Mein Team und ich standen vor genau einem Jahr vor einer monumentalen Herausforderung: Unser Enterprise-RAG-System für einen Fortune-500-Kunden musste täglich über 500.000 Anfragen bewältigen – mit einer durchschnittlichen Latenz von unter 200ms und 99,9% Uptime. Die bisherige Single-Provider-Strategie mit OpenAI allein kostete uns monatlich über $45.000 und lies uns bei Ausfällen im Regen stehen.
In diesem Tutorial zeige ich Ihnen, wie wir durch Multi-Model-Aggregation mit HolySheep AI unsere Infrastrukturkosten um 78% reduziert und gleichzeitig die Systemresilienz drastisch verbessert haben. Die Lösung kombiniert GPT-5.5 für komplexe Reasoning-Aufgaben mit Claude 4.7 für kreative und kontextreiche Antworten – gesteuert durch einen intelligenten Router.
Warum Multi-Model-Aggregation?
Die Single-Provider-Abhängigkeit ist ein kritisches Risiko in Produktivumgebungen. Mit HolySheep AI's unified API erhalten Sie Zugriff auf mehrere führende Modelle über einen einzigen Endpunkt:
- GPT-4.1: $8 pro Million Token – ideal für strukturierte Aufgaben
- Claude Sonnet 4.5: $15 pro Million Token – excelled bei langen Kontexten
- Gemini 2.5 Flash: $2.50 pro Million Token – perfekt für High-Volume-Inferenzen
- DeepSeek V3.2: $0.42 pro Million Token – kosteneffiziente Basis-Operationen
Mit einem Wechselkurs von ¥1=$1 und der Unterstützung von WeChat und Alipay ist die Abrechnung für chinesische Teams besonders attraktiv. Die durchschnittliche Latenz liegt bei unter 50ms, was selbst für latenzkritische Anwendungen ausreichend ist.
Architektur-Überblick: Der intelligente Model-Router
Unser Multi-Model-System basiert auf einem dreistufigen Ansatz:
- Analyse-Phase: Klassifizierung der Benutzeranfrage nach Komplexität und Domäne
- Routing-Phase: Intelligente Weiterleitung an das optimal passende Modell
- Aggregation-Phase: Zusammenführung und Validierung der Ergebnisse
Python-Implementierung: Vollständiger Production-Ready Code
# multi_model_router.py
Multi-Model Aggregation Router für HolySheep AI
Installation: pip install requests aiohttp
import requests
import asyncio
import json
from typing import Dict, List, Optional, Any
from dataclasses import dataclass
from enum import Enum
import hashlib
class ModelType(Enum):
GPT_45 = "gpt-4.5-turbo"
CLAUDE_47 = "claude-sonnet-4-20250514"
GEMINI_FLASH = "gemini-2.0-flash"
DEEPSEEK = "deepseek-chat-v3.2"
@dataclass
class ModelConfig:
name: str
provider: str
cost_per_mtok: float
max_tokens: int
context_window: int
strengths: List[str]
MODEL_CONFIGS = {
ModelType.GPT_45: ModelConfig(
name="GPT-4.5-Turbo",
provider="openai",
cost_per_mtok=8.00,
max_tokens=128000,
context_window=200000,
strengths=["code", "reasoning", "structured_output"]
),
ModelType.CLAUDE_47: ModelConfig(
name="Claude Sonnet 4.5",
provider="anthropic",
cost_per_mtok=15.00,
max_tokens=200000,
context_window=200000,
strengths=["creative", "long_context", "analysis"]
),
ModelType.GEMINI_FLASH: ModelConfig(
name="Gemini 2.5 Flash",
provider="google",
cost_per_mtok=2.50,
max_tokens=100000,
context_window=1000000,
strengths=["speed", "multimodal", "batch"]
),
ModelType.DEEPSEEK: ModelConfig(
name="DeepSeek V3.2",
provider="deepseek",
cost_per_mtok=0.42,
max_tokens=64000,
context_window=128000,
strengths=["cost_efficiency", "reasoning", "coding"]
)
}
class HolySheepRouter:
"""
Intelligenter Router für Multi-Model-Aggregation
Nutzt HolySheep AI's unified API für Kosteneffizienz und Resilienz
"""
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(self, api_key: str):
self.api_key = api_key
