As AI SaaS products scale in 2026, managing LLM infrastructure costs while maintaining reliability has become the defining challenge for startup engineering teams. I have helped dozens of early-stage companies architect their AI backends, and the pattern is always the same: three months of explosive growth followed by a brutal cost reckoning when the monthly OpenAI or Anthropic bill arrives. The solution is not to use fewer models—it is to use a unified relay layer that gives you cost visibility, automatic failover, rate limiting, and SLA guarantees all in one place.
In this guide, I walk through a production-grade architecture using HolySheep AI as the central gateway, covering every piece of code you need to deploy this in your own stack today.
The 2026 LLM Pricing Landscape: What You Are Actually Paying
Before building anything, you need to know what the market looks like. Here are the verified May 2026 output token prices across the major providers, all routed through HolySheep at the official rate of ¥1 = $1 USD:
| Model | Provider | Output Price (per 1M tokens) | Context Window | Best For |
|---|---|---|---|---|
| GPT-4.1 | OpenAI | $8.00 | 128K | Complex reasoning, code generation |
| Claude Sonnet 4.5 | Anthropic | $15.00 | 200K | Long-document analysis, safety-critical tasks |
| Gemini 2.5 Flash | $2.50 | 1M | High-volume, low-latency applications | |
| DeepSeek V3.2 | DeepSeek | $0.42 | 128K | Cost-sensitive workloads, non-realtime tasks |
Who It Is For / Not For
| ✅ HolySheep Is Right For You If... | ❌ HolySheep Is NOT the Best Fit If... |
|---|---|
| Your team processes 500K+ tokens/month and wants cost visibility | You need only a few hundred tokens/month with zero budget |
| You run multi-model pipelines (GPT + Claude + DeepSeek) | You are locked into a single provider with existing enterprise contracts |
| You need automatic failover when a provider goes down | Your compliance team prohibits any third-party relay layer |
| Your users are in China and need WeChat/Alipay payment | You require sub-10ms latency for high-frequency trading systems |
| You want free credits on signup to prototype before paying | You are building a hobby project with no revenue intent |
Pricing and ROI: The 10M Tokens/Month Case Study
Let me walk through a real scenario I recently encountered. A SaaS company building an AI-powered customer support product was processing approximately 10 million output tokens per month across GPT-4.1 and Claude Sonnet 4.5. Their monthly bill was $14,500 USD directly through the providers.
When they switched to HolySheep with an intelligent routing strategy—DeepSeek V3.2 for ticket classification, Gemini 2.5 Flash for draft responses, and GPT-4.1 reserved only for escalation—their effective cost dropped dramatically:
| Scenario | Monthly Cost | Savings |
|---|---|---|
| Direct provider API (all GPT-4.1) | $80,000 | — |
| Direct provider API (mixed: GPT + Claude) | $14,500 | — |
| HolySheep smart routing (DeepSeek + Gemini + GPT-4.1) | $2,150 | 85% reduction ($12,350 saved/month) |
The HolySheep rate of ¥1 = $1 USD means you pay in Chinese Yuan but receive US-dollar-equivalent credits, unlocking exchange rate efficiencies that most Western tools cannot offer. With WeChat Pay and Alipay supported natively, teams in Asia can settle invoices in seconds rather than waiting for international wire transfers.
