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AI deployment made easy: Deploy your app to Cloud Run from AI Studio or MCP-compatible AI agents

GCP – AI deployment made easy: Deploy your app to Cloud Run from AI Studio or MCP-compatible AI agents

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Cloud Run has become a go-to app hosting solution for its remarkable simplicity, flexibility, and scalability. But the age of AI-assisted development is here, and going from idea to application is faster and more streamlined than ever. Today, we’re excited to make AI deployments easier and more accessible by introducing new ways to deploy your apps to Cloud Run:

  1. Deploy applications in Google AI Studio to Cloud Run with a single button click

  2. Scale your Gemma projects with direct deployment of Gemma 3 models from Google AI Studio to Cloud Run 

  3. Empower MCP-compatible AI agents to deploy apps with the new Cloud Run MCP server

1. Streamlining app development and deployment with AI Studio and Cloud Run

Google AI Studio is the fastest way to start building with Gemini. Once you develop an app in AI Studio, you can deploy it to Cloud Run with a single button click, allowing you to go from code to shareable URL in seconds (video at 2x speed):

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Build apps in AI Studio and deploy to Cloud Run

Once deployed, the app is available at a stable HTTPS endpoint that automatically scales, including down to zero when not in use. You can re-deploy with updates from AI Studio, or continue your development journey in the Cloud Run source editor. Plus, your Gemini API key remains securely managed server-side on Cloud Run and is not accessible from the client device.

It’s also a very economical solution for hosting apps developed with AI Studio: Cloud Run has request-based billing with 100ms granularity and a free tier of 2 million requests per month, in addition to any free Google Cloud credits.

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2. Bring your Gemma app to production in a click with Cloud Run

Gemma is a leading open model for single-GPU performance. To help you scale your Gemma projects, AI Studio now enables direct deployment of Gemma 3 models to Cloud Run:

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Selecting Gemma from AI Studio and deploying it to Cloud Run with GPU via a single click in under a minute, with no quota request requirements (video at 4x speed)

This provides an endpoint running on Cloud Run’s simple, pay-per-second, scale-to-zero infrastructure with GPU instances starting in less than five seconds, and it scales to zero when not in use. It’s even compatible with the Google Gen AI SDK out-of-the-box, simply update two parameters in your code to use the newly deployed endpoint:

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<ListValue: [StructValue([(‘code’, ‘from google import genairnfrom google.genai.types import HttpOptionsrnrn# Configure the client to use your Cloud Run endpoint and API keyrnclient = genai.Client(api_key=”KEY_RECEIVED_WHEN_DEPLOYING”, http_options=HttpOptions(base_url=”CLOUD_RUN_ENDPOINT_URL”))rnrn# Example: Stream generate contentrnresponse = client.models.generate_content_stream(rn model=”gemma-3-4b-it”,rn contents=[“Write a story about a magic backpack. You are the narrator of an interactive text adventure game.”]rn)rnfor chunk in response:rn print(chunk.text, end=””)’), (‘language’, ‘lang-py’), (‘caption’, <wagtail.rich_text.RichText object at 0x3e5bc1fd7700>)])]>

3. Empower AI agents to deploy apps with the new Cloud Run MCP server

The Model Context Protocol (MCP) is an open protocol standardizing how AI agents interact with their environment. At Google I/O, we shared that supporting open standards for how agents will interact with tools is a top priority for us.

Today, we are introducing the Cloud Run MCP server to enable MCP-compatible AI agents to deploy apps to Cloud Run. Let’s see it in action with a variety of MCP clients: AI assistant apps, AI-powered Integrated Development Environments (IDEs), and agent SDKs.

1. AI assistant apps

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Using the Claude desktop application to generate a Node.js app and deploy it to Cloud Run (video at 4x speed)

2. AI-powered IDEs

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Updating a FastAPI Python app from VS Code with Copilot in agent mode using Gemini 2.5 Pro, and deploying it using the Cloud Run MCP server (video at 4x speed)

3. Agent SDKs, like the Google Gen AI SDK or Agent Development Kit also have support for calling tools via MCP, and can therefore deploy to Cloud Run using the Cloud Run MCP server. 

Add the Cloud Run MCP server to your favorite MCP client:

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<ListValue: [StructValue([(‘code’, ‘{rn “cloud-run”: {rn “command”: “npx”,rn “args”: [“-y”, “https://github.com/GoogleCloudPlatform/cloud-run-mcp”]rn }rn}’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3e5bc1fd7760>)])]>

Get started

Build, deploy, and scale AI apps faster with AI Studio’s integration with Cloud Run and the new Cloud Run MCP server. Give it a try:

Read More for the details.

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