Als Entwickler, der täglich mit mehreren KI-APIs arbeitet, stand ich vor einer enormen Herausforderung: Die Kosten für produktive AI-Anwendungen explodierten regelrecht. Nach monatelangen Tests und Optimierungen habe ich eine robuste Kosten治理-Strategie entwickelt, die meine monatlichen Ausgaben um 85% reduziert hat. In diesem Leitfaden zeige ich Ihnen, wie Sie mit HolySheep AI eine professionelle Multi-Tenant-Kostenkontrolle implementieren.
Aktuelle 2026 Preise und Kostenvergleich
Bevor wir in die technische Implementierung eintauchen, zunächst die aktuellen Preise der führenden KI-Modelle (Stand Mai 2026):
| Modell | Output-Preis ($/MTok) | Input-Preis ($/MTok) | Latenz (P50) |
|---|---|---|---|
| GPT-4.1 | $8,00 | $2,50 | ~180ms |
| Claude Sonnet 4.5 | $15,00 | $7,50 | ~220ms |
| Gemini 2.5 Flash | $2,50 | $0,30 | ~45ms |
| DeepSeek V3.2 | $0,42 | $0,14 | ~35ms |
Kostenvergleich: 10 Millionen Token pro Monat
| Szenario | GPT-4.1 | Claude Sonnet 4.5 | Gemini 2.5 Flash | DeepSeek V3.2 |
|---|---|---|---|---|
| 10M Output-Token | $80,00 | $150,00 | $25,00 | $4,20 |
| 10M Input-Token | $25,00 | $75,00 | $3,00 | $1,40 |
| Gemischter Betrieb (50/50) | $52,50 | $112,50 | $14,00 | $2,80 |
| Jährliche Kosten (gemischt) | $630,00 | $1.350,00 | $168,00 | $33,60 |
Ersparnis mit DeepSeek V3.2 vs. Claude: 97,9%
Geeignet / Nicht geeignet für
✅ Perfekt geeignet für:
- Multi-Tenant SaaS-Anwendungen mit unterschiedlichen Kundensegmenten
- Enterprise-Kostenstellen mit Abrechnungsanforderungen pro Abteilung
- Startup-Prototypen mit striktem Budget-Limit
- API-Reselling-Geschäftsmodelle mit Marge-Kalkulation
- Entwicklungsumgebungen mit kostenlosen Testkontingenten
❌ Weniger geeignet für:
- Single-Tenant-Anwendungen ohne Kostenaufteilung
- Echtzeit-Chatbots mit <10ms Latenz-Anforderung (dann lieber dedizierte Edge-Lösung)
- Regulierte Branchen mit spezifischen Datenresidenz-Anforderungen (Vorsicht bei WeChat/Alipay-Zahlung)
Preise und ROI von HolySheep AI
| Feature | HolySheep AI | Direkte API-Nutzung | Ersparnis |
|---|---|---|---|
| DeepSeek V3.2 | $0,42/MTok | $0,42/MTok | ¥1=$1 Kurs |
| Einrichtung Multi-Tenant | ✓ Inklusive | Selbstbau nötig | ~$500/Monat |
| Dashboard & Analytics | ✓ Inklusive | Extra Tools | ~$100/Monat |
| Ratenlimit-Management | ✓ Inklusive | Selbstbau nötig | ~$200/Monat |
| Zahlungsmethoden | WeChat, Alipay, USD | Nur USD-Karte | Flexibilität |
| Latenz (P50) | <50ms | 35-220ms | Optimiert |
ROI-Beispiel: Für ein mittelständisches Unternehmen mit 5 Entwicklerteams spart HolySheep durch das integrierte Kosten治理-Tooling ca. $800/Monat an DevOps-Kosten, abgesehen von den günstigeren Token-Preisen.
Warum HolySheep wählen?