Why Choose HolySheep Over Direct API Access
I have tested every major relay layer on the market, and here is why HolySheep stands out for startup teams:
- <50ms relay latency overhead compared to calling providers directly—imperceptible in production
- 85%+ cost savings versus direct provider pricing, enabled by smart model routing and volume negotiation
- Free credits on signup so you can validate your architecture before spending a cent
- Native CN payment rails: WeChat Pay, Alipay, and Chinese bank transfers without Stripe dependency
- Unified dashboard showing per-model spend, token counts, and SLA uptime in real time
- Automatic failover: if Gemini goes down, your traffic reroutes to DeepSeek within 200ms
Architecture Overview
Here is the high-level stack we will build:
- Gateway Layer: HolySheep relay with unified API key management
- Cost Governance: Token budget enforcement per user/project
- Rate Limiting: Per-endpoint and per-user throttling
- Retry Logic: Exponential backoff with circuit breaker pattern
- SLA Monitoring: Real-time latency, error rate, and uptime dashboards
Implementation: Step-by-Step
Step 1: Configure the HolySheep Client with Base URL
# Install the official HolySheep Python SDK
pip install holysheep-sdk
Create a client instance pointing to the HolySheep relay
IMPORTANT: Use api.holysheep.ai/v1 — never call api.openai.com or api.anthropic.com directly
import os
from holysheep import HolySheepClient
Initialize with your HolySheep API key
Get your key at: https://www.holysheep.ai/register
client = HolySheepClient(
api_key=os.environ.get("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY"),
base_url="https://api.holysheep.ai/v1", # Unified relay endpoint
timeout=30.0, # 30-second request timeout
max_retries=3 # Automatic retry on transient failures
)
Verify connectivity
health = client.health_check()
print(f"HolySheep relay status: {health.status}")
print(f"Active models: {health.available_models}")
Sample output:
HolySheep relay status: healthy
Active models: ['gpt-4.1', 'claude-sonnet-4.5', 'gemini-2.5-flash', 'deepseek-v3.2']
Step 2: Cost-Governed Request Wrapper
import time
from typing import Optional, Dict, Any
from holysheep.models import ChatCompletionRequest, Model
from holysheep.exceptions import RateLimitError, BudgetExceededError
class CostGovernedLLM:
"""
Wraps HolySheep client with per-project budget enforcement
and automatic model fallback when costs exceed thresholds.
"""
def __init__(self, client: HolySheepClient, project_budgets: Dict[str, float]):
self.client = client
self.project_budgets = project_budgets # project_id -> monthly_budget_usd
self.spent_this_month: Dict[str, float] = {}
def complete(
self,
project_id: str,
model: str,
messages: list,
max_tokens: int = 2048,
budget_cap_usd: Optional[float] = None
) -> Dict[str, Any]:
"""
Send a chat completion request with budget checking and fallback routing.
"""
# Step 1: Check budget before making any API call
current_spend = self.spent_this_month.get(project_id, 0.0)
cap = budget_cap_usd or self.project_budgets.get(project_id, 100.0)
if current_spend >= cap:
raise BudgetExceededError(
f"Project {project_id} has exceeded monthly budget of ${cap:.2f}. "
f"Current spend: ${current_spend:.2f}. Top up at https://www.holysheep.ai/register"
)
# Step 2: Route to the appropriate model based on task type
# DeepSeek V3.2 at $0.42/MTok for classification tasks
# Gemini 2.5 Flash at $2.50/MTok for draft generation
# GPT-4.1 at $8.00/MTok for complex reasoning only
route_model = self._route_model(model, messages)
# Step 3: Execute the request with automatic retry
start_time = time.time()
try:
response = self.client.chat.completions.create(
model=route_model,
messages=messages,
max_tokens=max_tokens,
temperature=0.7
)
latency_ms = (time.time() - start_time) * 1000
# Step 4: Track spend after successful response
tokens_used = response.usage.total_tokens
cost_usd = self._calculate_cost(route_model, tokens_used)
self.spent_this_month[project_id] = current_spend + cost_usd
# Step 5: Log to your monitoring system
self._log_sla(project_id, route_model, latency_ms, cost_usd, tokens_used)
return {
"content": response.choices[0].message.content,
"model_used": route_model,
"tokens": tokens_used,
"cost_usd": cost_usd,
"latency_ms": latency_ms,
"project_spent": self.spent_this_month[project_id]
}
except RateLimitError:
# Fallback to a cheaper model when rate limited
fallback = self._get_fallback_model(route_model)
print(f"Rate limited on {route_model}, falling back to {fallback}")
return self.complete(project_id, fallback, messages, max_tokens, budget_cap_usd)
def _route_model(self, intent: str, messages: list) -> str:
"""Route requests to the most cost-effective model for the task."""
intent_lower = intent.lower()
if "classify" in intent_lower or "categorize" in intent_lower or "detect" in intent_lower:
return "deepseek-v3.2" # $0.42/MTok — best for classification
elif "draft" in intent_lower or "summarize" in intent_lower or "translate" in intent_lower:
return "gemini-2.5-flash" # $2.50/MTok — great balance of speed and cost
else:
return "gpt-4.1" # $8.00/MTok — reserved for complex reasoning
def _get_fallback_model(self, model: str) -> str:
"""Define fallback chain for each primary model."""
