Introduction
En tant qu'ingénieur DevOps avec plus de huit ans d'expérience dans l'optimisation d'infrastructures distribuées, j'ai souvent été confronté à des défis de performance critiques lors de l'intégration d'API d'IA dans des environnements de production. Après avoir stress-testé des dizaines d'API tierces et développé des frameworks internes robustes, je souhaite partager avec vous les techniques avancées que j'ai perfectionnées. Aujourd'hui, nous allons explorer comment mener des tests de charge efficaces sur les API d'intelligence artificielle, en utilisant HolySheep AI comme plateforme de référence — une solution qui offre une latence inférieure à 50ms et des tarifs préférentiels avec un taux de ¥1=$1.Comprendre l'Architecture des API d'IA
Avant de procéder aux tests de charge, il est essentiel de comprendre l'architecture sous-jacente. Les API d'IA modernes comme celles proposées par HolySheep AI fonctionnent sur des modèles de transformation de type transformer qui requieren des ressources de calcul considérables. La plateforme HolySheep agrège multiple fournisseurs (OpenAI, Anthropic, Google, DeepSeek) derrière une API unifiée, offrant des tarifs compétitifs : DeepSeek V3.2 à $0.42/MTok, Gemini 2.5 Flash à $2.50/MTok, GPT-4.1 à $8/MTok, et Claude Sonnet 4.5 à $15/MTok. L'architecture typique implique un système de files d'attente (queue) pour gérer la concurrence, un cache intelligent pour les requêtes similaires, et un système de limitation de débit (rate limiting) qui peut devenir un goulot d'étranglement si mal configuré.Framework de Stress Testing en Python
Installation et Configuration
# Installation des dépendances
pip install aiohttp asyncio matplotlib pandas locust
Structure du projet de test
project/
├── stress_test/
│ ├── __init__.py
│ ├── config.py
│ ├── load_generator.py
│ ├── metrics_collector.py
│ └── report_generator.py
├── benchmarks/
│ ├── test_sequential.py
│ ├── test_concurrent.py
│ └── test_burst.py
├── requirements.txt
└── main.pyConfiguration Centralisée
# stress_test/config.py
import os
from dataclasses import dataclass
from typing import Dict, List
@dataclass
class APIConfig:
"""Configuration pour les tests d'API HolySheep AI"""
base_url: str = "https://api.holysheep.ai/v1"
api_key: str = "YOUR_HOLYSHEEP_API_KEY"
model: str = "gpt-4.1"
timeout: int = 120
max_retries: int = 3
# Tarifs HolySheep 2026 (USD par million de tokens)
pricing: Dict[str, float] = None
def __post_init__(self):
self.pricing = {
"gpt-4.1": 8.00, # GPT-4.1: $8/MTok
"claude-sonnet-4.5": 15.00, # Claude Sonnet 4.5: $15/MTok
"gemini-2.5-flash": 2.50, # Gemini 2.5 Flash: $2.50/MTok
"deepseek-v3.2": 0.42, # DeepSeek V3.2: $0.42/MTok
}
@property
def cost_per_1k_tokens(self) -> float:
return self.pricing.get(self.model, 0) / 1000
@dataclass
class LoadTestConfig:
"""Configuration des paramètres de charge"""
duration_seconds: int = 300
warmup_seconds: int = 30
cooldown_seconds: int = 30
# Patterns de charge
concurrent_users: List[int] = None
requests_per_second: List[int] = None
# Seuils d'alerte
max_latency_p95_ms: int = 500
max_error_rate_percent: float = 1.0
min_throughput_rps: int = 10
def __post_init__(self):
self.concurrent_users = [1, 5, 10, 25, 50, 100]
self.requests_per_second = [10, 50, 100, 200, 500]
Configuration globale
CONFIG = APIConfig()
LOAD_CONFIG = LoadTestConfig()Générateur de Charge Asynchrone
# stress_test/load_generator.py
import asyncio
import aiohttp
import time
from typing import List, Dict, Optional
from dataclasses import dataclass, field
from collections import defaultdict
import statistics
@dataclass
class RequestResult:
"""Résultat d'une requête individuelle"""
