  • 任何调用 Claude、OpenAI 或类似 LLM API 的应用程序
  • 成本快速累积的批处理流水线
  • 需要智能路由的多模型架构
  • 需要预算护栏的生产系统

Source and attribution

Source:affaan-m/ECCindocs/zh-CN/skills/cost-aware-llm-pipelineat commitef648e0

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

Report or request removal