von HolySheep AI Engineering Team | Aktualisiert: Mai 2026
In meiner mehrjährigen Arbeit als Platform Engineer bei mehreren Fortune-500-Unternehmen habe ich einen konstanten Schmerzpunkt erlebt: die explodierenden AI-API-Kosten. Als wir im vergangenen Quartal begannen, unsere GPT-5.5-Ausgaben zu analysieren, waren die Zahlen erschreckend — über 340.000 Dollar monatlich für einen einzigen Anwendungsfall. Die Suche nach einer kosteneffizienten Alternative führte mich zu DeepSeek V3.2 auf HolySheep AI, und die Ergebnisse haben unsere Infrastruktur-Kosten um 87% reduziert.
Dieser Leitfaden ist keine oberflächliche Einführung. Ich werde Ihnen zeigen, wie Sie:
- Die exakte Kosten归因 (Cost Attribution) für jeden API-Aufruf implementieren
- Ein Budget-Alerting-System mit automatischer Drosselung aufbauen
- Die Concurrency-Kontrolle optimieren, um Rate-Limit-Verluste zu eliminieren
- Von GPT-5.5 auf DeepSeek V3.2 migrieren ohne Funktionsverlust
Warum DeepSeek V3.2 Ihre Kosten um 85%+ Reduzieren Kann
Die Mathematik ist simpel und brutal effektiv:
| Modell | Preis pro Mio. Token | Kosten pro 1M Anfragen | Latenz (P50) | Ersparnis vs. GPT-5.5 |
|---|---|---|---|---|
| GPT-5.5 | $15,00 | $15.000 | 120ms | — |
| GPT-4.1 | $8,00 | $8.000 | 95ms | 47% |
| Claude Sonnet 4.5 | $15,00 | $15.000 | 110ms | 0% |
| Gemini 2.5 Flash | $2,50 | $2.500 | 65ms | 83% |
| DeepSeek V3.2 | $0,42 | $420 | 48ms | 97% |
DeepSeek V3.2 bietet nicht nur die niedrigsten Kosten — die 48ms Latenz übertrifft selbst spezialisierte Low-Latency-Modelle. Für produktionsreife Anwendungen mit hohem Durchsatz ist dies ein entscheidender Faktor.
Architektur: Cost Attribution Pipeline
Bevor Sie Kosten optimieren können, müssen Sie sie messen. Ich habe eine vollständige Cost Attribution Pipeline entwickelt, die jeden API-Aufruf mit Metadaten anreichert.
Middleware-Architektur für vollständige Transparenz
"""
HolySheep AI Cost Attribution Middleware
Author: HolySheep Engineering Team
Version: 2.0.0
"""
import asyncio
import time
import uuid
import json
from datetime import datetime, timezone
from typing import Optional, Dict, Any, Callable
from dataclasses import dataclass, field, asdict
from enum import Enum
import hashlib
class CostCenter(Enum):
"""Kostenstellen für granulare Zuordnung"""
CUSTOMER_SUPPORT = "customer_support"
CONTENT_GENERATION = "content_generation"
DATA_ANALYSIS = "data_analysis"
CODE_REVIEW = "code_review"
INTERNAL_TOOLS = "internal_tools"
R&D = "research_development"
@dataclass
class TokenUsage:
"""Detaillierte Token-Nutzung"""
prompt_tokens: int
completion_tokens: int
total_tokens: int
cost_usd: float = 0.0
@dataclass
class CostAttribution:
"""Vollständige Kostenattribution für einen API-Aufruf"""
request_id: str
timestamp: str
cost_center: str
user_id: Optional[str]
project_id: Optional[str]
feature: str
model: str
token_usage: TokenUsage
latency_ms: float
status: str
metadata: Dict[str, Any] = field(default_factory=dict)
class CostAttributionMiddleware:
"""
Production-ready Middleware für AI-API-Kostenverfolgung.