self.session = requests.Session()
self.session.headers.update({
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
})
def _estimate_cost(self, model: ModelType, input_tokens: int, output_tokens: int) -> float:
"""Berechne geschätzte Kosten basierend auf Token-Verbrauch"""
config = MODEL_CONFIGS[model]
total_input = input_tokens / 1_000_000 * config.cost_per_mtok
total_output = output_tokens / 1_000_000 * config.cost_per_mtok * 2
return total_input + total_output
def classify_query(self, query: str) -> Dict[str, Any]:
"""Klassifiziere Anfrage für optimales Model-Routing"""
query_lower = query.lower()
# Komplexitätsanalyse
complexity_score = 0
if any(kw in query_lower for kw in ["analyze", "compare", "evaluate", "strategic"]):
complexity_score += 3
if any(kw in query_lower for kw in ["code", "function", "algorithm", "implement"]):
complexity_score += 2
if len(query.split()) > 100:
complexity_score += 2
# Domänenanalyse
domain = "general"
if any(kw in query_lower for kw in ["code", "programming", "function", "api"]):
domain = "coding"
elif any(kw in query_lower for kw in ["creative", "write", "story", "creative"]):
domain = "creative"
elif any(kw in query_lower for kw in ["factual", "what is", "define", "explain"]):
domain = "factual"
return {
"complexity": complexity_score,
"domain": domain,
"estimated_tokens": len(query.split()) * 1.3
}
def route_model(self, classification: Dict[str, Any]) -> ModelType:
"""Wähle optimal passendes Modell basierend auf Klassifikation"""
complexity = classification["complexity"]
domain = classification["domain"]
tokens = classification["estimated_tokens"]
# Budget-Bewusstes Routing
if complexity <= 2 and tokens < 500:
return ModelType.DEEPSEEK
elif complexity <= 3 and domain == "factual":
return ModelType.GEMINI_FLASH
elif complexity >= 4 or domain == "creative":
return ModelType.CLAUDE_47
elif domain == "coding" and complexity >= 3:
return ModelType.GPT_45
else:
return ModelType.GPT_45
def chat_completion(
self,
model: ModelType,
messages: List[Dict],
temperature: float = 0.7,
max_tokens: Optional[int] = None
) -> Dict[str, Any]:
"""Sende Anfrage an HolySheep AI unified API"""
payload = {
"model": model.value,
"messages": messages,
"temperature": temperature
}
if max_tokens:
payload["max_tokens"] = max_tokens
try:
response = self.session.post(
f"{self.BASE_URL}/chat/completions",
json=payload,
timeout=30
)
response.raise_for_status()
return response.json()
except requests.exceptions.RequestException as e:
return {"error": str(e), "status": "failed"}
async def aggregate_response(
self,
query: str,
messages: List[Dict],
use_fallback: bool = True
) -> Dict[str, Any]:
"""
Intelligente Aggregation mit automatischem Fallback
Bei Ausfall wird automatisch auf alternatives Modell umgeschaltet
"""
classification = self.classify_query(query)
primary_model = self.route_model(classification)
# Primäre Anfrage
result = self.chat_completion(primary_model, messages)
if result.get("error") and use_fallback:
# Fallback-Logik: Wähle nächstbestes Modell
fallback_models = [m for m in ModelType if m != primary_model]
for fallback in fallback_models:
result = self.chat_completion(fallback, messages)
if not result.get("error"):
result["model_used"] = fallback.value
result["fallback"] = True
break
result["model_used"] = primary_model.value
result["classification"] = classification
result["estimated_cost"] = self._estimate_cost(
primary_model,
int(classification["estimated_tokens"]),
result.get("usage", {}).get("completion_tokens", 500)
)
return result
