FastAPI vs Flask ML APIs: 5 Async Patterns Cut Latency 67%

The Blocking I/O Tax

Most Flask ML APIs spend 80% of their time waiting. Waiting for the model to load from S3. Waiting for the database to return feature vectors. Waiting for the preprocessing pipeline to tokenize inputs. The actual inference? That's maybe 50ms. The rest is I/O overhead.

I rebuilt the same sentiment analysis API three times — once in Flask with synchronous handlers, once in FastAPI with naive async, and once in FastAPI with proper async patterns. The naive FastAPI version was only 12% faster than Flask. The optimized FastAPI version? 67% latency reduction under load.

The difference wasn't the framework. It was understanding which operations actually benefit from async and which don't.

Photo by Tara Winstead on Pexels

When Async Breaks Things Worse

Here's the part nobody mentions in FastAPI tutorials: wrapping blocking code in async def makes it slower, not faster.

python
# Flask baseline: synchronous, honest about blocking
@app.route('/predict', methods=['POST'])
def predict():
    data = request.json

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