[StreamBright]

1. Thanks Jon, this is exactly the sort of comment I was looking for. Yes I totally agree, if you have a memcache use case your are going to hit network limitations before you hit CPU. I was just pointing out that HTML rendering is different from running memcache or a distributed disk persisted key-value store. Amazon figured out the need for different use cases and introduced R3 instance types with few cores, large amount of memory and enhanced networking support. This is why I found a little-bit unfortunate the make general statements like "4 core instance has better networking on GCP". Depends which instance type you are using.

https://aws.amazon.com/about-aws/whats-new/2014/04/10/r3-ann...

2. Agreed, making it easier for the customers is always better.

Heh, I was working there when Retail moved to EC2, much fun! :)

[vgt]

Google Cloud platform offers Custom Machine types specifically to help you configure the most optimal CPU/RAM combinations:

https://cloud.google.com/custom-machine-types/

Quizlet's post alludes to Google's attitude as well. With exception of GPU instances, Google's VMs are generic. You are able to get incredibly fast SSDs, best in class networking, etc, on just typical instances. Benefits are pricing is simpler, spot instance/preemptible VM market is simpler, and you get much more architectural flexibility.

(Disclaimer - work on Big Data @ Google Cloud)

[abrookewood]

That should probably be emphasised a bit more in both the article & in general. It's fairly common to have wasted RAM or CPU or whatever because you had to pick a particular instance type in AWS ("I need better networking, so I'll have to pick a larger instance ... pity I don't need those extra cores").