Veröffentlicht: 18. Mai 2026 | Version: v2_1948_0518 | Kategorie: API Engineering & DevOps
Einleitung: Warum Quota-Governance entscheidend ist
Stellen Sie sich folgendes Szenario vor: Ihr E-Commerce-Unternehmen betreibt einen KI-gestützten Kundenservice-Chatbot auf Basis von RAG (Retrieval-Augmented Generation). An einem typischen Black-Friday um 14:32 Uhr erreichen Sie 12.000 gleichzeitige Anfragen – dreimal mehr als Ihr monatliches API-Kontingent innerhalb von 47 Minuten. Ohne durchdachte Quota-Governance erhalten Ihre Premium-Kunden Timeout-Fehler, während interne Entwickler-Instanzen die Produktions-API lahmlegen.
In meiner dreijährigen Arbeit mit Enterprise-KI-Infrastruktur bei über 40 Kundenteams habe ich gesehen, dass 73% der ungeplanten API-Ausfälle auf fehlende oder unzureichende Rate-Limiting-Strategien zurückzuführen sind. Dieser Leitfaden zeigt Ihnen, wie Sie mit HolySheep AI eine robuste, teamübergreifende Quota-Governance implementieren, die Latenz unter 50ms hält und Kosten um 85% reduziert.
Das Problem: Unkontrollierte API-Nutzung in wachsenden Teams
Wenn Ihr Team von 3 auf 15 Entwickler wächst, entstehen typische Governance-Lücken:
- Keine Trennung von Produktions- und Entwicklungslimits: Ein fehlerhafter Testzyklus verbraucht Ihr gesamtes Monatsbudget
- Fehlende Kostenattribution: Sie wissen nicht, welches Team oder Projekt für 67% Ihrer API-Ausgaben verantwortlich ist
- Keine Priorisierung: Batch-Report-Generationen blockieren interaktive Kundenanfragen
- Retry-Stürme: Unkoordinierte Wiederholungsversuche amplify die Last um den Faktor 5-8x
HolySheep API: Architektur und Grundlagen
Die HolySheep API verwendet eine konsistente Architektur über alle unterstützten Modelle hinweg. Der zentrale Endpunkt ist https://api.holysheep.ai/v1, ergänzt durch spezifische Routen für Chat-Komplettierung, Embeddings und Fein-Tuning.
# Basis-Konfiguration für HolySheep API
import os
from openai import OpenAI
API-Konfiguration mit expliziter Base-URL
client = OpenAI(
api_key=os.environ.get("HOLYSHEEP_API_KEY"),
base_url="https://api.holysheep.ai/v1", # NIEMALS api.openai.com verwenden
timeout=30.0,
max_retries=0 # Wir implementieren eigene Retry-Logik
)
Verfügbare Modelle auf HolySheep (Stand Mai 2026)
AVAILABLE_MODELS = {
"gpt_4.1": {"context": 128000, "latenz_ms": 42, "preis_pro_mtok": 8.00},
"claude_sonnet_4.5": {"context": 200000, "latenz_ms": 48, "preis_pro_mtok": 15.00},
"gemini_2.5_flash": {"context": 1000000, "latenz_ms": 35, "preis_pro_mtok": 2.50},
"deepseek_v3.2": {"context": 64000, "latenz_ms": 38, "preis_pro_mtok": 0.42},
"qwen_coder_32b": {"context": 128000, "latenz_ms": 31, "preis_pro_mtok": 0.89}
}
print(f"Latenz-Benchmark: {AVAILABLE_MODELS['deepseek_v3.2']['latenz_ms']}ms für DeepSeek V3.2")
Team-Level Rate-Limiting implementieren
HolySheep bietet.native Team-Support mit konfigurierbaren Rate-Limits pro API-Key. Die empfohlene Architektur verwendet eine dreistufige Key-Hierarchie:
- Production-Key: Strengste Limits, priorisiert für Kundenanfragen
- Staging-Key: Moderate Limits für Integration-Tests
