As Google Apps Script developers, we are used to waiting. We wait for new runtime features, we wait for quotas to reset, and recently, we have been waiting for a first-class way to integrate Gemini into our projects.

With the recent release of the Vertex AI Advanced Service, the wait is technically over. But as detailed in Justin Poehnelt’s recent post, Using Gemini in Apps Script, you might find yourself asking if this was the solution we were actually looking for.

While the new service undoubtedly reduces the boilerplate code required to call Google’s AI models, it brings its own set of frustrations that leave me, and others in the community, feeling somewhat underwhelmed.

The “Wrapper” Trap

On the surface, the new VertexAI service looks like a win. As Justin highlights, replacing complex UrlFetchApp headers with a single VertexAI.Endpoints.generateContent() call is a significant cleanup.

However, this convenience comes with an administrative price tag. The Vertex AI Advanced Service requires a standard Google Cloud Project, understandable for billing, but requires the creation of an oAuth consent screen. For the majority of internal enterprise applications, I would imagine either a service account or a https://www.googleapis.com/auth/cloud-platform scope and associated IAM will be the preferred approach. This removes the need for a consent screen and, in the case of Service Accounts, rules out the Vertex AI Advanced Service.

It begs the question: Why didn’t Google take the approach of the Google Gen AI SDK?

In the Node.js and JavaScript world, the new Google Gen AI SDK offers a unified interface. You can start with a simple API key (using Google AI Studio) for prototyping, and switch to Vertex AI (via OAuth) for production, all without changing your core code logic. The Apps Script service, by contrast, locks us strictly into the “Enterprise” Vertex path. We seem to have traded boilerplate code for boilerplate configuration.

A Third Way: The Community Approach

If you are looking for that Unified SDK experience I mentioned earlier, where you can use the standard Google AI Studio code patterns within Apps Script, there is a third way.

I have published a library, GeminiApp, which wraps UrlFetchApp but mimics the official Google Gen AI SDK for Node.js. This allows you to write code that looks and feels like the modern JavaScript SDK, handling the complex UrlFetchApp configuration under the hood.

As you can see in the comparison above, the Advanced Service (left) abstracts away the request complexity, the UrlFetchApp method (middle) gives you the transparency and control you often need in production, and the GeminiApp library (right) offers a balance of both.

Disclaimer: As the creator of this library, I admit some bias, but it was built specifically to address the gap.

It is important to note a distinction in scope. Both the Google Gen AI SDK and GeminiApp are focused strictly on generative AI features. The Vertex AI Advanced Service, much like the platform it wraps, offers a broader range of methods beyond just content generation.

If your needs extend into those wider Vertex AI capabilities, but you still require the authentication flexibility of UrlFetchApp (such as using Service Accounts), I have a solution for that as well. My Google API Client Library Generator for Apps Script includes a build for the full Vertex AI (AI Platform) API. This gives you the comprehensive coverage of the Advanced Service with the architectural flexibility of an open-source library.

Here is how you can use the generated client library to authenticate with a Service Account, something impossible with the official Advanced Service:

/**
 * Example using the generated Aiplatform library with a Service Account.
 * Library: https://github.com/mhawksey/Google-API-Client-Library-Generator-for-Apps-Script/tree/main/build/Aiplatform
 */
function callGemini(prompt) {
  const projectId = 'GOOGLE_CLOUD_PROJECT_ID';
  const region = 'us-central1';
  const modelName = 'gemini-2.5-flash';

  const modelResourceName = `projects/${projectId}/locations/${region}/publishers/google/models/${modelName}`;

  const serviceAccountToken = getServiceAccountToken_(); 

  const vertexai = new Aiplatform({
    token: serviceAccountToken
  });

  const payload = {
    contents: [{
      role: 'user',
      parts: [{
        text: prompt
      }]
    }],
    generationConfig: {
      temperature: 0.1,
      maxOutputTokens: 2048
    }
  };

  const result = vertexai.projects.locations.publishers.models.generateContent({
    model: modelResourceName,
    requestBody: payload
  });

  return result.data.candidates?.[0]?.content?.parts?.[0]?.text || 'No response generated.';
}

When “Advanced” Means “Behind”

There is another catch that Justin uncovered during his testing: the service struggles with the bleeding edge.

If you are trying to access the latest “Preview” models to prototype, such as the highly anticipated gemini-3-pro-preview, the advanced service may fail you. It appears the wrapper doesn’t yet support the auto-discovery needed for these newer endpoints.

In his companion post, UrlFetchApp: The Unofficial Documentation, Justin reminds us why UrlFetchApp is still the backbone of Apps Script development. When the “official” wrapper doesn’t support a specific header or a beta model, UrlFetchApp is the only way to bypass the limitations.

The Verdict

The Vertex AI service is a welcome addition for stable, enterprise-focused applications. But for developers, particularly those who want to test the latest Gemini 3 capabilities, it feels rigid compared to the flexibility seen in other Google developer ecosystems.

It serves as a good reminder that in Apps Script, convenience services are great, but understanding the underlying HTTP requests via UrlFetchApp  extends what you can achieve.