AI-Assisted Development Tools vs. Low-Code/No-Code: What to Use When

There are pros and cons to using AI-assisted development tools versus low-code/no-code platforms for creating applications with minimal coding. Here are the key differences between the two techniques.

Christopher Tozzi, Technology analyst

June 27, 2024

4 Min Read
blurred programmer behind screen of code

For years, low-code/no-code platforms offered an easy way of developing applications without having deep programming skills. Then, AI-assisted development tools came along, providing another approach to coding without having to code.

This means that anyone seeking to build software today with minimal coding faces a major choice: whether to use low-code/no-code tools on the one hand or AI on the other to develop applications.

We can't tell you which solution is best. But we can walk through the pros and cons of each approach and explain the major differences between low-code/no-code programming and AI-assisted programming.

What Is Low-Code/No-Code?

Low-code/no-code is an approach to programming that relies on prebuilt code modules to build applications. Typically, low-code/no-code tools allow users to select from a menu of application features and functionality, then put them together to create a complete app.

Applications created using a low-code/no-code approach sometimes require a bit of custom coding in order to tweak functionality. But in other cases, they involve no coding at all — hence the "no code" label.

Low-code/no-code development tools and techniques have been around for decades. They're a mature, tried-and-true type of solution.

Related:4 Ways AI-Assisted Coding Can Benefit ITOps Engineers

What Is AI-Assisted Development?

AI-assisted development is the use of generative AI to write software. This approach usually involves describing what you want your application to do, then letting an AI tool spit out the code to do it.

AI-assisted development became popular just within the past few years, following the launch of solutions like GitHub Copilot and Amazon CodeWhisperer.

The Similarities Between Low-Code/No-Code and AI-Assisted Coding

In some key respects, low-code/no-code and AI-assisted coding tools are quite similar:

  • They both accelerate the coding process by automatically generating most or all of the code necessary to run an application.

  • They both reduce the skill level needed to create software.

  • They both can support a wide range of use cases and application types.

Low-Code/No-Code vs. AI: Key Differences

On the other hand, there are major differences between low-code/no-code programming and AI-assisted programming tools. These differences are the key to why you might want to choose one type of solution over another.

Target audiences

Historically, low-code/no-code programming tools have catered both to professional programmers (who can use them to accelerate the coding process by generating boilerplate code) and "business" users who do not know how to code at all but want to develop software.

Related:Why AI Coding Assistants May Be Greatest Cybersecurity Threat Facing Your Business

In contrast, the AI-assisted development tools available today are really designed only for professional programmers. They assume that users have some understanding of how software works and that they will use the tools already knowing which languages, frameworks, and architectures they intend to use.


While both low-code/no-code and AI programming tools can be used to develop a wide range of apps, AI offers a more flexible and open-ended solution.

That's because most low-code/no-code tools restrict users to developing applications using certain languages. The apps also must often be deployed on a special platform.

AI-assisted development tools are different because they allow you to write generic source code that can run anywhere.

Licensing concerns

Unlike low-code/no-code tools, AI-assisted coding is currently subject to some legal debates. They center on claims that because generative AI models are trained on open source code, any new code generated by AI tools must comply with the open source licenses that govern the training code.

It remains to be seen whether courts will agree with those claims. But if they do, businesses that use AI to write code could find themselves facing complicated software licensing issues.

Choosing Between Low-Code/No-Code and AI

So, which type of solution is best? To answer that question, ask yourself the following:

  • Are you a professional coder? If not, you should use a low-code/no-code platform designed for non-programmers. AI coding tools won't be a good fit for you.

  • Do you want to use a certain language or framework? As long as it's a common language or framework, any AI coding tool can probably generate code using it. But a low-code/no-code platform may not support it.

  • Where will you deploy your app? If you need to deploy it in an environment or platform not supported by a low-code/no-code solution, your only viable approach may be to create an app using AI so that you can run it anywhere.

  • Are you worried about software licensing? If you don't want to worry about potential future licensing violation claims related to AI-assisted coding tools, stick with low-code/no-code solutions.

Using Low-Code/No-Code and AI Together

On a parting note, it's worth keeping in mind that low-code/no-code and AI-assisted coding aren't necessarily either-or propositions. You can use both types of solutions simultaneously; for instance, you could develop the main components of an application using a low-code/no-code platform but use AI to write custom extensions or integrations. Some low-code/no-code software vendors have added generative AI features to their products over the past year or two, making it easier than ever to use both techniques simultaneously.

About the Author(s)

Christopher Tozzi

Technology analyst, Fixate.IO

Christopher Tozzi is a technology analyst with subject matter expertise in cloud computing, application development, open source software, virtualization, containers and more. He also lectures at a major university in the Albany, New York, area. His book, “For Fun and Profit: A History of the Free and Open Source Software Revolution,” was published by MIT Press.

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