When AI Builders Are Not the Right Choice

Signs You Need Traditional Development

AI builders are powerful but not universal. Here's when to look elsewhere.

Highly Custom Algorithms

If your app's value is in the algorithm itself - a novel recommendation engine, complex financial modeling, custom machine learning - you need developers who understand the math.

AI builders generate standard patterns. Novel algorithms need novel code.

Performance-Critical Systems

Real-time trading systems, game engines, high-frequency data processing - these need hand-optimized code. AI generates correct code, not necessarily the fastest.

Complex Legacy Integration

Talking to a 20-year-old mainframe system with undocumented APIs? That's archaeological work. AI builders expect modern, well-documented integrations.

Highly Regulated Domains

Medical devices, aviation software, critical infrastructure - these have approval processes that require specific development practices, documentation, and testing that AI builders don't provide.

When You Already Have a Team

If you have experienced developers with capacity, they might be faster using familiar tools. AI builders shine when dev resources are scarce.

Signs AI Building Is Fine

Prototyping: Test ideas before investing heavily. Even if you rebuild later, learning fast is valuable.

Standard CRUD apps: Create, read, update, delete. This is AI builders' sweet spot.

Internal tools: Lower stakes, faster iteration, good fit.

MVPs: Get to market, validate demand, then decide on next steps.

Landing pages: Marketing sites, waitlist pages, product showcases.

The Hybrid Approach

Many teams use AI builders for some parts:

• Prototype with AI, rebuild core pieces
• AI for frontend, custom backend
• AI for internal tools, traditional for customer-facing

There's no rule against mixing approaches.