request_id: int
timestamp: float
latency_ms: float
status_code: int
success: bool
error_message: Optional[str] = None
tokens_used: int = 0
model: str = ""
@dataclass
class LoadTestResult:
"""Agrégation des résultats de test"""
total_requests: int = 0
successful_requests: int = 0
failed_requests: int = 0
latencies_ms: List[float] = field(default_factory=list)
tokens_consumed: int = 0
start_time: float = 0
end_time: float = 0
@property
def duration_seconds(self) -> float:
return self.end_time - self.start_time
@property
def throughput_rps(self) -> float:
return self.total_requests / self.duration_seconds if self.duration_seconds > 0 else 0
@property
def error_rate_percent(self) -> float:
return (self.failed_requests / self.total_requests * 100) if self.total_requests > 0 else 0
@property
def latency_stats(self) -> Dict[str, float]:
if not self.latencies_ms:
return {"min": 0, "max": 0, "mean": 0, "median": 0, "p95": 0, "p99": 0}
sorted_latencies = sorted(self.latencies_ms)
n = len(sorted_latencies)
return {
"min": sorted_latencies[0],
"max": sorted_latencies[-1],
"mean": statistics.mean(sorted_latencies),
"median": sorted_latencies[n // 2],
"p95": sorted_latencies[int(n * 0.95)],
"p99": sorted_latencies[int(n * 0.99)],
}
def total_cost_usd(self, pricing_per_mtok: float) -> float:
return (self.tokens_consumed / 1_000_000) * pricing_per_mtok
class StressTestRunner:
"""Exécuteur de tests de charge pour API HolySheep AI"""
def __init__(self, config: 'APIConfig'):
self.config = config
self.results: List[LoadTestResult] = []
self._session: Optional[aiohttp.ClientSession] = None
async def _create_session(self) -> aiohttp.ClientSession:
"""Crée une session HTTP avec configuration optimale"""
timeout = aiohttp.ClientTimeout(
total=self.config.timeout,
connect=10,
sock_read=30
)
connector = aiohttp.TCPConnector(
limit=200, # Limite de connexions simultanées
limit_per_host=100,
ttl_dns_cache=300,
enable_cleanup_closed=True
)
return aiohttp.ClientSession(
timeout=timeout,
connector=connector,
headers={
"Authorization": f"Bearer {self.config.api_key}",
"Content-Type": "application/json",
}
)
async def _make_request(
self,
session: aiohttp.ClientSession,
request_id: int,
prompt: str
) -> RequestResult:
"""Exécute une requête unique vers l'API"""
start_time = time.perf_counter()
payload = {
"model": self.config.model,
"messages": [
{"role": "user", "content": prompt}
],
"max_tokens": 500,
"temperature": 0.7
}
try:
async with session.post(
f"{self.config.base_url}/chat/completions",
json=payload
) as response:
latency = (time.perf_counter() - start_time) * 1000
if response.status == 200:
data = await response.json()
tokens = data.get("usage", {}).get("total_tokens", 0)
return RequestResult(
request_id=request_id,
timestamp=start_time,
latency_ms=latency,
status_code=200,
success=True,
tokens_used=tokens,
model=self.config.model
)
else:
error_text = await response.text()
return RequestResult(
request_id=request_id,
timestamp=start_time,
latency_ms=latency,
status_code=response.status,
success=False,
error_message=f"HTTP {response.status}: {error_text[:200]}"
)
except asyncio.TimeoutError:
return RequestResult(
request_id=request_id,
timestamp=start_time,
latency_ms=(time.perf_counter() - start_time) * 1000,
status_code=0,
success=False,
error_message="TimeoutError"
)
except Exception as e:
return RequestResult(
request_id=request_id,
timestamp=start_time,
latency_ms=(time.perf_counter() - start_time) * 1000,
status_code=0,
success=False,
error_message=str(e)
)
async def run_concurrent_load_test(
self,
num_users: int,
requests_per_user: int,
prompt: str = "Expliquez brièvement le concept de latence en informatique."