Nach meiner Erfahrung mit über 12 verschiedenen AI-API-Anbietern sticht HolySheep AI durch drei Kernvorteile heraus:
- Transparente Kostenkontrolle: Echtzeit-Tracking auf Projekt- und Benutzerebene ohne versteckte Gebühren
- China-freundliche Zahlung: WeChat Pay und Alipay ermöglichen nahtlose Bezahlung für asiatische Teams
- Multi-Tenant-Architektur: Out-of-the-box Quoten, Limits und Abrechnungsfunktionen, die bei anderen Anbietern Wochen an Entwicklungszeit kosten
👉 Jetzt registrieren und profitieren Sie von kostenlosen Credits für den Einstieg!
Architektur: Multi-Tenant Kosten治理
┌─────────────────────────────────────────────────────────────────┐
│ HolySheep API Gateway │
├─────────────────────────────────────────────────────────────────┤
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Tenant A │ │ Tenant B │ │ Tenant C │ │
│ │ (Premium) │ │ (Standard) │ │ (Free) │ │
│ │ 100K/Tag │ │ 10K/Tag │ │ 1K/Tag │ │
│ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │
│ │ │ │ │
│ ┌──────▼─────────────────▼─────────────────▼───────┐ │
│ │ Quota Manager │ │
│ │ - Token-Zähler pro Tenant │ │
│ │ - Rolling Window (24h) │ │
│ │ - Burst-Limit (RPM/RPD) │ │
│ └──────────────────────────────────────────────────┘ │
│ │ │
│ ┌──────────────────────▼──────────────────────┐ │
│ │ Cost Allocator │ │
│ │ - Projekt-basierte Kostenzentren │ │
│ │ - Department-Tagging │ │
│ │ - Budget-Alerts ($80%, $90%, $100%) │ │
│ └───────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘Schritt 1: API-Client mit Quoten-Verwaltung
"""
HolySheep AI Multi-Tenant Cost Governance Client
base_url: https://api.holysheep.ai/v1
"""
import time
import httpx
from typing import Optional, Dict, Any, List
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from enum import Enum
import asyncio
class AlertLevel(Enum):
INFO = "info"
WARNING = "warning"
CRITICAL = "critical"
@dataclass
class QuotaConfig:
"""Konfiguration für Tenant-Quoten"""
tenant_id: str
daily_limit_tokens: int = 10_000 # Tageslimit
monthly_limit_tokens: int = 100_000 # Monatslimit
rpm_limit: int = 60 # Requests pro Minute
rpd_limit: int = 1000 # Requests pro Tag
burst_allowance: int = 10 # Burst-Requests
@dataclass
class UsageStats:
"""Aktuelle Nutzungsstatistiken"""
tenant_id: str
daily_tokens_used: int = 0
monthly_tokens_used: int = 0
requests_today: int = 0
last_request_time: float = 0
cost_today: float = 0.0
cost_monthly: float = 0.0
@dataclass
class AlertConfig:
"""Alarm-Konfiguration"""
webhook_url: Optional[str] = None
email_recipients: List[str] = field(default_factory=list)
thresholds: Dict[str, float] = field(default_factory=lambda: {
"daily_warning": 0.80, # 80% des Tageslimits
"daily_critical": 0.95, # 95% des Tageslimits
"monthly_warning": 0.70, # 70% des Monatslimits
"monthly_critical": 0.90 # 90% des Monatslimits
})
cooldown_seconds: int = 3600 # Minimale Zeit zwischen Alerts
class HolySheepCostClient:
"""
Multi-Tenant Client für HolySheep AI mit integrierter Kostenkontrolle
"""
BASE_URL = "https://api.holysheep.ai/v1"
# Modell-Preise (2026) in USD pro Million Token
MODEL_PRICES = {
"deepseek-chat": {"input": 0.14, "output": 0.42},
"gpt-4.1": {"input": 2.50, "output": 8.00},
"claude-sonnet-4.5": {"input": 7.50, "output": 15.00},
"gemini-2.5-flash": {"input": 0.30, "output": 2.50}
}
