As a Business Analyst and Project Manager here in Brocoders, building prototypes is part of my job. UX prototypes help capture requirements and align stakeholders before development begins. For years, I worked with tools like Balsamiq and Figma to create early drafts of products.
Now, with AI-driven low-code platforms, I can build something that feels like a real product - without writing code. The choice usually comes down to two tools: V0 and Replit. Both promise speed, but they serve very different needs.
In this article, I’ll share how we approached these tools in one of our client projects and what we learned along the way.
Client Background: TransLogiX
TransLogiX (name was changed) is a logistics company that manages carrier payments and reconciliation with USPS contracts. Their business relies on processing thousands of records, matching vouchers against carrier loads, and resolving discrepancies in billing.
When they came to us, the challenge was clear: they needed a prototype to visualise these processes before building a full system. The goal was not just to design screens, but to test workflows, handle edge cases, and help stakeholders understand what a real solution might look like.
That’s where we turned to low-code AI platforms.
Prototyping with V0
When I first used V0 for TransLogiX, it felt powerful - we had screens ready in a single evening. However, as soon as we began mapping real business processes, the cracks started to appear.
The data we worked with wasn’t simple. Imagine a giant spreadsheet with more than fifty columns, each row having several status labels. V0 could make the table look nice for a demo, but as soon as we tried to represent this complexity, it fell apart. During workshops, even small changes from stakeholders - like adding a new status or tweaking a field - caused the screens to break, and we had to spend extra time fixing them manually.
The operations team also needed something practical: tables that let them filter, group, and scan thousands of records quickly. In V0, these tables behaved like a wobbly Jenga tower. They looked fine until you tried to actually use them for work.
Data uploads became another weak point. Our client had to bring in large CSV and Excel files from different sources, and V0 simply couldn’t process them reliably. Imports failed, retries were clunky, and people stopped trusting what they saw in the prototype.
Financial accuracy was even more critical. When you’re reconciling payments, rounding rules and thresholds have to be consistent. In V0, formulas were scattered across different components, so results weren’t always reproducible. From a business perspective, that meant the numbers on the screen couldn’t be fully trusted.
Finally, collaboration slowed down. Behind the scenes, developers had trouble reviewing changes because V0’s generated code was messy. For me as a BA, this meant slower iterations, less visibility, and more risk of hidden errors slipping through
As I worked through these pains, I came across developers voicing the same frustrations on Reddit. One comment stood out:
Bolt and Lovable get you a nicer looking landing page. But I find Replit is better for more complex apps.
Another one echoed my own struggles:
Rather than just a nice UI, [Replit] got me closest to a functioning prototype.
That captured the heart of the issue. V0 delivered momentum at the start, but as soon as the prototype needed to behave like a real system, the structure began to collapse.
Moving to Replit
Shifting TransLogiX into Replit changed the project completely. The platform gave us a full environment, not just UI screens. We imported the two V0 prototypes into Git and reorganized them into a single codebase with proper layers — domain, services, adapters, and UI. Secrets, environment variables, server logs, and quick preview links were all available in one place without any extra local setup.
With backend and frontend side by side, we could build APIs, run ingestion processes, and set up background jobs like queues, cron tasks, and webhooks. Heavy operations, such as large file processing, were offloaded into separate workers. External integrations like S3 storage, databases, or analytics plugged in naturally.
The user experience for operations improved dramatically. We deliberately chose TanStack Table with virtualization, added saved filters and views, status chips, bulk actions, hotkeys, and consistent date utilities. Our financial formulas and thresholds were packaged into testable modules with unit tests, ensuring accuracy and reproducibility.
Growth became manageable. We relied on TypeScript with schema validation through Zod/Valibot, centralized converters, and standardized rounding logic. Continuous Integration ran on every pull request, with linting, tests, and preview deployments — no hidden “magic generation” to break unexpectedly.
Collaboration also shifted. Replit gave us a shared space where Git diffs were clean and preview links were live. PMs, BAs, and stakeholders could all see progress transparently. The team adopted repeatable practices - CLI skeletons for imports, migration templates — reducing manual coordination.
The transition itself was straightforward:
From V0 to Git → we cleaned up structure, extracted utilities and formulas into separate modules, and added typing.
