marshall0524/everythingclaudecode/cost-aware-llm-pipeline

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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。

Install

npx vskill@latest install marshall0524/everythingclaudecode/cost-aware-llm-pipeline

SKILL.md

The same SKILL.md was published earlier as boyapati13/octo/cost-aware-llm-pipeline.

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-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. 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(2025-2026)

模型输入(美元/百万令牌)输出(美元/百万令牌)相对成本
Haiku 4.5$0.80$4.001x
Sonnet 4.6$3.00$15.00~4x
Opus 4.5$15.00$75.00~19x

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

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