Out of all the comments on numbers, SREs, and scaling, you get the response for meeting numbers with numbers!

> $120K isn't going to cover the fully loaded costs of an SRE who can set up and run that.

Literally this. I can do SRE on-prem and cloud, and my 50/30/20 budget break-even point (as in, needs and savings but no wants - so 70%) is $170k before taxes. Rent is astonishingly high right now, and the sort of mid-career professional you want to handle SRE for your single DC is going to take $150k in this market before fucking off to the first $200k job they get.

Know your market, and pay accordingly. You cannot fuck around with SREs.

> Hiring 1 person to run the infrastructure means that 1 person is on-call 24/7 forever.

This is less of an issue than you might think, but strongly dependent upon the quality of talent you’ve retained and the budget you’ve given them. Shitbox hardware or cheap-ass talent means you’ll need to double or triple up locally, but a quality candidate with discretion can easily be supported by a counterpart at another office or site, at least short-term. Ideally though, yeah, you’ll need two engineers to manage this stack, but AWS savings on even a modest (~700 VMs) estate will cover their TC inside of six months, generally.

> There's a second bus factor: What happens when that 8xH100 starts to get flakey? You can't move the jobs to another server because you only have one. You can start diagnosing things and replacing parts and hope it gets to the root issue, but that's more downtime.

This strikes at another workload I neglected to mention, and one I highly recommend keeping in the public cloud: GPUs.

GPUs on-prem suck. Drivers are finnicky, firmware is flakey, vendor support inconsistent, and SR-IOV is a pain in the ass to manage at scale. They suck harder than HBAs, which I didn’t think was possible.

If you’re consuming GPUs 24x7 and can afford to support them on-prem, you’re definitely not here on HN killing time. For everyone else, tune your scaling controls on your cloud provider of choice to use what you need, when you need it, and accept the reality that hyperscalers are better suited for GPU workloads - for now.

> Going on-prem like this is highly risky.

Every transaction is risky, but the risk calculus for “static” (ADDS) or “stable” (ERP, HRIS, dev/test) work makes on-prem uniquely appealing when done right. Segment out your resources (resist the urge for HPC or HCI), build sensible redundancies (on-prem or in the cloud), and lean on workhorse products over newer, fancier platforms (bulletproof hypervisors instead of fragile K8s clusters), and you can make the move successful and sensible. The more cowboy you go with GPUs, K8s, or local Terraform, the more delicate your infra becomes on-prem - and thus the riskier it is to keep there.

Keep it simple, silly.