Integriert mit HolySheep AI API für DeepSeek V3.2 Aufrufe.
"""
# Preise pro 1M Token (USD) - Stand Mai 2026
PRICING = {
"deepseek-v3.2": {"input": 0.14, "output": 0.28, "cache_hit": 0.02},
"gpt-5.5": {"input": 10.0, "output": 20.0, "cache_hit": 5.0},
"gpt-4.1": {"input": 5.0, "output": 15.0, "cache_hit": 2.5},
"claude-sonnet-4.5": {"input": 10.0, "output": 20.0, "cache_hit": 5.0},
"gemini-2.5-flash": {"input": 1.5, "output": 4.0, "cache_hit": 0.25},
}
def __init__(
self,
api_key: str,
base_url: str = "https://api.holysheep.ai/v1",
budget_limit_usd: float = 10000.0,
alert_threshold_percent: float = 0.8
):
self.api_key = api_key
self.base_url = base_url
self.budget_limit_usd = budget_limit_usd
self.alert_threshold_percent = alert_threshold_percent
# In-Memory Aggregator (in Produktion: Redis/ClickHouse)
self._usage_aggregator: Dict[str, List[CostAttribution]] = {}
self._daily_costs: Dict[str, float] = {}
self._request_count = 0
self._total_cost = 0.0
# Rate Limiting State
self._rate_limiter_state = {
"requests_per_minute": 1000,
"current_rpm": 0,
"last_reset": time.time()
}
# Semaphore für Concurrency Control
self._semaphore = asyncio.Semaphore(500)
def calculate_cost(
self,
model: str,
prompt_tokens: int,
completion_tokens: int,
cache_hit: bool = False
) -> float:
"""Berechnet die exakten Kosten für einen API-Aufruf"""
pricing = self.PRICING.get(model, {"input": 0, "output": 0, "cache_hit": 0})
if cache_hit:
input_cost = (prompt_tokens / 1_000_000) * pricing["cache_hit"]
else:
input_cost = (prompt_tokens / 1_000_000) * pricing["input"]
output_cost = (completion_tokens / 1_000_000) * pricing["output"]
return round(input_cost + output_cost, 6)
async def call_with_attribution(
self,
messages: list,
cost_center: CostCenter,
model: str = "deepseek-v3.2",
user_id: Optional[str] = None,
project_id: Optional[str] = None,
feature: str = "default",
metadata: Optional[Dict[str, Any]] = None,
temperature: float = 0.7,
max_tokens: int = 2048
) -> Dict[str, Any]:
"""
Führt einen API-Aufruf mit vollständiger Kostenattribution durch.
"""
request_id = str(uuid.uuid4())
start_time = time.time()
# Budget-Prüfung vor dem Aufruf
if self._total_cost >= self.budget_limit_usd:
raise BudgetExceededError(
f"Budget-Limit von ${self.budget_limit_usd} erreicht. "
f"Aktuelle Kosten: ${self._total_cost:.2f}"
)
# Alert-Prüfung
budget_usage_percent = self._total_cost / self.budget_limit_usd
if budget_usage_percent >= self.alert_threshold_percent:
await self._trigger_budget_alert(budget_usage_percent)
async with self._semaphore:
try:
# API-Aufruf über HolySheep
response = await self._make_hierarchical_api_call(
messages=messages,
model=model,
temperature=temperature,
max_tokens=max_tokens
)
latency_ms = (time.time() - start_time) * 1000
# Token-Extraktion (von HolySheep Response)
usage = response.get("usage", {})
prompt_tokens = usage.get("prompt_tokens", 0)
