4-2. ストリーミング＋実時間コスト集計
import os
import time
from openai import OpenAI
client = OpenAI(
api_key=os.environ["HOLYSHEEP_API_KEY"],
base_url="https://api.holysheep.ai/v1"
)
def stream_with_realtime_cost(model: str, prompt: str, max_tokens: int = 4096):
"""
ストリーミングで各チャンク到着ごとに概算コストを加算。
TTFTと累積トークン数、最終コストを返す。
"""
price_per_tok = PRICE_PER_MTOK[model] / 1_000_000
start = time.perf_counter()
ttft_ms = None
cumulative_tokens = 0
text_buffer = []
stream = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": prompt}],
stream=True,
max_tokens=max_tokens,
)
for chunk in stream:
delta = chunk.choices[0].delta.content
if delta is None:
continue
if ttft_ms is None:
ttft_ms = (time.perf_counter() - start) * 1000
text_buffer.append(delta)
# ストリームにはusageが来ないため、概算で+1tok/chunk
cumulative_tokens += 1
print(delta, end="", flush=True)
total_ms = (time.perf_counter() - start) * 1000
final_cost = cumulative_tokens * price_per_tok
print(
f"\n[Metrics] TTFT: {ttft_ms:.1f}ms | Total: {total_ms:.1f}ms | "
f"~Tokens: {cumulative_tokens} | Cost: ${final_cost:.6f} (¥{final_cost:.6f})"
)
return "".join(text_buffer)
4-3. 非同期バッチ処理＋同時実行制御
import os
import asyncio
from openai import AsyncOpenAI
from asyncio import Semaphore
from collections import deque
client = AsyncOpenAI(
api_key=os.environ["HOLYSHEEP_API_KEY"],
base_url="https://api.holysheep.ai/v1"
)
同時実行数を20に制限（HolySheepのTier-1上限）
sem = Semaphore(20)
トークンバケット：100,000 tok/分の出力上限
class TokenBucket:
def __init__(self, capacity: int, refill_per_sec: float):
self.capacity = capacity
self.tokens = capacity
self.refill = refill_per_sec
self.last = time.monotonic()
self.lock = asyncio.Lock()
async def acquire(self, amount: int = 1):
async with self.lock:
now = time.monotonic()
self.tokens = min(
self.capacity,
self.tokens + (now - self.last) * self.refill
)
self.last = now
if self.tokens >= amount:
self.tokens -= amount
return True
# 不足なら待機時間計算
wait = (amount - self.tokens) / self.refill
await asyncio.sleep(wait)
return await self.acquire(amount)
bucket = TokenBucket(capacity=5000, refill_per_sec=1666) # ≈100k/min
async def bounded_generate(model: str, prompt: str):
async with sem:
await bucket.acquire()
resp = await client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": prompt}],
max_tokens=1024,
)
return resp.choices[0].message.content, resp.usage.completion_tokens
async def batch_process(prompts: list, model: str = "gemini-2.5-pro"):
tasks = [bounded_generate(model, p) for p in prompts]
results = await asyncio.gather(*tasks, return_exceptions=True)
success = [r for r in results if not isinstance(r, Exception)]
total_tokens = sum(r[1] for r in success)
total_cost = total_tokens * PRICE_PER_MTOK[model] / 1_000_000
print(f"Processed {len(success)}/{len(prompts)} | Tokens: {total_tokens} | Cost: ${total_cost:.4f}")
return results
5. コスト試算：月間ROI
私のチームの実例で計算します。1日600万tok（出力のみ）、月間約1.8億tokをClaude Opus 4.7で処理するケースです。