) -> LoadTestResult:
"""Lance un test de charge avec N utilisateurs simultanés"""
if self._session is None:
self._session = await self._create_session()
result = LoadTestResult()
result.start_time = time.time()
semaphore = asyncio.Semaphore(num_users)
request_counter = 0
async def user_worker(user_id: int):
nonlocal request_counter
async with semaphore:
for i in range(requests_per_user):
request_id = request_counter
request_counter += 1
req_result = await self._make_request(
self._session, request_id, prompt
)
result.latencies_ms.append(req_result.latency_ms)
result.tokens_consumed += req_result.tokens_used
if req_result.success:
result.successful_requests += 1
else:
result.failed_requests += 1
# Lancer tous les utilisateurs en parallèle
tasks = [user_worker(i) for i in range(num_users)]
await asyncio.gather(*tasks)
result.total_requests = request_counter
result.end_time = time.time()
return result
async def run_rps_target_test(
self,
target_rps: int,
duration_seconds: int,
prompt: str = "Donnez un exemple de code Python pour trier une liste."
) -> LoadTestResult:
"""Lance un test ciblant un throughput spécifique (requêtes/seconde)"""
if self._session is None:
self._session = await self._create_session()
result = LoadTestResult()
result.start_time = time.time()
interval = 1.0 / target_rps
request_id = 0
async def continuous_requester():
nonlocal request_id
while time.time() - result.start_time < duration_seconds:
req_result = await self._make_request(
self._session, request_id, prompt
)
request_id += 1
result.latencies_ms.append(req_result.latency_ms)
result.tokens_consumed += req_result.tokens_used
if req_result.success:
result.successful_requests += 1
else:
result.failed_requests += 1
# Respecter le rythme target
await asyncio.sleep(interval)
# Pool de requêtables
num_workers = max(10, target_rps // 5)
tasks = [continuous_requester() for _ in range(num_workers)]
await asyncio.gather(*tasks)
result.total_requests = request_id
result.end_time = time.time()
return result
async def close(self):
if self._session:
await self._session.close()
self._session = NoneScripts de Benchmark Exécutables
Benchmark Complet Multi-Modèles
#!/usr/bin/env python3
"""
Benchmark complet HolySheep AI - Tests de performance multi-modèles
Auteur: Équipe HolySheep AI
"""
import asyncio
import json
import time
from datetime import datetime
from stress_test.config import APIConfig, LoadTestConfig
from stress_test.load_generator import StressTestRunner
MODELS_TO_TEST = [
("gpt-4.1", "Explication technique complexe"),
("claude-sonnet-4.5", "Analyse de code legacy"),
("gemini-2.5-flash", "Résumé de document"),
("deepseek-v3.2", "Génération de code simple"),
]
async def run_model_benchmark(
model: str,
test_config: LoadTestConfig
) -> dict:
"""Benchmark un modèle spécifique avec plusieurs niveaux de charge"""
api_config = APIConfig()
api_config.model = model
runner = StressTestRunner(api_config)
results = {
"model": model,
"tests": [],
"timestamp": datetime.now().isoformat()
}
print(f"\n{'='*60}")
print(f" Benchmarking {model}")
print(f"{'='*60}")
for num_users in test_config.concurrent_users:
print(f"\n Test avec {num_users} utilisateurs simultanés...")
test_result = await runner.run_concurrent_load_test(
num_users=num_users,
requests_per_user=20,
prompt="Expliquez les avantages de l'architecture microservices."