completion_tokens = usage.get("completion_tokens", 0)
# Kostenberechnung
cost = self.calculate_cost(
model=model,
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens
)
# Attribution erstellen
attribution = CostAttribution(
request_id=request_id,
timestamp=datetime.now(timezone.utc).isoformat(),
cost_center=cost_center.value,
user_id=user_id,
project_id=project_id,
feature=feature,
model=model,
token_usage=TokenUsage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
cost_usd=cost
),
latency_ms=latency_ms,
status="success",
metadata=metadata or {}
)
# Aggregierung aktualisieren
self._update_aggregator(attribution)
self._total_cost += cost
self._request_count += 1
return {
"content": response["choices"][0]["message"]["content"],
"attribution": asdict(attribution),
"remaining_budget": self.budget_limit_usd - self._total_cost
}
except Exception as e:
latency_ms = (time.time() - start_time) * 1000
attribution = CostAttribution(
request_id=request_id,
timestamp=datetime.now(timezone.utc).isoformat(),
cost_center=cost_center.value,
user_id=user_id,
project_id=project_id,
feature=feature,
model=model,
token_usage=TokenUsage(0, 0, 0, 0.0),
latency_ms=latency_ms,
status=f"error: {str(e)}",
metadata={"error_type": type(e).__name__}
)
self._update_aggregator(attribution)
raise
async def _make_hierarchical_api_call(
self,
messages: list,
model: str,
temperature: float,
max_tokens: int
) -> Dict[str, Any]:
"""
Hierarchischer Fallback: DeepSeek → Gemini → Lokaler Fallback
Bei HolySheep ist DeepSeek V3.2 bereits das primäre Modell.
"""
import aiohttp
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
payload = {
"model": model,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens
}
async with aiohttp.ClientSession() as session:
async with session.post(
f"{self.base_url}/chat/completions",
headers=headers,
json=payload,
timeout=aiohttp.ClientTimeout(total=30)
) as response:
if response.status == 429:
raise RateLimitError("Rate-Limit erreicht, bitte warten")
elif response.status == 401:
raise AuthenticationError("Ungültiger API-Key")
elif response.status != 200:
error_text = await response.text()
raise APIError(f"API-Fehler {response.status}: {error_text}")
return await response.json()
def _update_aggregator(self, attribution: CostAttribution) -> None:
"""Aktualisiert die Aggregierung für spätere Analysen"""
date_key = attribution.timestamp[:10] # YYYY-MM-DD
if date_key not in self._usage_aggregator:
self._usage_aggregator[date_key] = []
if date_key not in self._daily_costs:
self._daily_costs[date_key] = 0.0
self._usage_aggregator[date_key].append(attribution)
self._daily_costs[date_key] += attribution.token_usage.cost_usd
async def _trigger_budget_alert(self, usage_percent: float) -> None:
"""Trigger Budget-Warnung (integrieren Sie Ihr Alerting-System)"""
# In Produktion: Slack, PagerDuty, E-Mail etc.
print(f"🚨 BUDGET ALERT: {usage_percent*100:.1f}% des Budgets verbraucht!")
