Today, innovation is no longer hidden behind thick walls of code, but lies bare on the desks of founders, product managers and everyone who drives innovation in companies. The toolbox could hardly be fuller: from visual drag-and-drop platforms to AI-supported code copilots and classic IDEs, everything is just a click away.
This is precisely why the crucial question immediately arises: Which technology will get your project to the finish line the fastest, cheapest and most reliably? Traditional programming provides maximum control, no/low-code promises speed and accessibility, while AI coding takes both approaches to a new level of speed.
In this blog article, we sort through the jungle of tools and show you - in line with our VisualMakers motto "Empowering People " - how to find the optimal mix for your requirements: quickly and without any nasty surprises later on in terms of costs, maintenance or scalability.
1. introduction
Before we dive into features and specific tips, it's worth taking a quick pit stop.We explain why no/low-code has become so fast, what AI copilots really automate and when classic code is still indispensable.
What is no-code or low-code?
No-code refers to development platforms that allow you to create digital applications without having to write code yourself. Instead, you work with a graphical interface and combine ready-made modules (e.g. for login forms, databases or automation) using drag-and-drop. Low-code goes in a similar direction, but allows for more in-depth code adjustments in certain places. This means that low-code is often suitable for more complex projects in which individual features need to be "customized", i.e. adapted to your own needs.
Well-known no-code or low-code tools include bubble, Glide, FlutterFlow and Make. You can use them to build functioning MVPs (minimum viable products) or prototypes in record time. Companies often use them to quickly test innovations without having to hire a full team of developers or hire an expensive agency.
→ You can find out more about no-code & low-code in our article: No-code vs. low-code - what's the difference?
What is AI coding?
AI coding refers to the use of artificial intelligence when coding. This can be an "AI Copilot", for example, which makes suggestions at every step, provides debugging hints or even generates entire blocks of code. A prominent example is GitHub Copilot. But AI-native development environments (IDEs) such as Windsurf, Cursor and other AI assistants also play a role here.
In addition to these copilots, platforms such as Lovable, Bolt.new or v0.dev, which go one step further: You describe your project in natural language or use a community template, and the AI immediately puts together an executable web app based on modern stacks (such as Next.js, Tailwind CSS or shadcn/ui). After each iteration, you see a live preview, can refine changes via prompt and often get the app deployed automatically - including the URL, which you can share directly with colleagues. This merges prototyping, code generation and hosting into a single, extremely fast workflow.
However, AI coding does not necessarily mean that the AI develops the entire application on its own. Rather, it speeds up the development process by taking over repetitive tasks, detecting possible errors or designing prototypes in the shortest possible time. Depending on the tool, this can go so far as to generate entire websites or apps if you describe your requirements sufficiently.
→ By the way: If you want to get off to the right start with Lovable, get our free Introduction to coding with AI.
How is AI coding changing traditional development?
Software development is traditionally based on manual programming in languages such as Java, Python or JavaScript. This has proven itself over decades, but can be time-consuming and costly. With the advent of AI coding, developers are now faced with a new reality: many lines of code are now automatically suggested or even completely generated.
Of course, this does not completely replace human expertise. Instead, the focus is shifted to understanding and evaluating the code. If you are already an experienced developer, you can now work faster with the help of AI because the AI acts as a kind of co-programmer. However, without a clear understanding of the code base, it can be dangerous to follow AI suggestions without reservation. Trust is good, but code review is better.
Now you're asking yourself: "Should I go for no-code + AI or a traditional approach + AI today?"
There is no one-size-fits-all answer to this question. It depends on what goal you are pursuing, what resources you have available and how complex your project is. We will take a closer look at this in the rest of this article.
2. what you should consider
Tools promise speed, but without the right know-how and a realistic cost window, every project drags on like chewing gum.This chapter therefore sorts out which skills and investments you should plan for.
You need these skills
No-code/low-code tools are designed so that you can get started quickly. Nevertheless, you need a basic understanding of data structures and processes. Even if you don't write any code, it's important to know how a database works or the logic behind a workflow.
With AI coding, you should have at least a rough knowledge of a programming language and an idea of what "good code" means. When the AI makes you a suggestion, it is helpful to assess whether it is robust and understandable.
