klingai-reference-architecture

name	klingai-reference-architecture
description	Production reference architecture for Kling AI video generation platforms. Use when designing scalable systems. Trigger with phrases like 'klingai architecture', 'kling ai system design', 'video platform architecture', 'klingai production setup'.
allowed-tools	Read, Write, Edit, Bash(npm:*), Grep
version	1.18.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","kling-ai","architecture","scaling"]
compatibility	Designed for Claude Code, also compatible with Codex and OpenClaw

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(, )

    
    job_id =  queue.enqueue({
        : req.prompt,
        : req.model,
        : (req.duration),
        : req.mode,
    })

     {: job_id, : , : credits}