How-To Guide
You asked an AI to build a website. It wrote the HTML, CSS, and JavaScript. The preview in your editor looks right. Now what?
Getting AI-generated code from your editor to a live URL used to mean exporting files, setting up a Git repo, configuring a deployment platform, and running build commands. In 2026, MCP (Model Context Protocol) changed that. AI coding agents can now deploy websites directly — without you leaving the conversation.
This guide covers three ways to deploy AI-generated websites, from the simplest (one sentence) to the most flexible (CLI tools).
MCP lets AI coding agents interact with external tools through a standard protocol. If your editor supports MCP, you can deploy directly from the conversation — no export, no terminal, no context switch.
Supported AI coding agents
Step-by-step with VibeHost (any MCP editor)
https://api.vibehost.com/mcp The same pattern works with other MCP-compatible deployment platforms. Vercel, Netlify, Render, Railway, and Cloudflare all offer MCP servers as of 2026. The setup varies — some require API tokens, Git configuration, or CLI installation alongside MCP.
When to use this method: You want the fastest path from AI-generated code to a live URL. One setup, then every future deploy is a single sentence.
CLI (command-line interface) tools give you more control over the deployment process. Most deployment platforms offer a CLI that you can run from your terminal.
| CLI tool | Platform | Install | Deploy command |
|---|---|---|---|
vibehost | VibeHost | curl -fsSL https://vibehost.com/install.sh | sh | vibehost deploy |
vercel | Vercel | npm i -g vercel | vercel |
netlify | Netlify | npm i -g netlify-cli | netlify deploy |
wrangler | Cloudflare Pages | npm i -g wrangler | wrangler pages deploy |
railway | Railway | npm i -g @railway/cli | railway up |
render | Render | Dashboard or Blueprint | git push (via Git integration) |
Step-by-step with the VibeHost CLI
vibehost login. This opens a browser window for Google OAuth. vibehost app create my-site. vibehost deploy from the directory with your files. When to use this method: You are comfortable with the terminal and want to script deployments, integrate with other tools, or deploy from a CI/CD pipeline.
If you do not use MCP or CLI tools, most platforms still accept traditional deployment methods.
When to use this method: You already have a Git workflow, or you want a one-time deploy without installing anything.
| Method | Setup time | Technical skill needed | Repeatable | Best for |
|---|---|---|---|---|
| MCP (from AI editor) | Under 2 minutes | None — natural language | Every deploy is one sentence | Non-technical builders using AI coding agents |
| CLI | 5 minutes | Basic terminal knowledge | Scriptable, CI/CD compatible | Developers who want control and automation |
| Git push | 5-10 minutes | Git knowledge required | Automatic on every push | Teams with existing Git workflows |
| Drag-and-drop | 1 minute | None | Manual each time | One-off deploys or quick tests |
Not every deployment platform is designed for AI-built code. When choosing a platform, consider:
Privacy by default. If you are building something for a client or testing an idea, you may not want the preview URL to be publicly accessible. Some platforms make previews public by default. VibeHost makes them private by default.
No-config setup. AI-generated code often does not include build configuration files, environment variables, or framework-specific settings. Platforms that require these add friction. Look for platforms that detect your project type automatically or work without config.
Team sharing built in. If you are working with a team or sharing previews with stakeholders, built-in collaboration features save time. VibeHost includes team management for up to 3 members on the free plan. Most competitors charge for team features.
MCP tool coverage. Having an MCP server is one thing. Having 46 tools that cover deploy, preview, rollback, custom domains, sharing, password protection, redirects, and logs is another. More tool coverage means your AI coding agent can do more without you switching contexts.
MCP (Model Context Protocol) is a standard that lets AI coding agents interact with external tools. For deployment, it means your AI agent can deploy, preview, rollback, and manage websites without you leaving the conversation. Most major deployment platforms adopted MCP in 2025-2026.
Yes. Claude Desktop supports MCP connectors. Add a deployment platform's MCP URL in your Claude Desktop settings, and you can deploy by typing a command like "deploy this to VibeHost" or using the platform's MCP tools. This also works with Claude Code from the terminal.
Most AI coding agents produce static websites (HTML, CSS, JavaScript) or Next.js applications. All platforms in this guide support static sites. For Next.js with server-side rendering, Vercel and Netlify offer the most complete support. VibeHost supports static sites and Next.js.
With MCP deployment, no. The AI agent handles the technical steps. You describe what you want to do in natural language — "deploy this," "share it with a password," "roll back to the previous version" — and the agent executes through the deployment platform's tools.
It depends on your needs. Vercel and Netlify are best for developers who want mature infrastructure with AI tooling added on. VibeHost is best for non-technical builders who want the entire experience designed around deploying from AI coding agents, with private previews and team sharing included on the free plan.
Free plan includes 100 apps, team management, and up to 3 members.