You can use Kling AI via API by following the common pattern used by hosted video generation services: create an async generation task, then poll (or receive a webhook) for completion, and finally download the result. In practical terms, your backend sends an authenticated POST request containing your prompt and parameters (for text-to-video) or a reference image plus prompt (for image-to-video). The API returns a task_id (or similar). Your system then checks task status until it becomes succeeded or failed, at which point you fetch the output URL(s). This async design is important because video generation can take long enough that synchronous HTTP requests would time out. If you’re building a product, you should plan your UI around job states: queued → running → finished, with clear failure reasons and retry options.
Implementation details vary by provider and model version, but the core request shape is usually consistent. You’ll typically supply:
- Authentication: an API key (often in
Authorization: Bearer ...or a vendor-specific header) - Prompt: text describing subject, scene, camera, and motion
- Optional negative prompt: what must not appear (text overlays, logos, extra limbs)
- Duration / length: commonly short clips like 5s or 10s
- Aspect ratio:
16:9,9:16, or1:1 - Mode/quality: standard vs pro/high quality
- Image input (for image-to-video): either a direct upload or a URL to a stored image Operationally, you should add idempotency (so retries don’t create duplicate jobs), rate-limit handling (429 responses), and structured error parsing (invalid params, insufficient credits). If the API supports webhooks, prefer them over polling: webhooks reduce latency and cost, and they scale better when you have many concurrent jobs.