This is the tight, conversational loop you use to create and perfect a specific piece of code.

Vibe coding doesn't stop at code generation. Vibe deploying is the ability to launch your application to a live, production-grade environment (like Cloud Run) with a single click or prompt. This removes the "DevOps bottleneck," allowing you to test your ideas with real users immediately.

Vibe coding operates on two levels: the low-level iterative loop of refining code, and the high-level lifecycle of building and deploying a full application.

This is the broader process of taking a high-level idea from concept to a deployed application.

With traditional programming, you focus on the details of implementation, manually writing the specific commands, keywords, and punctuation a language requires. Vibe coding lets you focus on the desired outcome instead, describing your goal in plain language, like "create a user login form," while the AI handles the actual code.

Here’s a comparison:

Google Cloud offers several tools for vibe coding. Choosing which tool you use should depend on your goal, and not necessarily your job title. A developer might use AI Studio for a quick prototype, an enthusiast might build a full application in Firebase Studio, and a data scientist might use Gemini CLI to write a script.

After you finish prototyping, your deployment path depends on the tool you select. You can continue to iterate by editing the source code directly or by returning to your vibe coding environment to provide more instructions.

Use this guide to find the best tool for the task at hand.

AI Studio is the quickest way to go from an idea to a live, shareable web app, often with a single prompt. It's perfect for rapid prototyping and building simple, generative AI applications.

To get started, go to Build in AI Studio. In the main prompt area, simply describe the application you want to create. Start with a fun, creative idea, and then simply run the prompt. Once you run the prompt, you’ll see AI Studio generate the necessary code and files, with a live preview of your app appearing on the right-hand side.

Now that you have a live preview, you can use the chat interface to refine its look and functionality with follow-up prompts. You could add features, change visual elements, and more.

Once you’re happy with the result, you can deploy to Cloud Run. AI Studio now automatically provisions a database and publishes your app to a public URL. This allows your app to handle persistent data (like user profiles or industry lists) without any manual infrastructure setup.

Key features:

• Zero-friction access: You can launch your first applications quickly.
• Integrated database provisioning: The deployment process includes support for backend services to handle data persistence automatically. It automatically provisions and configures a database—such as Cloud SQL or Firestore—based on your app's requirements. This ensures your data storage is ready to use without any manual setup.
• Scalable infrastructure: It uses Cloud Run on the backend, ensuring your app can scale to handle traffic if it goes viral.

Firebase Studio is a powerful, web-based environment for building production-ready applications, especially those that need a robust backend with features like user authentication or a database.

To get started, open Firebase Studio and describe the complete application you want to build in the prompt area. You can describe a robust, multi-page application from the very beginning.

After submitting your initial prompt, Firebase Studio generates an app blueprint for you to review. This blueprint is a detailed plan outlining the features, style guidelines, and technology stack the AI intends to use.

Here, you can provide feedback to refine the blueprint, ensuring the initial code generation is closer to what you have in mind. Making changes to the plan at this stage is much easier than editing the final code, helping you get to your desired state faster.

When you're happy with the blueprint, go ahead and click the "Prototype this App" button. Firebase Studio will then generate a working prototype based on your approved plan. After a moment, a live, interactive preview of your new app will appear.

With your interactive prototype running in the preview panel, you can continue the conversation to make edits. For example, ask for visual changes, add or change features, or even introduce new logic to your application.

When your application is ready, you can deploy it directly from the environment. To do so, simply click "Publish" in the top right-hand corner. Firebase Studio handles the entire deployment process, publishing your app to a public URL using Cloud Run. Because it's built for production, your application is ready to scale and handle traffic from day one.

Gemini Code Assist acts as an AI pair programmer directly within your existing code editor (like VS Code or JetBrains). It’s best used for helping professional developers work faster and more efficiently directly in their IDE, and on existing projects.

To get started, open a project file in your IDE. Instead of writing code manually, you can use the Gemini chat window or an in-line prompt to describe the function or code block you need. The AI will generate the code and insert it directly into your file.

Highlight the code you just created (or any block of existing code) and use follow-up prompts to modify or improve it. This is perfect for adding new features, adding error handling, improving performance, or changing logic without having to manually refactor.

To ensure your code is production-quality, you can ask Gemini to generate unit tests. This automates a crucial but often time-consuming part of app development.

Gemini CLI is an open-source AI agent that brings Gemini directly into your terminal. It’s designed for developers who want a terminal-first vibe coding experience.

After installing the agent in your terminal, you can launch Gemini CLI in any directory by typing gemini. It can automatically analyze your local files to understand the project context.

Gemini CLI supports the model context protocol (MCP), which allows the AI to connect to external tools and data sources.

• You can connect Gemini to a database, a GitHub repository, or Google Search
• By pointing Gemini CLI to an MCP server, you give it "new skills," such as the ability to read your Jira tickets or deploy code to a specific server
• Gemini CLI has an ecosystem of extensions from popular service providers and Google services which package MCP servers with context for how it should be used by Gemini to carry out tasks on your behalf

You can toggle "shell mode" within Gemini CLI to run terminal commands directly. This allows you to ask the AI to "Fix the error in my last build," and the AI can execute the fix and re-run the build command for you.

Vibe coding with Google Antigravity shifts the focus from writing syntax to directing a mission. Instead of micro-managing lines of code, you guide autonomous agents that handle the heavy lifting across your editor, terminal, and browser.

Launch the Antigravity application. Note that for enterprise users, Antigravity is supported via the Google AI Ultra for Business add-on, granting higher usage limits and prioritized traffic for mission-critical tasks. You can choose to import existing settings from VS Code or start fresh to explore the agent-native interface.

In the Agent Manager, you'll select your primary model, such as Gemini 3 Pro, and configure your Review Policy.

For a true "vibe" experience, many developers set terminal execution to auto, which allows the agent to run routine commands like npm install or git status without stopping to ask for permission every time.

In the Agent Panel, describe what you want to build using natural language. For example, you might say, "Build a responsive personal finance dashboard using Next.js and Tailwind CSS."

Antigravity doesn't just start typing; it begins by analyzing your request and proposing a task checklist. This checklist outlines the entire project lifecycle, from scaffolding the file structure to final UI polish.

Before any code is committed, the agent generates an Implementation Plan (usually as an implementation_plan.md artifact). This document serves as a technical blueprint, detailing exactly which files will be created or modified and what logic will be used.

You can review this plan, leave comments or "vibes" on specific sections, like asking for a different color palette or a specific state management library, and the agent will adjust its strategy before proceeding.

Once you approve the plan, the agent moves into the execution phase.

You can watch as it opens the terminal to install dependencies, creates component files in the editor, and fixes its own linting errors in real-time. If you hit a roadblock or want to pivot, you can switch between Planning Mode (for complex architecture) and Fast Mode (for quick edits) to keep the momentum going.

Antigravity moves beyond text-based logs by providing visual proof of its work. If your project includes a frontend, the agent can launch a Browser Sub-Agent to test the UI. It will capture screenshots and browser recordings of itself clicking buttons and navigating pages to ensure everything works as intended. You can verify the "vibe" of the final product by reviewing these artifacts directly in your mission control dashboard.

As your project grows, you can teach your agents new tricks using Agent Skills. By adding a SKILL.md file to your project's .agent/skills/ directory, you can define specific workflows or coding standards unique to your team. For instance, you could create a "database migration" skill that teaches the agent how to safely update your schema using your company’s specific CLI tools.