Asynchronous support in Django: when to use it, how it works, and when it's NOT appropriate.

Django has always been synonymous with productivity: a powerful ORM, a production-ready admin, and a mature ecosystem. However, for years it carried a reputation that was hard to ignore: not standing out for performance. Much of that perception stems from its synchronous-by-default model.

Since Django 3.x, the framework incorporated asynchronous support, and with it, a very common question arose:

is it really worth using async in Django?

The short answer is: it depends on the type of load.

The long answer is exactly what you are about to read here.

What is asynchronous support in Django?

Asynchronous support in Django allows defining asynchronous views (async def) and executing the entire request chain in asynchronous mode when the application is deployed on ASGI (Asynchronous Server Gateway Interface).

In simple terms:

• Django can avoid blocking the server while waiting for I/O operations.
• A single worker can handle many concurrent requests.
• It is especially useful for external API calls, streaming, long-polling, or slow databases.

Something important that often goes unnoticed:

Async views also work under WSGI, but without real performance benefits and with additional penalties.

Django Sync vs Async: key differences that truly matter

Synchronous model: how it works and why it limits performance

In the traditional model (Sync):

• One worker → one request at a time.
• If the request waits for I/O (database, network, disk), the worker remains blocked.
• To scale, more threads or processes are added… with higher memory consumption.

In practice, this makes Django suffer especially when:

• There is network latency.
• Many database queries are made.
• It depends on external services.

This was evident when analyzing endpoints that simply "waited" for the database: the server was idle, but it couldn't handle new requests.

Asynchronous model: collaborative multitasking and real concurrency

With Async, the model changes:

• A worker can handle many requests at the same time.
• When a task reaches an await, it releases control.
• Other requests move forward in the meantime.

This approach is known as collaborative multitasking and is much more efficient than traditional context switching between threads.

Mind you: async does not speed up the code by itself.

What it does is make better use of I/O idle time.

ASGI vs WSGI in Django: why the server matters

What happens if you use async under WSGI

When you run an async view under WSGI:

• Django creates an "emergency" event loop.
• You can use await, but…
• Each request still uses a thread.

Result:

• ✔ It works
• ❌ It doesn't scale
• ❌ You don't get real concurrency

Why ASGI unlocks true asynchronous performance

To get real benefits you need:

• ASGI
• A server like Uvicorn, Daphne, or Hypercorn
• Async-compatible middleware

It can be used for:

• Hundreds of concurrent connections
• WebSockets
• Slow streaming
• Long-polling

But beware: a single synchronous middleware forces Django to go back to the thread-based model, nullifying much of the gain.

Asynchronous views in Django: how they are implemented

Async views with async def

Creating an asynchronous view is trivial:

from django.http import JsonResponse 

async def my_view(request): 
   return JsonResponse({"mensaje": "Hello sinse async"}) 

For I/O operations, you can use async-native libraries like httpx, aiofiles, etc.

Classes, middleware, and compatibility

In class-based views:

• HTTP methods (get, post) can be async def.
• __init__ and as_view() remain synchronous.

If you need to use parts of Django that are still sync (like part of the ORM), you must use: 

from asgiref.sync import sync_to_async 
resultado = await sync_to_async(funcion_sync, thread_sensitive=True)()

Real performance: what happens in CPU-bound vs IO-bound loads

Why async performs worse in CPU tasks

In load tests with CPU-bound routes:

• Async showed less predictable behavior.
• Sync had greater stability and more total successes.
• The event loop cannot parallelize intensive CPU tasks.

For CPU-bound loads, Sync remains the best option.

Why async multiplies performance in IO and high latency

In IO-bound routes (database reading and writing):

• With 200 ms latency, async was:
  • ~40× better in reads
  • ~230× better in writes
• Sync degraded quickly because workers were left waiting.

This confirms a key thing I learned while testing:
latency is the worst enemy of the synchronous model.

Async, on the other hand, takes advantage of that idle time.

Current limitations of asynchronous support in Django

The real state of the Asynchronous ORM

The async ORM is still evolving:

• Not everything is 100% asynchronous.
• Some operations are still sync internally.
• You have to mix await with sync_to_async.

It works, but it requires judgment.

Synchronous middleware and other bottlenecks

Other critical points:

• Database connection limits (Postgres was a key factor).
• Middleware not compatible with async.
• Additional complexity in debugging.

Async does not simplify, it optimizes.

Should you use asynchronous support in Django in your project?

Cases where async does make sense

• APIs that consume other APIs
• Operations with high latency
• Streaming or WebSockets
• Intensive IO (DB, network, queues)
• Services with high concurrency

Cases where sync remains the best option

• Heavy CPU processing
• Simple logic and low concurrency
• Small apps where complexity is not justified

Best practices for using async in Django without breaking your app

• Use async only where it adds value
• Measure before and after
• Avoid mixing unnecessary sync middleware
• Control database connections
• It does not replace Celery: they complement each other

Conclusion

Asynchronous support in Django is not a silver bullet, but used correctly it can completely transform an application's performance.

The practical rule that experimentation left me with is simple:

CPU-bound → Sync
IO-bound → Async

If you understand that difference and choose wisely, Django remains an extremely solid option even in high-concurrency scenarios.

Quick FAQs

• Is Django truly asynchronous?
  • Yes, under ASGI and without synchronous middleware.
• Do I need to rewrite my entire app in async?
  • No. You can mix sync and async views.
• Does Async replace Celery?
  • No. Async handles concurrency; Celery handles background tasks.