Like the title says, looking at the cost of entry-level GPU instances on the major clouds:

AWS: p2.xlarge — 1 Tesla K80, 4 vCPUs, 61gb ram $0.900/hr

Azure: NC6 — 1 Tesla K80, 6 vCPUs, 56gb ram $0.900/hr

GCP: 1 Tesla K80, 6 vCPUs, 52gb ram $0.663/hr

Further, for training CNNs on the K80 I never exceed 4-5gb of memory usage or reasonable utilization of 4 vCPUs. Since GCP is the only cloud that gives me ability to finely tune specs I can even further decrease cost for ML applications. For example:

GCP: 1 Tesla K80, 4 vCPUs, 5gb ram $0.424/hr

When benchmarking resnet50, this cheaper configuration provides no performance decrease compared to the more expensive instance.

Perhaps spot instance pricing (low-priority for azure, preemptible for GCP) comes into play, where GCP is in the middle of the pack:

AWS: p2.xlarge — 1 Tesla K80, 4 vCPUs, 61gb ram $0.270/hr

Azure: NC6 — 1 Tesla K80, 6 vCPUs, 56gb ram $0.180/hr

GCP: 1 Tesla K80, 6 vCPUs, 52gb ram $0.236/hr

This kind of instance, however, does not work for every use case so the regular on-demand pricing difference is still significant.

This all leaves me wondering:

If you train your models on the cloud, which provider do you use?

Can you imagine any reasons/use cases/etc that might warrant picking a provider other than GCP?

What is GCPs business model? How can they make money selling for so much less? Is this a loss leader to gain market share?