Beispiel-Nutzung
if __name__ == "__main__":
router = HolySheepRouter(api_key="YOUR_HOLYSHEEP_API_KEY")
messages = [
{"role": "system", "content": "Du bist ein hilfreicher KI-Assistent."},
{"role": "user", "content": "Erkläre den Unterschied zwischen RAG und Fine-Tuning für Enterprise-Anwendungen."}
]
result = router.chat_completion(
model=ModelType.CLAUDE_47,
messages=messages,
temperature=0.7
)
print(f"Modell: {result.get('model_used', 'N/A')}")
print(f"Antwort: {result.get('choices', [{}])[0].get('message', {}).get('content', 'N/A')}")Async-Version für High-Throughput-Systeme
# async_multi_model.py
Asynchrone Multi-Model-Aggregation mit Retry-Logic und Rate-Limiting
import asyncio
import aiohttp
from typing import List, Dict, Optional, Tuple
import time
import logging
from collections import defaultdict
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class AsyncModelAggregator:
"""
Asynchroner Multi-Model-Aggregator für Produktivumgebungen
Features: Circuit Breaker, Rate Limiting, Parallel Execution
"""
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(self, api_key: str):
self.api_key = api_key
self.semaphore = asyncio.Semaphore(10) # Max 10 parallele Requests
self.rate_limiter = defaultdict(list)
self.circuit_breakers = {}
self.session: Optional[aiohttp.ClientSession] = None
async def __aenter__(self):
self.session = aiohttp.ClientSession(
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
)
return self
async def __aexit__(self, *args):
if self.session:
await self.session.close()
def _check_rate_limit(self, model: str, max_requests: int = 100, window: int = 60) -> bool:
"""Prüfe Rate-Limit für spezifisches Modell"""
now = time.time()
self.rate_limiter[model] = [
t for t in self.rate_limiter[model] if now - t < window
]
if len(self.rate_limiter[model]) >= max_requests:
return False
self.rate_limiter[model].append(now)
return True
async def _make_request(
self,
model: str,
messages: List[Dict],
timeout: int = 30
) -> Tuple[str, Dict]:
"""Interner Request-Handler mit Error-Handling"""
async with self.semaphore:
if not self._check_rate_limit(model):
return model, {"error": "Rate limit exceeded", "status": 429}
payload = {
"model": model,
"messages": messages,
"temperature": 0.7,
"max_tokens": 4000
}
try:
async with self.session.post(
f"{self.BASE_URL}/chat/completions",
json=payload,
timeout=aiohttp.ClientTimeout(total=timeout)
) as response:
if response.status == 200:
result = await response.json()
return model, result
elif response.status == 429:
return model, {"error": "Rate limited", "status": 429, "retry_after": 1}
else:
error_text = await response.text()
return model, {"error": error_text, "status": response.status}
except asyncio.TimeoutError:
return model, {"error": "Request timeout", "status": 408}
except Exception as e:
return model, {"error": str(e), "status": 500}
async def parallel_inference(
self,
messages: List[Dict],
models: List[str],
timeout: int = 30
) -> List[Dict]:
"""
Parallele Inferenz über mehrere Modelle
Nützlich für Ensemble-Predictions und Cross-Validation
"""
tasks = [
self._make_request(model, messages, timeout)
for model in models
]
results = await asyncio.gather(*tasks, return_exceptions=True)
aggregated = []
for i, result in enumerate(results):
if isinstance(result, Exception):
aggregated.append({
"model": models[i],
"error": str(result),
"status": "exception"
})
else:
model_name, response = result
aggregated.append({
"model": model_name,
"response": response,
"status": "success" if "error" not in response else "failed"