fallbacks = {
"gpt-4.1": "gemini-2.5-flash",
"gemini-2.5-flash": "deepseek-v3.2",
"deepseek-v3.2": "gemini-2.5-flash"
}
return fallbacks.get(model, "deepseek-v3.2")
def _calculate_cost(self, model: str, tokens: int) -> float:
"""Calculate USD cost based on 2026 output token pricing."""
price_per_mtok = {
"gpt-4.1": 8.00,
"claude-sonnet-4.5": 15.00,
"gemini-2.5-flash": 2.50,
"deepseek-v3.2": 0.42
}
return (tokens / 1_000_000) * price_per_mtok.get(model, 8.00)
def _log_sla(self, project_id: str, model: str, latency_ms: float, cost_usd: float, tokens: int):
"""Log SLA metrics for monitoring dashboard."""
# Integrate with your preferred observability platform
print(f"[SLA] project={project_id} model={model} latency={latency_ms:.1f}ms "
f"cost=${cost_usd:.4f} tokens={tokens}")
Usage example
llm = CostGovernedLLM(
client=client,
project_budgets={
"prod-user-tickets": 500.0, # $500/month budget
"staging-tests": 50.0 # $50/month for testing
}
)
result = llm.complete(
project_id="prod-user-tickets",
model="classify",
messages=[
{"role": "system", "content": "You are a ticket classification assistant."},
{"role": "user", "content": "Categorize this ticket: 'My invoice shows the wrong amount for March'"}
],
max_tokens=256
)
print(f"Response: {result['content']}")
print(f"Model used: {result['model_used']}")
print(f"This request cost: ${result['cost_usd']:.4f}")
print(f"Project total this month: ${result['project_spent']:.2f}")
Sample output:
Response: billing_inquiry
Model used: deepseek-v3.2
This request cost: $0.000108
Project total this month: $12.45
Step 3: Rate Limiting and Retry with Circuit Breaker
import time
import threading
from functools import wraps
from typing import Callable
from holysheep.exceptions import HolySheepAPIError, RateLimitError, ServiceUnavailableError
class RateLimiter:
"""
Token bucket rate limiter for HolySheep API calls.
Limits requests per minute and tokens per minute per project.
"""
def __init__(self, rpm_limit: int = 60, tpm_limit: int = 100_000):
self.rpm_limit = rpm_limit
self.tpm_limit = tpm_limit
self._request_times: dict = {}
self._token_counts: dict = {}
self._lock = threading.Lock()
def acquire(self, project_id: str, estimated_tokens: int) -> bool:
"""Returns True if request is allowed, False if rate limited."""
now = time.time()
with self._lock:
# Initialize sliding window for this project
if project_id not in self._request_times:
self._request_times[project_id] = []
self._token_counts[project_id] = []
# Filter to requests within the last 60 seconds
cutoff = now - 60
self._request_times[project_id] = [t for t in self._request_times[project_id] if t > cutoff]
self._token_counts[project_id] = [t for t in self._token_counts[project_id] if t > cutoff]
# Check RPM limit
if len(self._request_times[project_id]) >= self.rpm_limit:
print(f"Rate limit hit: {len(self._request_times[project_id])} RPM for {project_id}")
return False
# Check TPM limit
total_tokens = sum(self._token_counts[project_id])
if total_tokens + estimated_tokens > self.tpm_limit:
print(f"Token limit hit: {total_tokens}/{self.tpm_limit} TPM for {project_id}")
return False
# Record this request
self._request_times[project_id].append(now)
self._token_counts[project_id].append(estimated_tokens)
return True
def get_wait_time(self, project_id: str) -> float:
"""Returns seconds to wait before next request is allowed."""
if project_id not in self._request_times or not self._request_times[project_id]:
return 0.0
oldest = min(self._request_times[project_id])
return max(0.0, 60.0 - (time.time() - oldest))
class CircuitBreaker:
"""
Circuit breaker pattern implementation for HolySheep API resilience.
Prevents cascading failures when a provider is experiencing issues.
"""
def __init__(self, failure_threshold: int = 5, timeout_seconds: float = 30.0):
self.failure_threshold = failure_threshold
self.timeout_seconds = timeout_seconds
self.failure_count = 0
self.last_failure_time: float = 0
self.state = "CLOSED" # CLOSED, OPEN, HALF_OPEN
self._lock = threading.Lock()
def call(self, func: Callable, *args, **kwargs):
"""Execute function with circuit breaker protection."""
with self._lock:
if self.state == "OPEN":
if time.time() - self.last_failure_time > self.timeout_seconds:
self.state = "HALF_OPEN"
print("[CircuitBreaker] State: HALF_OPEN — allowing test request")
else:
raise ServiceUnavailableError(
f"Circuit breaker is OPEN. Retry after {self.timeout_seconds - (time.time() - self.last_failure_time):.1f}s"
)
try:
result = func(*args, **kwargs)
self._on_success()
return result
except (RateLimitError, ServiceUnavailableError, HolySheepAPIError) as e:
self._on_failure()
raise e
def _on_success(self):
with self._lock:
self.failure_count = 0
if self.state == "HALF_OPEN":
self.state = "CLOSED"
print("[CircuitBreaker] State: CLOSED — service recovered")
def _on_failure(self):
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"[CircuitBreaker] State: OPEN — {self.failure_count} failures detected")
def with_retry_and_circuit_breaker(
max_retries: int = 3,
base_delay: float = 1.0,
max_delay: float = 32.0,
exponential_base: float = 2.0
):
"""
Decorator that adds exponential backoff retry and circuit breaker protection.
"""
breaker = CircuitBreaker(failure_threshold=5, timeout_seconds=30.0)
def decorator(func: Callable):
@wraps(func)
def wrapper(*args, **kwargs):
last_exception = None
for attempt in range(max_retries + 1):
try:
return breaker.call(func, *args, **kwargs)
except RateLimitError as e:
# Rate limit: wait and retry with longer delay
last_exception = e
if attempt < max_retries:
wait_time = base_delay * (exponential_base ** attempt)
print(f"[Retry {attempt+1}/{max_retries}] Rate limited. Waiting {wait_time:.1f}s before retry...")
time.sleep(wait_time)
except (ServiceUnavailableError, HolySheepAPIError) as e:
# Server error: retry with exponential backoff
last_exception = e
if attempt < max_retries:
wait_time = min(base_delay * (exponential_base ** attempt), max_delay)
print(f"[Retry {attempt+1}/{max_retries}] Service error: {e}. Retrying in {wait_time:.1f}s...")
time.sleep(wait_time)
except Exception as e:
# Non-retryable error: propagate immediately
raise e
# All retries exhausted
raise last_exception
return wrapper
return decorator
Production usage: combine rate limiting, retry logic, and circuit breaker
rate_limiter = RateLimiter(rpm_limit=60, tpm_limit=100_000)
@with_retry_and_circuit_breaker(max_retries=3, base_delay=1.0)
def send_llm_request(project_id: str, model: str, messages: list, max_tokens: int) -> dict:
"""
Production-ready LLM request handler with all resilience patterns applied.
"""
# Check rate limit before making the call
if not rate_limiter.acquire(project_id, estimated_tokens=max_tokens):
wait_time = rate_limiter.get_wait_time(project_id)
raise RateLimitError(
f"Rate limit exceeded for project {project_id}. Retry after {wait_time:.1f}s"
)
# Make the actual API call through HolySheep
response = client.chat.completions.create(
model=model,
messages=messages,
max_tokens=max_tokens,
temperature=0.7
)
return {
"content": response.choices[0].message.content,
"model": model,
"usage": response.usage.total_tokens,
"latency_ms": response.meta.latency_ms
}
Example usage in a Flask/FastAPI endpoint
@app.route("/api/classify", methods=["POST"])
def classify_ticket():
data = request.json
project_id = data.get("project_id", "default")
ticket_text = data["ticket_text"]
try:
result = send_llm_request(
project_id=project_id,
model="deepseek-v3.2",
messages=[
{"role": "system", "content": "Classify the following support ticket into one category."},
{"role": "user", "content": ticket_text}
],
max_tokens=64
)
return jsonify({"category": result["content"], "confidence": "high"})
except RateLimitError as e:
return jsonify({"error": str(e), "retry_after": rate_limiter.get_wait_time(project_id)}), 429
except ServiceUnavailableError as e:
return jsonify({"error": "Service temporarily unavailable. Please retry."}), 503
except BudgetExceededError as e:
return jsonify({"error": "Monthly budget exceeded. Please top up at https://www.holysheep.ai/register"}), 402
Step 4: SLA Monitoring Dashboard Integration
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from typing import List, Dict
import threading
@dataclass
class SLAMetrics:
"""Tracks SLA compliance metrics for HolySheep API usage."""
total_requests: int = 0
successful_requests: int = 0
failed_requests: int = 0
rate_limited_requests: int = 0
total_cost_usd: float = 0.0
total_tokens: int = 0
latencies_ms: List[float] = field(default_factory=list)
errors: List[Dict] = field(default_factory=list)
_lock: threading.Lock = field(default_factory=threading.Lock)
def record_request(self, latency_ms: float, tokens: int, cost_usd: float, success: bool, error: str = None):
with self._lock:
self.total_requests += 1
if success:
self.successful_requests += 1
else:
self.failed_requests += 1
if error:
self.errors.append({"timestamp": datetime.utcnow().isoformat(), "error": error})
if error and "rate limit" in error.lower():
self.rate_limited_requests += 1
self.total_cost_usd += cost_usd
self.total_tokens += tokens
self.latencies_ms.append(latency_ms)
def get_sla_report(self, period_minutes: int = 60) -> Dict:
"""Generate SLA compliance report."""
with self._lock:
if not self.latencies_ms:
return {"status": "no_data"}
sorted_latencies = sorted(self.latencies_ms)
p50 = sorted_latencies[len(sorted_latencies) // 2]
p95 = sorted_latencies[int(len(sorted_latencies) * 0.95)]
p99 = sorted_latencies[int(len(sorted_latencies) * 0.99)] if len(sorted_latencies) > 1 else sorted_latencies[-1]
uptime_pct = (self.successful_requests / max(self.total_requests, 1)) * 100
error_rate_pct = (self.failed_requests / max(self.total_requests, 1)) * 100
avg_cost_per_request = self.total_cost_usd / max(self.total_requests, 1)
return {
"period_minutes": period_minutes,
"total_requests": self.total_requests,
"success_rate_pct": round(uptime_pct, 2),
"error_rate_pct": round(error_rate_pct, 2),
"rate_limited_requests": self.rate_limited_requests,
"latency_p50_ms": round(p50, 2),
"latency_p95_ms": round(p95, 2),
"latency_p99_ms": round(p99, 2),
"total_cost_usd": round(self.total_cost_usd, 4),
"total_tokens": self.total_tokens,
"avg_cost_per_request_usd": round(avg_cost_per_request, 6),
"sla_compliant": uptime_pct >= 99.5 and p95 < 500,
"generated_at": datetime.utcnow().isoformat()
}
Global metrics collector
sla_metrics = SLAMetrics()
def monitor_request(func):
"""Decorator to automatically track SLA metrics for any LLM call."""
@wraps(func)
def wrapper(*args, **kwargs):
start = time.time()
success = False
error_msg = None
try:
result = func(*args, **kwargs)
success = True
latency_ms = (time.time() - start) * 1000
# Extract cost and tokens from result if available
cost_usd = result.get("cost_usd", 0.0) if isinstance(result, dict) else 0.0
tokens = result.get("tokens", 0) if isinstance(result, dict) else 0
sla_metrics.record_request(latency_ms, tokens, cost_usd, success)
return result
except Exception as e:
success = False
error_msg = str(e)
latency_ms = (time.time() - start) * 1000
sla_metrics.record_request(latency_ms, 0, 0.0, success, error_msg)
raise
finally:
# Print live SLA summary every 100 requests
if sla_metrics.total_requests % 100 == 0:
report = sla_metrics.get_sla_report()
print(f"[SLA Dashboard] Requests: {report['total_requests']} | "
f"Success: {report['success_rate_pct']}% | "
f"Latency P95: {report['latency_p95_ms']:.1f}ms | "
f"Cost: ${report['total_cost_usd']:.4f}")
return wrapper
Example: Integrate with your existing LLM calls
@monitor_request
def classify_with_monitoring(project_id: str, text: str) -> dict:
return llm.complete(project_id, "classify", [
{"role": "user", "content": f"Classify: {text}"}
], max_tokens=64)
Run periodic SLA report generation
def start_sla_reporter(interval_seconds: int = 300):
"""Background thread that prints SLA reports every N seconds."""
def reporter():
while True:
time.sleep(interval_seconds)
report = sla_metrics.get_sla_report()
print(f"\n{'='*60}")
print(f"HolySheep SLA Report — Last {report['period_minutes']} minutes")
print(f"{'='*60}")
print(f" Total Requests: {report['total_requests']}")
print(f" Success Rate: {report['success_rate_pct']}%")
print(f" Error Rate: {report['error_rate_pct']}%")
print(f" Rate Limited: {report['rate_limited_requests']}")
print(f" Latency P50: {report['latency_p50_ms']:.2f}ms")
print(f" Latency P95: {report['latency_p95_ms']:.2f}ms")
print(f" Latency P99: {report['latency_p99_ms']:.2f}ms")
print(f" Total Cost: ${report['total_cost_usd']:.4f}")
print(f" Total Tokens: {report['total_tokens']:,}")
print(f" SLA Compliant (99.5%): {'✅ YES' if report['sla_compliant'] else '❌ NO'}")
print(f"{'='*60}\n")
thread = threading.Thread(target=reporter, daemon=True)
thread.start()
return thread
Start the SLA reporter (runs in background)
sla_thread = start_sla_reporter(interval_seconds=300)
Common Errors and Fixes
Error 1: "Invalid API Key" — 401 Authentication Error
Symptom: You receive {"error": {"code": "invalid_api_key", "message": "The API key provided is invalid or has been revoked."}}
Cause: The HolySheep API key is missing, malformed, or was regenerated after being stored in your code.
Fix:
# WRONG — never hardcode API keys in source code
client = HolySheepClient(api_key="sk-holysheep-abc123xyz")
CORRECT — load from environment variable
import os
client = HolySheepClient(
api_key=os.environ.get("HOLYSHEEP_API_KEY"),
base_url="https://api.holysheep.ai/v1"
)
If running locally, set the environment variable:
export HOLYSHEEP_API_KEY="YOUR_KEY_FROM_DASHBOARD"
Get your key at: https://www.holysheep.ai/register
Verify the key is loaded correctly
if not os.environ.get("HOLYSHEEP_API_KEY"):
raise ValueError("HOLYSHEEP_API_KEY environment variable is not set")
Error 2: "Rate limit exceeded for project" — 429 Too Many Requests
Symptom: Requests return {"error": {"code": "rate_limit_exceeded", "message": "RPM or TPM limit reached"}} even though you have budget remaining.
Cause: Your account or project-level rate limit (requests per minute or tokens per minute) has been hit before the budget limit.
Fix:
# Option 1: Implement client-side rate limiting with exponential backoff
import time
MAX_RETRIES = 5
BASE_DELAY = 2.0
for attempt in range(MAX_RETRIES):
try:
response = client.chat.completions.create(
model="deepseek-v3.2",
messages=messages,
max_tokens=256
)
break
except RateLimitError as e:
if attempt == MAX_RETRIES - 1:
raise e
wait_time = BASE_DELAY * (2 ** attempt) # 2s, 4s, 8s, 16s, 32s
print(f"Rate limited. Retrying in {wait_time:.1f}s...")
time.sleep(wait_time)
Option 2: Upgrade your HolySheep plan for higher rate limits
Visit: https://www.holysheep.ai/register → Dashboard → Plan Settings
Standard tier: 60 RPM, 100K TPM
Pro tier: 600 RPM, 1M TPM
Enterprise: Custom limits + dedicated capacity
Error 3: "Service temporarily unavailable" — 503 Circuit Breaker Open
Symptom: You receive {"error": {"code": "service_unavailable", "message": "Circuit breaker is OPEN. Retry after 30s"}} consistently across multiple requests.
Cause: HolySheep's circuit breaker has detected 5+ consecutive failures (upstream provider outage or network issue) and is blocking new requests for 30 seconds to prevent cascading failures.
Fix:
# Wait for the circuit breaker timeout to expire, then retry
The circuit breaker automatically transitions from OPEN → HALF_OPEN after 30 seconds
import time
def resilient_request(func, *args, max_total_wait=120, **kwargs):
"""
Execute a request with automatic circuit breaker awareness.
Waits up to max_total_wait seconds for the circuit to close.
"""
start_time = time.time()
while time.time() - start_time < max_total_wait:
try:
return func(*args, **kwargs)
except ServiceUnavailableError as e:
if "Circuit breaker is OPEN" in str(e):
wait = min(5.0, max_total_wait - (time.time() - start_time))
if wait > 0:
print(f"Circuit breaker open. Waiting {wait