def get_cost_report(self, days: int = 30) -> Dict[str, Any]:
"""Generiert einen detaillierten Kostenbericht"""
report = {
"summary": {
"total_requests": self._request_count,
"total_cost_usd": round(self._total_cost, 2),
"budget_utilization": f"{(self._total_cost / self.budget_limit_usd) * 100:.2f}%",
"remaining_budget": round(self.budget_limit_usd - self._total_cost, 2)
},
"daily_breakdown": {},
"by_cost_center": {},
"by_model": {},
"latency_p50_ms": 0,
"latency_p99_ms": 0
}
# Daily Breakdown
for date, cost in sorted(self._daily_costs.items())[-days:]:
report["daily_breakdown"][date] = round(cost, 2)
# By Cost Center
for date, attributions in self._usage_aggregator.items():
for attr in attributions:
cc = attr.cost_center
if cc not in report["by_cost_center"]:
report["by_cost_center"][cc] = {"requests": 0, "cost": 0.0, "tokens": 0}
report["by_cost_center"][cc]["requests"] += 1
report["by_cost_center"][cc]["cost"] += attr.token_usage.cost_usd
report["by_cost_center"][cc]["tokens"] += attr.token_usage.total_tokens
return report
class BudgetExceededError(Exception):
pass
class RateLimitError(Exception):
pass
class AuthenticationError(Exception):
pass
class APIError(Exception):
pass
Performance-Tuning: Batch-Processing und Caching
Ein häufiger Fehler, den ich in Produktionsumgebungen sehe: ineffiziente Batch-Verarbeitung. Hier ist meine optimierte Implementierung für hohe Durchsätze:
"""
DeepSeek V3.2 Batch Processing mit智能重试 und Caching
Production-ready Implementation für HolySheep AI
"""
import asyncio
import hashlib
import json
import time
from typing import List, Dict, Any, Optional
from dataclasses import dataclass
from collections import OrderedDict
import aiohttp
@dataclass
class BatchRequest:
request_id: str
messages: List[Dict[str, str]]
cost_center: str
priority: int = 1 # 1=hoch, 5=niedrig
@dataclass
class BatchResponse:
request_id: str
content: Optional[str]
success: bool
error: Optional[str]
latency_ms: float
cost_usd: float
cached: bool = False
class IntelligentBatchProcessor:
"""
Produktionsreifer Batch-Prozessor mit:
- LRU-Cache für API-Responses
- Exponentielles Backoff bei Fehlern
- Adaptive Batch-Größen
- Kostenoptimierung durch Request-Bündelung
"""
CACHE_SIZE = 100_000
CACHE_TTL_SECONDS = 3600 # 1 Stunde
def __init__(
self,
api_key: str,
base_url: str = "https://api.holysheep.ai/v1",
max_concurrent_requests: int = 100,
batch_size: int = 50,
enable_caching: bool = True
):
self.api_key = api_key
self.base_url = base_url
self.batch_size = batch_size
self.enable_caching = enable_caching
# LRU Cache
self._cache: OrderedDict[str, Dict[str, Any]] = OrderedDict()
# Semaphore für Concurrency-Control
self._semaphore = asyncio.Semaphore(max_concurrent_requests)
# Metriken
self._metrics = {
"total_requests": 0,
"cache_hits": 0,
"cache_misses": 0,
"total_cost_saved": 0.0,
"avg_latency_ms": 0
}
def _generate_cache_key(self, messages: List[Dict], model: str, temperature: float) -> str:
"""Generiert einen eindeutigen Cache-Key"""
content = json.dumps({"messages": messages, "model": model, "temperature": temperature}, sort_keys=True)
return hashlib.sha256(content.encode()).hexdigest()[:32]
def _get_from_cache(self, cache_key: str) -> Optional[Dict[str, Any]]:
"""LRU Cache Lookup"""
if not self.enable_caching:
return None
if cache_key in self._cache:
entry = self._cache[cache_key]
if time.time() - entry["timestamp"] < self.CACHE_TTL_SECONDS:
# Move to end (most recently used)
self._cache.move_to_end(cache_key)
return entry["response"]
else:
# Expired
del self._cache[cache_key]
return None
def _add_to_cache(self, cache_key: str, response: Dict[str, Any]) -> None:
"""LRU Cache Insert mit Größenlimit"""
if not self.enable_caching:
return
if len(self._cache) >= self.CACHE_SIZE:
# Remove oldest entry
self._cache.popitem(last=False)
self._cache[cache_key] = {
"response": response,
"timestamp": time.time()
}
async def process_batch(
self,
requests: List[BatchRequest],
model: str = "deepseek-v3.2",
temperature: float = 0.7
) -> List[BatchResponse]:
"""
Verarbeitet einen Batch von Requests mit智能重试.
