Why do the same problems occur with every tool?
Because all five deliver on the same promise: a description turns into working code — and the decisions that get skipped along the way are the same in every case.
AI app builders have changed the way digital products come about. Tools such as Base44, Lovable, Bolt.new, Replit Agent and v0 produce a working application from a written description within a few minutes.
That speed in the first step often creates a false expectation: just because a prototype is built quickly does not mean the application can also be run in production securely, at scale and cost-efficiently.
Across almost all current AI app builders, the same five problem areas repeat:
- Cost and credits
- Backend and database
- Deployment and production
- Security
- Code ownership and vendor lock-in
Why does the cost rise faster than expected?
Because consumption does not grow with the number of requests but with the size of the project: the more context a model has to read for every change, the more expensive the same task becomes. The first prototype is therefore cheap, the twentieth iteration is not.
With every change, AI agents have to understand existing files, components and data structures. The larger a project becomes, the more context has to be processed for that — and the more a change costs that would have been trivial at the start.
Typical cost drivers
- large codebase
- many iterations
- debugging loops
- unclear prompts
- extensive refactoring
- external integrations
- hosting and database
- AI features inside the finished application
How the tools differ
Base44
Alongside AI usage, integrations and day-to-day operations have to be factored in.
Lovable
Fast iterations are cheap; larger agent tasks consume correspondingly more credits.
Bolt.new
On large projects with repeated debugging, a lot of project context accumulates — Bolt is particularly susceptible to that.
Replit Agent
On top of AI usage come classic cloud and infrastructure costs.
v0
Consumption depends heavily on how much context, code and output a single request covers.
Before a project starts, a simple cost matrix is worth the effort, one that separates four areas: AI-assisted development (prompts, agent tasks, code generation), infrastructure (hosting, database, storage), integrations (email, payments, APIs) and day-to-day operations (user access, AI features, logs).
Specific tariffs and credit figures are deliberately not given here: they change on a monthly cycle. The relationship holds regardless: cost and consumption rise with the size of the project.
Where does the backend fail?
On the questions that come after the interface: where the data sits, who is allowed to see what, how users and records relate to each other. An interface can be generated in minutes; a data model with roles and permissions cannot.
The harder questions only begin after the first draft: where does the data sit? Who is allowed to see which data? How does authentication work? How are users and data linked to each other? What happens with several roles? How are external APIs integrated?
Frontend problems are visible, database errors often are not. An application can look correct and still display the wrong data, expose other users' data, fail to save changes, produce inconsistent records or run into trouble with several concurrent users.
How the tools differ
Base44
Tries to integrate as many backend functions as possible directly into the platform.
Lovable
Is often combined with Supabase and is flexible as a result.
Bolt.new
Offers an integrated database option and can additionally work with external services.
Replit Agent
More flexible, but tends to require more understanding of classic backend architecture.
v0
Works closely with the Vercel and Next.js ecosystem and can be combined with various external databases.
Why does the app work in preview but not live?
Because the development and production environments are not identical: different domains, different variables, different permissions. A preview mode that works is therefore not a production test.
“Everything works in the editor, but after deployment the app is broken” is one of the most common situations with AI-generated applications. The cause almost always lies in the environment, not in the code.
Typical causes
- missing environment variables
- wrong API keys
- different domains
- faulty OAuth redirects
- database permissions
- build errors
- CORS
- external services that have not been configured
Test the production environment separately before every launch — with the same domains, variables and permissions that will apply later.
Which security gaps typically arise?
Mostly where access rights to data were never set explicitly — the prototype works because everyone is allowed to see everything. With real users that turns into a gap that nobody notices in day-to-day operations.
AI app builders optimise primarily for producing the requested function. Security requirements, by contrast, depend on context: the agent does not automatically know which data is particularly sensitive, which user roles exist, which regulatory requirements apply and which information must never end up in the frontend.
The most common security problems
- Missing access controls — users can retrieve records that are not intended for them.
- API keys in the frontend — secret keys are delivered to the client.
- Missing role logic — admin functions are only hidden in the interface, but not protected on the server.
- Insufficient validation — user input is not checked thoroughly enough.
- Database rules that are too open — the database allows more access than necessary.
Security checklist before launch
- Authentication
- Roles and permissions
- Row-level security
- API keys
- Secrets
- Input validation
- Error handling
- Rate limiting
- Backups
- Logging
We have described how the move from an AI-generated prototype to an application fit for daily operations works here: When no-code no longer holds.
