How Do I Build a Queue for AI Video Generation?

Overview

Build an AI video queue by accepting user requests into your own durable job table, then letting workers submit provider tasks at a controlled rate. Store a local job ID before making an API call and a prediction ID when one is returned. The queue protects your app from traffic bursts and gives users a stable place to see progress.

Define states such as waiting, submitting, provider-running, completed, failed and needs-review. The worker should validate inputs and rights, select a route, and check current account concurrency before submitting. WaveSpeedAI’s FAQ describes concurrent individual calls rather than a dedicated batch-submission endpoint, and its account tiers have differing limits. After submission, the worker can poll the result or process a supported webhook. Keep the provider task ID and outputs attached to the local job; store final approved media outside temporary result URLs.

Make the transition from “submitting” robust. If a network timeout hides the POST outcome, do not blindly create another task: WaveSpeedAI warns that the first may already have been accepted and billed. Reconcile or escalate the ambiguous job, while safe result reads can use bounded retries. Give time-sensitive jobs an explicit priority policy rather than letting every customer request jump the line.

Test the queue with a burst and a simulated worker crash. A good queue resumes observable work and never turns an uncertain submission into an invisible duplicate.

Source: WaveSpeedAI FAQ; WaveSpeedAI account levels; WaveSpeedAI API quickstart. Source check: 2026-09-29.