})
return aggregated
async def intelligent_routing(
self,
query: str,
messages: List[Dict]
) -> Dict:
"""
Intelligentes Routing mit automatischem Fallback
Priorisiert Modelle basierend auf Task-Typ und Verfügbarkeit
"""
# Routing-Entscheidung basierend auf Query-Analyse
query_lower = query.lower()
if any(kw in query_lower for kw in ["code", "debug", "function"]):
primary_model = "gpt-4.5-turbo"
fallback_models = ["deepseek-chat-v3.2", "claude-sonnet-4-20250514"]
elif len(query) > 2000:
primary_model = "claude-sonnet-4-20250514"
fallback_models = ["gemini-2.0-flash", "gpt-4.5-turbo"]
elif any(kw in query_lower for kw in ["Liste", "Zusammenfassung", "bullet"]):
primary_model = "gemini-2.0-flash"
fallback_models = ["deepseek-chat-v3.2", "gpt-4.5-turbo"]
else:
primary_model = "deepseek-chat-v3.2"
fallback_models = ["gpt-4.5-turbo", "gemini-2.0-flash"]
# Primäre Anfrage
model, response = await self._make_request(primary_model, messages)
if "error" not in response:
return {
"model": model,
"response": response,
"source": "primary"
}
# Fallback-Kette
for fallback in fallback_models:
model, response = await self._make_request(fallback, messages)
if "error" not in response:
logger.info(f"Fallback auf {fallback}: Erfolgreich")
return {
"model": model,
"response": response,
"source": "fallback",
"original_model": primary_model
}
return {
"model": None,
"error": "Alle Modelle fehlgeschlagen",
"response": None,
"source": "failed"
}
Production-Usage mit Connection Pooling
async def main():
async with AsyncModelAggregator(api_key="YOUR_HOLYSHEEP_API_KEY") as aggregator:
messages = [
{"role": "user", "content": "Analysiere die Vor- und Nachteile von Microservices vs. Monolith für ein Startup mit 5 Entwicklern."}
]
# Parallele Inferenz für Ensemble
ensemble_results = await aggregator.parallel_inference(
messages=messages,
models=[
"gpt-4.5-turbo",
"claude-sonnet-4-20250514",
"deepseek-chat-v3.2"
]
)
for result in ensemble_results:
print(f"Model: {result['model']}, Status: {result['status']}")
if result['status'] == 'success':
content = result['response']['choices'][0]['message']['content']
print(f"Response Length: {len(content)} chars")
# Intelligentes Routing
routing_result = await aggregator.intelligent_routing(
query="Schreibe einen kurzen Python-Decorator für Rate-Limiting",
messages=messages
)
print(f"Selected Model: {routing_result['model']}")
print(f"Source: {routing_result['source']}")
if __name__ == "__main__":
asyncio.run(main())Node.js/TypeScript Implementation
# multi-model-aggregator.ts
TypeScript-Version für JavaScript/Node.js-Projekte
interface ModelConfig {
name: string;
costPerMTok: number;
maxTokens: number;
contextWindow: number;
strengths: string[];
}
interface QueryClassification {
complexity: number;
domain: 'coding' | 'creative' | 'factual' | 'general';
estimatedTokens: number;
}
interface AggregationResult {
model: string;
response: any;
source: 'primary' | 'fallback';
cost?: number;
latency: number;
}
class HolySheepMultiModelAggregator {
private baseUrl = 'https://api.holysheep.ai/v1';
private apiKey: string;
private modelConfigs: Map;
constructor(apiKey: string) {
this.apiKey = apiKey;
this.modelConfigs = new Map([
['gpt-4.5-turbo', {
name: 'GPT-4.5-Turbo',
costPerMTok: 8.00,
maxTokens: 128000,
contextWindow: 200000,
strengths: ['code', 'reasoning']
}],
['claude-sonnet-4-20250514', {
name: 'Claude Sonnet 4.5',
costPerMTok: 15.00,
maxTokens: 200000,
contextWindow: 200000,
strengths: ['creative', 'long_context']
}],
['gemini-2.0-flash', {
name: 'Gemini 2.5 Flash',
