They are all called AI website building, but what AI does is different.
Open any website-building product today, and the homepage keywords are almost always the same set: AI website building, one-sentence generation, build a website with zero foundation.
But with the same phrase, what AI actually does in different products can be completely different. Some help you pick one from thousands of templates and then fill in the copy and images; some write out a set of working code for you; and there is another kind where AI is the user of the system from the very beginning—building, updating, and maintaining the website, it is doing it all from start to finish.
The difference is not in “whether a website can be generated”—anyone can do that now. The real difference is decided before generation even begins. The table below puts the underlying mechanisms of the three paths side by side, so you can get the full picture first:
| Comparison dimension | Template-matching Fanke, Meicheng Website Builder Star | Code-generation Alibaba Cloud Wanxiaozhi | Blueprint-driven HOLYPro Digital Platform |
|---|---|---|---|
| Role of AI | An auxiliary tool for choosing templates and filling in content | An engine for generating code | A permanent operator of the system |
| After AI finishes the work | Exits, leaving a template-based site | Exits, leaving a traditional website | Stays resident, continuing to read, write, modify, and maintain |
| Object generated | Pages with content filled into templates | Frontend + backend + database | Structure (blueprint) separated from content (content blocks) |
| Source of skeleton | Thousands of fixed templates | Code regenerated each time | Blueprint + design system |
| Content and structure | Content embedded in pages | Content in the database, but bound together with structure in the code | Content blocks independent, injected into lists, the two decoupled |
| Dynamic content | Locked within the template framework | Entry-level tier is mainly static presentation, with dynamic capabilities provided in tiers | Content blocks natively support dynamic content |
| Post-delivery updates | Drag-and-drop editing / find a service provider | Regenerate | AI conversation + self-service backend |
| Redesign iterations | Switch to another template | Regenerate; old and new versions require manual connection | Development slot overwrite write + snapshots + one-click rollback |
| Multilingual | One set of pages per language / translation plugin | Version switching | One master record + one translation version per language |
| SEO / GEO | Basic trio | Basic trio | Basic trio + discovery pages for AI search engines |
| Content production | Mainly manual editing | Mainly manual editing | AI continuously produces through the toolchain |
Every row in this table can be taken one layer deeper.
The first fork: Is AI a tool or an operator?
This is the most fundamental difference among the three paths; every difference that follows grows out of this one.
In one approach, AI is a generation tool. You tell it the industry, the style, and which pages you need; it matches against a template library, picks out a suitable set, and then fills the text and images into the corresponding positions. Once generation is complete, AI’s job is done, and what you have left is still a template site; to change it later, you return to drag-and-drop editing or the old path of finding a service provider.
In another approach, AI is the operator of the system. This system has a tool layer built in at the foundation for AI to use—dozens of read-only tools and more than a dozen write tools. Through this toolchain, AI reads content, writes articles, uploads images, and updates templates. AI does not “exit after generation”; instead, it remains in the system, and after the website goes live, it continues to handle updates, maintenance, version changes, and other work.
The gap between the two is not “who is faster at the moment of generation,” but “whether AI is still there after generation is done.”
The second fork: is what gets generated pages, or structure and content?
Go one level deeper and see what it actually generates.
With the template-matching approach, what is generated is a page: the layout is fixed, and the AI changes the text and images inside it. The skeleton stays the same; what you get is still that template, just with content filled in.
With the code-generation approach, what is generated is code: front end, back end, and database. The AI writes all three from a single requirement and delivers a working website. This is more flexible than templates, but what is delivered is still essentially “a traditional website”—pages plus a backend plus a database—just written by AI instead of by hand.
The third approach generates “structure and content,” and the two are separate. Which pages the website needs and how the navigation is arranged come from a blueprint—a description of what each page should do; what content goes on a page comes from independent content blocks. Structure stays structure, content stays content; changing content does not require touching structure, and changing structure does not require rewriting content.
The third fork: where does content grow
This layer is the most practical; it directly determines how long a website can remain in use.
In a template site, content is embedded in the pages. If you want to change a product introduction, you have to find that page, open the editor, make the change, and save it; if you want to adjust the layout structure, you often have to modify the template itself. The content is locked in by the structure.
In a code-generated site, content is stored in a database, which is an advantage over templates. But its problem is this: the structure and the content both grow on the “code.” Redesigning basically means generating a new version. After launch, how the content you edited in the backend is supposed to match the newly generated structure is a very real hassle.
In the third path, content consists of independent content blocks. The four content types—articles, products, cases, and notes—use the same structure: one master record manages shared information such as the cover, category, and visibility, while each language’s own title, body, and SEO hang beneath it. A page is just a container, and through the “list injection” mechanism, content blocks are retrieved and filled into it.
The result of this design is that content can be updated independently, migrated independently, and read and rewritten independently by AI, without being tied to any particular page. When the website is redesigned, the content follows along rather than being redone from scratch.
The Fourth Fork: How Does the Website Change After Delivery?
A website isn’t fixed on the day it’s delivered—it keeps changing. The three approaches differ enormously in their ability to handle change.
To change the structure of a template site, you basically have to go back to the provider or apply another template.
For a code-generated site, redesign depends on regeneration—redescribe the requirements, and the AI produces another version. There’s a gap in between: how versions connect, and how old content migrates to the new structure, requires manual cleanup.
The third path provides a dedicated mechanism: template packages are uploaded to a "development slot" and written in overwrite mode; the system automatically retains the most recent few snapshot versions, and you can roll back with one click in the admin backend. In other words, a website can be repeatedly reworked, re-versioned, and rolled back—the previous version can return at any time.
This difference isn’t visible day to day; it shows up when the website has been live for six months, the product line has changed, and the structure needs to be replanned.
The fifth fork: multilingual and SEO practices
These two things reveal most clearly how deeply an approach has thought through “long-term operation.”
In the template-and-code-generation approach, the usual multilingual method is basically “build a separate set of pages for each language,” or rely on a translation plugin to swap out the text while the structure and content remain the same, so as languages increase, maintenance costs rise with them.
The multilingual approach of the third path is “one master record plus one translated version for each language”: cover, category, and visibility are shared across all languages, while title, body, and SEO are independent per language. A piece of content only needs translations added, not to be redone; if a language is missing, the system can flag it.
For SEO, template and code generation provide the basic title, keywords, and description. In addition to these three, the third path also has a built-in “discovery page” in the system specifically for AI search engines—listing the site’s externally exposed data API inventory so that AI search engines can directly understand the website’s data structure and then cite it.
Today, when “fewer people click links and more AI directly gives answers,” this latter capability is becoming as important as traditional SEO.
How should companies compare?
There is no absolute good or bad among the three paths; they serve different needs.
Template matching suits clients with standard requirements who want to launch quickly—it is mature and low-barrier; code generation suits those who want flexibility and can accept tinkering on their own later; the path that makes AI the operator suits businesses that treat the website as a long-term operating tool and value content and continuous iteration.
But one thing is common: don't just look at whether AI can "generate" a website; look at what happens after it is generated—whether the content is easy to change, whether the structure is easy to adjust, whether multilingual and SEO can keep up, and whether the website can keep iterating forward. These are what determine how many years a website can be used.
HOLYPro digital platform takes the third path. We won't expand on ourselves in this article; we only lay out the three paths; if you take the questions in the table above to ask any provider, the answers will tell you which path they belong to.