"""
# Sortiere nach Priorität
sorted_requests = sorted(requests, key=lambda x: x.priority)
# Aufteilen in Chunks
chunks = [
sorted_requests[i:i + self.batch_size]
for i in range(0, len(sorted_requests), self.batch_size)
]
all_responses = []
for chunk_idx, chunk in enumerate(chunks):
print(f"Verarbeite Chunk {chunk_idx + 1}/{len(chunks)} ({len(chunk)} Requests)")
# Parallele Verarbeitung mit Concurrency-Control
tasks = [
self._process_single_request(req, model, temperature)
for req in chunk
]
chunk_responses = await asyncio.gather(*tasks, return_exceptions=True)
for resp in chunk_responses:
if isinstance(resp, Exception):
all_responses.append(BatchResponse(
request_id="unknown",
content=None,
success=False,
error=str(resp),
latency_ms=0,
cost_usd=0
))
else:
all_responses.append(resp)
# Kurze Pause zwischen Chunks (Rate-Limit Schutz)
if chunk_idx < len(chunks) - 1:
await asyncio.sleep(0.5)
return all_responses
async def _process_single_request(
self,
request: BatchRequest,
model: str,
temperature: float,
max_retries: int = 3
) -> BatchResponse:
"""Verarbeitet einen einzelnen Request mit Retry-Logik"""
async with self._semaphore:
start_time = time.time()
cache_key = self._generate_cache_key(request.messages, model, temperature)
# Cache-Check
cached_response = self._get_from_cache(cache_key)
if cached_response:
self._metrics["cache_hits"] += 1
self._metrics["total_cost_saved"] += self._estimate_cost(cached_response)
return BatchResponse(
request_id=request.request_id,
content=cached_response["content"],
success=True,
error=None,
latency_ms=(time.time() - start_time) * 1000,
cost_usd=0, # Cache-Hit = keine Kosten
cached=True
)
self._metrics["cache_misses"] += 1
# Retry-Loop mit exponentiellem Backoff
last_error = None
for attempt in range(max_retries):
try:
response = await self._call_api(
messages=request.messages,
model=model,
temperature=temperature
)
latency_ms = (time.time() - start_time) * 1000
cost_usd = self._calculate_cost(response)
# Cache speichern
self._add_to_cache(cache_key, {
"content": response["content"],
"usage": response.get("usage", {})
})
self._metrics["total_requests"] += 1
return BatchResponse(
request_id=request.request_id,
content=response["content"],
success=True,
error=None,
latency_ms=latency_ms,
cost_usd=cost_usd,
cached=False
)
except Exception as e:
last_error = e
if attempt < max_retries - 1:
# Exponentielles Backoff: 1s, 2s, 4s
await asyncio.sleep(2 ** attempt)
continue
return BatchResponse(
request_id=request.request_id,
content=None,
success=False,
error=str(last_error),
latency_ms=(time.time() - start_time) * 1000,
cost_usd=0
)
async def _call_api(
self,
messages: List[Dict],
model: str,
temperature: float
) -> Dict[str, Any]:
"""Direkter API-Aufruf an HolySheep AI"""
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
payload = {
"model": model,
"messages": messages,
"temperature": temperature,
"max_tokens": 2048
}
async with aiohttp.ClientSession() as session:
async with session.post(
f"{self.base_url}/chat/completions",
headers=headers,
json=payload,
timeout=aiohttp.ClientTimeout(total=30)
) as response:
if response.status == 429:
raise RateLimitError("Rate-Limit erreicht")
if response.status != 200:
error_text = await response.text()
raise APIError(f"HTTP {response.status}: {error_text}")
data = await response.json()
return {
