---
EDIT: Done!
I have updated the notebook with the digital Ocean offering using their General Purpose (dedicated CPU) droplets.
The major takeaways for DO are that they:
- Also do not charge for the control plane resources
- $/vCPU is less expensive than the other providers
- $/GB memory is more expensive than the other providers
- No preemptible or committed use discounts available
For smaller clusters and/or clusters running CPU bound workloads, DO looks like the most affordable option!Also, I would need to do some further research to understand how to fairly compare the physical CPU cores on the bare metal systems w/ the vCPUs offered across the major cloud VMs.
My (admittedly small) cluster of 3x 4Gb droplets, an external load balancer, and volumes enough for logs, databases and filesystems costs about 70 USD/Month. It's been absolutely rock solid too. I have very few minor gripes and a lot of positive things to say about it.
[1] https://www.digitalocean.com/docs/kubernetes/how-to/autoscal...
Having said that, DO is enough for virtually everything I've ever worked on, and the user experience and price are so much better. They're a clear winner for almost everything I do these days.
Whether a conscious decision or not, I think offering what the 80ish percent (just a guess) actually use, and streamlining it, is the right decision.
The article and Jupyter Notebook now reflect that change.