Why I Was Wrong About NextJS and LLMs: A Django Advocate's Reckoning

A while ago, I wrote a blog post arguing that NextJS was a better choice than Django for projects leveraging Large Language Models (LLMs). My reasoning was simple: if LLMs can auto-generate boilerplate code, why bother with Django’s “batteries-included” philosophy? Why not embrace a minimalistic framework like NextJS and let the AI handle the rest?

I was wrong.

After experimenting with both frameworks in LLM-driven development, I’ve had to confront hard truths about code quality, maintainability, and the hidden costs of “barebones” architectures. Here’s why I’ve reversed my stance—and why Django might still be the wiser choice for AI-powered applications.

LLMs Generate Code, Not Quality

The initial allure of NextJS was its flexibility. If an LLM can scaffold API routes, React components, and even simple ORM-like logic, why tie yourself to Django’s conventions? But here’s the catch: LLMs are only as good as the data they’re trained on.

NextJS, while wildly popular, has a fragmented ecosystem. Its codebases range from pristine to spaghetti, and LLMs often regurgitate patterns from low-quality, poorly structured examples. The result? Code that works but doesn’t endure—prone to technical debt, security holes, and scalability issues.

Django, by contrast, enforces structure. Its patterns are mature, its documentation is canonical, and its community has spent years refining best practices. When an LLM generates Django code, it’s drawing from a corpus of stable, battle-tested conventions. The framework itself acts as a guardrail, nudging the AI toward solutions that are inherently more maintainable.

NextJS Is Minimalistic to a Fault

NextJS excels at frontend-heavy applications, but when you venture into backend logic, its minimalism becomes a liability. Let’s compare:

Background Tasks: In Django, Celery integration is straightforward. Need to offload LLM inference or batch processing? A few lines of code and you’re done. In NextJS, you’re forced to jury-rig solutions using serverless functions, third-party services, or makeshift cron jobs—none of which feel native.

ORM and Database Layer: Django’s ORM is a masterpiece of ergonomics. LLMs can generate models, queries, and migrations with surgical precision. NextJS, even with Prisma or Drizzle, requires manual plumbing because these packages are notorious for always changing their APIs for no good reason.

Third-Party Packages: Django’s ecosystem thrives on interoperability. Need authentication? django-allauth plugs directly into your user model. Need real-time updates? Channels integrates seamlessly. In NextJS, every package feels like a standalone tool—no shared conventions, no signals, no middleware hooks.

The result? With NextJS, you spend more time debugging generated code than actually building features. Django’s “boring” consistency saves time in the long run.

Stability Matters More Than Hype

Django’s maturity isn’t just about features—it’s about culture. The framework’s users are disproportionately experienced developers who prioritize stability, security, and scalability. When an LLM generates Django code, it’s mimicking patterns refined over 15+ years of production use.

NextJS, while powerful, is still evolving. App Router vs. Pages Router? Server Actions vs. API Routes? The constant churn means even human developers struggle to keep up. For LLMs, this translates to confusion: outdated examples, conflicting patterns, and code that’s brittle by design.

The Hidden Cost of “Just Build It”

LLMs promise to democratize development, but they don’t absolve us of the need for robust architecture. Django’s built-in admin, authentication, and security features aren’t just conveniences—they’re force multipliers.

Imagine an LLM-generated NextJS app:

• User authentication requires stitching together NextAuth, database calls, and session management.
• Admin panels need a custom React UI or a third-party SaaS tool.
• Security headers, CSRF protection, and rate-limiting? All manual.

In Django, these come out of the box. Even if an LLM writes the code, Django ensures it’s secure by default.

Conclusion: Choose the Right Tool for the Job

NextJS isn’t “bad”—it’s phenomenal for frontend-centric projects, marketing sites, or lightweight full-stack apps. But for AI-driven backends, Django’s maturity, integrated ecosystem, and enforced conventions make it the safer bet.

LLMs are powerful, but they’re not architects. They need guardrails, and Django provides them. NextJS, for all its flexibility, leaves too much room for error—especially when the training data itself is a minefield of anti-patterns.

So, to my past self: sometimes, “boring” is better. Sometimes, the framework that does less for you ends up costing more. And sometimes, the AI needs a grown-up in the room.

Django, it turns out, is that grown-up.