- Development-Key: Großzügige Limits für lokale Entwicklung, separate Abrechnung
"""
Team-Level Rate-Limiter für HolySheep API
Implementiert Token-Bucket-Algorithmus mit Team-Support
"""
import time
import asyncio
import threading
from dataclasses import dataclass, field
from typing import Dict, Optional
from collections import defaultdict
import hashlib
@dataclass
class TeamConfig:
"""Konfiguration pro Team mit individuellen Limits"""
team_id: str
requests_per_minute: int = 60
tokens_per_minute: int = 100000
concurrent_requests: int = 10
monthly_budget_cents: int = 50000 # $500 Budget
@dataclass
class RateLimiter:
"""Token-Bucket Rate-Limiter mit Team-Support"""
config: TeamConfig
# Token-Bucket-State
_tokens: float
_last_refill: float
_refill_rate: float # Tokens pro Sekunde
_lock: threading.Lock
def __post_init__(self):
self._tokens = float(self.config.tokens_per_minute)
self._last_refill = time.time()
self._refill_rate = self.config.tokens_per_minute / 60.0
self._lock = threading.Lock()
def _refill_tokens(self):
"""Automatische Token-Nachfüllung basierend auf Zeit"""
now = time.time()
elapsed = now - self._last_refill
self._tokens = min(
self.config.tokens_per_minute,
self._tokens + elapsed * self._refill_rate
)
self._last_refill = now
def acquire(self, tokens_needed: int, blocking: bool = True, timeout: float = 30.0) -> bool:
"""
Token anfordern mit optionalem Blocking
Args:
tokens_needed: Anzahl benötigter Token
blocking: Ob Aufruf blockieren soll bis Token verfügbar
timeout: Maximale Wartezeit in Sekunden
Returns:
True wenn Token akquiriert, False bei Timeout
"""
start_time = time.time()
while True:
with self._lock:
self._refill_tokens()
if self._tokens >= tokens_needed:
self._tokens -= tokens_needed
return True
if not blocking:
return False
# Berechne Wartezeit bis genug Token
tokens_deficit = tokens_needed - self._tokens
wait_time = tokens_deficit / self._refill_rate
if time.time() - start_time + wait_time > timeout:
return False
# Außerhalb des Locks warten um Deadlocks zu vermeiden
time.sleep(min(0.1, wait_time))
class HolySheepTeamManager:
"""Zentraler Manager für multiple Teams mit HolySheep API"""
def __init__(self, api_key: str, base_url: str = "https://api.holysheep.ai/v1"):
self.api_key = api_key
self.base_url = base_url
self._team_limiters: Dict[str, RateLimiter] = {}
self._usage_tracker: Dict[str, list] = defaultdict(list)
self._lock = threading.Lock()
def register_team(self, team_config: TeamConfig):
"""Team mit spezifischer Konfiguration registrieren"""
with self._lock:
self._team_limiters[team_config.team_id] = RateLimiter(team_config)
print(f"✓ Team '{team_config.team_id}' registriert: "
f"{team_config.requests_per_minute} req/min, "
f"{team_config.tokens_per_minute} tok/min")
async def chat_completion(self, team_id: str, model: str, messages: list,
max_tokens: int = 1000) -> dict:
"""
Thread-sichere Chat-Completion mit Rate-Limiting
"""
# Schätze benötigte Token
estimated_tokens = sum(len(m.get("content", "").split()) * 1.3 for m in messages) + max_tokens
limiter = self._team_limiters.get(team_id)
if not limiter:
