Kling AI Rate Limits
Overview
Kling AI enforces rate limits per API key. When exceeded, the API returns 429 Too Many Requests. This skill covers detection, backoff strategies, request queuing, and concurrent job management.
Rate Limit Tiers
| Tier | Concurrent Tasks | Requests/Min | Notes |
|---|
| Free | 1 | 10 | 66 daily credits cap |
| Standard | 3 | 30 | Per API key |
| Pro | 5 | 60 | Per API key |
| Enterprise | 10+ | Custom | Contact sales |
Exponential Backoff with Jitter
import time, random, requests
def exponential_backoff(attempt: int, base: float = 1.0, max_wait: float = 60.0) -> float:
"""Calculate wait time with jitter to avoid thundering herd."""
wait = min(base * (2 ** attempt), max_wait)
jitter = random.uniform(0, wait * 0.5)
return wait + jitter
def request_with_retry(method, url, headers, json=None, max_retries=5):
for attempt in range(max_retries + 1):
response = method(url, headers=headers, json=json, timeout=30)
if response.status_code == 429:
if attempt == max_retries:
raise RuntimeError("Rate limit: max retries exceeded")
wait = exponential_backoff(attempt)
print(f"429 rate limited. Waiting {wait:.1f}s (attempt {attempt + 1})")
time.sleep(wait)
continue
if response.status_code >= 500:
if attempt == max_retries:
response.raise_for_status()
time.sleep(exponential_backoff(attempt, base=2.0))
continue
response.raise_for_status()
return response
raise RuntimeError("Unreachable")
Concurrent Task Limiter (asyncio)
import asyncio
class TaskLimiter:
"""Limit concurrent Kling AI tasks to stay within API tier."""
def __init__(self, max_concurrent: int = 3):
self._semaphore = asyncio.Semaphore(max_concurrent)
self._active = 0
async def submit(self, coro):
async with self._semaphore:
self._active += 1
try:
return await coro
finally:
self._active -= 1
@property
def active_count(self) -> int:
return self._active
limiter = TaskLimiter(max_concurrent=3)
tasks = [limiter.submit(generate_video(p)) for p in prompts]
results = await asyncio.gather(*tasks, return_exceptions=True)
Rate Limit Monitor
class RateLimitMonitor:
"""Track API call frequency and warn before hitting limits."""
def __init__(self, max_per_minute: int = 30):
self.max_per_minute = max_per_minute
self._calls = []
def record_call(self):
now = time.time()
self._calls = [t for t in self._calls if now - t < 60]
self._calls.append(now)
@property
def usage_pct(self) -> float:
now = time.time()
recent = sum(1 for t in self._calls if now - t < 60)
return (recent / self.max_per_minute) * 100
def wait_if_needed(self):
if self.usage_pct > 80 and self._calls:
wait = 60 - (time.time() - self._calls[0])
if wait > 0:
()
time.sleep(wait)
Request Queue Pattern
from collections import deque
import threading
class RequestQueue:
"""FIFO queue with rate-limit-aware dispatch."""
def __init__(self, client, max_per_minute: int = 30):
self.client = client
self.interval = 60.0 / max_per_minute
self._queue = deque()
def enqueue(self, endpoint: str, body: dict, callback=None):
self._queue.append((endpoint, body, callback))
def process_all(self):
while self._queue:
endpoint, body, callback = self._queue.popleft()
try:
result = self.client._post(endpoint, body)
if callback:
callback(result, error=None)
except Exception as e:
if callback:
callback(None, error=e)
time.sleep(self.interval)
Error Reference
| Scenario | HTTP Code | Action |
|---|
| Soft rate limit | 429 + Retry-After | Wait specified seconds |
| Hard rate limit | 429 no header | Backoff from 1s, double each attempt |
| Concurrent limit hit | 429 or task rejection | Wait for active tasks to complete |
| Burst detection | Multiple 429s | Aggressive backoff (30-60s) |
Resources