Traditional Development requires a deeper technical foundation. You or your team should be able to master various programming languages, frameworks and development processes. The topics of versioning (e.g. using Git), testing and software architecture are also more important here.
Simple vs. more complex no-code/low-code tools
There are very simple platforms such as Glide, which require hardly any programming knowledge, but are also more suitable for smaller apps without overly complex logic. Tools like Bubble or FlutterFlow offer significantly more options, but also have a steeper learning curve. You should ask yourself whether you are prepared to familiarize yourself more intensively in order to end up with a more flexible tool.
There are also simpler and more complex tools for automation. Make, for example, is one of the tools with which you can see initial results within a very short time. The connection of ChatGPT and co. is extremely simple.
👇 In this video, Alex shows you how you can use automation to make your everyday life easier with Make and ChatGPT. 👇
AI Coding Tools
These range from GitHub Copilot, which embeds directly into your IDE (e.g. Visual Studio Code), to AI-supported editors such as Cursor or native AI functions in low-code platforms such as WeWeb. It is important to note that every AI has different strengths. Some are very good when it comes to code examples in Python, others are better at JavaScript or SQL. Also, some tools are more adept at handling large code bases, while others are designed for smaller projects.
Learning the tools or coding
The good news is that nowadays most of these tools come with detailed online courses - often even free of charge. In our VisualMakers Academy and on our YouTube channel you will find plenty of tutorials to support you. In addition, platforms such as Bubble, Make or GitHub Copilot offer their own documentation and learning paths.
One key learning: AI assistants make learning to code more accessible than ever before. So if you've shied away from programming so far, an AI-supported approach can be a great way to get started because you get direct feedback while programming.
What it will cost you
Before the first line of code is written - whether by you or an AI - it's worth taking a close look at the cost structure, as this will help determine whether your project remains profitable or is stranded after the MVP.
Subscriptions and hosting costs
No- and low-code platforms almost always charge on a subscription model. The monthly or annual fee then scales either with the number of users, API calls or data volume; AI coding services often also license per team member. If hosting is not included in the package, server and traffic fees add up and increase with each new wave of users.
Learning the tools
Many providers offer solid free tutorials, but if you want to delve deeper - be it via a certificate, bootcamp or 1-on-1 coaching - you have to pay extra. AI assistants such as ChatGPT or GitHub Copilot significantly lower this entry barrier because they answer questions in real time and translate coding hurdles. Nevertheless, plan for training time if you want to roll out AI coding team-wide: everyone needs to understand how Copilots work, how to optimize prompts and how to review suggestions cleanly. These learning and personnel costs are just as much a part of the project business plan as servers and licenses.
3. time-to-market
Time-to-market is the period of time between brainwave and release. If you shorten it, you can gather feedback faster, win customers sooner and generate initial sales sooner. How much time should you allow in each case?
Traditional development takes the most breath here. Architecture, coding, testing and deployment easily add up - complex platforms often take many months, often several years, before a first version even goes online.
You think: "Programming used to take time - AI is changing that." And yes, copilots suggest boilerplates, search for bugs and speed up individual tasks. Nevertheless, classic code remains a craft. You have to check the suggestions, weave them into the overall architecture and test them.
The risks of relying unconditionally on copilots are real. Unseen security gaps, unreadable logic or code that doesn't fit in with the rest can later catch up with you as so-called technical debt. There is also a legal component: IT lawyer Chan-jo Jun warns that AI-generated code often contains strikingly similar sequences to the training material. Depending on the license of the source code, this can mean that your project suddenly falls under a strict copyleft obligation or that individual components may not be used commercially at all. Anyone purchasing or delivering software should therefore check (or have someone check) whether lines of AI code are copyright-free, establish suitable review processes and contractually stipulate who will assume liability in the event of an emergency - otherwise the hoped-for shortcut will quickly turn into an expensive detour.
Low-code reverses the relationship between effort and speed. Drag-and-drop platforms such as Plasmic, WeWeb or FlutterFlow require hardly any upfront investment and deliver quickly executable interfaces. An MVP is therefore often ready in weeks rather than months, internal tools with tools such as Glide or Softr even in a few days. The running costs can be planned because the platform provider takes care of technical updates and maintenance.
