AWS Outperforms GCP in the 2018 Cloud Report

Very promising results. Look forward to seeing what’s in store for 2019!

I've also benchmarked GCP vs. AWS [0], and, for the tests that I ran, found that GCP outperformed AWS by a factor of 3:1. Specifically, a GCP instance n1-highcpu-8 with a 256GB pd-ssd disk, clocked in at 11,728 IOPS vs an AWS c4.xlarge with a 256GB gp2 disk, clocking in at 3,634 IOPS.

To put that in context of the blog post, it means your setup can drastically affect your results. Using local NVMe disk, for example, yields excellent results at the expense of increased risk. Also, AWS's io1 disk is very expensive—after my first io1 bill from AWS, I never used that disk type again.

[0] http://engineering.pivotal.io/post/gobonniego_results/

There might be a networking cap & disk I/O issues with the instances you picked on GCP vs AWS.

The GCP instance has 8 Gbps vs 10 Gbps for AWS. I don't really know without seeing the graphs from the instances, if you hit a cap, but this could make a difference in both transfer speeds and latency #'s for GCP. Also, for your local disk test, on GCP, disk size makes a difference to get the best performance. The larger the disk, the better the performance. PD disk read/write performance also comes out of the available network bandwidth! So, the instance you picked on GCP was at a disadvantage right from the start [3]. This likely explains the I/O Experiment graph and the "67x difference in throughput" as you're likely hitting caps, both in terms of network bandwidth, and disk performance compared to AWS. Seeing anything where it is x67 difference is a pretty big red flag that something strange is going on and needs further investigation.

GCP's n1-standard-16 = 8 Gbps max [1]

AWS's c5d.4xlarge = 10 Gbps max [2]

I guess the problem with comparing clouds, it is never apples vs apples, and I don't fault you for picking what do you (as it is not obvious). GCP typically gives you (core count / 2) = # Gbps network bandwidth. A good followup to your comparison might be to investigate why they #'s are different. Does adding more cpus, memory, network bandwidth increase performance?

[1] https://cloud.google.com/blog/products/gcp/5-steps-to-better... (see section #3).

[2] https://aws.amazon.com/blogs/aws/ec2-instance-update-c5-inst...

[3] https://cloud.google.com/compute/docs/disks/performance#size... (see the table re: disk size to bandwidth)

I would compare against the C5 instance type; it uses the newer "Nitro" hypervisor.

Developer experience on GCP is vastly superior to AWS.

- Pricing on GCP is much easier, no need to purchase reserved instances, figure out all the details and buried AWS billing rules. Run your GCP instances and automatically get discounts. AWS reserved instances requires knowing your instance types, knowing that you can purchase the smallest type of an instance class and combine, knowing that you can only purchase 20 reserved instances per zone/per region in an account. So many gotchas.

- GCP projects by default span all regions. It is much easier if you run multiple regions, all services can communicate with all regions. Multi-region in AWS is sort of a nightmare, setting up VPC peering, can't reference security groups across regions, etc..

- Custom machine types. With GCE, you simply select the number of cores you need and memory. No trying to decipher the crazy amount of AWS instance types T2, T3, M5, M5a, R5, R5a, C5, C5n, I3...

- Instance attached block storage is easier to grok and in my experience is much faster than EBS. The bigger the disk on GCE, the more IOPS. No provisioned IOPS madness.