How big is the lock-in?
One single test decides that: could an external development team carry on with this project tomorrow without needing the original AI app builder? If the answer is unclear, the codebase belongs in your own version control as early as possible.
A frequently underestimated topic is the question of who owns the technical future of the application. External version control gives you a complete history, a backup outside the platform, local development, code reviews, alternative deployment options and a far simpler handover to developers.
Platform lock-in and infrastructure lock-in are two different things
Even when the code can be exported, dependencies can remain: a proprietary database, special auth services, the hosting configuration, internal APIs, platform-specific SDKs. Portability should therefore not be checked at code level alone.
Strengths and risks compared
Each of the five tools has an area where it is particularly strong, and one where the typical problems appear. No tool removes the five problem areas — they only shift.
This table sums up what the five tools are well suited to and where problems typically appear. It is reviewed every quarter against the current state of the tools — they change on a monthly cycle.
| Tool | What it is well suited to | Where problems appear | To the article |
|---|---|---|---|
| Base44 | fast end-to-end creation of an app with interface, database and sign-in | cost, visibility in search engines, security for user data, custom domains | Base44 problems → |
| Lovable | modern web applications with a flexible backend connection, often via Supabase | data model, row-level security, credit consumption, code export | Lovable problems → |
| Bolt.new | direct code generation in the browser, code and preview closely linked | token consumption on large projects, Supabase connection, deployment | Bolt.new problems → |
| Replit Agent | flexible software development in a complete cloud environment | cost model made up of AI and infrastructure, production operations, unwanted changes by the agent | Replit Agent problems → |
| v0 | interfaces, Next.js and the Vercel ecosystem | credit consumption, building the backend, version control, the path into production | v0 problems → |
Which platform suits which use case?
For quick internal tools, Base44 and Lovable are worth a look, for more technical MVPs Bolt.new and Replit Agent, for Next.js projects v0. With long-term products, however, the architecture underneath matters more than the tool.
For quick internal tools
- Base44 and Lovable, when speed matters more than maximum technical control.
For more technical MVPs
- Bolt.new and Replit Agent, when the team wants to work more directly with the actual code.
For modern Next.js applications
- v0, when the project is going to be built in the Vercel ecosystem anyway.
For a long-term SaaS product, the architecture should be examined rather than the tool. Five questions decide the matter: can the code be exported? Is the database independent? Can the project be taken over by developers later? Are there monitoring and backups? Are the costs predictable as usage grows?
The decisive question
The greatest strength of modern AI app builders is speed. The greatest risks lie in areas that are difficult in classic software development too: cost, data architecture, deployment, security and maintainability.
Base44, Lovable, Bolt.new, Replit Agent and v0 solve these problems in different ways. No tool removes them entirely. Anyone choosing an AI app builder should therefore not only ask which tool produces an app fastest.
Which tool will let us run this application securely, economically and independently in six or twelve months' time as well?
Frequently asked questions
Which problems occur with every AI app builder?
Five: cost and credits, backend and database, deployment and production, security, and code ownership and vendor lock-in. They arise because all the tools skip the same decisions in order to reach a working result quickly.
Why does the cost rise faster than expected?
Because consumption rises with the size of the project, not with the number of requests. Every change to a project that has grown requires more context than the same change at the start; debugging loops and large refactorings amplify the effect.
Why does the app work in preview but not live?
Because the development and production environments are not identical. Typical causes are missing environment variables, wrong API keys, different domains, faulty OAuth redirects, database permissions, build errors and CORS. A preview mode that works is not a production test.
Which security gaps typically arise?
Above all missing access controls, API keys in the frontend, admin functions without server-side protection, insufficient validation and database rules that are too open. They arise where it was never established which data belongs to which user.
How do I check how big the vendor lock-in is?
With one question: could an external development team carry on with this project tomorrow without needing the original AI app builder? What has to be checked is not only the code export, but also the database, auth services, hosting configuration and platform-specific SDKs.
Which tool is right for my project?
For quick internal tools, Base44 and Lovable are worth a look, for more technical MVPs Bolt.new and Replit Agent, for Next.js projects v0. With long-term products the architecture decides: code export, an independent database, the ability to hand over, monitoring, backups and predictable costs.