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Google Cloud Certified Professional Machine Learning Engineer · Google Cloud
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I think the "high-throughput" is the critical differentiator between choosing B or D. Dataflow is designed for large-scale stream or batch processing, not for low-latency, single-request online prediction. The startup latency of a Dataflow job makes it unsuitable for this pattern.
I would argue that the dataflow job is 'always on' in streaming mode for this use case, whereas the cloud function would incur startup latency. So I chose B rather than D.
Dataflow has autoscale. And in my experience, you use Cloud Functions to small stuff.