How Do I Integrate an AI Video API into My App?

Overview

Integrate an AI video API as a background job, not a page request that waits for a finished movie. Keep the API key server-side, choose a route that matches your inputs, submit the task once, save the prediction ID and update the user interface as the task moves to completion or failure. That pattern works for both prompt-led and image-led video workflows.

Start with the product contract. Decide whether users provide only a scene description or an approved starting image, what aspect ratio they need, and which controls the chosen model actually exposes. WaveSpeedAI lists text-to-video and image-to-video as different route types. If an input image is uploaded, use the current upload flow and retain its provenance and permission record. Validate files and policy-sensitive prompts before sending a billable task.

For job orchestration, persist a local ID, model route, submitted settings, provider task ID, timestamps and terminal result. WaveSpeedAI documents polling and an HTTPS webhook path for asynchronous results. Make callback handling idempotent, and reconcile missed callbacks by checking the result endpoint. If the submission outcome is unclear after a network timeout, investigate the recorded task state before deciding whether to retry.

Once a clip is approved, copy it into your own media storage and serve that asset through your application. Temporary provider output URLs are delivery mechanisms, not a durable video library. Launch with one well-observed route before adding a model switcher.

Source: WaveSpeedAI API quickstart; WaveSpeedAI video generator guide; WaveSpeedAI upload API; WaveSpeedAI webhook guide; WaveSpeedAI result API. Source check: 2026-09-29.