Batch image generation API: how many images can run at once?

Sume queues extra generation jobs instead of rejecting them. The per-plan concurrency and queue table, the 429 queue_full case, and how to size an image batch.

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How many images run at once depends on your plan: Sume's docs list 1 processing job on Free, 4 on Pro, 8 on Startup and 20 on Scale, and valid extra jobs wait as queued instead of failing. Only when the queue is also full does a submit return 429 queue_full.

From the Generation admission and Image API docs, read 2026-09-29. The dashboard Concurrency tab and the generation_limits field in submit responses are the live source, and the docs say to prefer them over the static table.

What are the limits per plan?

Default generation limits from the Generation admission docs, read 2026-09-29.
PlanProcessing at onceQueue capacityAccepted jobs
Free156
Pro42024
Startup84048
Scale20100120

What happens when I submit more than the limit?

Valid jobs are accepted as queued while queue capacity remains, then move to processing as slots open. queued is not a failure. When both are full the submit fails with 429 queue_full; wait for jobs to finish or cancel queued ones, then retry with the same idempotency key.

How should I submit a batch?

Use mode: "async" with an Idempotency-Key per item, store each job id, and poll with backoff. Do not resubmit a paid request just because your local worker timed out.

for i in 1 2 3 4; do
  curl -s -X POST https://api.sume.com/v1/images \
    -H "Authorization: Bearer $SUME_API_KEY" \
    -H "Content-Type: application/json" \
    -H "Idempotency-Key: catalog-batch-001-item-$i" \
    -d "{\"model\": \"google/nano-banana-2\", \"prompt\": \"Studio photo, item $i\", \"mode\": \"async\"}"
done

Does a queued job cost anything while it waits?

A submit needs the estimate reserved from your balance, and without enough balance it returns 402 insufficient_credits before any provider work starts. A job that fails or is canceled is not billed for images. Check GET /v1/balance before a large batch.

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