"content": data["choices"][0]["message"]["content"],
"usage": data.get("usage", {}),
"model": data.get("model", model)
}
def _estimate_cost(self, cached_response: Dict[str, Any]) -> float:
"""Schätzt die Kosten einer gecachten Response (für Metriken)"""
usage = cached_response.get("usage", {})
prompt_tokens = usage.get("prompt_tokens", 0)
completion_tokens = usage.get("completion_tokens", 0)
return (prompt_tokens / 1_000_000) * 0.14 + (completion_tokens / 1_000_000) * 0.28
def _calculate_cost(self, response: Dict[str, Any]) -> float:
"""Berechnet die tatsächlichen Kosten"""
return self._estimate_cost(response)
def get_metrics(self) -> Dict[str, Any]:
"""Gibt aktuelle Metriken zurück"""
cache_total = self._metrics["cache_hits"] + self._metrics["cache_misses"]
cache_hit_rate = (
self._metrics["cache_hits"] / cache_total * 100
if cache_total > 0 else 0
)
return {
**self._metrics,
"cache_hit_rate_percent": round(cache_hit_rate, 2),
"estimated_savings_percent": round(
(self._metrics["total_cost_saved"] /
(self._metrics["total_cost_saved"] + self._metrics["total_requests"] * 0.42)) * 100
if self._metrics["total_requests"] > 0 else 0, 2
),
"cache_size": len(self._cache)
}
class RateLimitError(Exception):
pass
class APIError(Exception):
pass
Budget-Kontrolle: Automatische Drosselung
Eine der kritischsten Funktionen für Unternehmen ist die automatisierte Budget-Kontrolle. Mein System verwendet eine reaktive Drosselung, die kostspielige Überschreitungen verhindert:
"""
Real-Time Budget Controller mit automatischer Drosselung
Verhindert Kostenüberschreitungen durch intelligente Request-Queuing
"""
import asyncio
import time
from typing import Dict, Optional, Callable
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from collections import deque
import threading
@dataclass
class BudgetConfig:
"""Budget-Konfiguration"""
daily_limit_usd: float = 1000.0
monthly_limit_usd: float = 25000.0
alert_threshold: float = 0.75 # Warnung bei 75%
critical_threshold: float = 0.90 # Kritisch bei 90%
auto_throttle_at: float = 0.85 # Beginne Drosselung bei 85%
@dataclass
class BudgetState:
"""Aktueller Budget-Status"""
daily_spent: float = 0.0
monthly_spent: float = 0.0
last_reset_daily: datetime = field(default_factory=datetime.now)
last_reset_monthly: datetime = field(default_factory=datetime.now)
throttle_active: bool = False
throttle_factor: float = 1.0
requests_queued: int = 0
requests_processed_today: int = 0
class BudgetController:
"""
Echtzeit-Budget-Controller mit automatischer Drosselung.
Features:
- Tägliche und monatliche Limits
- Progressives Throttling bei Budget-Erschöpfung
- Request-Queueing mit Priority
- Alert-Integration
"""
def __init__(
self,
config: BudgetConfig,
on_alert: Optional[Callable[[str, float], None]] = None
):
self.config = config
self.on_alert = on_alert
self._state = BudgetState()
self._request_queue: asyncio.PriorityQueue = asyncio.PriorityQueue()
self._lock = asyncio.Lock()
# Request-Tracking (Rolling Window)
self._recent_costs: deque = deque(maxlen=1000)
self._recent_timestamps: deque = deque(maxlen=1000)
# Metriken
self._total_requests_throttled = 0
self._total_requests_rejected = 0
async def check_and_reserve_budget(
self,
estimated_cost: float,
priority: int = 5
) -> bool:
"""
Prüft ob Budget verfügbar ist und reserviert es.
Gibt True zurück wenn Request durchgeführt werden darf.
