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"Its not even that you'll hit Google scale, its that you'll hit popular scale and still serve the whole thing out of your Digital Ocean droplet."

Are you saying that model inference is slower or less efficient for a model built and trained in Keras, than the same model architecture built directly in tensorflow?

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Actually, with Tensorflow as a Keras backend, I would expect them to be the same. I am not sure where the performance difference between TF and TF as a backend come from.

I do think that pure TF would be easier to scale up over multiple servers etc. but that's only because I don't know how it would work in Keras. Maybe its easy.

Its pretty straightforward to convert a keras model to a tf graph, as long as you used a tf backend in keras.

I would think the difference would be from the data input pipeline, efficiency in batching, updating online models. The inference itself would be the exact same.

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