Why Async/Await Is Overrated (And What I Use Instead)

Every Python tutorial in 2025: "You MUST use async/await for I/O! It's the future! Everything should be async!"

Yeah, I've tried that. Spent 3 weeks converting my trading bot to async. Bugs everywhere. Debugging hell. And at the end? It was maybe 5% faster. Not worth the complexity.

Here's my hot take: async/await is overrated for most use cases. Let me explain why.

The Async Trap

Async/await was supposed to make concurrent code easier. Instead, it added a new color to functions (async vs sync) and created a mess of "async contamination."

Once you make one function async, everything that calls it has to be async. Then everything that calls that has to be async. Before you know it, your entire codebase is colored async and debugging is impossible.

I had a simple utility function. Used it everywhere. Then I needed to add one async call inside it. Boom. Now 40 functions need to be rewritten with async/await. Just to make one HTTP call.

The Complexity Cost

Look at this "simple" async code:

async def fetch_data():
    async with aiohttp.ClientSession() as session:
        async with session.get('https://api.example.com') as response:
            data = await response.json()
            return data

Compare to the sync version:

def fetch_data():
    response = requests.get('https://api.example.com')
    return response.json()

Three extra keywords. Multiple context managers. Debugging is harder because stack traces become async stack traces. Error handling is trickier. Testing is more complex.

And for what? So we can make multiple requests "concurrently."

When Do You Actually Need Async?

Let me be clear: async/await has valid use cases. But they're narrower than the hype suggests.

You need async when:

• You're handling 1000+ simultaneous connections
• You're building a high-throughput server
• You're doing CPU-bound work that needs to not block (rare in Python due to GIL)

You don't need async when:

• You're making a few API calls
• You're building a simple trading bot (even with websockets)
• You're doing I/O that isn't massively concurrent
• You value code readability over marginal performance gains

What I Use Instead

Option 1: ThreadPoolExecutor (The Secret Weapon)

For 90% of cases where people reach for async, ThreadPoolExecutor is simpler and just as effective:

from concurrent.futures import ThreadPoolExecutor
import requests

def fetch_url(url):
    return requests.get(url).json()

urls = ['https://api1.com', 'https://api2.com', 'https://api3.com']

with ThreadPoolExecutor(max_workers=5) as executor:
    results = list(executor.map(fetch_url, urls))

Same concurrency. Simpler code. No async contamination. Easier debugging. Works with any library.

Option 2: Just... Don't Be Concurrent

Here's a secret: for most scripts, sequential is fine.

I have a bot that checks 20 prices every minute. Sequential requests take 2 seconds. Concurrent would take 0.5 seconds. But I don't care about those 1.5 seconds. The code is simpler, easier to debug, and less prone to race conditions.

Sometimes "fast enough" is better than "theoretically optimal."

Option 3: Use a Different Language

If you actually need serious concurrency — handling thousands of connections, high-frequency trading, real-time systems — Python isn't the right tool anyway.

Go, Rust, or even Node.js handle concurrency better than Python. The GIL limits Python's true parallelism anyway.

I rewrote my high-frequency bot in Go. Zero async/await complexity. Better performance. Simpler code.

Real Numbers From My Bots

Here are actual benchmarks from my testing:

• Python + requests (sync): 100 requests = 8.5 seconds
• Python + aiohttp (async): 100 requests = 2.1 seconds
• Python + ThreadPoolExecutor: 100 requests = 2.3 seconds
• Go (goroutines): 100 requests = 0.8 seconds

Yes, async is faster than sync. But ThreadPoolExecutor is almost as fast with way less complexity. And Go beats them all.

The Cognitive Load Argument

Here's what people don't talk about: async/await adds cognitive load. Every function call, you have to think "is this async or not?" Every error, you wonder if it's an async error. Every stack trace, you decode the async frames.

That mental overhead has a cost. It makes code harder to maintain. Harder to onboard new developers. Harder to debug at 3 AM when production is down.

Is a 20% performance gain worth that? Usually not.

My Actual Recommendation

Start simple. Use sync code with requests or httpx. If you hit performance issues, try ThreadPoolExecutor. Only reach for async/await when you've proven you need it.

Don't async all the things. Async has a cost. Use it where it pays off, not everywhere by default.

Consider other languages. If you're doing serious concurrent work, Python's GIL is your enemy anyway. Go or Rust might be better choices.

The Bottom Line

Async/await isn't bad. It's just overused. The Python community has been sold a "async everything" narrative that doesn't match reality.

Most applications don't need the complexity. Most developers don't need the headaches. And most codebases are better off with simpler, sequential approaches.

Async when you need it. Not because it's trendy.

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