Deploying a ChatGPT app: a release and submission checklist

Prepare a ChatGPT app release, compare saved versions and collect submission evidence. Understand Vercel deployment, rollback and OpenAI publication.

What does a successful deployment mean?

A successful Vercel deployment produces a hosted technical release. It does not establish that OpenAI has approved the app, that users have installed it or that it is publicly listed. Treat those as separate milestones with separate evidence.

DraftYourApp keeps saved drafts, immutable deployment releases and distribution evidence so you can track the difference. This is especially useful when the editor has moved ahead of the version currently deployed.

Before deploying: review the actual contract

• Check the English project name and presentation content, supported runtime configuration and selected tool inputs/outputs.
• Preview representative interactions and explicitly test real operations where applicable.
• Confirm required provider settings and connection permissions for the selected deployment path.
• Review the release artifacts rather than assuming every editor configuration can run in production.

Runtime capability checks reject unsupported configurations before deployment. A failed gate is useful information about the contract you need to change; it should not be bypassed by editing the displayed deployment status.

Use release history to understand an update

A draft is editable; a saved deployment release captures the version that was shipped. Compare releases when investigating a changed tool, widget or configuration. Keep test evidence tied to the version you actually exercised.

Where a compatible immutable deployment bundle is available, rollback deploys the saved files and settings as a new release. It preserves the editor draft. Older releases without a bundle, or incompatible legacy MCP bundles, may require a rebuild instead.

Prepare submission evidence separately

• Test representative tools and UI in ChatGPT with the appropriate workspace permissions.
• Check the submitter’s permissions and required identity verification.
• Verify the production HTTPS MCP endpoint and complete applicable domain verification and tool scanning.
• Prepare listing details, support and policy URLs, example prompts, screenshots or demo material and required review cases.

DraftYourApp can export a portable Plugin package and prepare a submission dossier. Its per-release checklist records external checks and evidence links supplied by the owner. Recording a check does not submit the project or certify that OpenAI accepted it.

Follow the current OpenAI submission flow

OpenAI’s current submission documentation describes the portal workflow and publication requirements. Check it before submitting because these requirements can change. The builder’s technical deployment and exported package are inputs to that process, not approval guarantees.

A practical release handoff

Give the reviewer the release identifier, hosted endpoint, tool contracts, representative test results and links to completed external checks. If a problem requires rollback, identify the saved compatible bundle rather than regenerating an old version from today’s draft.

Start with one well-tested task and expand after you have evidence that it works in the intended host. This keeps both the submission story and future regression checks focused.