Gemini Developer API v.s. Vertex AI

When developing generative AI solutions with Gemini, Google offers two API products: the Gemini Developer API and the Vertex AI Gemini API.

The Gemini Developer API provides the fastest path to build, productionize, and scale Gemini powered applications. Most developers should use the Gemini Developer API unless there is a need for specific enterprise controls.

Vertex AI offers a comprehensive ecosystem of enterprise ready features and services for building and deploying generative AI applications backed by the Google Cloud Platform.

We've recently simplified migrating between these services. Both the Gemini Developer API and the Vertex AI Gemini API are now accessible through the unified Google Gen AI SDK.

Code comparison

This page has side-by-side code comparisons between Gemini Developer API and Vertex AI quickstarts for text generation.

Python

You can access both the Gemini Developer API and Vertex AI services through the google-genai library. See the libraries page for instructions on how to install google-genai.

from google import genai

client = genai.Client(api_key="YOUR_API_KEY")

response = client.models.generate_content(
    model="gemini-2.0-flash", contents="Explain how AI works in a few words"
)
print(response.text)

from google import genai

client = genai.Client(
    vertexai=True, project='your-project-id', location='us-central1'
)

response = client.models.generate_content(
    model="gemini-2.0-flash", contents="Explain how AI works in a few words"
)
print(response.text)

JavaScript and TypeScript

You can access both Gemini Developer API and Vertex AI services through @google/genai library. See libraries page for instructions on how to install @google/genai.

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({ apiKey: "YOUR_API_KEY" });

async function main() {
  const response = await ai.models.generateContent({
    model: "gemini-2.0-flash",
    contents: "Explain how AI works in a few words",
  });
  console.log(response.text);
}

main();

import { GoogleGenAI } from '@google/genai';
const ai = new GoogleGenAI({
  vertexai: true,
  project: 'your_project',
  location: 'your_location',
});

async function main() {
  const response = await ai.models.generateContent({
    model: "gemini-2.0-flash",
    contents: "Explain how AI works in a few words",
  });
  console.log(response.text);
}

main();

Go

You can access both Gemini Developer API and Vertex AI services through google.golang.org/genai library. See libraries page for instructions on how to install google.golang.org/genai.

import (
  "context"
  "encoding/json"
  "fmt"
  "log"
  "google.golang.org/genai"
)

// Your Google API key
const apiKey = "your-api-key"

func main() {
  ctx := context.Background()
  client, err := genai.NewClient(ctx, &genai.ClientConfig{
    APIKey:  apiKey,
    Backend: genai.BackendGeminiAPI,
  })

  // Call the GenerateContent method.
  result, err := client.Models.GenerateContent(ctx, "gemini-2.0-flash", genai.Text("Tell me about New York?"), nil)

}

import (
  "context"
  "encoding/json"
  "fmt"
  "log"
  "google.golang.org/genai"
)

// Your GCP project
const project = "your-project"

// A GCP location like "us-central1"
const location = "some-gcp-location"

func main() {
  ctx := context.Background()
  client, err := genai.NewClient(ctx, &genai.ClientConfig
  {
        Project:  project,
      Location: location,
      Backend:  genai.BackendVertexAI,
  })

  // Call the GenerateContent method.
  result, err := client.Models.GenerateContent(ctx, "gemini-2.0-flash", genai.Text("Tell me about New York?"), nil)

}

Other use cases and platforms

Refer to use case specific guides on Gemini Developer API Documentation and Vertex AI documentation for other platforms and use cases.

Migration considerations

When you migrate:

• You'll need to use Google Cloud service accounts to authenticate. See the Vertex AI documentation for more information.

• You can use your existing Google Cloud project (the same one you used to generate your API key) or you can create a new Google Cloud project.

• Supported regions may differ between the Gemini Developer API and the Vertex AI Gemini API. See the list of supported regions for generative AI on Google Cloud.

• Any models you created in Google AI Studio need to be retrained in Vertex AI.

If you no longer need to use your Gemini API key for the Gemini Developer API, then follow security best practices and delete it.

To delete an API key:

1. Open the Google Cloud API Credentials page.

2. Find the API key you want to delete and click the Actions icon.

3. Select Delete API key.

4. In the Delete credential modal, select Delete.

   Deleting an API key takes a few minutes to propagate. After propagation completes, any traffic using the deleted API key is rejected.

Next steps

• See the Generative AI on Vertex AI overview to learn more about generative AI solutions on Vertex AI.