The outcome

Create a reviewable staging deployment before any AI-proposed infrastructure change reaches production.

Zeabur is a AI-assisted deployment platform and unified multi-model API hub. Using plain language to configure application deployments and accessing multiple model providers through a single OpenAI-compatible API and usage ledger. This guide narrows that broad capability into one repeatable outcome, with checkpoints that keep the source material and your judgment in the loop.

Before you begin

Set the boundary before the tool starts.

Choose one real task, identify who will use the result, and decide what evidence or test will make the result acceptable. Gather only the source material needed for that task. If the work contains confidential, personal, regulated, or client-owned information, confirm that the platform and account are approved before sharing it.

Troiana principle

AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.

Step by step

A workflow you can repeat.

  1. 01

    Inventory the repository, runtime, database, environment variables, region, budget, domain, and production restrictions before opening the Agent.

  2. 02

    Ask the AI Assistant for a staging deployment with explicit service names and limits, then review every proposed project and configuration change.

  3. 03

    Provide secrets only through Zeabur's protected variable controls, use least-privilege external credentials, and never paste credentials into chat.

  4. 04

    Inspect build and runtime logs, health checks, networking, storage persistence, generated domains, and behavior under restart before promotion.

  5. 05

    Separate staging from production, bind a production domain only after approval, and record the final settings, costs, owner, and rollback procedure.

Working standard

What good use looks like.

  • Deploy to staging first.
  • Enter secrets only in protected controls.
  • Review AI-proposed infrastructure changes.

AI-proposed infrastructure and model access can create services, spend credits, expose secrets, or change production behavior. Review every change, isolate staging and production, keep credentials out of chat and source control, use least-privilege keys, pin model and region settings, monitor usage history, and retain a tested rollback path.

Official references

Check the current product documentation.

Features, plan limits, availability, and data controls change. These official pages are the starting points used for this collection.