Cost of on-premise operation and TCO

A 50-person company buys five servers. The invoice arrives and gets paid, and it feels like the job is done. It is only the start. The servers need a rack and a room to stand in. They burn electricity every hour of every day, and an air conditioner has to remove the heat they make. Someone has to install them, configure them and patch them. In year three a disk dies, the RAM is too small, and the cycle of buying begins again.

That is the life of Legacy IT. In part 03 you saw that in this model the customer manages every layer, from the building up to the application. The flip side is simple: the customer also pays for every layer. The honest way to price it is the Total cost of ownership (TCO), which adds up everything it costs to own and run the system over its life, not just what it costs to buy.

Five buckets of cost

1. Hardware purchase. Servers, storage and network gear.
2. Housing. The enclosure (rack, power strips, console switch) and the room it lives in.
3. Operations. Power, cooling and maintenance.
4. Personnel. The people who install, configure and maintain the machines.
5. Upgrades and refresh. Extending or replacing hardware as it ages.

The buckets behave differently over time. Hardware, housing kit and refreshes are lumpy: big amounts at a few moments. Accountants call this kind of spending Capital expenditure (CapEx). Operations and personnel recur month after month, and that is Operational expenditure (OpEx). The next concepts make this split precise. For now, notice which buckets the case study will actually price, because the gap matters at the end.

The company in the case study is a small news streaming firm with 50 employees. It needs three things from its IT: networking, web hosting and backups. The question is what it costs to run that on its own hardware for three years, so it can later be set against a cloud bill for the same job.

Fix the assumptions before you add anything up

A Total cost of ownership (TCO) comparison is only fair when both scenarios use the same scope and the same assumptions. Otherwise the cheaper option may simply be covering less. The case study fixes three assumptions up front.

• The software is open source, so licence fees are zero on both sides.
• Maintenance costs the same on-premise and in the cloud, so it cancels out of the comparison and is left out of both totals.
• The horizon is 3 years. That matches how long servers are typically used before replacement. Barroso et al. note that servers have a shorter lifetime than buildings and are usually depreciated over 3 to 4 years, while data center buildings are depreciated over 15to 20 years.

The hardware

The firm buys five identical 1U rack servers. Each has two Intel Xeon E5-2640 v2 processors, a 2013 server part that Intel lists with 8 cores, 16 threads, a 2.0 GHz base clock, a 20 MB cache and a 95 W thermal design power, for two-socket boards only.

Two quick derivations make this concrete. Compute first: 5 × 2 × 8 = 80 physical cores, or 160 hardware threads, across the fleet. Power second: the two CPUs alone can dissipate 2 × 95 = 190 W, the power supply is rated for 460 W, and the measured draw used later is 308 W, about 67% of the rating.

The five servers cost 17,500 EUR. They are bought once and used for years. The electricity bill arrives every month. A technician who comes to replace a failed fan is paid for that visit, even though the fan sits inside a long-lived asset. Those three payments fall into two different accounting categories, and the difference is the heart of the cloud's financial argument.

The accounting definitions

Capital expenditure (CapEx) is spending on fixed assets that will be used for more than one period. The international standard IAS 16 defines such property, plant and equipment as tangible items “expected to be used during more than one period”. Their cost is not charged all at once. Instead it is spread over the useful life by depreciation, “the systematic allocation of the depreciable amount of an asset over its useful life”. Barroso et al. put it in data center terms: CapEx is the investment made upfront and then depreciated over a certain timeframe.

Operational expenditure (OpEx) is the recurring cost of actually running things, excluding depreciation: electricity, repairs and maintenance, salaries of on-site staff, rent, and service subscriptions such as Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). IAS 16 paragraph 12 is explicit that the costs of the day-to-day servicing of an asset are not added to its carrying amount. They are expensed as they occur.

Why the cloud talks about this split

The Berkeley “Above the Clouds” report notes that the cloud's appeal is often described as converting capital expenses into operating expenses, but argues that “pay as you go” captures the real benefit better. Without an upfront purchase, money stays free for the core business, and capacity can follow demand instead of being bought for a guessed peak. AWS's Well-Architected cost guidance says the same in practical terms: adopt a consumption model, and stop spending money on the undifferentiated heavy lifting of racking, stacking and powering servers. That argument is exactly what part 06 tests against this case study.

Now the firm goes shopping. Everything on this list is a durable asset paid for on day one, so all of it is Capital expenditure (CapEx). The rule is a plain sum: quantity times unit price, over every item bought.

Storage, not compute, dominates

The surprise in the bill is where the money goes. The servers, the part everyone pictures, are only about a quarter of it. The SAN alone is 35,000 / 68,824 ≈ 50.9% of the CapEx, and once running costs are added it is still 39.0% of the whole three-year Total cost of ownership (TCO). A SAN is a dedicated network that gives servers shared, redundant, block-level access to pooled disks, and that redundancy and shared access are expensive. This is one reason managed storage and Object storage are such strong selling points for the cloud: the provider amortises that cost over thousands of customers.

Decoding the small items

• PDU, a power distribution unit: the rack's managed power strip that feeds every device in it.
• KVM here means a keyboard-video-mouse switch, which lets one console control many servers.
• SAN, a storage area network: the shared storage pool described above.

With the hardware bought, the meter starts running. Every watt the servers draw is paid for, every watt of heat they make has to be pumped out by the cooling, and the room has a monthly rent. These recurring costs are the Operational expenditure (OpEx), and adding them to the Capital expenditure (CapEx) gives the Total cost of ownership (TCO).

Cooling costs more than computing

Look again at the two energy lines. Cooling, at 3,702 EUR/yr, costs more than the servers' own power at 2,962 EUR/yr. For every 308 W of IT load, the room spends another 385 W getting rid of the heat. The standard way to express this is power usage effectiveness, defined by The Green Grid as total facility energy divided by IT equipment energy. Here that ratio is at least (308 + 385) / 308 = 2.25, and that is before counting the UPS, lighting or any other overhead.

The Uptime Institute's surveys put the industry average PUE at about 2.50 in 2007 and about 1.56 in 2024, and large hyperscale sites run well below that average. A small server room cooled by a generic air conditioner is simply much less efficient than a purpose-built data center. That gap is one of the structural reasons the cloud can undercut on-premise running costs.

What the TCO leaves out

The 89,715 EUR is a clean, recomputable number, and that is its value. It is also a lower bound, because several real costs sit outside it. Read the list below against the cost buckets from the first concept.

The energy lines are also sensitive to price. At 0.30 EUR/kWh instead of 0.22, three years of power plus cooling rise from about 20,033 EUR to about 27,318 EUR, about 36% more, exactly the price ratio 0.30 / 0.22, because energy cost is linear in price. When you build such a model for research, vary the inputs you are least sure about and report the range, not only the point estimate.