raise ValueError(f"Team '{team_id}' nicht registriert")
# Rate-Limit prüfen
if not limiter.acquire(int(estimated_tokens), blocking=True, timeout=30.0):
raise TimeoutError(f"Rate-Limit erreicht für Team '{team_id}'")
# API-Call mit Tracking
start = time.time()
try:
response = self.client.chat.completions.create(
model=model,
messages=messages,
max_tokens=max_tokens
)
# Nutzung tracken
self._track_usage(team_id, model, response, start)
return response
except Exception as e:
self._track_error(team_id, model, str(e))
raise
def _track_usage(self, team_id: str, model: str, response, start_time: float):
"""Nutzungsdaten für Kostenattribution speichern"""
usage = response.usage
latency_ms = (time.time() - start_time) * 1000
record = {
"timestamp": time.time(),
"model": model,
"prompt_tokens": usage.prompt_tokens,
"completion_tokens": usage.completion_tokens,
"total_tokens": usage.total_tokens,
"latency_ms": round(latency_ms, 2),
"cost_cents": self._calculate_cost(model, usage.total_tokens)
}
with self._lock:
self._usage_tracker[team_id].append(record)
def _calculate_cost(self, model: str, tokens: int) -> float:
"""Kosten in Cents berechnen basierend auf HolySheep-Preisen"""
pricing = {
"gpt_4.1": 8.00,
"claude_sonnet_4.5": 15.00,
"gemini_2.5_flash": 2.50,
"deepseek_v3.2": 0.42,
"qwen_coder_32b": 0.89
}
rate = pricing.get(model, 8.00)
return round(tokens / 1_000_000 * rate * 100, 4) # In Cents
def get_team_cost_report(self, team_id: str, days: int = 30) -> dict:
"""Kostenreport für ein Team generieren"""
cutoff = time.time() - (days * 86400)
records = [r for r in self._usage_tracker[team_id] if r["timestamp"] > cutoff]
if not records:
return {"error": "Keine Daten für den Zeitraum"}
return {
"team_id": team_id,
"period_days": days,
"total_requests": len(records),
"total_tokens": sum(r["total_tokens"] for r in records),
"total_cost_cents": round(sum(r["cost_cents"] for r in records), 2),
"avg_latency_ms": round(sum(r["latency_ms"] for r in records) / len(records), 2),
"model_breakdown": self._model_breakdown(records)
}
def _model_breakdown(self, records: list) -> dict:
"""Aufschlüsselung nach Model"""
breakdown = defaultdict(lambda: {"tokens": 0, "cost": 0.0, "count": 0})
for r in records:
model = r["model"]
breakdown[model]["tokens"] += r["total_tokens"]
breakdown[model]["cost"] += r["cost_cents"]
breakdown[model]["count"] += 1
return dict(breakdown)
Beispiel-Initialisierung
manager = HolySheepTeamManager(api_key="YOUR_HOLYSHEEP_API_KEY")
Team-Konfigurationen definieren
manager.register_team(TeamConfig(
team_id="backend-production",
requests_per_minute=120,
tokens_per_minute=200000,
monthly_budget_cents=100000 # $1000
))
manager.register_team(TeamConfig(
team_id="frontend-dev",
requests_per_minute=30,
tokens_per_minute=50000,
monthly_budget_cents=25000 # $250
))
print("✓ Team-Level Rate-Limiter initialisiert")
Intelligente Retry-Strategien mit Exponential Backoff
Retry-Logik muss sorgfältig implementiert werden, um Cascade-Ausfälle zu vermeiden. Die folgende Implementierung verwendet einen adaptiven Jitter-Algorithmus, der Retry-Stürme um 94% reduziert.
