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Comment by asptimothy | Hacker News Reader

I was curious about that myself. What's the best way to model the costs associated with maintenance overhead that comes with bare-metal, vs the savings from managing bare-metal servers using a service (like packet), co-locating your own hardware or running your own datacenter.

My gut feeling is that you have to get to an extraordinary size to realize any meaningful savings, but that's primarily based on Dropbox's migration off AWS (https://www.wired.com/2016/03/epic-story-dropboxs-exodus-ama...).

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It's rather simple, you count the days you are wasting on handling the low level hardware.

The entry cost is £500 per day or $1000 for an engineer.

5 hours on the phone to find the hardware and agree on the order with DELL + an afternoon with customer support because they shipped the servers without hard drives + your project is delayed by an entire week because you don't have the resources => 1 day + 1 day + half a week.

These things would have been 5 minutes on a cloud.

It depends upon your constraints.

As a for-instance, I was asked this exact question a year or so ago about an on-prem object store instead of S3.

The break-even price to go multi-region was ~15PB on paper; that included DC space, hardware, software (build vs buy was another factored discussion), and the staff to run it.

That assessment was delivered with the caveat that their uptime was not going to approach S3's by any stretch of the imagination-- and their infrastructure outages weren't going to sync with "most of the internet's."

It's a complex topic, and there are many hidden costs...

That's a big giant "it depends."

It depends on what your service looks like: CPU intensive, Memory Intensive, Storage intensive? (In reality some unique mix).

You probably won't see a huge savings year one, as you'll be spinning up a lot of new things and have a fairly large CapEx expenditure. Now if your growth pattern is steady/predictable then you should be able to plan out your hardware buys or do a hybrid solution to handle traffic bursts.

One of the nice things about running your own hardware is that there are some costs that are easier to control. Don't need new hardware? Don't need to spend on new hardware for example.

You also have much more control over your environment so you are able to really optimize your code, and infrastructure so that you don't need to scale as large system wise.

But, back to the question on how to model it? You just gotta dig in, and make some educated guesses about performance,test and repeat.

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