Is Django Outdated? Reflections from ASGI to the AI Era

Foreword

This article was originally just a thought in the blog post for the new release of DjangoStarter v3.2.1, but as I wrote more and more, I decided to split it into a separate article. 😄

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"Is Django outdated?"

This is a question that can almost be seen daily on forums, Reddit, or Zhihu.

But upon closer thought, the underlying issue behind this question is not that "Django has become bad," but rather that technological trends have changed.

Async Pain Points

In summary, Django's biggest issue isn't performance—it's its poor support for asynchronous operations, a limitation that becomes even more pronounced in the AI era. WSGI seems truly outdated; large model streaming requires SSE, and real-time collaboration/conversations require WebSocket. In this regard, Django is indeed at a disadvantage.

While Django can use ASGI servers like Daphne from the Python ecosystem, the fact that Django ORM still doesn't support asynchronous operations as of 2025 makes development painfully cumbersome.

Although Django can handle asynchronous communication using Channels + Daphne, the overall development experience is far from smooth. I've encountered these pitfalls before; for reference: Using Django-Channels for WebSocket Communication + Large Model Conversations.

Ecosystem and Engineering

But this does not mean Django is outdated. On the contrary, Django's value remains irreplaceable:

• Mature ecosystem: Almost any functionality has a pre-built solution ("reinventing the wheel")—authentication, admin panels, permissions, payments, etc.
• Well-established engineering practices: From project structure to configuration, from security to ORM, Django has already thought it through for you.
• Proven over time: Companies like Instagram and YouTube still use it, demonstrating its reliability.

Over the years, Django has built up a vast ecosystem and accumulated excellent engineering practices.

There's a saying that "any Python web project will eventually evolve to resemble Django."

This isn't an exaggeration—when a project scales, you'll realize Django's approach is actually the most stable.

This clearly demonstrates Django's strengths in developing large-scale projects, further backed by its adoption in products like Instagram and YouTube.

New Challenges in the AI Era

However, from the perspective of independent developers or AI-native applications, Django's shortcomings become more pronounced:

• Real-time capability: Large language model interactions inherently rely on SSE/WebSocket. Django can handle this, but it's not built-in natively.
• Lightweight nature: Frameworks like Next.js and FastAPI are better suited to the approach of "full-stack integration + async-first."
• Trial-and-error cost: Starting a complete project with Django involves higher costs and is less flexible compared to lightweight frameworks.

Therefore, for me, while DjangoStarter will continue to be maintained, I may not prioritize Django for my future new products.

Conclusion

Django is not outdated; it remains an extremely robust choice.

• If you are part of an enterprise team working on a medium-to-large, long-term maintenance project: Django is still a top-tier framework;
• If you are an independent developer aiming for rapid iteration and exploring AI-native applications: you may need to consider alternatives like LiteStar, Next.js, etc.

The final choice of framework depends on your project type and goals.

For me, Django is no longer my first choice, but it will always be the most reliable old companion in my toolbox.