)
latency_stats = test_result.latency_stats
cost = test_result.total_cost_usd(api_config.pricing[model])
test_data = {
"concurrent_users": num_users,
"total_requests": test_result.total_requests,
"successful": test_result.successful_requests,
"failed": test_result.failed_requests,
"error_rate_percent": test_result.error_rate_percent,
"throughput_rps": test_result.throughput_rps,
"latency_ms": {
"min": round(latency_stats["min"], 2),
"max": round(latency_stats["max"], 2),
"mean": round(latency_stats["mean"], 2),
"median": round(latency_stats["median"], 2),
"p95": round(latency_stats["p95"], 2),
"p99": round(latency_stats["p99"], 2),
},
"tokens_consumed": test_result.tokens_consumed,
"estimated_cost_usd": round(cost, 4),
}
results["tests"].append(test_data)
print(f" ├─ Throughput: {test_result.throughput_rps:.1f} req/s")
print(f" ├─ Latence P95: {latency_stats['p95']:.1f}ms")
print(f" ├─ Taux d'erreur: {test_result.error_rate_percent:.2f}%")
print(f" └─ Coût: ${cost:.4f}")
# Pause entre les tests
await asyncio.sleep(2)
await runner.close()
return results
async def main():
"""Point d'entrée principal du benchmark"""
test_config = LoadTestConfig()
all_results = []
print("\n" + "="*60)
print(" HOLYSHEEP AI - BENCHMARK DE PERFORMANCE 2026")
print("="*60)
print(f"\nPlateforme: HolySheep AI")
print(f"API Base: https://api.holysheep.ai/v1")
print(f"Taux de change: ¥1 = $1 (économie 85%+ vs alternatives)")
print(f"Paiement: WeChat / Alipay acceptés")
print(f"Latence moyenne: <50ms")
start_benchmark = time.time()
for model, _ in MODELS_TO_TEST:
result = await run_model_benchmark(model, test_config)
all_results.append(result)
total_duration = time.time() - start_benchmark
# Génération du rapport
report = {
"benchmark_info": {
"date": datetime.now().isoformat(),
"duration_seconds": round(total_duration, 2),
"platform": "HolySheep AI",
"api_endpoint": "https://api.holysheep.ai/v1"
},
"results": all_results,
"summary": generate_summary(all_results)
}
# Sauvegarde des résultats
filename = f"benchmark_results_{int(time.time())}.json"
with open(filename, "w", encoding="utf-8") as f:
json.dump(report, f, indent=2, ensure_ascii=False)
print(f"\n\n{'='*60}")
print(" RÉSUMÉ DU BENCHMARK")
print(f"{'='*60}")
print_summary(report["summary"])
print(f"\n Rapport sauvegardé: {filename}")
print(f" Durée totale: {total_duration:.1f} secondes")
def generate_summary(all_results: list) -> dict:
"""Génère un résumé agrégé des benchmarks"""
summary = {
"best_throughput": {"model": "", "rps": 0},
"best_latency": {"model": "", "p95_ms": float("inf")},
"best_cost_efficiency": {"model": "", "cost_per_1k": float("inf")},
"most_reliable": {"model": "", "error_rate": 100}
}
for result in all_results:
model = result["model"]
for test in result["tests"]:
if test["throughput_rps"] > summary["best_throughput"]["rps"]:
summary["best_throughput"] = {
"model": model,
"rps": test["throughput_rps"]
}
if test["latency_ms"]["p95"] < summary["best_latency"]["p95_ms"]:
summary["best_latency"] = {
"model": model,
"p95_ms": test["latency_ms"]["p95"]
}
if test["error_rate_percent"] < summary["most_reliable"]["error_rate"]:
summary["most_reliable"] = {
"model": model,
"error_rate": test["error_rate_percent"]
}
return summary
def print_summary(summary: dict):
"""Affiche le résumé formaté"""
print(f"\n Meilleure performance (throughput):")
print(f" {summary['best_throughput']['model']} avec {summary['best_throughput']['rps']:.1f} req/s")
print(f"\n Meilleure latence (P95):")
print(f" {summary['best_latency']['model']} avec {summary['best_latency']['p95_ms']:.1f}ms")
print(f"\n Fiabilité maximale:")
print(f" {summary['most_reliable']['model']} avec {summary['most_reliable']['error_rate']:.2f}% d'erreurs")
if __name__ == "__main__":
asyncio.run(main())Optimisation de la Concurrence et du Contrôle de Débit
Rate Limiter Intelligent avec Backoff Exponentiel
# stress_test/rate_limiter.py
import asyncio
import time
from typing import Dict, Optional, Callable
from dataclasses import dataclass, field
from collections import deque
import logging
logger = logging.getLogger(__name__)
@dataclass
class RateLimitConfig:
"""Configuration du rate limiting"""
requests_per_second: int = 50
burst_size: int = 100
max_queue_size: int = 1000
backoff_base_seconds: float = 1.0
backoff_max_seconds: float = 60.0
backoff_multiplier: float = 2.0
class TokenBucket:
"""Implémentation du algorithme Token Bucket pour le rate limiting"""
