The outcome

Ship a tested model integration with pinned behavior, protected keys, regional routing, and measurable usage.

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

    Define the task, data classification, latency and quality targets, required modalities, region, budget, and evaluation cases before choosing a model.

  2. 02

    Add limited credits, create a project-specific API key, store it in an environment secret, and select the closest approved AI Hub endpoint.

  3. 03

    Configure the OpenAI-compatible base URL and an explicit supported model ID, then send a minimal non-sensitive request and handle API errors.

  4. 04

    Benchmark candidate models on the same factual, safety, structured-output, latency, and cost cases while recording model IDs and settings.

  5. 05

    Set application limits and fallbacks, monitor usage history for anomalies, rotate keys, and rerun the benchmark before changing models or regions.

Working standard

What good use looks like.

  • Use one key per application.
  • Pin and benchmark model IDs.
  • Monitor credits and usage history.

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.