Vertex AI quickstart

This quickstart shows you how to install the Google Gen AI SDK for your language of choice and then make your first API request.

Requirements

The requirements for getting started with Vertex AI depend on your Google Cloud workflow. You need to:

Choose your authentication method:

Before you begin

If you have already configured ADC, skip to the next step.

To configure ADC, do the following:

Configure your project

Select a project, enable billing, enable the Vertex AI API, and install the gcloud CLI:

Create local authentication credentials

Create local authentication credentials for your user account:

gcloud auth application-default login

If an authentication error is returned, and you are using an external identity provider (IdP), confirm that you have signed in to the gcloud CLI with your federated identity.

Set up required roles

If you're using a standard API key or ADC, your project also needs to be granted the appropriate Identity and Access Management permissions for Vertex AI. If you're using an express mode API key, you can skip to the next step.

To get the permissions that you need to use Vertex AI, ask your administrator to grant you the Vertex AI User (roles/aiplatform.user) IAM role on your project. For more information about granting roles, see Manage access to projects, folders, and organizations.

You might also be able to get the required permissions through custom roles or other predefined roles.

Install the SDK and set up your environment

On your local machine, click one of the following tabs to install the SDK for your programming language.

Python

Install and update the Gen AI SDK for Python by running this command.

pip install --upgrade google-genai

Set environment variables:

# Replace the `GOOGLE_CLOUD_PROJECT_ID` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT_ID
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_VERTEXAI=True
    

Go

Install and update the Gen AI SDK for Go by running this command.

go get google.golang.org/genai

Set environment variables:

# Replace the `GOOGLE_CLOUD_PROJECT_ID` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT_ID
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_VERTEXAI=True
    

Node.js

Install and update the Gen AI SDK for Node.js by running this command.

npm install @google/genai

Set environment variables:

# Replace the `GOOGLE_CLOUD_PROJECT_ID` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT_ID
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_VERTEXAI=True
    

Java

Install and update the Gen AI SDK for Java by running this command.

Maven

Add the following to your pom.xml:

<dependencies>
  <dependency>
    <groupId>com.google.genai</groupId>
    <artifactId>google-genai</artifactId>
    <version>0.7.0</version>
  </dependency>
</dependencies>
    

Set environment variables:

# Replace the `GOOGLE_CLOUD_PROJECT_ID` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT_ID
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_VERTEXAI=True
    

REST

Set environment variables:

GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT_ID
GOOGLE_CLOUD_LOCATION="global"
API_ENDPOINT="https://aiplatform.googleapis.com"
MODEL_ID="gemini-2.5-flash"
GENERATE_CONTENT_API="generateContent"
    

Replace GOOGLE_CLOUD_PROJECT_ID with your Google Cloud project ID.

Make your first request

Use the generateContent method to send a request to the Gemini API in Vertex AI:

Python

Go

Node.js

Java

C#

REST

To send this prompt request, run the curl command from the command line or include the REST call in your application.

curl \
-X POST \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
"${API_ENDPOINT}/v1/projects/${GOOGLE_CLOUD_PROJECT}/locations/${GOOGLE_CLOUD_LOCATION}/publishers/google/models/${MODEL_ID}:${GENERATE_CONTENT_API}" -d \
$'{
  "contents": {
    "role": "user",
    "parts": {
      "text": "Explain how AI works in a few words"
    }
  }
}'

The model returns a response. Note that the response is generated in sections with each section separately evaluated for safety.

Generate images

Gemini can generate and process images conversationally. You can prompt Gemini with text, images, or a combination of both to achieve various image-related tasks, such as image generation and editing. The following code demonstrates how to generate an image based on a descriptive prompt:

You must include responseModalities: ["TEXT", "IMAGE"] in your configuration. Image-only output is not supported with these models.

Image understanding

Gemini can understand images as well. The following code uses the image generated in the previous section and uses a different model to infer information about the image:

Code execution

The Gemini API in Vertex AI code execution feature enables the model to generate and run Python code and learn iteratively from the results until it arrives at a final output. Vertex AI provides code execution as a tool, similar to function calling. You can use this code execution capability to build applications that benefit from code-based reasoning and that produce text output. For example:

For more examples of code execution, check out the code execution documentation.

What's next

Now that you made your first API request, you might want to explore the following guides that show how to set up more advanced Vertex AI features for production code:

Guide

Get started with Gemini 3

Learn about Gemini 3, our most intelligent model family to date, built on a foundation of state-of-the-art reasoning.

Overview

Explore Google models

Explore the latest Google models supported in Vertex AI, including Gemini, Imagen, Veo, and Gemma.

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Last updated 2026-02-19 UTC.