def __init__(self, rate: float, burst: int):
self.rate = rate # Tokens par seconde
self.burst = burst # Taille du seau (burst)
self.tokens = float(burst)
self.last_update = time.monotonic()
self._lock = asyncio.Lock()
async def acquire(self, tokens: int = 1) -> bool:
"""Acquiert des tokens, retourne True si réussi"""
async with self._lock:
now = time.monotonic()
elapsed = now - self.last_update
# Ajout des tokens selon le taux
self.tokens = min(self.burst, self.tokens + elapsed * self.rate)
self.last_update = now
if self.tokens >= tokens:
self.tokens -= tokens
return True
return False
async def wait_for_token(self, tokens: int = 1):
"""Attend jusqu'à ce que les tokens soient disponibles"""
while not await self.acquire(tokens):
wait_time = (tokens - self.tokens) / self.rate
await asyncio.sleep(max(0.01, wait_time))
class AdaptiveRateLimiter:
"""Rate limiter intelligent avec adaptation automatique"""
def __init__(self, config: RateLimitConfig):
self.config = config
self.bucket = TokenBucket(config.requests_per_second, config.burst_size)
# Métriques
self._request_times: deque = deque(maxlen=1000)
self._error_times: deque = deque(maxlen=100)
self._consecutive_errors = 0
self._current_backoff = config.backoff_base_seconds
self._last_success = time.time()
# Callback pour les changements de taux
self._on_rate_change: Optional[Callable] = None
def set_rate_change_callback(self, callback: Callable[[int], None]):
"""Configure un callback appelé lors des changements de taux"""
self._on_rate_change = callback
async def acquire(self) -> bool:
"""Tente d'acquérir une requête selon le rate limit courant"""
current_time = time.time()
self._request_times.append(current_time)
# Détection de rate limiting HTTP 429
if self._consecutive_errors > 3:
self._increase_backoff()
# Vérification du backoff
if time.time() - self._last_success < self._current_backoff:
wait_time = self._current_backoff - (time.time() - self._last_success)
logger.debug(f"Backoff actif, attente de {wait_time:.2f}s")
await asyncio.sleep(wait_time)
acquired = await self.bucket.acquire(1)
if not acquired:
await self.bucket.wait_for_token(1)
return True
def record_success(self):
"""Enregistre une requête réussie"""
self._consecutive_errors = 0
self._current_backoff = self.config.backoff_base_seconds
self._last_success = time.time()
self._error_times.clear()
def record_error(self, status_code: Optional[int] = None):
"""Enregistre une erreur"""
self._consecutive_errors += 1
self._error_times.append(time.time())
if status_code == 429:
logger.warning("Rate limit HTTP 429 détecté - réduction du throughput")
self._decrease_rate()
def _increase_backoff(self):
"""Augmente le backoff exponentiellement"""
self._current_backoff = min(
self._current_backoff * self.config.backoff_multiplier,
self.config.backoff_max_seconds
)
logger.info(f"Backoff augmenté à {self._current_backoff:.1f}s")
def _decrease_rate(self):
"""Diminue temporairement le taux de requêtes"""
new_rate = int(self.config.requests_per_second * 0.5)
self.config.requests_per_second = max(10, new_rate)
# Recréer le bucket avec le nouveau taux
self.bucket = TokenBucket(self.config.requests_per_second, self.config.burst_size // 2)
if self._on_rate_change:
self._on_rate_change(self.config.requests_per_second)
logger.info(f"Taux réduit à {self.config.requests_per_second} req/s")
@property
def current_rps(self) -> int:
"""Retourne le taux de requêtes actuel"""
return self.config.requests_per_second
def get_metrics(self) -> Dict:
"""Retourne les métriques du rate limiter"""
now = time.time()
# Requêtes par minute récentes
recent_requests = sum(1 for t in self._request_times if now - t < 60)
# Erreurs récentes
recent_errors = sum(1 for t in self._error_times if now - t < 60)
return {
"current_rps": self.current_rps,
"requests_last_minute": recent_requests,
"errors_last_minute": recent_errors,
"consecutive_errors": self._consecutive_errors,
"current_backoff_seconds": round(self._current_backoff, 2),
}Optimisation des Coûts avec HolySheep AI
Avec HolySheep AI, l'optimisation des coûts devient un exercice mathématique précis. En utilisant le taux avantageux de ¥1=$1, les économies sont substantielles comparées aux plateformes traditionnelles. Par exemple, pour un volume de 10 millions de tokens par jour avec GPT-4.1, le coût atteint $80 — mais en optant pour DeepSeek V3.2 à $0.42/MTok, ce même volume ne coûte que $4.20, soit une économie de 95% pour des cas d'usage appropriés.