From Git to Replit → we set up environment variables and secrets, added ingestion and reconciliation workers, and configured error logs and reports.
On the UI side → we replaced generated screens with responsive flows that included progress indicators, clear error and empty states, saved filters, and stable keys.
The results:
Stability: large file imports stopped failing, with visible progress and retries. Quality: formulas and rounding were covered by tests; regressions surfaced at PR stage. Speed of change: post-workshop updates modified only the domain layer, leaving UI intact. Transparency: stakeholders had preview links, logs, and artifacts they could verify independently.
For TransLogiX, Replit meant keeping the iteration pace of V0 but gaining the reliability and scalability needed for a real product.
When to Use V0 vs Replit
| Scenario | Use V0 | Use Replit |
|---|---|---|
| Purpose | Super effective for quick UI demonstrations — workshops, investor pitches, internal validation. | Designed for controlled architecture with backend, background jobs, and tested business logic. |
| Time-to-first-demo | Overnight UI screens, instant stakeholder momentum. | Same iteration pace, but with stability for live use. |
| Complexity of data | Simple CRUD, small datasets, minimal statuses. | Multi-table models, 50+ fields, complex statuses, reconciliation logic. |
| Workflows | Linear flows, light interactions. | Ingestion pipelines, retries, reconciliation, cron/webhooks. |
| User experience | Mockups with limited interactivity. | Rich grids, virtualization, saved filters/views, bulk actions, hotkeys. |
| Accuracy & testing | Weak for precision — formulas scattered, no test coverage. | Strong — centralized formulas, schema validation, unit tests, CI pipelines. |
| Collaboration | Best for solo founders or small demos — noisy diffs in Git. | Team-friendly — clean Git diffs, preview links, transparent for PM/BA/stakeholders. |
| Longevity | Disposable prototype, likely to be rebuilt. | “Speed + control” → scalable foundation for a live product. |
To complete the picture, it’s not only about functionality - pricing and credit usage also influence when V0 or Replit makes sense.
Comparison of Pricing and Credit Usage
| Aspect | V0 | Replit |
|---|---|---|
| Entry cost | Lower — suited for quick demos and short-lived prototypes. | Higher baseline — often described as “a bit expensive,” with bills of $50–$100+ for complex use. |
| Usage model | Pay for AI-driven UI generation, works well for early iterations. | Credit-based system; users warn credits can “burn really fast,” especially with AI Agent or frequent checkpoints. |
| Predictability | Costs remain low if prototype stays simple and disposable. | Less predictable — some praise all-in-one convenience, others find the credit system unclear. |
| Scalability of cost | Fine for throwaway MVPs, but migration needed for growth. | Scales with usage; “Your wallet won’t thank you” was one user’s warning. |
| Alternative strategies | Often abandoned once real product development begins. | Some users combine other tools (e.g. Lovable + Windsurf) for “far less $$$” than running everything in Replit. |
Reddit voices:
- “Beware of unexpectedly high bills.”
- “Replit doesn’t need connections from external apps and keeps secrets in one place.”
- “Credits in Windsurf are a bit more clear than checkpoints in Replit.”
Conclusion
Prototyping has always been about speed, but speed alone isn’t enough once real workflows, data, and accuracy come into play. V0 is excellent for quick UI demonstrations - it creates momentum, gets stakeholders engaged, and helps validate assumptions in the earliest phase. But it struggles once prototypes evolve into products that demand controlled architecture, background jobs, and testable business logic.
Replit kept the same pace of iteration but added what V0 lacked: structure, stability, and transparency. It gave our team and stakeholders confidence that the prototype could actually grow into a product. That came with a trade-off - higher costs and sometimes confusing credit usage - but for projects like TransLogiX, the combination of “speed + control” outweighed the financial overhead.
From my perspective as a Business Analyst, the lesson is simple:
Use V0 when you need to demonstrate ideas fast and expect to throw the prototype away.
Use Replit when you want the first prototype to also serve as the foundation for a live product.
The best MVP tool isn’t the cheapest or the flashiest. It’s the one you can grow out of gracefully without starting over - and for us, that meant moving from V0 to Replit.