It's hard to do that with cloud too. Your instances can go down at any time.
What you're trading off is a small number of people on-site at the data center (and it can in fact be small!) plus some slightly-old-school sysadminning versus new-school cloud-native development. Maybe the latter is easier to hire for (although I doubt that) or your current people find it more fun. But it's not like physical hardware dies all the time, or that you're immune from seeing the effects of physical hardware dying just because you're on the cloud.
You might save on hiring one person to replace disks when they fail and one person to write some PXE boot automation, but you'll need more than two people to run a Kubernetes at the same scale of compute capacity than those two people could have run for you.
https://thehftguy.com/2018/01/15/the-inevitable-demise-of-ib...
If you are willing to pay a lot of money upfront, which you are since you're talking about building your own datacenter, AWS and Google will both give you huge discount and be significantly cheaper than SoftLayer.
Memory is easy to compare. But how does it stack up CPU-wise.
Another question is whether lower tier servers compare favorably or not. How does a $200-500/month server compare?
Lastly, is it possible that SL is just not competitive anymore, but another provider is? I have gotten out of that gig a few years ago so I honestly don’t know, but is it possible that Hetzner or someone similar is now the leader in leased hardware?
SoftLayer is a disaster on lower tier servers. You only go to them for higher tiers.
The market is very competitive and moving fast. Both Google Cloud and AWS have been rolling out high memory hardware recently at a reasonable rate.
Hetzner is amateur tier. It's cheap second hands servers with no ECC. It's not comparable to enterprise infrastructure.
(But then they were bought by a company who's "cloud strategy" is to put the word "cloud" in as many DB2 and mainframe contracts as possible.)
It's noteworthy that SoftLayer is now worse on all aspects.
I wasn't sure what your use "heavy" means here -- is it "a lot of" or "disproportionate"? Years ago there was much less flexibility with IaaS machine shapes, but I was super impressed when Google launched "custom machine types". There's a little slider to scale up/down your RAM/CPU independently, and of course storage is already independent. In fairness, there is some correlation between CPU allocation and storage IOPS, but that's inherent to scheduling a portion of the host machine reasonably.
https://cloud.google.com/compute/docs/instances/creating-ins...
Second, it depends on your expertise. And you don’t need scale. I ran a successfully bit of infrastructure on SL with seven servers that was quite a bit cheaper than an equivalent AWS setup. I was pretty much solely responsible for the infrastructure and still my responsibilities included more than 80% coding and later management. Given my salary at the time, it was quite cost effective.
if you have experience (and/or your team does) with dedicated, that should probably be for a non-trivial in decision making. likewise if you have more aws/cloud experience (or have those skills available on staff), there's benefits to that.
cloud skills don't magically happen - someone with minimal AWS skills can provide you with a setup that is more expensive to run, and possibly more insecure than a locked down dedicated box.
But the calculation is harder than that. People are terrible at estimating the ops personnel cost for DIY. Turns out it's hard running with minimal downtime, or engineering hardware + software to be fault-tolerant. It's hard to put a price on dev tools/APIs/doco/the whole eco-system.
Especially for that last reason, I have never been "blown away" by Softlayer, even when their instances were way beefier than anything AWS/GCP offered. YMMV.