Google AI Studio Published App Not Updating: Why Changes Aren’t Going Live

You’ve changed your Google AI Studio app, checked the preview and pressed Publish. AI Studio says the update is complete. But when you open the public link, your old design or broken feature is still there. You’ve republished, refreshed the browser and perhaps changed the same code again. Nothing seems to move.

When a Google AI Studio app doesn’t update after publishing, first establish whether the changed code reached the active Cloud Run deployment and whether the public URL is serving that revision. A saved preview, a completed build and a live release aren’t interchangeable. Caching can also obscure a successful update, but blaming the cache before checking the deployment can send you in circles.

Why can the preview and published app show different versions?

Google AI Studio’s Build mode provides a development preview and a publishing route to Cloud Run. Google explains the current workflow in its deployment documentation. The public application runs separately from the editing session.

That separation is useful, because visitors shouldn’t automatically see every experiment you make. It also introduces several points where an update can fail or go to the wrong place: the current source, build output, deployment target, active revision and browser response.

Reports in Google’s developer community during 2026 describe successful-looking publish operations that left the previous version live. Such accounts demonstrate a real symptom, not one confirmed universal Google fault. You still need evidence from the particular app to determine what happened.

Start with one harmless change you can identify

Before doing more prompting, decide which exact change should be visible. Choose a harmless piece of text or a recognisable UI adjustment that doesn’t alter data, permissions or payment behaviour. Record what the editor currently shows and when it was changed.

Then check whether that change exists in the saved project state. If your app has version history or GitHub integration enabled, note the relevant revision or commit. A change that appears temporarily in a preview isn’t necessarily the version the publisher is building.

Be particularly careful after restoring previous versions, remixing projects or switching between browser tabs. It’s possible to be looking at one AI Studio project while checking a public URL associated with another. Compare the project name, account and actual deployment destination.

Don’t add several changes just to create a more obvious difference. Every additional AI edit makes it harder to isolate the first release that went wrong.

Check that you’re publishing to the intended application

AI Studio can publish to Cloud Run and may offer a custom ai.studio URL. Google explains that deployments correspond to Cloud Run services and that URL assignments follow specific rules. Confirm the public address you’re testing belongs to the app you just updated.

Write down the current public URL, associated Google Cloud project where applicable, service name, region and last successful release details available in the interface. If there are multiple test apps or copies, label them clearly.

Compare the page at the provider URL and any branded domain or link you’ve been sharing. If one is current and the other is old, the problem may be routing or an intervening cache, not the AI-generated source code.

If both remain old, turn your attention to the deployment. Don’t change DNS or unpublish an app without first understanding where customers are being sent.

Did the deployment actually complete?

A green-looking button isn’t a substitute for checking the release result. Review the publish status and any available deployment logs. Look for errors during dependency installation, compilation, container startup or health checks. A build can appear to begin normally but fail before a replacement revision is serving requests.

When you have access to the connected Cloud Run service, confirm whether a new revision was created and is receiving traffic. If the service still routes all requests to an older revision, the latest files may exist without being the version visitors use.

Also distinguish a source-code problem from an infrastructure problem. If a newly deployed revision fails to start because it requires a missing environment variable, silently reprompting the UI won’t fix the runtime configuration. The actionable evidence is the failed revision and its logs.

Google’s Cloud Run troubleshooting guide explains the sorts of deployment and serving errors to investigate. Some Starter Tier deployments have fewer visible controls than a fully managed Google Cloud project, so don’t assume every user has the same console access.

Rule out caching with controlled tests

Caching is worth checking, but do it systematically. Open the exact public URL in a private browser window, then test from another device or network if possible. In your browser’s developer tools, inspect the document response, JavaScript assets and their cache headers.

A stale page may be served from a browser cache, CDN layer or service worker. If the application uses a service worker, check whether an older bundle is still controlling the page. Don’t delete application storage or unregister workers indiscriminately on a production device if that could remove unsynchronised offline records.

If an HTML document references an older script bundle, that suggests the published artefacts or cache are stale. If the new bundle is loaded but the UI still looks old, inspect the application’s routing, feature flags and runtime data.

Avoid repeatedly adding random query strings and then declaring the issue fixed. A cache-busted URL might prove that one layer stores stale assets, but you still need to correct the caching behaviour for ordinary visitors.

Check whether the new code depends on live configuration

Sometimes the latest version is present but its behaviour doesn’t match preview. The interface may look unchanged because the feature is controlled by an API response, environment variable, database value or logged-in user role.

For example, you might update a pricing panel in the code while the live app still requests prices from the production database. Or you may be viewing an old account record that doesn’t contain the newly introduced field.

Compare one specific browser request in preview and live. Record the endpoint, status, response shape and which environment it reaches. Keep keys and personal data out of screenshots shared for support.

AI Studio’s Build mode documentation describes server-side secrets and deployments. A runtime dependency that differs between the two environments can produce apparently stale functionality without a stale deployment.

Avoid these fixes until you’ve identified the cause

• Don’t delete and recreate the app to force an update. You may lose a working deployment and make the original URL harder to recover.
• Don’t rotate API keys at random. Key changes can break integrations and don’t guarantee that a different code revision will be served.
• Don’t make a private backend public because the new release has an access error.
• Don’t overwrite production records with whatever exists in the preview or test environment.
• Don’t keep reprompting unrelated code. You’re changing the evidence while still trying to understand the original release.

If you’ve already made several unsuccessful changes, stop and record the last revision that you know actually worked on the live URL.

What evidence should you collect before seeking help?

You don’t need to be a developer to prepare a useful fault report. Capture the intended public URL, approximate publication times, a description of the exact missing change, the project/version identifier and screenshots showing preview versus published results.

Where available, include the deployment status, service/revision identity, relevant build or runtime log messages, and the response to a fresh private-window test. Remove API credentials, user data and confidential headers.

This separates a failed publish from a stale route, cached asset or environment-specific behaviour. It also makes a Google support escalation much more precise if the fault is inside Google’s publishing infrastructure.

If the entire live app returns a 403 rather than an older working page, that’s a different diagnostic route. Read my guide to Google AI Studio 403 errors after publishing rather than treating permissions as a cache issue.

When should you stop republishing and get the release investigated?

Once you’ve confirmed the correct app, repeated the failure in a clean browser and checked whether a new revision is serving traffic, more blind publishing attempts are unlikely to tell you much. At that point a controlled diagnosis should establish whether the source, build, deployment, routing or runtime data is responsible.

A straightforward cache issue can be resolved once identified. A broken build or configuration change may need developer work. If Google’s publishing service is failing, it may require Google support. I won’t pretend an external infrastructure incident can be repaired from your website’s code.

For a commercial application, also think about customer trust. If you’ve promised that a booking, pricing or account fix is live and visitors continue to see the old problem, you need a release process that can prove what version is running.

Start with the actual revision serving the public URL. Preserve the last working release, collect the logs and change only the component shown to be at fault.

If your AI Studio project keeps reporting successful releases that never appear live, I can investigate the deployment path and recommend the next steps through a paid diagnostic. Any subsequent repair is quoted separately.