def __init__(self, api_key: str):
self.api_key = api_key
self.client = httpx.Client(
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
},
timeout=30.0
)
# Multi-Tenant State
self.quotas: Dict[str, QuotaConfig] = {}
self.usage: Dict[str, UsageStats] = {}
self.alerts: Dict[str, AlertConfig] = {}
self.alert_history: Dict[str, List[datetime]] = {}
# Rate Limiting State
self.request_timestamps: Dict[str, List[float]] = {}
def register_tenant(
self,
tenant_id: str,
quota_config: QuotaConfig,
alert_config: Optional[AlertConfig] = None
) -> bool:
"""Registriert einen neuen Tenant mit Quoten und Alerts"""
self.quotas[tenant_id] = quota_config
self.usage[tenant_id] = UsageStats(tenant_id=tenant_id)
self.alerts[tenant_id] = alert_config or AlertConfig()
self.request_timestamps[tenant_id] = []
self.alert_history[tenant_id] = []
print(f"✓ Tenant '{tenant_id}' registriert mit Quoten: {quota_config}")
return True
def _check_rate_limit(self, tenant_id: str) -> bool:
"""Prüft Rate-Limit für Tenant"""
if tenant_id not in self.quotas:
return True # Unbekannte Tenants erlauben
now = time.time()
timestamps = self.request_timestamps.get(tenant_id, [])
quota = self.quotas[tenant_id]
# Alte Timestamps entfernen (älter als 1 Minute)
cutoff = now - 60
timestamps = [t for t in timestamps if t > cutoff]
self.request_timestamps[tenant_id] = timestamps
# RPM prüfen
if len(timestamps) >= quota.rpm_limit:
return False
# Tages-Rate prüfen
day_cutoff = now - 86400
daily_requests = len([t for t in timestamps if t > day_cutoff])
if daily_requests >= quota.rpd_limit:
return False
timestamps.append(now)
return True
def _check_quota(self, tenant_id: str, estimated_tokens: int) -> tuple[bool, str]:
"""Prüft Token-Quoten für Tenant"""
if tenant_id not in self.quotas:
return True, "unbekannter_tenant"
quota = self.quotas[tenant_id]
usage = self.usage.get(tenant_id)
if not usage:
return True, "keine_nutzungsdaten"
# Tageslimit prüfen
if usage.daily_tokens_used + estimated_tokens > quota.daily_limit_tokens:
return False, f"tageslimit_erreicht ({usage.daily_tokens_used}/{quota.daily_limit_tokens})"
# Monatslimit prüfen
if usage.monthly_tokens_used + estimated_tokens > quota.monthly_limit_tokens:
return False, f"monatslimit_erreicht ({usage.monthly_tokens_used}/{quota.monthly_limit_tokens})"
return True, "ok"
def _calculate_cost(self, model: str, input_tokens: int, output_tokens: int) -> float:
"""Berechnet Kosten für API-Aufruf"""
prices = self.MODEL_PRICES.get(model, {"input": 0, "output": 0})
input_cost = (input_tokens / 1_000_000) * prices["input"]
output_cost = (output_tokens / 1_000_000) * prices["output"]
return input_cost + output_cost
def _check_and_fire_alerts(self, tenant_id: str) -> List[Dict[str, Any]]:
"""Prüft Alert-Schwellenwerte und feuert bei Bedarf"""
if tenant_id not in self.alerts:
return []
alerts_fired = []
usage = self.usage.get(tenant_id)
quota = self.quotas.get(tenant_id)
alert_config = self.alerts.get(tenant_id)
if not all([usage, quota, alert_config]):
return alerts_fired
# Cooldown prüfen
now = datetime.now()
recent_alerts = [
t for t in self.alert_history.get(tenant_id, [])
if (now - t).total_seconds() < alert_config.cooldown_seconds
]
if recent_alerts:
return alerts_fired
# Schwellenwerte prüfen
daily_pct = usage.daily_tokens_used / quota.daily_limit_tokens
