AI-powered cloud deployment
How to use
Zeabur.
Using plain language to configure application deployments and accessing multiple model providers through a single OpenAI-compatible API and usage ledger.
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 covers the whole path in one place: official access, a first session that produces something reviewable, the checks that make output trustworthy, and the permissions worth limiting before you connect real work.
AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.
Access & setup
Find, install, and sign in to Zeabur
Get into the official Zeabur experience with the right account and a setup you understand.
- 01
Start at https://zeabur.com/ and confirm the domain before entering account or payment information.
- 02
Availability: Zeabur is a browser-based cloud platform with Git-based deployment, an AI Assistant, AI Hub APIs, templates, and a public developer API.
- 03
A Zeabur account, verified billing or credits where required, a repository or deployment specification, and securely managed application secrets.
- 04
Sign in with the account you intend to keep using, then review plan, data, notification, and permission settings.
- 05
Run one low-risk test task before connecting sensitive files, repositories, or workspace data.
- Use official download pages.
- Review permissions during setup.
- Keep installers and applications updated.
First session
Your first useful Zeabur session
Learn the interaction loop using a small task with a clear outcome.
- 01
Choose a non-production repository or simple service and document its runtime, variables, region, and budget.
- 02
Ask Zeabur's AI Assistant to create a staging deployment, then review the proposed project and service configuration.
- 03
Enter required secrets through protected environment-variable controls and run build and health checks.
- 04
Verify the generated endpoint, logs, persistence, costs, and rollback path before considering a production domain.
- State the outcome before the background.
- Provide the real source material.
- Review the result before expanding the task.
Quality control
Check the quality of Zeabur output
Establish that a change is safe to run in production and reversible if it is not.
- 01
Restate the intended end state and the blast radius before applying anything.
- 02
Review the generated configuration line by line against the provider's current documentation.
- 03
Apply to a non-production environment first and confirm the observed result matches the intended one.
- 04
Confirm the rollback path works by actually exercising it, not by assuming it exists.
- 05
Check cost, scaling limits, and network exposure before the change reaches production traffic.
- Test the rollback, do not assume it.
- Check what a configuration exposes to the public internet.
- Watch cost and rate limits as closely as correctness.
Privacy & permissions
Use Zeabur safely
Keep credentials, network exposure, and cost under deliberate control.
- 01
Use scoped, short-lived credentials, and never paste production secrets into a prompt or config file.
- 02
Confirm what each change exposes publicly before applying it, especially storage, databases, and admin endpoints.
- 03
Separate environments so a mistake in development cannot reach production data.
- 04
Set billing alerts and hard quotas before enabling autoscaling or usage-based services.
- 05
Review audit logs and revoke access for integrations that are no longer in use.
- 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.
- Follow your organisation's approved-use policy.
- Never treat fluent output as authorization to act.
Core workflows
Step-by-step ways to use Zeabur for the work it does best.
Each workflow below has its own outcome, steps and review checkpoints.
Use plain-language deployment help while preserving explicit control over code, services, variables, domains, and environments.
↓ 02 WorkflowUse Zeabur AI Hub as a governed multi-model APIConnect an OpenAI-compatible application to Zeabur AI Hub and compare models without losing security or cost visibility.
↓Workflow 01
Deploy an application with Zeabur's AI Assistant
Use plain-language deployment help while preserving explicit control over code, services, variables, domains, and environments.
Outcome: Create a reviewable staging deployment before any AI-proposed infrastructure change reaches production.
- 01
Inventory the repository, runtime, database, environment variables, region, budget, domain, and production restrictions before opening the Agent.
- 02
Ask the AI Assistant for a staging deployment with explicit service names and limits, then review every proposed project and configuration change.
- 03
Provide secrets only through Zeabur's protected variable controls, use least-privilege external credentials, and never paste credentials into chat.
- 04
Inspect build and runtime logs, health checks, networking, storage persistence, generated domains, and behavior under restart before promotion.
- 05
Separate staging from production, bind a production domain only after approval, and record the final settings, costs, owner, and rollback procedure.
What good use looks like
- Deploy to staging first.
- Enter secrets only in protected controls.
- Review AI-proposed infrastructure changes.
Workflow 02
Use Zeabur AI Hub as a governed multi-model API
Connect an OpenAI-compatible application to Zeabur AI Hub and compare models without losing security or cost visibility.
Outcome: Ship a tested model integration with pinned behavior, protected keys, regional routing, and measurable usage.
- 01
Define the task, data classification, latency and quality targets, required modalities, region, budget, and evaluation cases before choosing a model.
- 02
Add limited credits, create a project-specific API key, store it in an environment secret, and select the closest approved AI Hub endpoint.
- 03
Configure the OpenAI-compatible base URL and an explicit supported model ID, then send a minimal non-sensitive request and handle API errors.
- 04
Benchmark candidate models on the same factual, safety, structured-output, latency, and cost cases while recording model IDs and settings.
- 05
Set application limits and fallbacks, monitor usage history for anomalies, rotate keys, and rerun the benchmark before changing models or regions.
What good use looks like
- Use one key per application.
- Pin and benchmark model IDs.
- Monitor credits and usage history.
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 guide.