Kling AI Reference Architecture

Overview

Production architecture for video generation platforms built on Kling AI. Covers API gateway, job queue, worker pool, storage, and monitoring layers.

Architecture Diagram

User Request
    |
[API Gateway / Load Balancer]
    |
[Application Server]
    |--- validate prompt & estimate cost
    |--- enqueue job to Redis/SQS
    |
[Job Queue (Redis / SQS / Pub/Sub)]
    |
[Worker Pool (N workers)]
    |--- generate JWT token
    |--- POST https://api.klingai.com/v1/videos/text2video
    |--- receive task_id
    |--- register callback_url OR poll
    |
[Webhook Receiver / Poller]
    |--- receive completion callback
    |--- download video from Kling CDN
    |--- upload to S3/GCS
    |--- update job status in DB
    |--- notify user
    |
[Object Storage (S3 / GCS)]
    |
[CDN (CloudFront / Cloud CDN)]
    |
User views video

Component Details

API Layer

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

app = FastAPI()

class VideoRequest(BaseModel):
    prompt: str
    model: str = "kling-v2-master"
    duration: int = 5
    mode: str = "standard"

@app.post("/api/videos")
async def create_video(req: VideoRequest):
    # 1. Validate
    if len(req.prompt) > 2500:
        raise HTTPException(400, "Prompt exceeds 2500 chars")

    # 2. Estimate cost
    credits = estimate_credits(req.duration, req.mode)
    if not budget_guard.check(credits):
        raise HTTPException(402, "Budget exceeded")

    # 3. Enqueue
    job_id = await queue.enqueue({
        "prompt": req.prompt,
        "model": req.model,
        "duration": str(req.duration),
        "mode": req.mode,
    })

    return {"job_id": job_id, "status": "queued", "estimated_credits": credits}

Worker Service

import redis
import json

class VideoWorker:
    def __init__(self, kling_client, storage_client, redis_url="redis://localhost"):
        self.kling = kling_client
        self.storage = storage_client
        self.redis = redis.Redis.from_url(redis_url)

    def process_loop(self):
        while True:
            raw = self.redis.brpop("kling:jobs:pending", timeout=5)
            if not raw:
                continue

            job = json.loads(raw[1])
            try:
                # Submit to Kling API
                result = self.kling.text_to_video(
                    job["prompt"],
                    model=job["model"],
                    duration=int(job["duration"]),
                    mode=job["mode"],
                    callback_url=os.environ.get("WEBHOOK_URL"),
                )

                # If using polling (no callback)
                if isinstance(result, dict) and "videos" in result:
                    video_url = result["videos"][0]["url"]
                    stored_url = self.storage.download_and_upload(video_url, job["id"])
                    self.redis.publish("kling:events", json.dumps({
                        "type": "completed",
                        "job_id": job["id"],
                        "video_url": stored_url,
                    }))

            except Exception as e:
                self.redis.lpush("kling:jobs:failed", json.dumps({
                    **job, "error": str(e)
                }))

Scaling Guidelines

Component	Scaling Strategy
Workers	Scale by queue depth (1 worker per 3 concurrent API tasks)
API servers	Horizontal, behind load balancer
Redis	Single instance for <1K jobs/day, cluster for more
Storage	S3/GCS scales automatically
CDN	CloudFront/Cloud CDN for global delivery

Concurrency Limits by Tier

Tier	Max Concurrent Tasks	Workers Needed
Free	1	1
Standard	3	1
Pro	5	2
Enterprise	10+	3-4

Docker Compose Setup

# docker-compose.yml
services:
  api:
    build: ./api
    ports: ["8000:8000"]
    environment:
      - REDIS_URL=redis://redis:6379
      - KLING_ACCESS_KEY=${KLING_ACCESS_KEY}
      - KLING_SECRET_KEY=${KLING_SECRET_KEY}

  worker:
    build: ./worker
    deploy:
      replicas: 2
    environment:
      - REDIS_URL=redis://redis:6379
      - KLING_ACCESS_KEY=${KLING_ACCESS_KEY}
      - KLING_SECRET_KEY=${KLING_SECRET_KEY}
      - S3_BUCKET=${S3_BUCKET}

  webhook:
    build: ./webhook
    ports: ["8001:8001"]
    environment:
      - REDIS_URL=redis://redis:6379

  redis:
    image: redis:7-alpine
    volumes: ["redis-data:/data"]

volumes:
  redis-data:

Resources

• API Reference
• Developer Portal