Is Async Python Really Faster? Uncover the Truth Behind Sync vs Async

Many people claim that asynchronous Python code runs faster than regular (synchronous) code. This article examines what “sync” and “async” mean, how they are realized in web applications, and when async can actually be faster.

1. What do “synchronous” and “asynchronous” mean?

Web applications typically handle many requests from different clients, so concurrency is required to avoid latency. A synchronous server achieves concurrency by using a pool of workers implemented as processes, threads, or a combination of both. Each worker handles one request, and the number of workers is usually set to the number of CPU cores. If more clients arrive than workers, excess requests are queued, causing delays.

An asynchronous server runs in a single process with an event loop that creates a task for each incoming request. Tasks are paused when they need to wait for I/O (e.g., a database response) and resumed by the loop when the I/O is ready. This allows hundreds or thousands of active tasks to be managed efficiently.

2. Two ways to achieve async in Python

The standard way is using the asyncio package, which provides async, await and yield keywords to build coroutine‑based asynchronous code. Other coroutine frameworks include Trio, Curio, and the older Twisted. For web development, async‑capable frameworks such as aiohttp, Sanic, FastAPI and Tornado are available.

A second approach relies on the greenlet library (installable via pip). Greenlets, like coroutines, allow a function to pause and resume, but they do not require special syntax. This enables many existing synchronous codebases to run asynchronously. Greenlet‑based async libraries include Gevent, Eventlet and Meinheld, all of which provide their own event loops and monkey‑patching of blocking standard‑library functions. Flask is the only major web framework that automatically detects a greenlet server and adjusts accordingly, while Django, Bottle and others can also benefit from greenlet servers with appropriate monkey‑patching.

3. Is async faster than sync?

Performance misconceptions abound: raw Python execution speed is essentially the same for sync and async code. The two main factors that affect a concurrent application's performance are context switching and scalability.

Context switching in synchronous programs is handled by the operating system, while async programs perform switches inside the event loop. Optimized async loops (e.g., uvloop, Gevent, Meinheld) can be more efficient than OS‑level switches, but noticeable gains require very high concurrency.

Scalability is where async shines. When a workload is I/O‑bound and the system experiences high load, an async server can start handling all requests immediately, whereas a synchronous server would queue most of them. If the tasks are CPU‑bound, both models perform similarly because the CPU execution speed does not change.

Async may be faster only when:

There is high load (many concurrent requests).

The tasks are I/O‑bound.

Throughput (requests per unit time) is the metric of interest, not per‑request latency.

4. Conclusion

Async applications outperform synchronous ones only under high load and I/O‑bound workloads.

Greenlet‑based solutions let you reap async benefits even when using traditional frameworks like Flask or Django.