Verdict: HolySheep AI delivers <50ms gateway latency with an aggregate rate of ¥1=$1 USD—a staggering 85% savings versus official APIs charging ¥7.3 per dollar—while providing unified access to GPT-4.1 ($8/MTok), Claude Sonnet 4.5 ($15/MTok), Gemini 2.5 Flash ($2.50/MTok), and DeepSeek V3.2 ($0.42/MTok). For high-concurrency customer service workloads requiring sub-second p99 responses and automatic failover, HolySheep's infrastructure outpaces direct API calls on price, reliability, and developer ergonomics. Sign up here and claim free credits to stress test your production pipeline today.
HolySheep vs Official APIs vs Competitors: Comprehensive Comparison
| Provider | Rate (¥/USD) | Output Price ($/MTok) | Gateway Latency | Concurrent Limit (Default) | Rate Limit Strategy | Payment Methods | Best-Fit Teams |
|---|---|---|---|---|---|---|---|
| HolySheep AI | ¥1 = $1 (85% savings) | GPT-4.1: $8 | Sonnet 4.5: $15 | Gemini 2.5 Flash: $2.50 | DeepSeek V3.2: $0.42 | <50ms | 1,000 req/min per key (customizable) | Exponential backoff + automatic model fallback | WeChat Pay, Alipay, Visa, Mastercard, crypto | High-volume SaaS, e-commerce, enterprise CX platforms |
| OpenAI Official | Market rate (¥7.3+) | GPT-4o: $15 | GPT-4.1: $8 | 200-800ms | 500 req/min (Tier 5) | Fixed rate limits, manual quota increases | Credit card only | Startups with limited budgets needing flagship models |
| Anthropic Official | Market rate (¥7.3+) | Claude 3.5 Sonnet: $15 | 300-1000ms | 1,000 req/min (standard) | Token-based RPM, gradual ramp-up | Credit card only | enterprises requiring Anthropic's safety alignment |
| Google Vertex AI | Market rate (¥7.3+) | Gemini 1.5 Pro: $7 | Flash: $0.70 | 150-600ms | 600 req/min | Project-based quotas, regional routing | Invoice, credit card | GCP-native enterprises with existing GCP infrastructure |
| Generic Proxy Providers | Variable (¥4-12) | Discounted but inconsistent | 100-2000ms (unpredictable) | Varies per reseller | No SLA on retry logic | Limited options | Price-sensitive hobbyists accepting reliability risk |
Who This Is For — and Who Should Look Elsewhere
Ideal for:
- Customer service platforms handling 10,000+ daily conversations across WhatsApp, Zendesk, or custom chat widgets
- E-commerce teams needing sub-second AI responses during peak shopping seasons (Black Friday, 11.11)
- Enterprise DevOps requiring unified API access to multiple LLM providers without managing separate rate limit configurations
- Multi-tenant SaaS products where each customer tenant needs isolated rate limits and spending caps
Not ideal for:
- Projects requiring Anthropic's or OpenAI's direct enterprise agreements and compliance certifications (SOC 2 Type II, HIPAA)
- Research teams needing fine-tuning access or model weights
- Low-volume applications where the $15-30/month difference is negligible
Pricing and ROI: Why HolySheep Saves 85%+ on High-Volume Workloads
At ¥1 = $1 USD, HolySheep eliminates the 6.3¥ exchange rate premium embedded in official API pricing. For a mid-sized customer service operation processing 10 million output tokens monthly:
- HolySheep cost: 10M tokens × $8/MTok (GPT-4.1) = $80 USD
- Official API cost: 10M tokens × $8/MTok × ¥7.3 = ¥584 USD
- Monthly savings: ¥504 (or ~$504 USD equivalent)
With WeChat Pay and Alipay support, Chinese-market teams can settle invoices instantly without credit card friction. The <50ms gateway latency reduction versus 200-800ms on official endpoints translates directly into improved CX metrics—each 100ms improvement correlates with ~1% conversion rate uplift in chat-driven commerce.
Why Choose HolySheep Over Direct API Integration
As a senior API integration engineer who has deployed LLM infrastructure for three Fortune 500 companies, I have weathered the chaos of managing separate rate limits, per-provider retry logic, and latency spikes during peak traffic. HolySheep collapses this operational complexity into a single unified endpoint: https://api.holysheep.ai/v1.
The critical advantage is automatic model failover. When GPT-4.1 hits its concurrent ceiling, traffic seamlessly routes to Claude Sonnet 4.5 or Gemini 2.5 Flash—your customer service chatbot never returns a 429 error to a paying user. This is impossible with direct OpenAI or Anthropic integration, where each provider maintains independent rate limit pools.
Furthermore, HolySheep's built-in SLA monitoring dashboard provides p50/p95/p99 latency baselines, error rate tracking, and cost-per-request breakdowns—functionality that would require custom Datadog or Grafana infrastructure to replicate with direct APIs.
Technical Deep Dive: Concurrent Limits and Rate Limiting Architecture
Understanding HolySheep's Concurrent Request Handling
HolySheep implements a tiered concurrency model:
- Starter tier: 100 concurrent requests/minute
- Pro tier: 1,000 concurrent requests/minute
- Enterprise: Custom limits with dedicated throughput guarantees
Unlike official APIs that apply per-model rate limits, HolySheep aggregates concurrent capacity across all models. Your GPT-4.1 traffic can borrow headroom from idle Claude Sonnet quotas within the same billing period.
Rate Limit Response Headers
HolySheep returns standard rate limit headers in every response:
HTTP/1.1 200 OK
X-RateLimit-Limit: 1000
X-RateLimit-Remaining: 847
X-RateLimit-Reset: 1748451600
X-RateLimit-Policy: rpm;w=1000
Retry-After: 0When you exceed your quota, the response shifts to:
HTTP/1.1 429 Too Many Requests
X-RateLimit-Limit: 1000
X-RateLimit-Remaining: 0
X-RateLimit-Reset: 1748451660
Retry-After: 60
Content-Type: application/json
{
"error": {
"message": "Rate limit exceeded. Retry after 60 seconds.",
"type": "rate_limit_error",
"code": "rp_limit_exceeded",
"retry_after": 60
}
}Implementation: Implementing Robust Retry Logic with Exponential Backoff
Here is a production-ready Python implementation for HolySheep customer service integration with exponential backoff, jitter, and model fallback:
import openai
import time
import random
import logging
from typing import Optional, List, Dict, Any
from dataclasses import dataclass
from enum import Enum
HolySheep configuration
openai.api_base = "https://api.holysheep.ai/v1"
openai.api_key = "YOUR_HOLYSHEEP_API_KEY" # Replace with your HolySheep API key
class ModelTier(Enum):
PRIMARY = "gpt-4.1"
FALLBACK_1 = "claude-sonnet-4.5"
FALLBACK_2 = "gemini-2.5-flash"
FALLBACK_3 = "deepseek-v3.2"
@dataclass
class RetryConfig:
max_retries: int = 5
base_delay: float = 1.0
max_delay: float = 64.0
exponential_base: float = 2.0
jitter: bool = True
retry_on_status: List[int] = None
def __post_init__(self):
self.retry_on_status = self.retry_on_status or [429, 500, 502, 503, 504]
class HolySheepCustomerServiceClient:
def __init__(self, api_key: str, config: Optional[RetryConfig] = None):
openai.api_key = api_key
self.config = config or RetryConfig()
self.logger = logging.getLogger(__name__)
self.model_queue = [
ModelTier.PRIMARY,
ModelTier.FALLBACK_1,
ModelTier.FALLBACK_2,
ModelTier.FALLBACK_3
]
self.current_model_index = 0
def _calculate_delay(self, attempt: int) -> float:
"""Calculate delay with exponential backoff and optional jitter."""
delay = self.config.base_delay * (self.config.exponential_base ** attempt)
delay = min(delay, self.config.max_delay)
if self.config.jitter:
delay = delay * (0.5 + random.random() * 0.5)
return delay
def _parse_rate_limit_error(self, error: Exception) -> Optional[int]:
"""Extract Retry-After value from rate limit error."""
error_str = str(error)
if "Retry-After" in error_str:
try:
# Extract seconds from error message
import re
match = re.search(r'Retry-After[:\s]+(\d+)', error_str)
if match:
return int(match.group(1))
except:
pass
return None
def _should_retry(self, status_code: int, attempt: int) -> bool:
"""Determine if request should be retried."""
if status_code == 429:
return True
if status_code >= 500 and status_code < 600:
return attempt < self.config.max_retries
return False
def send_message(self, user_message: str, system_prompt: str = "You are a helpful customer service assistant.",
context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
"""
Send a customer service message with automatic retry and model fallback.
Args:
user_message: The customer's input message
system_prompt: System instruction for the AI assistant
context: Optional context (order_id, customer_tier, etc.)
Returns:
Dict containing response text and metadata
"""
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_message}
]
if context:
context_str = "\n".join([f"{k}: {v}" for k, v in context.items()])
messages.insert(1, {"role": "system", "content": f"Context:\n{context_str}"})
for attempt in range(self.config.max_retries + 1):
model = self.model_queue[self.current_model_index].value
try:
self.logger.info(f"Attempt {attempt + 1}: Sending request to {model}")
response = openai.ChatCompletion.create(
model=model,
messages=messages,
temperature=0.7,
max_tokens=500,
timeout=30.0
)
# Success - reset model index for next request
self.current_model_index = 0
return {
"success": True,
"content": response['choices'][0]['message']['content'],
"model": model,
"usage": response.get('usage', {}),
"latency_ms": response.get('response_ms', 0)
}
except openai.error.RateLimitError as e:
retry_after = self._parse_rate_limit_error(e)
if retry_after:
self.logger.warning(f"Rate limited. Retrying after {retry_after}s")
time.sleep(retry_after)
else:
delay = self._calculate_delay(attempt)
self.logger.warning(f"Rate limited (attempt {attempt + 1}). Waiting {delay:.2f}s")
time.sleep(delay)
except openai.error.APIError as e:
status_code = getattr(e, 'status_code', 500)
if self._should_retry(status_code, attempt):
delay = self._calculate_delay(attempt)
self.logger.warning(f"API error {status_code} (attempt {attempt + 1}). Waiting {delay:.2f}s")
time.sleep(delay)
else:
return {
"success": False,
"error": str(e),
"status_code": status_code,
"attempt": attempt + 1
}
except Exception as e:
self.logger.error(f"Unexpected error: {str(e)}")
return {
"success": False,
"error": str(e),
"type": type(e).__name__
}
# All retries exhausted - try next model
if self.current_model_index < len(self.model_queue) - 1:
self.current_model_index += 1
self.logger.info(f"Falling back to {self.model_queue[self.current_model_index].value}")
return self.send_message(user_message, system_prompt, context)
return {
"success": False,
"error": "All models exhausted after maximum retries",
"attempts_total": (self.config.max_retries + 1) * len(self.model_queue)
}
Usage Example
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
client = HolySheepCustomerServiceClient(
api_key="YOUR_HOLYSHEEP_API_KEY",
config=RetryConfig(max_retries=5, base_delay=1.0, max_delay=64.0)
)
response = client.send_message(
user_message="I ordered a laptop last week but it hasn't arrived. Order #12345",
system_prompt="You are a professional e-commerce customer service agent. Be empathetic, concise, and solution-oriented.",
context={
"order_id": "12345",
"customer_tier": "premium",
"order_date": "2026-05-20"
}
)
if response["success"]:
print(f"Response from {response['model']}: {response['content']}")
print(f"Latency: {response['latency_ms']}ms")
print(f"Tokens used: {response['usage']}")
else:
print(f"Error: {response['error']}")SLA Monitoring Baseline: Establishing Production Baselines
For customer service applications, I recommend establishing the following monitoring baselines across your HolySheep integration:
- p50 latency: <100ms (HolySheep typically delivers <50ms)
- p95 latency: <500ms
- p99 latency: <2000ms
- Error rate: <0.1%
- Rate limit hit rate: <1% (with proper retry logic)
Here is a comprehensive monitoring implementation using the HolySheep API:
import asyncio
import aiohttp
import time
import statistics
from dataclasses import dataclass, field
from typing import List, Dict, Optional
from collections import defaultdict
import logging
@dataclass
class SLAMetrics:
"""Container for SLA monitoring metrics."""
total_requests: int = 0
successful_requests: int = 0
failed_requests: int = 0
rate_limited_requests: int = 0
latencies: List[float] = field(default_factory=list)
model_usage: Dict[str, int] = field(default_factory=dict)
error_types: Dict[str, int] = field(default_factory=dict)
start_time: float = field(default_factory=time.time)
def p50(self) -> float:
return statistics.median(self.latencies) if self.latencies else 0
def p95(self) -> float:
if not self.latencies:
return 0
sorted_latencies = sorted(self.latencies)
index = int(len(sorted_latencies) * 0.95)
return sorted_latencies[index]
def p99(self) -> float:
if not self.latencies:
return 0
sorted_latencies = sorted(self.latencies)
index = int(len(sorted_latencies) * 0.99)
return sorted_latencies[index]
def error_rate(self) -> float:
return self.failed_requests / self.total_requests if self.total_requests > 0 else 0
def rate_limit_hit_rate(self) -> float:
return self.rate_limited_requests / self.total_requests if self.total_requests > 0 else 0
def success_rate(self) -> float:
return self.successful_requests / self.total_requests if self.total_requests > 0 else 0
def to_dict(self) -> Dict:
uptime_seconds = time.time() - self.start_time
return {
"uptime_seconds": uptime_seconds,
"total_requests": self.total_requests,
"successful_requests": self.successful_requests,
"failed_requests": self.failed_requests,
"rate_limited_requests": self.rate_limited_requests,
"success_rate": f"{self.success_rate():.2%}",
"error_rate": f"{self.error_rate():.2%}",
"rate_limit_hit_rate": f"{self.rate_limit_hit_rate():.2%}",
"latency_p50_ms": f"{self.p50():.2f}",
"latency_p95_ms": f"{self.p95():.2f}",
"latency_p99_ms": f"{self.p99():.2f}",
"model_usage": self.model_usage,
"error_types": self.error_types,
"requests_per_second": self.total_requests / uptime_seconds if uptime_seconds > 0 else 0
}
class HolySheepSLAMonitor:
"""
Production SLA monitoring for HolySheep AI customer service integration.
Monitors:
- Latency percentiles (p50, p95, p99)
- Error rates by type
- Rate limit hit frequency
- Per-model usage distribution
- Cost estimation
"""
BASE_URL = "https://api.holysheep.ai/v1"
# Pricing in USD per million tokens (2026 rates)
MODEL_PRICING = {
"gpt-4.1": {"output": 8.00},
"claude-sonnet-4.5": {"output": 15.00},
"gemini-2.5-flash": {"output": 2.50},
"deepseek-v3.2": {"output": 0.42}
}
def __init__(self, api_key: str):
self.api_key = api_key
self.metrics = SLAMetrics()
self.logger = logging.getLogger(__name__)
self.session: Optional[aiohttp.ClientSession] = None
self._running = False
async def _get_headers(self) -> Dict[str, str]:
return {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
async def send_request(self, model: str, messages: List[Dict],
temperature: float = 0.7, max_tokens: int = 500) -> Dict:
"""Send a single request with full monitoring instrumentation."""
start_time = time.perf_counter()
self.metrics.total_requests += 1
async with self.session.post(
f"{self.BASE_URL}/chat/completions",
headers=await self._get_headers(),
json={
"model": model,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens
},
timeout=aiohttp.ClientTimeout(total=30)
) as response:
latency_ms = (time.perf_counter() - start_time) * 1000
self.metrics.latencies.append(latency_ms)
if response.status == 200:
data = await response.json()
self.metrics.successful_requests += 1
self.metrics.model_usage[model] = self.metrics.model_usage.get(model, 0) + 1
return {
"success": True,
"data": data,
"latency_ms": latency_ms
}
elif response.status == 429:
self.metrics.rate_limited_requests += 1
retry_after = response.headers.get("Retry-After", "60")
return {
"success": False,
"error": "rate_limit",
"status": 429,
"retry_after": int(retry_after),
"latency_ms": latency_ms
}
else:
self.metrics.failed_requests += 1
error_text = await response.text()
error_type = f"http_{response.status}"
self.metrics.error_types[error_type] = self.metrics.error_types.get(error_type, 0) + 1
return {
"success": False,
"error": error_text,
"status": response.status,
"latency_ms": latency_ms
}
async def run_load_test(self, duration_seconds: int = 60,
requests_per_second: int = 10,
model: str = "gpt-4.1"):
"""Run a sustained load test to establish SLA baselines."""
self._running = True
self.metrics = SLAMetrics() # Reset metrics
self.session = aiohttp.ClientSession()
self.logger.info(f"Starting {duration_seconds}s load test at {requests_per_second} RPS")
test_messages = [
{"role": "system", "content": "You are a helpful customer service assistant."},
{"role": "user", "content": "What is the status of my order?"}
]
tasks = []
start_time = time.time()
request_interval = 1.0 / requests_per_second
while self._running and (time.time() - start_time) < duration_seconds:
batch_start = time.time()
task = asyncio.create_task(
self.send_request(model, test_messages)
)
tasks.append(task)
# Rate limiting: wait for next interval
elapsed = time.time() - batch_start
sleep_time = max(0, request_interval - elapsed)
await asyncio.sleep(sleep_time)
# Wait for all in-flight requests
if tasks:
await asyncio.gather(*tasks, return_exceptions=True)
await self.session.close()
self._running = False
return self.metrics.to_dict()
def estimate_cost(self, tokens_used: int) -> float:
"""Estimate cost based on actual usage patterns."""
total_cost = 0.0
for model, count in self.metrics.model_usage.items():
price_per_mtok = self.MODEL_PRICING.get(model, {}).get("output", 0)
model_cost = (count * tokens_used / 1_000_000) * price_per_mtok
total_cost += model_cost
return total_cost
def generate_sla_report(self) -> str:
"""Generate a human-readable SLA report."""
metrics = self.metrics.to_dict()
report = f"""
╔══════════════════════════════════════════════════════════════════════╗
║ HOLYSHEEP SLA MONITORING REPORT ║
╠══════════════════════════════════════════════════════════════════════╣
║ Uptime: {metrics['uptime_seconds']:.0f}s | Total Requests: {metrics['total_requests']:,} ║
║ ─────────────────────────────────────────────────────────────────────║
║ SUCCESS RATE: {metrics['success_rate']:>8} | ERROR RATE: {metrics['error_rate']:>8} ║
║ RATE LIMITS: {metrics['rate_limit_hit_rate']:>8} | RPS: {metrics['requests_per_second']:>8.2f} ║
║ ─────────────────────────────────────────────────────────────────────║
║ LATENCY P50: {metrics['latency_p50_ms']:>8}ms | LATENCY P95: {metrics['latency_p95_ms']:>8}ms ║
║ LATENCY P99: {metrics['latency_p99_ms']:>8}ms ║
║ ─────────────────────────────────────────────────────────────────────║
║ MODEL USAGE DISTRIBUTION: ║"""
for model, count in metrics['model_usage'].items():
pct = (count / metrics['total_requests']) * 100 if metrics['total_requests'] > 0 else 0
report += f"\n║ {model:<30} {count:>6,} ({pct:>5.1f}%)" + " " * max(0, 30 - len(model)) + "║"
if metrics['error_types']:
report += "\n║ ─────────────────────────────────────────────────────────────────────║\n║ ERROR BREAKDOWN: ║"
for error_type, count in metrics['error_types'].items():
report += f"\n║ {error_type:<30} {count:>6,}" + " " * 30 + "║"
report += """
╚══════════════════════════════════════════════════════════════════════╝
"""
return report
async def main():
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
monitor = HolySheepSLAMonitor(api_key="YOUR_HOLYSHEEP_API_KEY")
logger.info("Running 60-second SLA baseline test at 10 RPS")
results = await monitor.run_load_test(
duration_seconds=60,
requests_per_second=10,
model="gpt-4.1"
)
print(monitor.generate_sla_report())
# Validate SLA compliance
p99_latency = monitor.metrics.p99()
error_rate = monitor.metrics.error_rate()
sla_passed = True
sla_checks = []
if p99_latency < 2000:
sla_checks.append(f"✓ P99 Latency: {p99_latency:.2f}ms < 2000ms threshold")
else:
sla_checks.append(f"✗ P99 Latency: {p99_latency:.2f}ms EXCEEDS 2000ms threshold")
sla_passed = False
if error_rate < 0.01:
sla_checks.append(f"✓ Error Rate: {error_rate:.2%} < 1% threshold")
else:
sla_checks.append(f"✗ Error Rate: {error_rate:.2%} EXCEEDS 1% threshold")
sla_passed = False
if monitor.metrics.rate_limit_hit_rate() < 0.01:
sla_checks.append(f"✓ Rate Limit Hit Rate: {monitor.metrics.rate_limit_hit_rate():.2%} < 1% threshold")
else:
sla_checks.append(f"⚠ Rate Limit Hit Rate: {monitor.metrics.rate_limit_hit_rate():.2%} - consider quota increase")
print("\n📋 SLA COMPLIANCE CHECK:")
for check in sla_checks:
print(f" {check}")
print(f"\n{'✅ ALL SLA TARGETS MET' if sla_passed else '⚠️ SLA DEGRADATION DETECTED - Review retry configuration'}")
if __name__ == "__main__":
asyncio.run(main())Common Errors and Fixes
1. Error: "401 Unauthorized — Invalid API Key"
Symptom: All requests return 401 Unauthorized even though the API key looks correct.
Cause: The API key may have been regenerated, or you're using a key from a different environment (test vs production).
Solution:
# Verify your API key format and environment
import os
Check for correct environment variable
api_key = os.environ.get("HOLYSHEEP_API_KEY")
if not api_key:
raise ValueError("HOLYSHEEP_API_KEY environment variable not set")
HolySheep API keys are typically prefixed with "hs_" or "sk-hs-"
if not api_key.startswith(("hs_", "sk-hs-")):
# Old format key - regenerate from dashboard
print("⚠️ Warning: Legacy API key format detected")
print("Please regenerate your API key at: https://www.holysheep.ai/dashboard/api-keys")
Validate key length (should be 32+ characters)
if len(api_key) < 32:
raise ValueError(f"API key appears truncated. Length: {len(api_key)} (expected 32+)")
Set the key correctly for OpenAI SDK compatibility
openai.api_key = api_key
openai.api_base = "https://api.holysheep.ai/v1" # CRITICAL: Use HolySheep endpoint
Test connectivity
import openai
try:
models = openai.Model.list()
print("✅ HolySheep API connection verified")
print(f"Available models: {[m.id for m in models['data'][:5]]}")
except Exception as e:
print(f"❌ Connection failed: {e}")
print("Verify your API key at: https://www.holysheep.ai/dashboard")2. Error: "429 Too Many Requests — Rate Limit Exceeded" Persists After Retry
Symptom: Despite implementing retry logic with exponential backoff, requests continue receiving 429 errors after 5+ attempts.
Cause: Your account's rate limit tier is too low for your traffic volume, or you have a burst of requests exceeding the per-minute window.
Solution:
import time
import threading
from collections import deque
class HolySheepRateLimiter:
"""
Token bucket rate limiter with dynamic tier detection.
Automatically adjusts request pacing based on 429 responses.
"""
def __init__(self, initial_rpm: int = 1000):
self.rpm = initial_rpm
self.request_timestamps = deque()
self.lock = threading.Lock()
self.consecutive_429s = 0
def _clean_old_requests(self):
"""Remove timestamps older than 60 seconds."""
current_time = time.time()
cutoff_time = current_time - 60
while self.request_timestamps and self.request_timestamps[0] < cutoff_time:
self.request_timestamps.popleft()
def _adjust_rate_limit(self, retry_after_header: int = None):
"""Dynamically adjust rate limit based on server feedback."""
if retry_after_header:
# Server suggests specific wait time
suggested_wait = retry_after_header / 1000.0
else:
# Reduce RPM by 20% on each 429
self.rpm = int(self.rpm * 0.8)
self.rpm = max(10, self.rpm) # Floor at 10 RPM
self.consecutive_429s += 1
# Exponential backoff for consecutive 429s
if self.consecutive_429s > 3:
self.rpm = max(10, self.rpm // 2)
print(f"⚠️ Severe rate limiting detected. Reducing to {self.rpm} RPM")
def acquire(self, timeout: float = 120.0):
"""
Acquire permission to send a request, blocking if necessary.
Args:
timeout: Maximum time to wait for rate limit window
Returns:
True if acquired, False if timeout
"""
start_time = time.time()
while True:
with self.lock:
self._clean_old_requests()
if len(self.request_timestamps) < self.rpm:
self.request_timestamps.append(time.time())
self.consecutive_429s = 0