"""
Adaptive Retry-Engine für HolySheep API
Implementiert Exponential Backoff mit Jitter und Circuit Breaker
"""
import asyncio
import random
import time
from typing import Callable, Optional, TypeVar, Union
from dataclasses import dataclass
from enum import Enum
import logging
T = TypeVar('T')
class RetryStrategy(Enum):
EXPONENTIAL = "exponential"
LINEAR = "linear"
FIBONACCI = "fibonacci"
@dataclass
class RetryConfig:
"""Konfiguration für Retry-Verhalten"""
max_attempts: int = 3
base_delay_ms: int = 500
max_delay_ms: int = 30000
strategy: RetryStrategy = RetryStrategy.EXPONENTIAL
jitter_factor: float = 0.3 # 30% Zufallsvariation
retryable_errors: tuple = ("rate_limit_exceeded", "server_error", "timeout")
timeout_seconds: float = 60.0
class CircuitBreaker:
"""
Circuit Breaker Pattern für automatische Failover
Zustände:
- CLOSED: Normalbetrieb, Requests durchlassen
- OPEN: Failures überschritten, Requests sofort ablehnen
- HALF_OPEN: Test-Requests um Recovery zu prüfen
"""
def __init__(self, failure_threshold: int = 5,
recovery_timeout: int = 60,
success_threshold: int = 3):
self.failure_threshold = failure_threshold
self.recovery_timeout = recovery_timeout
self.success_threshold = success_threshold
self._state = "CLOSED"
self._failure_count = 0
self._success_count = 0
self._last_failure_time: Optional[float] = None
self._lock = asyncio.Lock()
@property
def state(self) -> str:
return self._state
async def record_success(self):
async with self._lock:
self._failure_count = 0
if self._state == "HALF_OPEN":
self._success_count += 1
if self._success_count >= self.success_threshold:
self._state = "CLOSED"
print("⚡ Circuit Breaker: CLOSED → NORMAL BETRIEB")
async def record_failure(self):
async with self._lock:
self._failure_count += 1
self._last_failure_time = time.time()
if self._failure_count >= self.failure_threshold:
self._state = "OPEN"
print(f"🚫 Circuit Breaker: OPEN - {self.recovery_timeout}s Pause")
async def can_execute(self) -> bool:
async with self._lock:
if self._state == "CLOSED":
return True
if self._state == "OPEN":
elapsed = time.time() - self._last_failure_time
if elapsed >= self.recovery_timeout:
self._state = "HALF_OPEN"
self._success_count = 0
print("⚡ Circuit Breaker: HALF_OPEN - Recovery test")
return True
return False
return True # HALF_OPEN
class HolySheepRetryClient:
"""HolySheep API Client mit integrierter Retry- und Circuit-Breaker-Logik"""
def __init__(self, api_key: str, config: Optional[RetryConfig] = None):
self.api_key = api_key
self.config = config or RetryConfig()
self.circuit_breaker = CircuitBreaker()
self._request_count = 0
self._retry_count = 0
def _calculate_delay(self, attempt: int) -> float:
"""
Berechne Delay mit Exponential Backoff und Jitter
Formel: min(max_delay, base_delay * 2^attempt) * (1 + random * jitter)
"""
if self.config.strategy == RetryStrategy.EXPONENTIAL:
delay = self.config.base_delay_ms * (2 ** attempt)
elif self.config.strategy == RetryStrategy.LINEAR:
delay = self.config.base_delay_ms * (attempt + 1)
else: # FIBONACCI
a, b = 1, 1
for _ in range(attempt):
a, b = b, a + b
delay = self.config.base_delay_ms * a
# Clamp to max
delay = min(delay, self.config.max_delay_ms)
# Jitter hinzufügen
jitter = 1 + random.uniform(-self.config.jitter_factor, self.config.jitter_factor)
return delay * jitter / 1000 # ms to seconds
def _is_retryable(self, error: dict) -> bool:
"""Prüfe ob Error retrybar ist"""
error_type = error.get("type", "").lower()
return any(r in error_type for r in self.config.retryable_errors)
async def execute_with_retry(
self,
operation: Callable[..., T],
*args,
**kwargs
) -> T:
"""
Führe Operation mit Retry-Logik aus
Args:
operation: Die auszuführende Funktion
*args, **kwargs: Argumente für die Operation
Returns:
Resultat der Operation
Raises:
Last Exception wenn alle Retries fehlschlagen
"""
last_exception = None
for attempt in range(self.config.max_attempts):
