I have written about it in the past. Yes, it beats the simplicity of hosting everything and “running massive machine learning algorithms” on datasets. One thing that never gets mentioned is the cost.
Here’s something from this perspective:
In other words, you get much better transparency if you vendor-lock your company to the IaaS provider (or simply have a less cost-optimized platform). Suppose you don’t want to vendor-lock your software to any cloud company and just use the bare-bones infrastructure so that you could migrate with ease to a different cloud provider in the future; well, good luck. If you’re going to get a glimpse of a complex pricing model, take a look at this guide that discusses only one provider’s data transfer pricing model!
After spending $100k in a year, we calculated that each thread-hour costs us about $0.06-$0.08 — including everything from storage to data transfer and CPU usage. (Not including GPUs in this analysis)
Running at about 120k CPU hours per month, this sum amounts to $7200-$9,600 per month!
This is the cost of a very well-equipped server with RTX 3090 GPU.
I have been a strong votary of the on-premises hardware. You own the hardware and factoring in the running costs (including licensing and other services like VPN/Firewalls etc), it’s still cheaper than paying the egress fees to a cloud service provider that is a strong disincentive for migration. Choose carefully!
We noticed that our RTXs, although considered gaming cards, are comparable (if not better) in performance to Tesla V100, which some cloud providers rent at the staggering price of $3.06 an hour.
Ouch!
RadOncNotes 