Stratégie de Sélection Dynamique de Modèle
# stress_test/cost_optimizer.py
"""
Optimiseur de coûts HolySheep AI
Sélectionne automatiquement le modèle optimal selon le cas d'usage
"""
import asyncio
from typing import List, Dict, Optional, Tuple
from dataclasses import dataclass
from enum import Enum
import hashlib
@dataclass
class ModelCapability:
"""Capacités estimées d'un modèle"""
name: str
price_per_mtok: float
speed_score: float # 1-10, plus élevé = plus rapide
quality_score: float # 1-10, plus élevé = meilleure qualité
context_window: int
supports_function_calling: bool
supports_vision: bool
class TaskType(Enum):
"""Types de tâches pour la sélection de modèle"""
SIMPLE_SUMMARIZATION = "simple_summary"
CODE_GENERATION = "code_generation"
COMPLEX_REASONING = "complex_reasoning"
CREATIVE_WRITING = "creative_writing"
FAST_RESPONSE = "fast_response"
BATCH_PROCESSING = "batch_processing"
Catalogue des modèles HolySheep 2026 avec capacités
HOLYSHEEP_MODELS = {
"gpt-4.1": ModelCapability(
name="GPT-4.1",
price_per_mtok=8.00,
speed_score=6,
quality_score=10,
context_window=128000,
supports_function_calling=True,
supports_vision=False
),
"claude-sonnet-4.5": ModelCapability(
name="Claude Sonnet 4.5",
price_per_mtok=15.00,
speed_score=5,
quality_score=10,
context_window=200000,
supports_function_calling=True,
supports_vision=True
),
"gemini-2.5-flash": ModelCapability(
name="Gemini 2.5 Flash",
price_per_mtok=2.50,
speed_score=9,
quality_score=8,
context_window=1000000,
supports_function_calling=True,
supports_vision=True
),
"deepseek-v3.2": ModelCapability(
name="DeepSeek V3.2",
price_per_mtok=0.42,
speed_score=8,
quality_score=7,
context_window=64000,
supports_function_calling=True,
supports_vision=False
),
}
class CostOptimizer:
"""Optimiseur intelligent de coûts pour HolySheep AI"""
# Mappage tâche -> modèle préféré
TASK_MODEL_PREFERENCE = {
TaskType.SIMPLE_SUMMARIZATION: ["deepseek-v3.2", "gemini-2.5-flash"],
TaskType.CODE_GENERATION: ["gpt-4.1", "deepseek-v3.2"],
TaskType.COMPLEX_REASONING: ["gpt-4.1", "claude-sonnet-4.5"],
TaskType.CREATIVE_WRITING: ["gpt-4.1", "claude-sonnet-4.5"],
TaskType.FAST_RESPONSE: ["gemini-2.5-flash"],
TaskType.BATCH_PROCESSING: ["deepseek-v3.2"],
}
def __init__(self, budget_limit_usd: float = 100.0):
self.budget_limit = budget_limit_usd
self.total_spent = 0.0
self.request_count = 0
self.savings_vs_gpt4 = 0.0
def estimate_cost(
self,
model: str,
input_tokens: int,
output_tokens: int,
input_cost_multiplier: float = 1.0
) -> float:
"""Estime le coût d'une requête en USD"""
if model not in HOLYSHEEP_MODELS:
return float('inf')
model_info = HOLYSHEEP_MODELS[model]
# HolySheep utilise des prix fixes pour input et output
total_tokens = input_tokens + output_tokens
cost = (total_tokens / 1_000_000) * model_info.price_per_mtok
return cost * input_cost_multiplier
def select_optimal_model(
self,
task_type: TaskType,
requires_quality: bool = False,
requires_speed: bool = False,
requires_vision: bool = False,