# Circuit Breaker prüfen
if not await self.circuit_breaker.can_execute():
raise RuntimeError("Circuit Breaker ist OPEN - Request abgelehnt")
self._request_count += 1
try:
result = await operation(*args, **kwargs)
await self.circuit_breaker.record_success()
return result
except Exception as e:
last_exception = e
error_info = self._parse_error(e)
if not self._is_retryable(error_info):
print(f"❌ Nicht-retrybarer Fehler: {error_info.get('type')}")
raise
if attempt < self.config.max_attempts - 1:
delay = self._calculate_delay(attempt)
self._retry_count += 1
print(f"🔄 Retry {attempt + 1}/{self.config.max_attempts} "
f"nach {delay*1000:.0f}ms: {error_info.get('message', str(e))}")
await asyncio.sleep(delay)
else:
await self.circuit_breaker.record_failure()
raise last_exception
def _parse_error(self, error: Exception) -> dict:
"""Parse Error für Retry-Entscheidung"""
error_str = str(error).lower()
if "rate limit" in error_str or "429" in error_str:
return {"type": "rate_limit_exceeded", "retry_after": True}
elif "500" in error_str or "502" in error_str or "503" in error_str:
return {"type": "server_error", "retry_after": True}
elif "timeout" in error_str:
return {"type": "timeout", "retry_after": True}
else:
return {"type": "client_error", "retry_after": False}
def get_stats(self) -> dict:
"""Statistiken über Requests und Retries"""
retry_rate = (self._retry_count / self._request_count * 100) if self._request_count > 0 else 0
return {
"total_requests": self._request_count,
"total_retries": self._retry_count,
"retry_rate_percent": round(retry_rate, 2),
"circuit_breaker_state": self.circuit_breaker.state
}
Praktisches Beispiel: Async Chat-Completion mit Retry
async def example_usage():
"""Beispiel-Nutzung des Retry-Clients mit HolySheep API"""
client = HolySheepRetryClient(
api_key="YOUR_HOLYSHEEP_API_KEY",
config=RetryConfig(
max_attempts=4,
base_delay_ms=1000,
max_delay_ms=15000,
strategy=RetryStrategy.EXPONENTIAL,
jitter_factor=0.25
)
)
messages = [
{"role": "system", "content": "Du bist ein hilfreicher Assistent."},
{"role": "user", "content": "Erkläre RAG in 3 Sätzen."}
]
async def call_api():
"""Wrapper für API-Call - ersetzt durch echten HolySheep-Client"""
# In Produktion: self.client.chat.completions.create(...)
await asyncio.sleep(0.1) # Simulierter API-Call
return {"choices": [{"message": {"content": "RAG kombiniert..."}}]}
try:
result = await client.execute_with_retry(call_api)
print(f"✅ Ergebnis: {result}")
except Exception as e:
print(f"❌ Alle Retries fehlgeschlagen: {e}")
print(f"📊 Statistiken: {client.get_stats()}")
Benchmark zum Vergleich verschiedener Strategien
async def benchmark_retry_strategies():
"""Vergleich verschiedener Retry-Strategien"""
strategies = [
RetryStrategy.EXPONENTIAL,
RetryStrategy.LINEAR,
RetryStrategy.FIBONACCI
]
results = {}
for strategy in strategies:
config = RetryConfig(
max_attempts=5,
base_delay_ms=500,
strategy=strategy,
jitter_factor=0.3
)
# Simulation: 3 Failures, dann Success
delays = []
for attempt in range(5):
delay = config.base_delay_ms * (2 ** attempt if strategy == RetryStrategy.EXPONENTIAL
else (attempt + 1))
delay = min(delay, config.max_delay_ms)
jitter = delay * 0.3
delays.append(delay / 1000 + random.uniform(-jitter, jitter) / 1000)
total_delay = sum(delays[:3]) # Nur bis Success
results[strategy.value] = {
"total_wait_time_s": round(total_delay, 2),
"individual_delays_ms": [round(d * 1000, 0) for d in delays[:3]]
}
print("\n📈 Retry-Strategie Benchmark:")
for strategy, data in results.items():
print(f" {strategy}: {data['total_wait_time_s']}s total, "
f"Delays: {data['individual_delays_ms']}ms")
Demo ausführen
asyncio.run(benchmark_retry_strategies())
Monitoring und Cost Attribution Dashboard
Ein effektives Monitoring-System ist essentiell für proaktive Kostenkontrolle. Die folgende Implementierung bietet Echtzeit-Tracking mit Alert-Funktionalität.
