How Can Teams Run Batch AI Image Generation Through an API?

Overview

Teams can run batch AI image generation by submitting a controlled set of individual API tasks and tracking each result. Do not assume that a platform offers a special one-call batch endpoint: WaveSpeedAI’s current FAQ says it does not have a dedicated batch-submission API, while concurrent individual requests are possible within account limits.

Start with a manifest of the images you need: source asset or prompt, model route, output format, campaign ID and approval owner. Your worker reads one row, submits that route’s required fields, and records the returned prediction ID before moving to the next queued item. Set concurrency below the account’s current allowance and adjust it from observed errors and wait times. Persist per-item states such as queued, running, completed, failed and approved so one bad image cannot erase progress for the entire set.

Separate technical completion from creative acceptance. A completed image may have misspelled packaging or an unsuitable crop. Keep failed tasks available for a targeted retry and rejected creative available for a revised prompt; they are different events with different cost implications. Export an itemized report that connects every approved file to its input and route.

For a first run, use a small representative set rather than a full catalog. It reveals whether upload, throttling, result retrieval and human review can keep pace before you increase throughput.

When the pilot finishes, reconcile submitted IDs against completed files and billed tasks. That check exposes missing callbacks, duplicate submissions and unreviewed outputs before the same gaps multiply across a larger catalog.

Source: WaveSpeedAI FAQ; WaveSpeedAI account levels and rate limits; WaveSpeedAI result API. Source check: 2026-09-29.