Rate Limits and Throttling

Every FourA API request passes through rate limiting. You'll encounter two types of limits: concurrency (simultaneous requests) and RPM (requests per minute). Both are enforced per service.

How Rate Limits Work

The API enforces limits at two levels:

1. Overall API limit: applies to all requests regardless of endpoint.
2. Per-service limit: applies separately to each endpoint (single, proxy, browser).

Each level tracks two metrics:

• Concurrency: how many requests are running at the same time.
• RPM: how many requests you've sent in the last 60 seconds.

A request must pass both the overall API check and the service-specific check before it reaches the backend.

Rate Limit Responses

When you hit a limit, the API returns a JSON response with your current usage and the limits in effect.

429: RPM Exceeded

{
  "error": "Rate limit exceeded",
  "status": 429,
  "service": "single",
  "retryAfter": 5,
  "current": {
    "concurrency": 12,
    "rpm": 3000
  },
  "limits": {
    "maxConcurrency": 500,
    "maxRpm": 3000
  }
}

You've sent too many requests in the last minute. Wait for the retryAfter period before sending more.

503: Concurrency Exceeded

{
  "error": "Service at capacity",
  "status": 503,
  "service": "proxy",
  "retryAfter": 2,
  "current": {
    "concurrency": 500,
    "rpm": 1200
  },
  "limits": {
    "maxConcurrency": 500,
    "maxRpm": 3000
  }
}

Too many requests are running at the same time. Some of your earlier requests haven't finished yet.

Response Fields

Handling Rate Limits

Use the retryAfter field to implement backoff:

import requests
import time

def fetch(url, max_retries=5):
    for attempt in range(max_retries):
        resp = requests.post(
            "https://eu.api.foura.ai/api/single/",
            headers={
                "X-API-Key": "YOUR_API_KEY",
                "Content-Type": "application/json"
            },
            json={"method": "GET", "url": url}
        )

        if resp.status_code in (429, 503):
            data = resp.json()
            wait = data.get("retryAfter", 5)
            time.sleep(wait)
            continue

        return resp

    raise Exception("Max retries exceeded")

Tips

• Check the current fields in rate limit responses to understand your usage patterns.
• If you're consistently hitting concurrency limits, reduce the number of parallel requests.
• If you're hitting RPM limits, add a small delay between requests or batch them over a longer window.
• The retryAfter value varies by limit type: 2 seconds for concurrency, 5 seconds for RPM.

Related

• API Endpoints: Full parameter reference
• Error Handling: All error codes and responses
• Troubleshooting: Common problems and fixes