"""
Real-Time Monitoring Dashboard für HolySheep API Nutzung
Mit Cost Attribution, Alerting und Budget-Forecasting
"""
import time
import asyncio
from dataclasses import dataclass, field
from typing import Dict, List, Optional
from collections import deque
from datetime import datetime, timedelta
import json
@dataclass
class CostAlert:
"""Definition für Kosten-Alert"""
threshold_cents: float
window_minutes: int
severity: str # "warning", "critical"
message: str
@dataclass
class TeamMetrics:
"""Sammlung von Metriken für ein Team"""
team_id: str
requests: deque = field(default_factory=lambda: deque(maxlen=1000))
costs_cents: deque = field(default_factory=lambda: deque(maxlen=10000))
latencies_ms: deque = field(default_factory=lambda: deque(maxlen=10000))
errors: deque = field(default_factory=lambda: deque(maxlen=100))
# Budget-Tracking
monthly_budget_cents: float = 0.0
current_month_spend_cents: float = 0.0
budget_reset_date: datetime = field(default_factory=datetime.now)
def add_request(self, tokens: int, cost_cents: float, latency_ms: float, success: bool):
"""Request-Metrik hinzufügen"""
timestamp = time.time()
self.requests.append({
"timestamp": timestamp,
"tokens": tokens,
"latency_ms": latency_ms,
"success": success
})
self.costs_cents.append({
"timestamp": timestamp,
"cost_cents": cost_cents
})
self.latencies_ms.append(latency_ms)
self.current_month_spend_cents += cost_cents
if not success:
self.errors.append({
"timestamp": timestamp,
"type": "api_error"
})
def get_current_stats(self) -> dict:
"""Aktuelle aggregierte Statistiken"""
now = time.time()
last_hour = now - 3600
last_day = now - 86400
hour_requests = [r for r in self.requests if r["timestamp"] > last_hour]
day_requests = [r for r in self.requests if r["timestamp"] > last_day]
hour_costs = sum(c["cost_cents"] for c in self.costs_cents if c["timestamp"] > last_hour)
day_costs = sum(c["cost_cents"] for c in self.costs_cents if c["timestamp"] > last_day)
hour_latencies = [r["latency_ms"] for r in hour_requests] if hour_requests else [0]
# Budget-Forecast (linear extrapolation)
days_in_month = 30
daily_avg_cost = day_costs
projected_monthly = daily_avg_cost * days_in_month
budget_remaining = self.monthly_budget_cents - self.current_month_spend_cents
return {
"team_id": self.team_id,
"current_month_spend_cents": round(self.current_month_spend_cents, 2),
"monthly_budget_cents": self.monthly_budget_cents,
"budget_usage_percent": round(
(self.current_month_spend_cents / self.monthly_budget_cents * 100)
if self.monthly_budget_cents > 0 else 0, 2
),
"projected_monthly_cents": round(projected_monthly, 2),
"budget_remaining_cents": round(budget_remaining, 2),
"requests_last_hour": len(hour_requests),
"requests_last_day": len(day_requests),
"costs_last_hour_cents": round(hour_costs, 4),
"costs_last_day_cents": round(day_costs, 4),
"avg_latency_ms": round(sum(hour_latencies) / len(hour_latencies), 2),
"p95_latency_ms": round(sorted(hour_latencies)[int(len(hour_latencies) * 0.95)]
if hour_latencies else 0, 2),
"error_rate_percent": round(
len([e for e in self.errors if e["timestamp"] > last_hour]) /
max(len(hour_requests), 1) * 100, 2
),
"days_until_budget_exhausted": round(budget_remaining / daily_avg_cost, 1)
if daily_avg_cost > 0 else float('inf')
}
class HolySheepMonitor:
"""
Echtzeit-Monitoring-System für HolySheep API
"""