max_latency_ms: Optional[int] = None
) -> Tuple[str, float]:
"""
Sélectionne le modèle optimal selon les contraintes
Retourne: (nom_du_modèle, score_total)
"""
candidates = []
preferred_order = self.TASK_MODEL_PREFERENCE.get(task_type, [])
for model_key, model_info in HOLYSHEEP_MODELS.items():
score = 0.0
# Critères éliminatoires
if requires_vision and not model_info.supports_vision:
continue
if max_latency_ms and model_info.speed_score < 5:
continue
# Score de base selon préférence
if model_key in preferred_order:
score += 100 - preferred_order.index(model_key) * 30
# Score qualité
if requires_quality:
score += model_info.quality_score * 10
# Score vitesse
if requires_speed:
score += model_info.speed_score * 15
# Bonus coût (plus cher = moins bon pour l'optimisation)
score -= (model_info.price_per_mtok / 15) * 20
candidates.append((model_key, score))
if not candidates:
return "deepseek-v3.2", 0.0
# Retourner le meilleur candidat
best = max(candidates, key=lambda x: x[1])
return best
def calculate_savings(
self,
tokens_used: int,
selected_model: str,
baseline_model: str = "gpt-4.1"
) -> Dict[str, float]:
"""Calcule les économies réalisées vs GPT-4.1"""
selected_cost = self.estimate_cost(selected_model, 0, tokens_used)
baseline_cost = self.estimate_cost(baseline_model, 0, tokens_used)
savings = baseline_cost - selected_cost
savings_percent = (savings / baseline_cost * 100) if baseline_cost > 0 else 0
return {
"baseline_cost_usd": baseline_cost,
"selected_cost_usd": selected_cost,
"savings_usd": savings,
"savings_percent": savings_percent,
"tokens_processed": tokens_used
}
def record_usage(self, model: str, tokens: int):
"""Enregistre l'utilisation pour les statistiques"""
cost = self.estimate_cost(model, 0, tokens)
self.total_spent += cost
self.request_count += 1
# Économies cumulées vs GPT-4.1
gpt4_cost = self.estimate_cost("gpt-4.1", 0, tokens)
self.savings_vs_gpt4 += (gpt4_cost - cost)
def get_daily_report(self) -> Dict:
"""Génère un rapport d'optimisation"""
return {
"total_requests": self.request_count,
"total_spent_usd": round(self.total_spent, 4),
"savings_vs_gpt4_usd": round(self.savings_vs_gpt4, 4),
"savings_percent": round(
(self.savings_vs_gpt4 / (self.total_spent + self.savings_vs_gpt4) * 100)
if self.total_spent > 0 else 0, 2
),
"remaining_budget_usd": round(self.budget_limit - self.total_spent, 4),
"budget_utilization_percent": round(
(self.total_spent / self.budget_limit * 100)
if self.budget_limit > 0 else 0, 2
)
}
Exemple d'utilisation
def demo_cost_optimizer():
optimizer = CostOptimizer(budget_limit_usd=500.0)
# Scénario: 1000 requêtes de résumé
print("=== OPTIMISATION DE COÛTS HOLYSHEEP AI ===\n")
for i in range(1000):
model, score = optimizer.select_optimal_model(
task_type=TaskType.SIMPLE_SUMMARIZATION,
requires_speed=True
)
# Simulation: ~2000 tokens par requête
tokens = 2000
optimizer.record_usage(model, tokens)
# Scénario: 500 requêtes de code
for i in range(500):
model, score = optimizer.select_optimal