"""
async with self._lock:
await self._check_and_reset()
# Kritische Prüfung: Budget überschritten
if self._state.daily_spent >= self.config.daily_limit_usd:
self._trigger_alert("DAILY_LIMIT_REACHED", self._state.daily_spent)
self._total_requests_rejected += 1
return False
if self._state.monthly_spent >= self.config.monthly_limit_usd:
self._trigger_alert("MONTHLY_LIMIT_REACHED", self._state.monthly_spent)
self._total_requests_rejected += 1
return False
# Progressives Throttling
daily_utilization = self._state.daily_spent / self.config.daily_limit_usd
if daily_utilization >= self.config.auto_throttle_at:
# Berechne Throttle-Faktor (0.0 - 1.0)
excess = daily_utilization - self.config.auto_throttle_at
range_size = 1.0 - self.config.auto_throttle_at
self._state.throttle_factor = max(0.1, 1.0 - (excess / range_size))
self._state.throttle_active = True
# Probability-basierte Drosselung
import random
if random.random() > self._state.throttle_factor:
self._total_requests_throttled += 1
return False
# Budget verfügbar
return True
async def record_cost(self, actual_cost: float) -> None:
"""Records the actual cost after API call"""
async with self._lock:
self._state.daily_spent += actual_cost
self._state.monthly_spent += actual_cost
self._state.requests_processed_today += 1
self._recent_costs.append(actual_cost)
self._recent_timestamps.append(time.time())
# Check thresholds
daily_utilization = self._state.daily_spent / self.config.daily_limit_usd
if daily_utilization >= self.config.critical_threshold:
self._trigger_alert(
"CRITICAL_BUDGET",
daily_utilization * 100
)
elif daily_utilization >= self.config.alert_threshold:
self._trigger_alert(
"BUDGET_WARNING",
daily_utilization * 100
)
async def _check_and_reset(self) -> None:
"""Prüft und setzt Limits bei Bedarf zurück"""
now = datetime.now()
# Daily Reset
if (now - self._state.last_reset_daily).days >= 1:
self._state.daily_spent = 0.0
self._state.last_reset_daily = now
self._state.requests_processed_today = 0
self._state.throttle_active = False
self._state.throttle_factor = 1.0
# Monthly Reset
if now.month != self._state.last_reset_monthly.month:
self._state.monthly_spent = 0.0
self._state.last_reset_monthly = now
def _trigger_alert(self, alert_type: str, value: float) -> None:
"""Trigger Alert via Callback oder Logging"""
message = f"[{alert_type}] Budget-Status: ${value:.2f}"
if self.on_alert:
self.on_alert(alert_type, value)
print(f"🚨 {message}")
def get_status(self) -> Dict:
"""Gibt aktuellen Budget-Status zurück"""
daily_utilization = (
self._state.daily_spent / self.config.daily_limit_usd * 100
if self.config.daily_limit_usd > 0 else 0
)
monthly_utilization = (
self._state.monthly_spent / self.config.monthly_limit_usd * 100
if self.config.monthly_limit_usd > 0 else 0
)
return {
"daily": {
"spent_usd": round(self._state.daily_spent, 2),
"limit_usd": self.config.daily_limit_usd,
"remaining_usd": round(self.config.daily_limit_usd - self._state.daily_spent, 2),
"utilization_percent": round(daily_utilization, 2),
"requests_processed": self._state.requests_processed_today
},
"monthly": {
"spent_usd": round(self._state.monthly_spent, 2),
"limit_usd": self.config.monthly_limit_usd,
"remaining_usd": round(self.config.monthly_limit_usd - self._state.monthly_spent, 2),
"utilization_percent": round(monthly_utilization, 2)
},
"throttling": {
"active": self._state.throttle_active,
"factor": self._state.throttle_factor,
"requests_throttled": self._total_requests_throttled,
"requests_rejected": self._total_requests_rejected
}
}
Usage Example
async def main():
# Alert-Callback
def my_alert_handler(alert_type: str, value: float):
# In Produktion: Slack, PagerDuty, etc.
print(f"📧 ALERT: {alert_type} - {value}")
controller = BudgetController(
config=BudgetConfig(
daily_limit_usd=500.0,
monthly_limit_usd=10000.0,
alert_threshold=0.70,
critical_threshold=0.90
),
on_alert=my_alert_handler
)
# Simuliere Request
estimated_cost = 0.15 # z.B. 1M Token Input
if await controller.check_and_reserve_budget(estimated_cost):
print("✅ Request erlaubt, führe API