I Tried Lovable, Bolt, Cursor, Replit, V0, and Claude. Here’s When I’d Use Each One.

There are tons of AI-powered prototyping tools today like Lovable. Bolt. Replit. Cursor. V0. Claude.

As a Product Manager, I was excited to start using these tools in real projects. But at the same time, I found myself overwhelmed too. With so many options available, I kept asking myself, “Which tool should I use, and when?”

But I believe in the fact that products are built to solve specific problems, and every tool is built with a different vision and excels in different areas. So now I stopped asking, “Which tool is the best?” and started asking, “Which problem is this tool best designed to solve?”

Before that, I asked:

Was I trying to understand whether users would even find value in the idea?

Was I trying to help stakeholders visualize the user experience?

Or do I need to dig deeper into the problem, like the workflow, business logic, and decisions happening in the background?

Once I had clarity on that, a few other questions naturally followed:

• What product question am I trying to answer?
• What do stakeholders need to understand?
• Which stakeholder concerns am I trying to address through this prototype?
• Do I need to demonstrate the user experience?
• Do I need to show the underlying workflow and business logic?
• Do I need to simulate backend processes or external API integrations?
• What data is required to make the experience realistic and believable?

And once I answered these questions, choosing the right tool was not that difficult.

Let’s dive into which tools fit different requirements and where each one delivers the most value.

Lovable

Lovable is usually my starting point when I want to bring a product idea to life quickly.

Imagine you have an idea for a meal subscription app, a fintech dashboard, a marketplace, or an AI assistant. You want stakeholders to click through it, understand the value proposition, and experience the user journey.

What I liked most about Lovable was how quickly I could go from an idea in my head to something tangible. Within a few prompts, I had a prototype that stakeholders could click through, react to, and discuss. Sharing it was simple, and the experience felt much closer to a real product than a traditional wireframe. For early conversations and stakeholder feedback, that speed was incredibly valuable.

What becomes challenging

• As the logic becomes more complex, you eventually need to understand what’s happening under the hood
• Debugging can become difficult if you’re not comfortable reading code
• Backend workflows can become limiting

I use it when the goal is to answer, "Will users understand this experience?”

Best for: SaaS products, onboarding flows, marketplaces, dashboards, customer-facing MVPs.

Bolt

Bolt sits somewhere between prototyping and product development.

The first thing I noticed was that it allows you to build more functional experiences.

Where, I could move quickly, experiment with different ideas, and still build something that felt functional rather than just a visual mockup. It was particularly useful when I wanted stakeholders or users to interact with the product and experience how it would actually work.

What becomes challenging

• You will eventually need to understand the generated code
• Complex changes often require some technical knowledge

When I use it When I need users to interact with a working version of the product rather than just viewing screens.

Best for: Startup MVPs, internal tools, rapid experimentation, proof of concepts.

Cursor

Cursor is where things start getting serious.

At first, a simple interface was enough. But as I started thinking about how the product would actually work, new questions started appearing, and I wanted more flexibility to present my prototype where I could start demonstrating how it actually behaved using the following:

• Backend workflows
• API integrations
• Authentication
• AI agents
• Complex business logic

What becomes challenging

• understanding of code, architecture, and and data flow

When I use it When the prototype involves the following:

• Agents
• Complex workflows
• External APIs
• Backend systems

Best for: AI agents, developer tools, custom workflows, backend-heavy products, technical prototypes.

Replit

Replit feels like the bridge between prototyping and deployment.

What I like about it is that I can build something and immediately share it with people.

Build and share it with no deployment drama.

What becomes challenging

• Requires more technical thinking
• Understanding data structures becomes important
• The quality of your prototype often depends on how clearly you understand the workflow

When I use it When stakeholders ask, "Can I actually try this?”

Best for: Functional MVPs, AI applications, internal tools, early-stage products

V0

V0 excels at generating beautiful frontend interfaces.

If stakeholders need to understand the look and feel of a product, V0 can save a significant amount of time. I found it particularly useful for generating clean, visually appealing interfaces quickly. It helped transform ideas into realistic product screens, making discussions around user experience much easier and more concrete.

What becomes challenging

• Doesn’t solve business logic
• Doesn’t solve backend complexity

When I use it When visual communication is more important than technical validation.

Best for: UI concepts, design systems, frontend prototypes, stakeholder presentations.

Claude

Claude is probably the tool I use before opening any of the others.

But because it helps me brainstorm.

Before building anything, I often use Claude, and What I like is

• Challenge assumptions
• Write user stories
• Draft PRDs
• Generate acceptance criteria
• Analyze customer feedback
• Refine product ideas

What becomes challenging

• It doesn’t replace actual prototyping
• Outputs still need validation

When I use it Before I open any prototyping tool.

Best for: Product discovery, customer research synthesis, PRDs, acceptance criteria, product strategy.

Most important is understanding the problem, the user, and the question you’re trying to answer.

Because at the end of the day, stakeholders don’t care which tool you used. Users don’t care which tool you used.

They care whether the product solves their problem.

Thanks for reading.

I’m constantly exploring product management, AI, customer behavior, and the decisions that shape successful products. If you enjoy these kinds of deep dives and want to follow my learning journey, consider subscribing to my Substack.

I share practical product lessons, case studies, and thoughts on AI from the perspective of someone who is actively learning, building, and asking questions.

See you in the next one.