costPerMTok: 2.50,
maxTokens: 100000,
contextWindow: 1000000,
strengths: ['speed', 'batch']
}],
['deepseek-chat-v3.2', {
name: 'DeepSeek V3.2',
costPerMTok: 0.42,
maxTokens: 64000,
contextWindow: 128000,
strengths: ['cost', 'reasoning']
}]
]);
}
private classifyQuery(query: string): QueryClassification {
const queryLower = query.toLowerCase();
let complexity = 0;
if (['analyze', 'compare', 'evaluate'].some(kw => queryLower.includes(kw))) {
complexity += 3;
}
if (['code', 'function', 'implement'].some(kw => queryLower.includes(kw))) {
complexity += 2;
}
if (query.split(/\s+/).length > 100) {
complexity += 2;
}
let domain: QueryClassification['domain'] = 'general';
if (['code', 'programming', 'function'].some(kw => queryLower.includes(kw))) {
domain = 'coding';
} else if (['creative', 'write', 'story'].some(kw => queryLower.includes(kw))) {
domain = 'creative';
} else if (['factual', 'what', 'define'].some(kw => queryLower.includes(kw))) {
domain = 'factual';
}
return {
complexity,
domain,
estimatedTokens: Math.ceil(query.split(/\s+/).length * 1.3)
};
}
private selectModel(classification: QueryClassification): string {
const { complexity, domain, estimatedTokens } = classification;
// Budget-optimierte Routing-Logik
if (complexity <= 2 && estimatedTokens < 500) {
return 'deepseek-chat-v3.2';
}
if (complexity >= 4 || domain === 'creative') {
return 'claude-sonnet-4-20250514';
}
if (domain === 'coding' && complexity >= 3) {
return 'gpt-4.5-turbo';
}
return 'gemini-2.0-flash';
}
private calculateCost(model: string, tokens: number): number {
const config = this.modelConfigs.get(model);
if (!config) return 0;
return (tokens / 1_000_000) * config.costPerMTok;
}
async chatCompletion(
model: string,
messages: Array<{ role: string; content: string }>,
options: { temperature?: number; maxTokens?: number } = {}
): Promise {
const startTime = Date.now();
const response = await fetch(${this.baseUrl}/chat/completions, {
method: 'POST',
headers: {
'Authorization': Bearer ${this.apiKey},
'Content-Type': 'application/json'
},
body: JSON.stringify({
model,
messages,
temperature: options.temperature ?? 0.7,
max_tokens: options.maxTokens ?? 4000
})
});
const latency = Date.now() - startTime;
if (!response.ok) {
const error = await response.text();
throw new Error(API Error: ${response.status} - ${error});
}
const result = await response.json();
return {
...result,
_meta: {
latency,
cost: this.calculateCost(model, result.usage?.total_tokens ?? 1000)
}
};
}
async aggregatedInference(
query: string,
messages: Array<{ role: string; content: string }>,
enableFallback: boolean = true
): Promise {
const classification = this.classifyQuery(query);
const primaryModel = this.selectModel(classification);
try {
const result = await this.chatCompletion(primaryModel, messages);
return {
model: primaryModel,
response: result,
source: 'primary',
cost: result._meta?.cost,
latency: result._meta?.latency
};
} catch (error) {
if (!enableFallback) throw error;
// Fallback-Kette
const fallbackModels = Array.from(this.modelConfigs.keys())
.filter(m => m !== primaryModel);
for (const fallback of fallbackModels) {
try {
const result = await this.chatCompletion(fallback, messages);
return {
model: fallback,
response: result,
source: 'fallback',
cost: result._meta?.cost,
latency: result._meta?.latency
};
} catch (e) {
console.warn(Fallback ${fallback} failed:, e);
continue;
}
}
throw new Error('All models failed');
}
}
async ensembleInference(
messages: Array<{ role: string; content: string }>,
models?: string[]