The comparison is therefore clear: Traditional development takes months to years by nature, but offers maximum control. Low-code reaches its goal in weeks to a few months, scores with low entry costs and little maintenance - as long as the limits of the platform fit your business model. The decisive factor is whether you want to validate quickly today or need maximum flexibility tomorrow - this will determine your optimal course to the finish line.
🎧 Speaking of Plasmic: In episode #125 of our podcast, we talked to Plasmic about developments in the no- and low-code world and how Plasmic uses AI in its own company. Listen to the episode here.
4 When is which approach suitable?
Not every method is suitable for every project. Here we give you some decision-making aids.
When should you use no-code + AI?
- If you're not a developer: You may not have an IT background, but you want to take the initiative and realize your idea. No-code tools help you avoid most hurdles, and AI features explain how to solve challenges without having to dig deep into the code yourself.
- When the app is not yet ready: You are starting from scratch and want to build a prototype quickly to get feedback and inspire potential investors. With no-code platforms such as Bubble or FlutterFlow, you can create an MVP in just a few days.
- If you only want a prototype or MVP to begin with, speed is often of the essence: test your idea on the market and optimize it iteratively. This minimizes the risk of investing a lot of time and money in a product that nobody needs in the end.
- If you want to tap into development but don't want to learn to code 100%: low-code platforms with AI assistants offer depth as required. You can build more than just click apps without having to write every line yourself and can integrate custom code for more complex requirements. Most tools allow snippets or plugins so that critical features can be solved individually or experts can be added later.
- If you want to quickly automate internal processes or workflows in specialist departments: No-code platforms with AI assistance allow teams from HR, Finance or Operations to set up forms, dashboards and approval flows independently. This means there are no queues in the IT roadmap, improvements are implemented directly by the specialists and your company saves handover and coordination time.
When should you rely on code + AI?
- If you already have a code base: Your company already has an extensive application that you would like to expand. Then it's worth combining AI coding with your familiar environment. AI can help you with refactoring or implementing new features.
- If you are a developer - and so is everyone who works in it: A pure team of developers will often prefer the classic code-based approach, as existing experience can be seamlessly integrated. AI is the icing on the cake to work faster or more creatively.
- If you have the skills to find bugs or fix bad code, the AI should let you down: AIs are great, but they can also generate nonsense. If you have the experience to check and debug code, you have a clear advantage.
- If you want to remain 100% flexible with regard to your code: Not all no-code or low-code tools allow you to export the code you have created or host it yourself. With traditional development, you retain full control over the architecture and infrastructure at all times.
- When compliance, audit trails or industry-specific regulations have top priority: In strictly regulated domains such as FinTech or MedTech, you need fully traceable source code, version control and security reviews. Classic development, supported by AI copilots, makes it possible to automatically document every change, incorporate tests against standards such as ISO 27001 or HIPAA and generate audit reports at the touch of a button - something that most no/low-code platforms do not (yet) offer in this depth.
5. conclusion
Digitalization and innovation are no longer topics that are reserved for large companies with huge development teams. Thanks to no-code, low-code and AI coding, anyone - whether founder, product manager or passionate innovator in the team - can quickly create their own digital products and present them to the market or within their own company.
- No-code + AI is unbeatable if you want a working application quickly to present it, collect feedback or simply try out new things experimentally. This keeps costs low and gets you into the test phase quickly. Our community at VisualMakers does exactly that every day: we offer guidance, inspiration and collaboration so that you quickly realize how much potential there is in this type of development.
- Traditional Code + AI, on the other hand, is ideal if you need full control in the long term, build scalable solutions for complex problems and have the necessary trust in your development team (or your own coding skills). Above all, AI can help you work smarter: Detect errors, automate repetitive tasks and give you fresh food for thought.
Our tip: Start small, test different tools and approaches. Be brave and don't be afraid to try things out. VisualMakers offers you an open, approachable community where you can share your learning progress. At the same time, you will find concentrated expertise to help you recognize potential pitfalls early on.
But remember: whether no-code, low-code, AI coding or traditional - in the end, it's always about providing people with a product or solution that adds value. If you take a structured, responsible approach and are willing to learn, you are well on your way to realizing your idea. There has never been a better time to bring your plans to life visually and with AI power!