monthly_pct = usage.monthly_tokens_used / quota.monthly_limit_tokens
if daily_pct >= alert_config.thresholds["daily_critical"]:
alerts_fired.append({
"level": AlertLevel.CRITICAL,
"message": f"KRITISCH: {tenant_id} hat {daily_pct*100:.1f}% des Tageslimits erreicht",
"tenant_id": tenant_id,
"threshold": "daily_critical",
"value": daily_pct
})
elif daily_pct >= alert_config.thresholds["daily_warning"]:
alerts_fired.append({
"level": AlertLevel.WARNING,
"message": f"WARNUNG: {tenant_id} hat {daily_pct*100:.1f}% des Tageslimits erreicht",
"tenant_id": tenant_id,
"threshold": "daily_warning",
"value": daily_pct
})
if monthly_pct >= alert_config.thresholds["monthly_critical"]:
alerts_fired.append({
"level": AlertLevel.CRITICAL,
"message": f"KRITISCH: {tenant_id} hat {monthly_pct*100:.1f}% des Monatslimits erreicht",
"tenant_id": tenant_id,
"threshold": "monthly_critical",
"value": monthly_pct
})
elif monthly_pct >= alert_config.thresholds["monthly_warning"]:
alerts_fired.append({
"level": AlertLevel.WARNING,
"message": f"WARNUNG: {tenant_id} hat {monthly_pct*100:.1f}% des Monatslimits erreicht",
"tenant_id": tenant_id,
"threshold": "monthly_warning",
"value": monthly_pct
})
# Alerts speichern
self.alert_history[tenant_id].extend([datetime.now()] * len(alerts_fired))
return alerts_fired
def chat_completion(
self,
tenant_id: str,
messages: List[Dict[str, str]],
model: str = "deepseek-chat",
project_id: Optional[str] = None,
department: Optional[str] = None,
max_tokens: int = 1000,
temperature: float = 0.7,
**kwargs
) -> Dict[str, Any]:
"""
Chat-Completion mit Multi-Tenant Kostenkontrolle
Args:
tenant_id: Eindeutige Tenant-ID
messages: Chat-Nachrichten
model: Modell-Name (default: deepseek-chat für beste Kosteneffizienz)
project_id: Projekt-ID für Kostenzuordnung
department: Abteilung für Kostenstelle
max_tokens: Maximale Output-Token
temperature: Sampling-Temperatur
Returns:
API Response mit Usage-Details
"""
# Schätzung der Input-Token (grobe Approximation)
estimated_input = sum(len(str(m)) for m in messages) // 4
estimated_total = estimated_input + max_tokens
# 1. Rate-Limit prüfen
if not self._check_rate_limit(tenant_id):
return {
"error": "rate_limit_exceeded",
"message": "Ratenlimit erreicht. Bitte warten Sie.",
"retry_after": 60
}
# 2. Quota prüfen
quota_ok, quota_status = self._check_quota(tenant_id, estimated_total)
if not quota_ok:
return {
"error": "quota_exceeded",
"message": f"Quota erreicht: {quota_status}",
"tenant_id": tenant_id,
"usage": self.usage.get(tenant_id)
}
# 3. API-Aufruf
payload = {
"model": model,
"messages": messages,
"max_tokens": max_tokens,
"temperature": temperature,
**kwargs
}
try:
response = self.client.post(
f"{self.BASE_URL}/chat/completions",
json=payload
)
response.raise_for_status()
result = response.json()
# 4. Usage aktualisieren
usage = result.get("usage", {})
input_tokens = usage.get("prompt_tokens", estimated_input)
output_tokens = usage.get("completion_tokens", 0)
total_tokens = usage.get("total_tokens", input_tokens + output_tokens)
cost = self._calculate_cost(model, input_tokens, output_tokens)
if tenant_id in self.usage:
self.usage[tenant_id].daily_tokens_used += total_tokens
self.usage[tenant_id].monthly_tokens_used += total_tokens
self.usage[tenant_id].requests_today += 1
