Source URL: https://cloud.google.com/blog/products/ai-machine-learning/ai-studio-to-cloud-run-and-cloud-run-mcp-server/
Source: Cloud Blog
Title: AI deployment made easy: Deploy your app to Cloud Run from AI Studio or MCP-compatible AI agents
Feedly Summary: 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:
Deploy applications in Google AI Studio to Cloud Run with a single button click
Scale your Gemma projects with direct deployment of Gemma 3 models from Google AI Studio to Cloud Run
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):
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:
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:
code_block
<ListValue: [StructValue([(‘code’, ‘from google import genai\r\nfrom google.genai.types import HttpOptions\r\n\r\n# Configure the client to use your Cloud Run endpoint and API key\r\nclient = genai.Client(api_key=”KEY_RECEIVED_WHEN_DEPLOYING", http_options=HttpOptions(base_url="CLOUD_RUN_ENDPOINT_URL"))\r\n\r\n# Example: Stream generate content\r\nresponse = client.models.generate_content_stream(\r\n model="gemma-3-4b-it",\r\n contents=["Write a story about a magic backpack. You are the narrator of an interactive text adventure game."]\r\n)\r\nfor chunk in response:\r\n print(chunk.text, end="")’), (‘language’, ‘lang-py’), (‘caption’, <wagtail.rich_text.RichText object at 0x3e6759eb5fa0>)])]>
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
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
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:
code_block
<ListValue: [StructValue([(‘code’, ‘{\r\n "cloud-run": {\r\n "command": "npx",\r\n "args": ["-y", "https://github.com/GoogleCloudPlatform/cloud-run-mcp"]\r\n }\r\n}’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3e6759eb5ee0>)])]>
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:
Build in AI Studio and deploy to Cloud Run
Install Cloud Run MCP server on your local machine
Chat with Gemma 3 in AI Studio and deploy Gemma 3 to Cloud Run
AI Summary and Description: Yes
**Summary:** The text discusses the integration between Google AI Studio and Cloud Run, enhancing the deployment of AI applications. Key features include one-click deployment from AI Studio, direct scaling of AI models, and support for AI agents through the Cloud Run MCP server. This streamlines workflow for developers in AI, offering economic benefits and immediate user accessibility.
**Detailed Description:**
The contents of the text provide substantial insights into the evolving landscape of application development, particularly in the realm of AI integration with cloud services. The announcement highlights key features that accelerate the pathway from idea conception to deployment, which is increasingly crucial for developers and organizations leveraging AI technology.
– **Key Innovations:**
– **One-Click Deployment:** AI developers can quickly deploy applications from Google AI Studio to Cloud Run with a single button click, which significantly reduces the time and complexity involved in deployment.
– **Scalability:** The integration allows apps to automatically scale, including downtimes (down to zero) when not in use, optimizing resource utilization and cost-effectiveness.
– **Economic Model:** Cloud Run employs a request-based billing model, including a free tier for initial usage, allowing developers to experiment without significant upfront costs.
– **Gemma Model Support:**
– The platform now allows users to deploy Gemma 3 models directly to Cloud Run seamlessly, improving the performance of single-GPU applications and facilitating faster iteration cycles for AI projects.
– **MCP Server for AI Agents:**
– Introduction of the Cloud Run Model Context Protocol (MCP) server, enabling AI agents – such as those used in IDEs or through various agent SDKs – to deploy applications effectively, further broadening the ecosystem of tools for developers.
– Real-world examples provided demonstrate how different clients and applications interact with the Cloud Run MCP server, enhancing the functionality suited for various development environments.
– **Practical Implications for Professionals:**
– **Enhanced Productivity:** Developers can leverage quick deployment options to focus on application functionality and user experience rather than overcoming deployment hurdles.
– **Flexible Development Environment:** The new features favor collaborative and iterative development utilizing AI capabilities, which aligns with modern software development practices.
– **Cost Efficiency:** With features such as pay-per-use billing, teams can manage budgets better while experimenting and scaling AI applications, which can be critical for startups and smaller projects.
In summary, the integration between Google AI Studio and Cloud Run represents a significant advancement in the deployment and scalability of AI applications, catering to a modern development landscape where speed and efficiency are paramount. Security and compliance measures surrounding API keys and data management are also implied as a core component of this seamless integration, emphasizing the importance of secure cloud application deployment.