OpenAI Models on Vertex AI: Setup and Access

Updated 9 November 2026 · Standard formula, shown below

You can access supported OpenAI models through Google Cloud’s Vertex AI Model Garden, using Google Cloud authentication and Vertex AI APIs rather than an OpenAI API key. This guide covers the prerequisites, model deployment, and a basic API request; model availability and exact console labels can vary by region and account.

What You’ll Need

A Google Cloud project with billing enabled and permission to use Vertex AI. An administrator may need to grant roles that allow you to enable services, deploy models, and make predictions.
Access to the OpenAI model listing in Vertex AI Model Garden. Availability, preview status, regions, and any required access approval can differ. Check the model card before proceeding.
The Google Cloud CLI (gcloud) for the command-line example, or access to the Google Cloud console. For application calls, use an appropriate Google Cloud identity and supported client library or REST endpoint.
A selected Vertex AI region and a deployment plan. The model card specifies the supported location and deployment requirements.

Step-by-Step

Confirm the model is available. Open the Google Cloud console, select your project, and go to Vertex AI Model Garden. Search for the OpenAI model you want. Open its model card and read the provider, availability, supported regions, input limits, pricing information, and deployment instructions. Do not assume every OpenAI model or feature is offered in every project or location.
Check access and permissions. If the model card offers an access request or requires accepting terms, complete that process using the account and project intended for use. Ask your cloud administrator to grant the least-privilege permissions needed to view and deploy the model and call its endpoint. Organization policies can also restrict model use.
Choose the project and region. In the console, verify the selected project. Use a location supported by the model card and keep it consistent across deployment and requests. If using the CLI, set your project and region, replacing the example values with your own:
```
gcloud config set project PROJECT_ID
gcloud config set ai/region REGION
```
The Vertex AI regional setting is used by some Google Cloud commands; follow the model’s instructions if they specify a different location or command.
Enable the Vertex AI API. In the console, open APIs & Services, choose Library, search for Vertex AI API, and select Enable. Alternatively, run gcloud services enable aiplatform.googleapis.com for the selected project. Enabling services usually requires a project-level permission. Wait for the operation to complete before deploying.
Deploy the model from Model Garden. Return to the model card and choose the deployment or use option shown there. Review the serving location, machine or capacity choices if shown, and estimated charges before confirming. Follow any consent or provider-specific prompts. Deployment may take several minutes, and some offerings may require an additional approval step. Record the endpoint or resource details displayed when deployment finishes.
Authenticate as a Google Cloud user or service account. For local testing, sign in with the CLI using gcloud auth application-default login. In a Google Cloud application, prefer its attached service account and grant it the required Vertex AI permissions. Avoid putting service-account keys or access tokens in source code, browser code, or public repositories.
Send a test request. Use the method shown on the model card, which may provide a REST example or a client-library example. For a deployed endpoint, requests generally go through Vertex AI’s prediction API and include the endpoint resource name and a payload matching the model’s documented schema. A typical REST URL pattern is https://REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/REGION/endpoints/ENDPOINT_ID:predict. This is a pattern, not a complete request: copy the exact endpoint, request fields, and headers from the model’s instructions. Authenticate with Google Cloud credentials; do not substitute an OpenAI API key.
Validate the response and monitor usage. Confirm that the response contains the expected generated output and check the model’s documented response format. Review Vertex AI monitoring, logs, and billing for the project. Test with non-sensitive sample input first, and set appropriate quotas, budgets, and access controls for your application.

Common Mistakes

Using the wrong API or credentials: Vertex AI access uses Google Cloud authentication and Vertex AI resource names. An OpenAI API key and OpenAI endpoint are not interchangeable with this setup.
Mixing regions: A model, endpoint, and request sent to different locations may fail. Use the location specified by the model card and endpoint.
Skipping enablement or permissions: A project can be selected but still lack the Vertex AI API, billing, or the necessary IAM permissions.
Assuming the request schema is identical: Do not copy an OpenAI API payload without checking the Vertex AI model documentation. Field names, response structure, and supported options may differ.
Leaving a deployment running: Review the model’s billing details and remove or scale down resources when they are no longer needed, using the model’s documented controls.

Tips

Start with the model card’s own deployment and request samples; they are the most reliable source for that model’s current interface.
Keep project ID, region, endpoint ID, and authentication configuration in environment-specific settings rather than hard-coding them.
Use a small, harmless prompt to verify connectivity before connecting an application or sending production data.
Check quotas, supported input sizes, and pricing before load testing. Set monitoring and budget alerts where available.
For production, use a service account with only the permissions the application needs, and rotate or revoke credentials according to your organization’s procedures.

Troubleshooting

Model Garden listing is missing or deployment is unavailable: Confirm the correct project and region, then review the model card’s availability and access requirements. Complete any approval or terms step, or ask your administrator whether organization policy blocks the offering.
Permission denied: Check that the signed-in identity is the one with access, the Vertex AI API is enabled, and the project has billing configured. Ask an administrator to verify the required IAM permissions rather than granting broad access.
API or endpoint not found: Check spelling and project, location, and endpoint IDs. Ensure the deployment completed and that the request URL uses the same region as the endpoint.
Invalid request or prediction error: Compare the JSON body with the exact schema in the model card. Remove unsupported parameters and check required fields, input format, and documented limits.
Authentication fails locally: Run gcloud auth application-default login again, confirm the intended account and project, and ensure the application uses Application Default Credentials. On Google Cloud, verify the attached service account and its permissions.
Slow responses or quota errors: Check Vertex AI quotas and service status, then retry transient failures with a measured backoff. Reduce request size or concurrency if the model’s limits require it; contact your cloud administrator if a quota increase is needed.

Frequently Asked Questions

Do I need an OpenAI API key?
No. When using an OpenAI model offered through Vertex AI, authenticate to Google Cloud and call the Vertex AI interface documented for that model.

Can I use every OpenAI model on Vertex AI?
No. Only models listed and available to your project are usable. Check the Model Garden listing for current regions, access requirements, and features.

Is deployment required?
Follow the model card. Some offerings require deploying an endpoint; others may provide a different managed access flow. Use the request method specified for the selected model.

Can I call the model from my application?
Yes, if the model’s serving option supports it. Use the documented Vertex AI client library or REST method, Google Cloud credentials, and the correct endpoint and payload.

Will an OpenAI-compatible SDK work?
Not automatically. Compatibility depends on the model’s Vertex AI interface and the SDK’s support for its authentication and request format. Follow the provider’s current integration instructions.

How do I stop charges?
Review the model’s billing and deployment controls in Vertex AI, then undeploy or delete resources you no longer need. Check for any remaining provisioned resources and confirm their status in the console.

Method: the standard formula for this calculation, rounded as each result states. See how the calculators work or report an error.