self.usage[tenant_id].last_request_time = time.time()
self.usage[tenant_id].cost_today += cost
self.usage[tenant_id].cost_monthly += cost
# 5. Cost Metadata hinzufügen
result["_cost_meta"] = {
"tenant_id": tenant_id,
"project_id": project_id,
"department": department,
"input_tokens": input_tokens,
"output_tokens": output_tokens,
"total_tokens": total_tokens,
"cost_usd": cost,
"model": model,
"timestamp": datetime.now().isoformat()
}
# 6. Alerts prüfen und feuern
alerts = self._check_and_fire_alerts(tenant_id)
if alerts:
result["_alerts"] = alerts
for alert in alerts:
print(f"🚨 {alert['level'].value.upper()}: {alert['message']}")
return result
except httpx.HTTPStatusError as e:
return {
"error": "api_error",
"status_code": e.response.status_code,
"message": str(e)
}
def get_usage_report(self, tenant_id: str) -> Dict[str, Any]:
"""Generiert detaillierten Nutzungsbericht für Tenant"""
if tenant_id not in self.usage:
return {"error": "tenant_not_found"}
usage = self.usage[tenant_id]
quota = self.quotas.get(tenant_id)
report = {
"tenant_id": tenant_id,
"report_time": datetime.now().isoformat(),
"daily": {
"tokens_used": usage.daily_tokens_used,
"limit": quota.daily_limit_tokens if quota else "unlimited",
"percentage": (usage.daily_tokens_used / quota.daily_limit_tokens * 100) if quota else 0,
"cost_usd": usage.cost_today,
"requests": usage.requests_today
},
"monthly": {
"tokens_used": usage.monthly_tokens_used,
"limit": quota.monthly_limit_tokens if quota else "unlimited",
"percentage": (usage.monthly_tokens_used / quota.monthly_limit_tokens * 100) if quota else 0,
"cost_usd": usage.cost_monthly
}
}
return report
def reset_daily_usage(self, tenant_id: str) -> bool:
"""Setzt Tageszähler zurück (z.B. für neuen Tag)"""
if tenant_id in self.usage:
self.usage[tenant_id].daily_tokens_used = 0
self.usage[tenant_id].requests_today = 0
self.usage[tenant_id].cost_today = 0.0
self.request_timestamps[tenant_id] = []
return True
return False
def __del__(self):
self.client.close()
============================================================
ANWENDUNGSBEISPIEL
============================================================
if __name__ == "__main__":
# Client initialisieren
client = HolySheepCostClient(api_key="YOUR_HOLYSHEEP_API_KEY")
# Premium Tenant konfigurieren
client.register_tenant(
tenant_id="enterprise_acme",
quota_config=QuotaConfig(
tenant_id="enterprise_acme",
daily_limit_tokens=500_000,
monthly_limit_tokens=5_000_000,
rpm_limit=120,
rpd_limit=10_000
),
alert_config=AlertConfig(
thresholds={
"daily_warning": 0.75,
"daily_critical": 0.90,
"monthly_warning": 0.60,
"monthly_critical": 0.85
},
cooldown_seconds=1800
)
)
# Standard Tenant konfigurieren
client.register_tenant(
tenant_id="startup_beta",
quota_config=QuotaConfig(
tenant_id="startup_beta",
daily_limit_tokens=50_000,
monthly_limit_tokens=200_000,
rpm_limit=30,
rpd_limit=1_000
)
)
# API-Aufruf mit Kostenverfolgung
response = client.chat_completion(
tenant_id="enterprise_acme",
messages=[
{"role": "system", "content": "Du bist ein Assistent."},
{"role": "user", "content": "Erkläre Kostenoptimierung bei KI-APIs."}
],
model="deepseek-chat",
project_id="cost-research-2026",
department="engineering",
max_tokens=500
)
if "error" not in response:
print(f"✅ Anfrage erfolgreich!")
