Best AI for Making Websites: Compare by What You Need to Build
The best AI for making a website depends on whether you need to write code, skip it entirely or maintain the site over time. Here is how to match the tool to the job.
6 min read
The best AI for making a website depends on what you are building, whether you write code and how much you want to maintain it afterward. No-code AI builders like Wix AI generate a full site from a short description, no technical knowledge needed. Developer-focused AI tools like Cursor and GitHub Copilot accelerate coding. General assistants like Claude and ChatGPT generate HTML, CSS and JavaScript that a developer pastes and adapts. Tools like fenbs keep the site brief, design decisions and handoff context in one place across sessions and tools, so those decisions do not get lost when a project moves between team members or AI assistants.
The choice matters most for long-running sites. A tool that generates a site quickly may make ongoing edits harder later. Think through how you will maintain the result before committing to a builder.
What kind of website are you building?
| Tool | Best fit | Coding needed? | Good for ongoing maintenance? |
|---|---|---|---|
| Wix AI website builder | Simple business sites and portfolios with no technical skill | No | Yes, through the Wix editor; changes stay within the platform |
| Framer AI | Design-forward sites for designers with some technical confidence | Light; optional code access | Yes; Framer editor handles most changes |
| Cursor | Full web apps and sites for developers who own the code | Yes | Yes; you edit code files directly |
| GitHub Copilot | Developers working in any editor or IDE | Yes | Yes; integrates into your normal coding workflow |
| Claude or ChatGPT | Generating code snippets or page content a developer pastes in | Some; developer assembles the result | Through the developer who maintains the code |
| fenbs | Storing the site brief, approved decisions and handoff context across tools | No | Yes; keeps the project record current across contributors |
No-code AI website builders
No-code AI builders are the fastest path to a published website if you do not code. Wix AI generates a site from a short description of your business or project: you answer a few questions, it proposes a layout with sample content and you edit from there. The result is a hosted website with no files to manage and no server to configure. For small businesses, portfolios, event sites and simple service pages, this approach works well.
Framer sits between a no-code builder and a developer tool. Designers use it to build interactive sites with precise visual control. Framer AI can generate layout sections from a description and style components from a prompt. You still need a design eye to get good results, but you do not need to write HTML or CSS. For design-forward marketing sites and portfolios, Framer gives more visual control than a standard website builder.
The constraint of no-code builders is that custom behavior requires workarounds. If your site needs server-side logic, a custom checkout flow or an unusual data integration, a no-code builder can become a limitation rather than a shortcut. Evaluate what your site needs to do in the next year, not just at launch, before committing to a platform. Check current feature availability and pricing directly on each vendor's website.
AI coding assistants for developers
Cursor is a code editor built around AI assistance. It reads your full project, not just the file you have open, and can make changes across multiple files at once. For building a web app or a complex site with components, routing and a backend, Cursor accelerates the coding more than a general AI chat tool can. You write the intent; Cursor implements it in your actual codebase.
GitHub Copilot integrates into most code editors and IDEs and assists as you type. It is strong for autocomplete, suggesting tests and explaining unfamiliar code. For website development it is useful throughout the build: generating component code, writing styles and reviewing logic. If your team already uses GitHub, Copilot fits naturally into that workflow without switching tools.
Claude and ChatGPT can both generate solid HTML, CSS and JavaScript from a description. Their usefulness for website work depends on how you use them: pasting a component description and getting working code back is efficient; managing a full project as a back-and-forth conversation is slower than a dedicated coding tool. They are most useful for specific generation tasks within a larger project, not as the primary environment for building a complete site.
Keep the site brief and decisions across tools
Record the approved brief, design decisions and technical constraints in fenbs as a project. When the work moves from design to development, or from one developer to another, the next person or assistant reads the same decisions rather than re-discovering them. Connect your coding assistant from Integrations and ask it to read the project context before starting each session.
Create a free accountFree for one person · No credit card · Works with Claude, ChatGPT, Cursor and more
Maintaining the site after launch
Launch is not the end. Every website needs content updates, design revisions and technical maintenance. How easy those changes are depends on the tool you used to build the site. No-code builders keep maintenance inside the platform: you log in and use the editor. Developer-built sites require someone who can work with the code. AI-assisted developer sites are as maintainable as any other code, which means they need a developer for non-trivial changes.
When a site has multiple contributors over time, decisions made at launch can get forgotten. Why was this section removed? Which version of the logo is approved? What is the navigation structure supposed to be? Keeping a record of those decisions outside the codebase means they are findable later without reading through commit messages or asking the original developer.
For teams using AI coding tools regularly, see what is Cursor AI for a closer look at how Cursor handles long-running projects. For an overview of how AI fits into a broader work setup, see how to use AI at work.
When requirements change mid-build
Client briefs change. Content strategies shift. A page that made sense at the start gets cut or replaced. When changes happen mid-project, the gap between what was planned and what was built widens quickly unless someone keeps the record current. An AI assistant that worked on version one of the site does not automatically know about version two unless you tell it.
Recording approved changes as decisions keeps the project record accurate. If the approved rule is "no sidebar on mobile" after the client review, that decision should be in the record that the next coding session reads. An assistant that reads the current decisions builds to the current requirements, not to the original ones.
How to do this with fenbs
- Create a free account. Add a project for the website. Write a project description: what the site is for, who it is for and the core pages or features it needs.
- Add tasks for each phase: design, development, content, review. Note what is in progress and what is waiting on the client.
- Record design decisions as they are approved: color choices, layout rules, which features were cut and why. Keep them in Decisions and rules so the next session starts with the approved version.
- Connect your coding assistant from Integrations. At the start of each session, ask it to read the project context. It sees your goals, the decisions and where work stands.
- Before each session ends, save progress. Record what was completed, what is next and what is waiting on a person. Keep the record current so handoffs between contributors stay smooth.