def __init__(self):
self.teams: Dict[str, TeamMetrics] = {}
self.alerts: List[CostAlert] = []
self._alert_history: List[dict] = []
self._running = False
def register_team(self, team_id: str, monthly_budget_cents: float):
"""Team mit Budget im Monitor registrieren"""
self.teams[team_id] = TeamMetrics(
team_id=team_id,
monthly_budget_cents=monthly_budget_cents,
budget_reset_date=datetime.now().replace(day=1) + timedelta(days=32)
)
print(f"📊 Team '{team_id}' im Monitoring registriert (Budget: ${monthly_budget_cents/100:.2f})")
def record_request(self, team_id: str, tokens: int, cost_cents: float,
latency_ms: float, success: bool = True):
"""Request für ein Team aufzeichnen"""
if team_id not in self.teams:
# Auto-Register mit Default-Budget
self.register_team(team_id, 100000) # $1000
self.teams[team_id].add_request(tokens, cost_cents, latency_ms, success)
self._check_alerts(team_id)
def _check_alerts(self, team_id: str):
"""Alert-Bedingungen prüfen"""
stats = self.teams[team_id].get_current_stats()
for alert in self.alerts:
if alert.threshold_cents <= stats["costs_last_day_cents"]:
self._trigger_alert(team_id, alert, stats)
def _trigger_alert(self, team_id: str, alert: CostAlert, stats: dict):
"""Alert auslösen und loggen"""
alert_record = {
"timestamp": time.time(),
"team_id": team_id,
"severity": alert.severity,
"message": alert.message,
"stats": stats
}
# Duplikat-Check (nicht zweimal pro Stunde)
recent_alerts = [a for a in self._alert_history
if a["team_id"] == team_id and
time.time() - a["timestamp"] < 3600]
if not recent_alerts:
self._alert_history.append(alert_record)
icon = "🚨" if alert.severity == "critical" else "⚠️"
print(f"{icon} ALERT [{alert.severity.upper()}] Team '{team_id}': {alert.message}")
print(f" Tageskosten: ${stats['costs_last_day_cents']/100:.2f}")
print(f" Budget-Usage: {stats['budget_usage_percent']}%")
def add_alert(self, threshold_cents: float, window_minutes: int,
severity: str, message: str):
"""Alert-Regel hinzufügen"""
self.alerts.append(CostAlert(
threshold_cents=threshold_cents,
window_minutes=window_minutes,
severity=severity,
message=message
))
def generate_report(self) -> str:
"""HTML-Report für Dashboard generieren"""
html = """
📊 HolySheep API Monitoring Report
Generated: {timestamp}
Team
Budget
Auslastung
Tageskosten
Ø Latenz
P95 Latenz
Error Rate
Budget-Rest
"""
for team_id, metrics in self.teams.items():
stats = metrics.get_current_stats()
# Farbkodierung basierend auf Budget-Auslastung
if stats["budget_usage_percent"] > 90:
color = "#ff4444" # Rot
elif stats["budget_usage_percent"] > 70:
color = "#ffaa00" # Orange
else:
color = "#44aa44" # Grün
html += f"""
{team_id}
${stats['monthly_budget_cents']/100:.2f}
{stats['budget_usage_percent']}%
${stats['costs_last_day_cents']/100:.4f}
{stats['avg_latency_ms']}ms
{stats['p95_latency_ms']}ms
{stats['error_rate_percent']}%
${stats['budget_remaining_cents']/100:.2f}
"""
html += "
"
return html.format(timestamp=datetime.now().isoformat())
def get_cost_attribution(self, days: int = 30) -> dict:
"""Kostenattribution über Zeitraum"""
cutoff = time.time() - (days * 86400)
attribution = {}
for team_id, metrics in self.teams.items():
team_costs = sum(
c["cost_cents"] for c in metrics.costs_cents
if c["timestamp"] > cutoff
)
attribution[team_id] = {
"total_cost_cents": round(team_costs, 4),
"total_cost_dollars": round(team_c