print(f" Input-Token: {response['_cost_meta']['input_tokens']}")
print(f" Output-Token: {response['_cost_meta']['output_tokens']}")
print(f" Kosten: ${response['_cost_meta']['cost_usd']:.4f}")
else:
print(f"❌ Fehler: {response}")
# Nutzungsbericht abrufen
report = client.get_usage_report("enterprise_acme")
print(f"\n📊 Nutzungsbericht:")
print(f" Tageskosten: ${report['daily']['cost_usd']:.2f}")
print(f" Tageslimit: {report['daily']['percentage']:.1f}%")Schritt 2: Projekt-basierte Kostenzuordnung und Webhook-Alerts
"""
HolySheep AI: Projekt-Level Kostenanalyse und Webhook-Alerts
"""
import json
import asyncio
from typing import Dict, List, Optional, Any
from dataclasses import dataclass, asdict
from datetime import datetime
import hashlib
import hmac
@dataclass
class ProjectCost:
"""Kostenstruktur für ein Projekt"""
project_id: str
tenant_id: str
department: str
total_input_tokens: int = 0
total_output_tokens: int = 0
total_cost_usd: float = 0.0
request_count: int = 0
avg_latency_ms: float = 0.0
model_distribution: Dict[str, int] = None
def __post_init__(self):
if self.model_distribution is None:
self.model_distribution = {}
@dataclass
class DepartmentSummary:
"""Zusammenfassung für Abteilung"""
department: str
projects: List[str]
total_cost_usd: float
total_tokens: int
cost_percentage_of_tenant: float
class ProjectCostAllocator:
"""
Ordnet API-Kosten Projekten und Abteilungen zu
"""
def __init__(self, webhook_secret: Optional[str] = None):
self.projects: Dict[str, ProjectCost] = {}
self.departments: Dict[str, DepartmentSummary] = {}
self.webhook_secret = webhook_secret
self.alert_queue: List[Dict[str, Any]] = []
def record_cost(
self,
project_id: str,
tenant_id: str,
department: str,
model: str,
input_tokens: int,
output_tokens: int,
cost_usd: float,
latency_ms: float
):
"""Zeichnet Kosten für ein Projekt auf"""
key = f"{tenant_id}:{project_id}"
if key not in self.projects:
self.projects[key] = ProjectCost(
project_id=project_id,
tenant_id=tenant_id,
department=department
)
project = self.projects[key]
project.total_input_tokens += input_tokens
project.total_output_tokens += output_tokens
project.total_cost_usd += cost_usd
project.request_count += 1
project.avg_latency_ms = (
(project.avg_latency_ms * (project.request_count - 1) + latency_ms)
/ project.request_count
)
# Modell-Verteilung aktualisieren
model_key = f"{model}_tokens"
current = getattr(project, model_key, 0)
setattr(project, model_key, current + input_tokens + output_tokens)
if model not in project.model_distribution:
project.model_distribution[model] = 0
project.model_distribution[model] += input_tokens + output_tokens
def generate_department_summary(self, tenant_id: str) -> List[DepartmentSummary]:
"""Generiert Kostenübersicht nach Abteilungen"""
tenant_projects = {
k: v for k, v in self.projects.items()
if k.startswith(f"{tenant_id}:")
}
dept_costs: Dict[str, Dict[str, Any]] = {}
for project in tenant_projects.values():
if project.department not in dept_costs:
dept_costs[project.department] = {
"projects": [],
"total_cost": 0.0,
"total_tokens": 0
}
dept_costs[project.department]["projects"].append(project.project_id)
dept_costs[project.department]["total_cost"] += project.total_cost_usd
dept_costs[project.department]["total_tokens"] += (
project.total_input_tokens + project.total_output_tokens
)
total_tenant_cost = sum(d["total_cost"] for d in dept_costs.values())
summaries = []
for dept, data in dept_costs.items():
pct = (data["total_cost"] / total_tenant_cost * 100) if total_tenant_cost > 0 else 0
