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Engineering notes from operating open infrastructure: the failures, design decisions, and upstream work that make open infrastructure better.
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Elasticity is a feature you pay for. It is worth it for a viral spike, but wasted on flat baseline load. A framework for measuring your peak-to-median ratio and placing workloads where they belong.
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Elasticity is a feature you pay for. It is worth it for a viral spike, but wasted on flat baseline load. A framework for measuring your peak-to-median ratio and placing workloads where they belong.
Elasticity is a product feature, and like any feature it has a price. The mistake most infrastructure arguments make is treating that price as always worth paying, or never worth paying, when the answer depends entirely on how much your demand moves.
Two situations illustrate the split better than any framework.
A platform team runs services averaging around 400 requests per second. A prospective customer then asks for a proof of concept at 3,000. Absorbing that on self-managed infrastructure would be a serious problem, and hyperscaler elasticity is the obvious answer.
A second team operates in a business that is stable in those dimensions, where next year's requirement is this year's plus or minus fifty percent. Going the elastic route means paying, continuously, for flexibility nobody uses.
Both conclusions are correct. The workloads are different.
The premium on on-demand capacity is not a markup for its own sake. It pays for tangible things: idle hardware waiting for your spike, the operational machinery to provision it in seconds, and the option value of never having to forecast correctly.
That option is worth a great deal when the alternative is losing a customer. The engineer facing a 7.5x traffic increase for a proof of concept is not weighing a cost optimization, he is weighing a deal. Paying a substantial premium on that capacity is straightforwardly correct, and would remain correct at several times the price.
The same option is worth very little when your traffic next quarter looks like your traffic this quarter. Paying spike pricing on baseline load is the most common form of cloud waste, and it persists because the spending is defensible in the abstract even when it is indefensible in the specific.
Not "how much traffic do we handle" but "what is the ratio between our peak and our baseline, and how much warning do we get."
Four rough shapes cover most production systems.
Flat. Internal platforms, batch processing, steady B2B services, most data infrastructure. Demand is knowable a year out. Elasticity here is a feature you fund and never exercise. This is the clearest case for owned or reserved capacity, and it describes far more enterprise workloads than the industry's messaging suggests.
Cyclical and predictable. Business-hours applications, overnight batch windows, seasonal retail. Peaks are large but scheduled, which means capacity can be planned rather than discovered. Scheduled scaling captures most of the benefit without paying for genuine on-demand.
Growing but smooth. Demand rises steadily and forecasts hold within a reasonable band. Procurement lead time is the constraint, and it is manageable with attention.
Spiky and unforecastable. Consumer applications with viral potential, event-driven traffic, the proof of concept that arrives with two weeks' notice. Elasticity earns its premium against demand like this.
Most organizations run all four simultaneously and buy infrastructure as though they only run the fourth.
The useful conclusion is that these should not all live in the same place.
Baseline capacity belongs on infrastructure you control, whether that is your own hardware, a hosted private cloud, or reserved instances. It is the largest and most predictable part of most estates, so per-unit cost compounds hardest against it over a three-to-five-year horizon.
Genuine burst belongs where capacity is somebody else's problem. Paying a premium for the top ten percent of a curve is rational when the alternative is buying for a peak you touch twice a year.
The hard part is the boundary, and it is an engineering problem rather than a slide. Workloads that span the boundary need consistent identity, network paths between environments, data locality that does not turn every request into a cross-site round trip, and observability that spans both without stitching two systems together by hand. Teams that underestimate this end up with two platforms, two sets of runbooks, and two on-call rotations, which costs more than either option alone.
Two rules make the boundary tractable. Keep stateful services on one side, since data gravity punishes indecision harder than anything else in the design. And use portable primitives, because a workload built on standard Kubernetes and S3-compatible storage can move; one built on a proprietary managed service cannot, whatever the architecture diagram claims.
The prospect of infinite scale is the most effective argument in infrastructure sales, and it is frequently sold to buyers whose demand curve is nearly flat.
Some questions that clarify quickly:
A team whose peak sits at 1.4x median and whose growth is forecastable has bought insurance against a risk it does not carry, and there are cheaper ways to hold that position.
The same scepticism belongs on the other side.
Self-managed infrastructure with genuinely unpredictable demand fails in a specific and expensive way: it fails during exactly the event that mattered. The proof of concept that arrives at 3,000 requests per second is not an infrastructure problem, it is a revenue event, and losing it to a capacity shortfall costs more than any efficiency gained the rest of the year.
Procurement lead times are the constraint people forget. Hardware ordered today is not serving traffic this month. Any plan for owned capacity needs headroom sized against realistic lead times, and that headroom is part of the cost comparison.
And owning capacity does not eliminate operational load. Somebody plans capacity, monitors utilization, and handles failures. A team without slack to do that will run out of capacity in either model, just more slowly.
Measure before deciding. Peak-to-median ratio, forecast accuracy over the past year, and the cost of a capacity shortfall are three numbers that resolve most of this argument, and most teams have never calculated any of them.
Then place by shape rather than by policy. Flat baseline on capacity you control. Genuine burst somewhere elastic. Invest properly in the boundary between them, because a badly designed hybrid is worse than either pure approach.
A private cloud makes this practical when the underlying primitives are standard. Atmosphere, VEXXHOST's OpenStack distribution built on Kubernetes, provides virtual machines, certified Kubernetes, bare metal, and Ceph-backed block and object storage through OpenStack APIs, with integrated monitoring, logging, and alerting so the baseline environment is observable on the same terms as anything else. It runs as a hosted private cloud, on-premises, or operated by VEXXHOST. Because the interfaces are standard rather than proprietary, workloads that need to sit elsewhere can, which is the property that makes a boundary between environments manageable instead of permanent.
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