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Deployments

Trigger builds, follow live logs, and roll back releases.

A deployment is one build-and-run cycle of your project. The project’s Deployments tab lists the full history with status, source, branch, duration, and age, and lets you manage each one.

Deployment sources

Git. Point the project at a repository URL and branch (or pick a repo through the GitHub integration). New pushes rebuild automatically when the webhook is connected — toggle Auto-deploy on the Deployments tab. Manual Deploy rebuilds from the latest commit or a specific SHA, with an option to clear the BuildKit cache.

Upload. Drop a .zip (at creation) or .zip / .tar / .tar.gz (on the Deployments tab) to deploy local code. An upload project can be migrated to Git later from the same tab without losing history.

Docker Compose. Repos with a docker-compose.yml build all services together; only the mapped ingress services get public URLs (see Project Configuration).

Lifecycle

Deployments move through pending → building → deploying → running. A failed build ends in failed; you can cancel a queued or active build. Only one deployment is live per project — each successful deploy replaces the previous container.

Click a row to expand its build logs, streamed live while the build runs and stored afterwards. The Runtime Logs view (under the Logs tab) shows the running container’s stdout instead.

Redeploy, rollback, delete

  • Redeploy clones the currently running deployment (same image and config) — the standard way to apply changed env vars or volumes.
  • Rollback points the route back at an earlier deployment’s image. Images are kept, so rollback is instant and does not rebuild.
  • Delete destroys a deployment’s containers. The running deployment cannot be deleted.

Failed builds and AI diagnosis

If a deployment fails, the log header shows a Diagnose button. It opens the AI diagnosis sheet: pick a configured LLM provider and model, and the agent investigates the logs, decides whether the fault is your code or the platform, and can open a fix pull request on your repo. See AI Diagnosis.

Next steps