I built something in an AI chat tool. How do I get it live under my own domain?

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Building something functional inside an AI chat tool and getting it onto your own domain used to be two separate, fiddly steps: export or copy the code, find a host, configure a deploy pipeline, then separately wire up DNS. Netlify's MCP Server collapses the first part of that into something the AI tool itself can do, since it lets AI coding agents like Claude, ChatGPT, Codex and several others deploy directly to and manage a Netlify site without you handling API keys or authentication yourself.

How the MCP connection actually works

MCP stands for Model Context Protocol, which is a standard way for an AI tool to talk to an external service, in this case Netlify, using a defined set of actions rather than you copy-pasting commands back and forth. Once you connect your AI coding tool to Netlify's MCP Server, the AI can deploy your project, check on a deploy's status, and manage basic site settings directly from within your conversation with it. Netlify publishes specific setup guides for the major agents, including Claude Code, Codex, Claude Web, Claude Desktop, ChatGPT and Grok, and keeps adding model support on a fast schedule: Netlify's own changelog shows new models landing in the same AI Gateway infrastructure this October alone, including OpenAI's GPT-6.1 Terra, Google's Gemini 4 Argon, and Mistral Large 4.

Step by step: getting your app live under your own domain

1. Connect your AI tool to Netlify's MCP Server following the setup guide for whichever tool you're using (Claude, ChatGPT, Codex and others each have their own short connection steps).
2. Ask the AI tool to deploy your project to Netlify directly from your conversation with it. It will hand you back a live netlify.app address once the deploy succeeds.
3. Test everything on that netlify.app address first, every page and every feature, before touching your domain. It's much easier to debug on a throwaway address than to troubleshoot DNS and a broken app at the same time.
4. Add your own domain in the site's domain settings and follow the DNS instructions shown there, typically pointing either a subdomain (with a CNAME record) or your root domain (with an A record or Netlify's own DNS) at your new site.
5. Wait for DNS to propagate, usually within a few hours and up to about 24, then confirm the site loads correctly on your actual domain with a valid SSL certificate, which Netlify provisions automatically once the domain is correctly pointed.
6. Keep your email's DNS records untouched if you're switching an existing domain over; changing name servers carelessly can break inbound email at the same time as your website, which is a much bigger problem than a website going down for a day.

The gap between "it deployed" and "it's production ready"

This is the part most people skip past, and it's worth being direct about: an AI tool successfully deploying your app proves the code runs, not that it's safe to run a business on. Backups, security review, and ongoing monitoring for errors are still entirely on you after the deploy succeeds. If your app handles customer data, payments, or anything where a bug has a real cost, that gap matters more the bigger your app gets, and it's exactly where I'd recommend a second look from a developer before you point real customer traffic at it, even if the AI-built version technically works.

A concrete example of where this bites people: an app that works fine during testing can still have no real error logging, meaning if something breaks for a visitor at 2am, you find out from a complaint rather than an alert. Netlify's own observability tools give you some visibility here, but "the deploy succeeded" and "I'll know if something goes wrong" are two different guarantees, and only one of them comes automatically.

How this compares to Vercel and Replit

Vercel offers a broadly similar story for AI-tool deploys, with its own set of integrations for AI coding assistants, and the choice between the two often comes down to which framework your AI tool generated (Next.js leans toward Vercel's own ecosystem, though both platforms support most modern frameworks) and which one your AI tool already has first-class support for. Replit's deployments are more tightly coupled to building inside Replit itself, which is convenient if that's where you built the app but less flexible if you want to build in one tool (say, Claude Code locally) and deploy somewhere else. If you're not sure which to pick, the practical test is simple: deploy to whichever platform your AI tool already has a clean, documented connection to, since fighting an awkward integration costs more time than any real difference in the platforms themselves for most straightforward projects.

What I'd actually tell you

If this is a genuinely simple app, a landing page with a form, a basic internal tool, a prototype you're testing with a few real users, getting it live on your domain through this flow is the right amount of effort for the job, and I wouldn't overthink it. Where I'd slow down is the moment the app starts holding anything you'd be upset to lose or embarrassed to have leak: customer records, anything with a login, anything collecting payment details. At that point, the question isn't "can an AI deploy this," it already has, it's "has anyone who understands security actually looked at it," and that's a different, more important question than the deploy process answers.

When to call a developer instead

If the simple deploy-and-point-your-domain path stops working, whether that's a DNS issue you can't resolve, an app that breaks in ways the AI tool can't fix when asked, or a growing feeling that this needs to actually be reliable rather than just functional, that's the point to bring in a developer rather than keep troubleshooting through the AI chat. I take on exactly this kind of project: finishing or hardening an AI-built app that's basically working but needs a professional pass before it's something you'd stake your business on.

How I'd approach this

My default advice is to let the AI-and-MCP flow handle getting a genuinely simple project live, since it's fast and it works for that case. The moment the stakes go up, whether that's data sensitivity, expected traffic, or just how much it would cost you if it went down for a day, I'd get a second, human pair of eyes on the actual code before pointing real customers at it, even if "it deployed successfully" feels like the project is done.