People often ask me where to even start if you want to build a working product quickly but do not have a team yet. Just two years ago, the answer was: find a developer, set aside a budget, wait for months. Today the answer is different: there are so many tools that choosing between them has become a separate task in itself.
According to data, as early as 2026 about 75% of all new applications will be built using low-code or no-code technologies. That is a jump from less than 25% in 2020. So the question is no longer whether to try AI tools, but which one to choose for your specific task.
What an AI Tool for Launching a Product Actually Means
Mykhailo Zborovskyi identifies at least three categories that are often confused:
- No-code AI builders. They generate a working application from a text description, without writing code by hand;
- AI assistants for developers. They help write and review code but require basic technical skills;
- AI agents for individual processes. They automate a specific function (analytics, support, data processing) rather than the entire product.
The first thing to do is to answer honestly which of these three categories you actually need. The most common mistake I see is a person looking for a no-code builder when what they really need is a narrow AI agent for a single task.
How to Tell Whether a Product Is Ready for AI Automation
In my opinion, there is a simple test. If a task can be clearly described in the words "do X when Y happens", it can probably be automated today. If a task requires constant contextual judgment (negotiations, complex exceptions, reputational risks), artificial intelligence is still more of an assistant than a replacement.
Industry data confirms this. According to estimates, in 2025, 90% of developers regularly use at least one AI tool in their work, and controlled experiments show up to a 55% speed-up in completing typical coding tasks thanks to AI. This refers specifically to typical, well-defined tasks, not to the entire product development process.
In practice, it looks like this:
- Ready for AI. Generating code from a description, initial data processing, standard support replies, basic analytics;
- Not fully ready yet. Complex legal decisions, final communications with key clients, strategic product priorities.
What to Choose? A Ready-Made Platform or In-House Development
This is the question Mykhailo Zborovskyi hears most often, and the honest answer is: it depends on the stage.
At the hypothesis-testing stage, a ready-made AI platform almost always wins: it is faster, cheaper and carries no risk of freezing the budget in development that may turn out to be unnecessary. But once the product has proven demand and started to grow, the situation changes. Requirements appear that ready-made platforms simply do not cover, including deep customization, data security and compliance with industry regulations.
In the niche where the expert works – iGaming – this is felt especially clearly. AI tools today really do allow one person to quickly assemble the technical framework of a product or test a specific mechanic. But as soon as it comes to a full-fledged product with licensing, compliance and payment security, that is infrastructure that no AI tool can replace yet. This is not a limitation of the technology, just a different category of tasks.
How Much Does It Really Cost to Launch an MVP on an AI Stack?
Here I deliberately avoid specific figures in hryvnias or dollars, because they become outdated in months, not years. But the overall scale of the market is telling in itself. According to a Precedence Research forecast, the global market for AI website and app builders will grow from $2.69 billion in 2025 to $17.43 billion in 2035, which means the barrier to entry keeps falling rapidly for anyone who wants to give it a try.
A practical guideline: compare not the absolute cost of a subscription, but the cost of one tested hypothesis. If an AI stack lets you test an idea in days instead of months, the savings are measured not in money spent on the tool, but in how quickly you find out whether it is worth continuing.
What to Look at When Comparing AI Tools
When I have to choose between several platforms, I check them against a short internal checklist:
- Can it be tested for free before paying?
- Are there real examples of finished products, not just demos?
- How easy is it to move the result off the platform if it stops being a good fit?
- Are the terms regarding data and security transparent?
I would highlight the last point separately. A tool that saves a week of development but creates a risk to user data costs much more than it seems at the start.
AI does not eliminate the need for a team; it changes the moment when that team becomes necessary. Today, one person with the right tool stack can get to a working prototype much faster than a few years ago. But once the product has proven its value, infrastructure, compliance and the people responsible for stability are still irreplaceable.
Why You Can Trust This Guide
This material is built on practical experience of launching products and is based on open research and recommendations, in particular from NIST and ENISA on risk management when using AI systems. Figures are given with a link to the source, and forecasts are marked as guidelines rather than guaranteed results.
How We Verify Information
Every market estimate is checked against the primary source, not a retelling. If data is outdated or contradictory, we point this out directly in the text.