An 18-Point Checklist Before Shipping an AI Video API

From input validation and async state to safety, spend limits, retention, and graceful degradation.

Key takeawayGeneration success is only an intermediate state. Production completion also requires safe delivery, traceable spend, and recoverable failure.

Request entry

Validate file type, size, dimensions, and reachability.
Limit prompt length and remove invisible characters.
Generate a stable request_id for every submission.
Use an idempotency key to prevent duplicate jobs.
Apply rate limits by user, team, and project.

Job execution

Use explicit pending, running, and terminal states.
Set separate connection, queue, and total-job timeouts.
Retry only recoverable errors with backoff and jitter.
Define a primary route, fallback, and stop condition.
Record version, parameters, routing reason, and provider job ID.

Result delivery

Verify webhook signatures and deduplicate events.
Copy results into owned storage instead of relying on short-lived URLs.
Validate media type, size, duration, and decodability.
Run safety and brand-policy checks before publishing.
Provide clear UI for failed, timed-out, and under-review states.

Operations and governance

Set per-job, per-user, and daily budget ceilings.
Define retention for inputs, prompts, logs, and outputs.
Rehearse provider incidents, price changes, and model retirement.

ModelRush recommends turning this checklist into a release gate with test evidence for every item. The most overlooked cases are not the happy path but duplicate callbacks, expired results, user cancellation, and situations where a provider bills after the client times out.

During the first production week, review failure classes, P95 delivery time, usable-output rate, and unit cost daily before increasing traffic.