How to deploy an AI-built app to production

Map what the application actually needs

A working browser screen can hide an incomplete backend. List every dependency: database, object storage, email service, model provider, authentication system, scheduled task and queue. Identify which calls happen in the browser and which must run on a trusted server. A secret bundled into client JavaScript is visible to visitors.

Record runtime requirements such as execution duration, memory, native libraries, persistent disk and inbound callbacks. A request that summarizes a short document may fit a synchronous server function. A multi-minute import may need a durable worker. Choose infrastructure after understanding that distinction.

Create a reproducible release

Put source, a dependency lockfile and build configuration under version control. Build from a clean checkout rather than relying on files generated during a coding session. Give the output an immutable identifier tied to the commit. The release record should explain which source and configuration reached production.

Separate environment-specific configuration from the artifact where your runtime supports it. A promotion should not unexpectedly pull a newer dependency or rebuild against a different toolchain. If your platform builds for each environment, record those inputs and verify that the resulting artifact matches the intended release.

Set up data, identity and configuration

Use persistent storage for anything the business needs to keep. Do not assume a server's local filesystem survives a restart or deploy. Define database migrations, backup retention, file upload limits and deletion behavior. Test the authorization rules using two distinct accounts so one customer cannot access another customer's records.

Keep production credentials in the server's secret mechanism. Use separate credentials and datasets for preview environments. Verify callback URLs, allowed origins and authentication redirects on the real hostname; these often behave differently from localhost.

• Database migration runs against the intended environment.
• Uploaded files survive a restart and remain access-controlled.
• Model and payment credentials stay server-side.
• Test users cannot read each other's data.
• Scheduled jobs and webhooks have a retry strategy.

Release through checks and observable verification

Run checks that exercise the product's real failure modes: login, a paid action, a denied request and an external dependency failure. Build an application health check that confirms readiness to serve the intended traffic. A process being alive does not prove its database connection or essential configuration works.

For suitable systems, expose a limited portion of traffic to a new release and compare customer-facing signals before expanding. Google's SRE guidance describes canarying as a way to evaluate a partial rollout. It requires meaningful signals and a control group; simply naming an environment canary does not supply that evidence.

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Prepare the first incident before the first launch

Write down how to identify the active release, disable a failing feature and restore service. Verify whether a rollback remains compatible with database changes. Name the person who receives alerts and the fallback contact. Keep a small runbook where someone can use it when the original builder is unavailable.

Launch to a limited audience, inspect real errors and cost per successful action, then expand. Latifold is designed to let your existing coding agent request infrastructure context and governed changes in an existing AWS, Azure or Google Cloud account. It does not replace application release testing or transfer responsibility for workload incidents. Confirm supported operations and retain a clear response owner.