Rate Limits & Pagination

Rate Limits

Rate limits are per API key and measured as requests per minute. Different plan tiers have different limits. Rate limits apply to all API endpoints uniformly — there are no per-endpoint limits.

Limits by Plan

Rate Limit Headers

Every API response includes headers that tell you about your current rate limit status:

Example response with rate limit headers:

HTTP/1.1 200 OK
X-RateLimit-Limit: 200
X-RateLimit-Remaining: 198
X-RateLimit-Reset: 1705312800
Content-Type: application/json

Handling Rate Limits

When you receive a 429 response, stop sending requests and wait for the duration specified in the Retry-After header. Implement exponential backoff in your client to gracefully handle rate limits without hammering the API. For high-throughput use cases, distribute requests evenly across the minute rather than bursting them all at once.

Python Retry Pattern

import time
import requests


def api_request_with_retry(url, headers, json_data, max_retries=3):
    for attempt in range(max_retries):
        response = requests.post(url, headers=headers, json=json_data)
        if response.status_code == 429:
            retry_after = int(response.headers.get("Retry-After", 2 ** attempt))
            time.sleep(retry_after)
            continue
        return response
    raise Exception("Max retries exceeded")

Pagination

The job listing endpoint (GET /v1/jobs) uses cursor-based pagination for efficient traversal of large result sets.

How Cursor Pagination Works

Your first request returns the first page of results plus a next_cursor value. Pass this cursor as a query parameter to get the next page. Continue until has_more is false, which indicates you’ve reached the end of the result set.

Cursor-based pagination is more reliable than offset pagination because it handles data changes (new or deleted items) gracefully. With offset pagination, inserting a new row can cause you to see duplicates or skip items. Cursors also perform consistently regardless of how deep you paginate — fetching page 1,000 is just as fast as fetching page 1.

Example

First request:

curl "https://videoconduit.com/v1/jobs?limit=10" \
  -H "Authorization: Bearer vc_your_api_key"

Response:

{
  "items": ["...10 jobs..."],
  "next_cursor": "eyJjcmVhdGVkX2F0IjogIjIwMjUtMDEtMTRUMTI6MDA6MDBaIn0=",
  "has_more": true
}

Next page — pass next_cursor as the cursor parameter:

curl "https://videoconduit.com/v1/jobs?limit=10&cursor=eyJjcmVhdGVkX2F0IjogIjIwMjUtMDEtMTRUMTI6MDA6MDBaIn0=" \
  -H "Authorization: Bearer vc_your_api_key"

Pagination Parameters

Paginating Through All Jobs

import requests

headers = {"Authorization": "Bearer vc_your_api_key"}
cursor = None
all_jobs = []

while True:
    params = {"limit": 50}
    if cursor:
        params["cursor"] = cursor

    response = requests.get(
        "https://videoconduit.com/v1/jobs",
        headers=headers,
        params=params,
    )
    data = response.json()
    all_jobs.extend(data["items"])

    if not data["has_more"]:
        break
    cursor = data["next_cursor"]

print(f"Total jobs: {len(all_jobs)}")