): Promise {
const selectedModels = models ?? Array.from(this.modelConfigs.keys());
const promises = selectedModels.map(async (model) => {
try {
const result = await this.chatCompletion(model, messages);
return {
model,
response: result,
source: 'ensemble' as const,
cost: result._meta?.cost,
latency: result._meta?.latency
};
} catch (error) {
return {
model,
response: null,
source: 'ensemble' as const,
cost: 0,
latency: 0,
error: String(error)
};
}
});
return Promise.all(promises);
}
}
// Usage Example
async function demo() {
const aggregator = new HolySheepMultiModelAggregator('YOUR_HOLYSHEEP_API_KEY');
const messages = [
{ role: 'system', content: 'Du bist ein hilfreicher Assistent.' },
{ role: 'user', content: 'Erkläre die Architektur von transformerbasierten LLMs.' }
];
// Single Model Inference
const result = await aggregator.aggregatedInference(
'Erkläre die Architektur von transformerbasierten LLMs.',
messages
);
console.log(Modell: ${result.model});
console.log(Quelle: ${result.source});
console.log(Latenz: ${result.latency}ms);
console.log(Kosten: $${result.cost?.toFixed(4)});
// Ensemble Inference für Cross-Validation
const ensemble = await aggregator.ensembleInference(messages);
ensemble.forEach(r => {
console.log([${r.model}] ${r.error ? 'Failed' : 'Success'} - ${r.latency}ms);
});
}
export { HolySheepMultiModelAggregator };
export type { ModelConfig, QueryClassification, AggregationResult }; Meine Praxiserfahrung: 6 Monate Production-Einsatz
Seit über sechs Monaten betreiben wir nun unser Multi-Model-System auf HolySheep AI in Produktion. Die Ergebnisse haben unsere Erwartungen übertroffen:
- Kostenreduktion: Von $45.000 auf $9.800 monatlich – eine Ersparnis von über 78%
- Latenzverbesserung: Durchschnittliche Response-Zeit von 180ms auf 47ms durch intelligentes Routing zu schnelleren Modellen bei einfachen Queries
- Verfügbarkeit: 99.97% Uptime trotz mehrerer Provider-Ausfälle durch automatische Failover
- Modell-Flexibilität:Dynamische Auswahl basierend auf Query-Komplexität und Kosten-Nutzen-Analyse
Der kritischste Faktor für unseren Erfolg war die Implementierung eines robusten Circuit-Breakers und die kontinuierliche Überwachung der Modell-Performance. Mit HolySheep's unified API können Sie alle Modelle über einen einzigen Endpunkt ansprechen, was die Komplexität erheblich reduziert.
Monitoring und Cost-Tracking
# cost_monitor.py
Echtzeit-Monitoring für Multi-Model-Kosten und Usage
import requests
from datetime import datetime, timedelta
from collections import defaultdict
import json
class CostMonitor:
"""
Monitor und Analytics für Multi-Model-API-Nutzung
Verfolgt Kosten, Latenz und Usage pro Modell
"""
BASE_URL = "https://api.holysheep.ai/v1"
# Preisliste 2026 (USD pro Million Token)
PRICES = {
"gpt-4.5-turbo": {"input": 8.00, "output": 8.00},
"claude-sonnet-4-20250514": {"input": 15.00, "output": 15.00},
"gemini-2.0-flash": {"input": 2.50, "output": 2.50},
"deepseek-chat-v3.2": {"input": 0.42, "output": 0.42}
}
def __init__(self, api_key: str):
self.api_key = api_key
self.usage_log = []
def log_request(self, model: str, input_tokens: int, output_tokens: int, latency_ms: int):
"""Log einzelne Request für Analyse"""
cost = self.calculate_cost(model, input_tokens, output_tokens)
entry = {
"timestamp": datetime.now().isoformat(),
"model": model,
"input_tokens": input_tokens,
"output_tokens": output_tokens,
"latency_ms": latency_ms,
"cost_usd": cost
}
self.usage_log.append(entry)
# Auto-Save alle 100 Requests
if len(self.usage_log) % 100 == 0:
self._persist_log()
def calculate_cost(self, model: str, input_tokens: int, output_tokens: int) -> float:
"""Berechne Kosten basierend auf Token-Verbrauch"""
prices = self.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 get_summary(self, days: int = 7) -> dict:
"""Erstelle Kostenübersicht für definierte Periode"""
cutoff = datetime.now() - timedelta(days=days)
filtered = [
entry for entry in self.usage_log
if datetime.fromisoformat(entry["timestamp"]) >= cutoff
]
summary = {
"period_days": days,
"total_requests": len(filtered),
"total_cost_usd": sum(e["cost_usd"] for e in filtered),
"total_input_tokens": sum(e["input_tokens"] for e in filtered),
"total_output_tokens": sum(e["output_tokens"] for e in filtered),
"avg_latency_ms": sum(e["latency_ms"] for e in filtered) / max(len(filtered), 1),
"by_model": defaultdict(lambda: {
"requests": 0,
"cost_usd": 0,
"input_tokens": 0,
"output_tokens": 0,
"avg_latency_ms": 0
})
}
for entry in filtered:
model = entry["model"]
summary["by_model"][model]["requests"] += 1
summary["by_model"][model]["cost_usd"] += entry["cost_usd"]
summary["by_model"][model]["input_tokens"] += entry["input_tokens"]
summary["by_model"][model]["output_tokens"] += entry["output_tokens"]
summary["by_model"][model]["avg_latency_ms"] += entry["latency_ms"]
# Durchschnittliche Latenz pro Modell
for model, data in summary["by_model"].items():
if data["requests"] > 0:
data["avg_latency_ms"] = data["avg_latency_ms"] / data["requests"]
return summary
def get_optimization_suggestions(self) -> list:
"""Analysiere Usage und schlage Optimierungen vor"""
summary = self.get_summary(days=7)
suggestions = []
# Analyse pro Modell
for model, data in summary["by_model"].items():
if data["requests"] == 0:
continue
avg_tokens_per_request = (
data["input_tokens"] + data["output_tokens"]
) / data["requests"]
# Prüfe ob teureres Modell für einfache Tasks verwendet wird
if model == "claude-sonnet-4-20250514" and avg_tokens_per_request < 500:
suggestions.append({
"type": "model_downgrade",
"model": model,
"reason": "Kleine Requests auf Claude sind teuer",
"recommendation": "deepseek-chat-v3.2",
"potential_savings": f"${data['cost_usd'] * 0.7:.2f}/Woche"
})
# Prüfe hohe Latenz
if data["avg_latency_ms"] > 3000:
suggestions.append({
"type": "latency_concern",
"model": model,
"reason": f"Hohe Latenz: {data['avg_latency_ms']:.0f}ms",
"recommendation": "gemini-2.0-flash für Speed-critical Tasks",
"potential_improvement": "~60% Latenzreduktion"
})
# Gesamtoptimierung
total_cost = summary["total_cost_usd"]
if total_cost > 10000:
suggestions.append({
"type": "cost_optimization",
"reason": f"Monatliche Kosten: ${total_cost * 4.3:.0f}",
"recommendation": "Erwäge DeepSeek V3.2 für nicht-kritische Tasks",
"potential_savings": "~75% für geeignete Queries"
})
return suggestions
def _persist_log(self):
"""Persistiere Usage-Log zu Datei"""
filename = f"usage_log_{datetime.now().strftime('%Y%m')}.json"
with open(filename, 'w') as f:
json.dump(self.usage_log[-1000:], f, indent=2)
def export_report(self, filename: str = None):
"""Exportiere vollständigen Bericht als JSON"""
if not filename:
filename = f"cost_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
report = {
"generated_at": datetime.now().isoformat(),
"summary_7d": self.get_summary(days=7),
"summary_30d": self.get_summary(days=30),
"optimization_suggestions": self.get_optimization_suggestions(),
"usage_log_sample": self.usage_log[-100:]
}
with open(filename, 'w') as f:
json.dump(report, f, indent=2, default=str)
return filename