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Engineering
Published Jun 28, 2026
7 min read
Batch Image Generation APIs: Queues, Idempotency, Budgets, and Partial Failure
Design batch image jobs that can pause, replay, enforce limits, and handle partial success.
Key takeawayA batch is not ten thousand images. It is ten thousand independently traceable and recoverable jobs.
Why this deserves its own decision
Batch generation exposes limits, provider capacity, duplicate submission, runaway spend, and partial failure at once. With only batch-level state, one failed child can force a full rerun, creating duplicate cost and ambiguous results.
Decision framework
- Give every input a stable job_id and idempotency key.
- Set concurrency, rate, per-job, and total batch budgets.
- Replay only failed or rejected children.
Putting it into a ModelRush workflow
A queue expands the batch into ModelRush child jobs. State records input, template, model, attempts, cost, and result. The scheduler controls concurrency using provider health and budget. Batch completion allows partial success and emits a repair list.
What to measure after launch
- Distribution of success, retry, rejection, and terminal failure.
- Duplicate billing and duplicate result count.
- Batch completion time and peak spend rate.
A batch is not ten thousand images. It is ten thousand independently traceable and recoverable jobs.
Next steps
Move straight from this article to model details, current pricing, API documentation, and the Playground.Open the API documentation
Map the article's architecture and reliability ideas to requests, job states, and errors.Keep reading
Continue building the surrounding decisions in your multi-model stack.
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