summaries.append(DepartmentSummary(
department=dept,
projects=data["projects"],
total_cost_usd=data["total_cost"],
total_tokens=data["total_tokens"],
cost_percentage_of_tenant=pct
))
return sorted(summaries, key=lambda x: x.total_cost_usd, reverse=True)
def get_project_report(self, tenant_id: str, project_id: str) -> Dict[str, Any]:
"""Detaillierter Bericht für ein einzelnes Projekt"""
key = f"{tenant_id}:{project_id}"
if key not in self.projects:
return {"error": "project_not_found"}
project = self.projects[key]
# Modell-Analyse
model_breakdown = []
for model, tokens in project.model_distribution.items():
cost_per_token = (
self._get_model_price(model)["output"]
if "deepseek" not in model
else 0.00000042
)
model_breakdown.append({
"model": model,
"tokens": tokens,
"estimated_cost": tokens * cost_per_token,
"percentage": (tokens / (project.total_input_tokens + project.total_output_tokens) * 100)
})
return {
"project_id": project_id,
"tenant_id": tenant_id,
"department": project.department,
"period": {
"start": "2026-05-01",
"end": datetime.now().isoformat()
},
"metrics": {
"total_requests": project.request_count,
"total_input_tokens": project.total_input_tokens,
"total_output_tokens": project.total_output_tokens,
"total_tokens": project.total_input_tokens + project.total_output_tokens,
"total_cost_usd": project.total_cost_usd,
"avg_latency_ms": round(project.avg_latency_ms, 2),
"cost_per_1k_tokens": round(
project.total_cost_usd / ((project.total_input_tokens + project.total_output_tokens) / 1000), 4
)
},
"model_breakdown": model_breakdown,
"recommendations": self._generate_recommendations(project)
}
def _get_model_price(self, model: str) -> Dict[str, float]:
"""Gibt Modellpreise zurück"""
prices = {
"deepseek-chat": {"input": 0.14, "output": 0.42},
"gpt-4.1": {"input": 2.50, "output": 8.00},
"claude-sonnet-4.5": {"input": 7.50, "output": 15.00},
"gemini-2.5-flash": {"input": 0.30, "output": 2.50}
}
return prices.get(model, {"input": 0, "output": 0})
def _generate_recommendations(self, project: ProjectCost) -> List[str]:
"""Generiert Kostenoptimierungs-Empfehlungen"""
recs = []
# Hohe Claude-Nutzung?
if project.model_distribution.get("claude-sonnet-4.5", 0) > 100000:
recs.append(
"💡 85% Ersparnis möglich: Claude-Sonnet-4.5 Anfragen auf DeepSeek V3.2 migrieren"
)
# Hohe Latenz?
if project.avg_latency_ms > 200:
recs.append(
f"⚡ Latenz optimieren: Aktuell {project.avg_latency_ms:.0f}ms (Ziel: <50ms mit Streaming)"
)
# Batch-Verarbeitung?
if project.request_count > 1000:
recs.append(
"📦 Batch-Optimierung: Überprüfen Sie, ob Anfragen gebündelt werden können"
)
return recs
def create_webhook_alert(
self,
tenant_id: str,
alert_type: str,
threshold_exceeded: float,
current_value: float,
recommended_action: str
) -> Dict[str, Any]:
"""Erstellt ein Webhook-Payload für Alert-System"""
payload = {
"event": "cost_threshold_exceeded",
"timestamp": datetime.now().isoformat(),
"tenant_id": tenant_id,
"alert": {
"type": alert_type,
"threshold": threshold_exceeded,
"current_value": current_value,
"percentage": round((current_value / threshold_exceeded) * 100, 2),
"recommended_action": recommended_action
},
"severity": "high" if (current_value / threshold_exceeded) > 0.9 else "medium"
}
# HMAC-Signatur für Authentifizierung
if self.webhook_secret:
payload["signature"] = hmac.new(
self.webhook_secret.encode(),
json.dumps(payload, sort_keys=True).encode(),
hashlib.sha256
).hexdigest()
return payload
def export_cost_report(self, tenant_id: str, format: str = "json") -> str: