API Rate Limiting¶
ALwrity implements rate limiting to ensure fair usage and maintain service quality for all users. This guide explains how rate limiting works and how to handle rate limits in your applications.
Rate Limiting Overview¶
Purpose¶
Rate limiting helps: - Prevent abuse: Protect against excessive API usage - Ensure fairness: Provide equal access to all users - Maintain performance: Keep the service responsive - Control costs: Manage infrastructure costs
How It Works¶
Rate limits are applied per API key and are based on: - Time windows: Requests per minute, hour, or day - User plan: Different limits for different subscription tiers - Endpoint type: Some endpoints have specific limits - Resource usage: Limits based on computational resources
Rate Limit Types¶
Request Rate Limits¶
Per Minute Limits¶
- Free Plan: 10 requests per minute
- Basic Plan: 60 requests per minute
- Pro Plan: 300 requests per minute
- Enterprise Plan: 1,000 requests per minute
Per Day Limits¶
- Free Plan: 100 requests per day
- Basic Plan: 1,000 requests per day
- Pro Plan: 10,000 requests per day
- Enterprise Plan: 100,000 requests per day
Resource-Based Limits¶
Content Generation¶
- Word Count: Limits based on content length
- Processing Time: Limits based on computational complexity
- Concurrent Requests: Limits on simultaneous processing
Data Usage¶
- Research Queries: Limits on research API calls
- Image Generation: Limits on image processing
- SEO Analysis: Limits on analysis requests
Rate Limit Headers¶
Standard Headers¶
Every API response includes rate limit information:
X-RateLimit-Limit: 60
X-RateLimit-Remaining: 59
X-RateLimit-Reset: 1640995200
X-RateLimit-Window: 60
Header Descriptions¶
| Header | Description |
|---|---|
X-RateLimit-Limit |
Maximum requests allowed in the window |
X-RateLimit-Remaining |
Requests remaining in current window |
X-RateLimit-Reset |
Unix timestamp when limit resets |
X-RateLimit-Window |
Time window in seconds |
Example Response¶
HTTP/1.1 200 OK
Content-Type: application/json
X-RateLimit-Limit: 60
X-RateLimit-Remaining: 58
X-RateLimit-Reset: 1640995200
X-RateLimit-Window: 60
{
"success": true,
"data": {
"content": "Generated content here..."
}
}
Rate Limit Responses¶
429 Too Many Requests¶
When rate limits are exceeded, the API returns a 429 status code:
HTTP/1.1 429 Too Many Requests
Content-Type: application/json
X-RateLimit-Limit: 60
X-RateLimit-Remaining: 0
X-RateLimit-Reset: 1640995200
Retry-After: 60
{
"error": {
"code": "RATE_LIMIT_EXCEEDED",
"message": "Rate limit exceeded. Please try again later.",
"details": {
"limit": 60,
"remaining": 0,
"reset_time": "2024-01-15T10:31:00Z",
"retry_after": 60
}
}
}
Retry-After Header¶
The Retry-After header indicates when you can retry:
Handling Rate Limits¶
Exponential Backoff¶
Implement exponential backoff for retries:
import time
import random
import requests
def make_request_with_backoff(url, headers, data, max_retries=3):
base_delay = 1
max_delay = 60
for attempt in range(max_retries):
response = requests.post(url, headers=headers, json=data)
if response.status_code == 429:
# Get retry delay from header or calculate
retry_after = int(response.headers.get('Retry-After', base_delay))
# Add jitter to prevent thundering herd
jitter = random.uniform(0.1, 0.5)
delay = min(retry_after + jitter, max_delay)
print(f"Rate limited. Retrying in {delay:.1f} seconds...")
time.sleep(delay)
# Exponential backoff for next attempt
base_delay *= 2
else:
return response
raise Exception("Max retries exceeded")
Request Queuing¶
Implement request queuing to manage rate limits:
import asyncio
import aiohttp
from asyncio import Semaphore
class RateLimitedClient:
def __init__(self, rate_limit=60, time_window=60):
self.semaphore = Semaphore(rate_limit)
self.time_window = time_window
self.requests = []
async def make_request(self, url, headers, data):
async with self.semaphore:
# Clean old requests
current_time = time.time()
self.requests = [req_time for req_time in self.requests
if current_time - req_time < self.time_window]
# Wait if at limit
if len(self.requests) >= self.semaphore._value:
sleep_time = self.time_window - (current_time - self.requests[0])
if sleep_time > 0:
await asyncio.sleep(sleep_time)
# Make request
self.requests.append(current_time)
async with aiohttp.ClientSession() as session:
async with session.post(url, headers=headers, json=data) as response:
return await response.json()
Caching Responses¶
Cache responses to reduce API calls:
import time
from functools import wraps
def cache_with_ttl(ttl_seconds):
def decorator(func):
cache = {}
@wraps(func)
def wrapper(*args, **kwargs):
# Create cache key
key = str(args) + str(sorted(kwargs.items()))
# Check cache
if key in cache:
data, timestamp = cache[key]
if time.time() - timestamp < ttl_seconds:
return data
# Make API call
result = func(*args, **kwargs)
# Cache result
cache[key] = (result, time.time())
return result
return wrapper
return decorator
# Usage
@cache_with_ttl(300) # Cache for 5 minutes
def get_blog_content(topic, word_count):
# API call here
pass
Rate Limit Monitoring¶
Track Usage¶
Monitor your rate limit usage:
class RateLimitMonitor:
def __init__(self):
self.usage_history = []
def track_request(self, response):
headers = response.headers
usage = {
'timestamp': time.time(),
'limit': int(headers.get('X-RateLimit-Limit', 0)),
'remaining': int(headers.get('X-RateLimit-Remaining', 0)),
'reset': int(headers.get('X-RateLimit-Reset', 0))
}
self.usage_history.append(usage)
# Alert if approaching limit
if usage['remaining'] < usage['limit'] * 0.1: # Less than 10% remaining
self.send_alert(usage)
def send_alert(self, usage):
print(f"Warning: Only {usage['remaining']} requests remaining!")
Usage Analytics¶
Analyze your API usage patterns:
def analyze_usage(usage_history):
if not usage_history:
return
# Calculate average usage
total_requests = sum(1 for _ in usage_history)
avg_remaining = sum(u['remaining'] for u in usage_history) / len(usage_history)
# Find peak usage times
peak_times = [u['timestamp'] for u in usage_history if u['remaining'] < 10]
# Calculate utilization
utilization = (usage_history[0]['limit'] - avg_remaining) / usage_history[0]['limit']
return {
'total_requests': total_requests,
'average_remaining': avg_remaining,
'peak_times': peak_times,
'utilization_percentage': utilization * 100
}
Best Practices¶
Efficient API Usage¶
- Batch Requests: Combine multiple operations when possible
- Cache Responses: Cache frequently accessed data
- Optimize Queries: Use specific parameters to reduce processing
- Monitor Usage: Track your rate limit consumption
- Plan Ahead: Consider rate limits in your application design
Error Handling¶
- Implement Backoff: Use exponential backoff for retries
- Handle 429 Errors: Properly handle rate limit responses
- Monitor Headers: Check rate limit headers in responses
- Queue Requests: Implement request queuing for high-volume usage
- Graceful Degradation: Provide fallbacks when rate limited
Application Design¶
- Async Processing: Use asynchronous requests when possible
- Request Prioritization: Prioritize important requests
- Load Balancing: Distribute requests across time
- Circuit Breakers: Implement circuit breakers for failures
- Monitoring: Monitor rate limit usage and errors
Rate Limit by Endpoint¶
Content Generation Endpoints¶
| Endpoint | Free | Basic | Pro | Enterprise |
|---|---|---|---|---|
/api/blog-writer |
5/min | 30/min | 150/min | 500/min |
/api/linkedin-writer |
5/min | 30/min | 150/min | 500/min |
/api/seo-dashboard/analyze |
10/min | 60/min | 300/min | 1000/min |
Research Endpoints¶
| Endpoint | Free | Basic | Pro | Enterprise |
|---|---|---|---|---|
/api/research |
5/min | 20/min | 100/min | 300/min |
/api/keywords/research |
10/min | 50/min | 200/min | 500/min |
Analytics Endpoints¶
| Endpoint | Free | Basic | Pro | Enterprise |
|---|---|---|---|---|
/api/analytics |
20/min | 100/min | 500/min | 1000/min |
/api/performance |
10/min | 50/min | 200/min | 500/min |
Upgrading Plans¶
When to Upgrade¶
Consider upgrading if you: - Hit rate limits frequently: Consistently exceed your limits - Need higher throughput: Require more requests per minute - Have growing usage: Usage is increasing over time - Need priority support: Require dedicated support
Plan Comparison¶
| Feature | Free | Basic | Pro | Enterprise |
|---|---|---|---|---|
| Requests/min | 10 | 60 | 300 | 1,000 |
| Requests/day | 100 | 1,000 | 10,000 | 100,000 |
| Priority Support | ❌ | ❌ | ✅ | ✅ |
| Custom Limits | ❌ | ❌ | ❌ | ✅ |
| SLA | ❌ | ❌ | ✅ | ✅ |
Troubleshooting¶
Common Issues¶
Frequent Rate Limiting¶
- Check usage patterns: Analyze when you hit limits
- Optimize requests: Reduce unnecessary API calls
- Implement caching: Cache responses to reduce calls
- Consider upgrading: Evaluate if you need a higher plan
Inconsistent Limits¶
- Check endpoint limits: Some endpoints have different limits
- Verify plan: Ensure you're on the expected plan
- Contact support: Reach out if limits seem incorrect
Performance Issues¶
- Monitor response times: Check if rate limiting affects performance
- Implement queuing: Use request queuing for better performance
- Optimize code: Improve request efficiency
Getting Help¶
- Documentation: Check API documentation for specific limits
- Support: Contact support for rate limit questions
- Community: Join developer community for best practices
- Status Page: Check for any service issues
Need help with rate limiting? Contact Support or Upgrade Your Plan for higher limits!