No need to choose at all: faster and cheaper in the cloud

You get to choose: speed or cost? For many IT decision makers, it seems like an unavoidable dilemma. If you want an application that responds at lightning speed, you automatically get a higher cloud bill. If you want savings, you sacrifice performance.

The truth? This is a persistent myth. You don't have to choose at all. In reality, with smart scaling and sizing in the cloud, you can achieve both goals: a faster user experience AND lower costs.

The “trick” is in thinking differently. Because the cloud is not a copy of your old data center. You no longer buy fixed boxes, but flexibility. You pay per second, per CPU, per request. And that means you also have to look at capacity in a completely different way. No longer sizing for peak load, but for what you really need. And when.

In this blog, we'll show you how to do that. We show you the difference between sizing (choosing the right amount) and scaling (scaling up and down as the load changes). You'll discover why the cloud requires different choices than you're used to on-prem and why it's possible that by reserving fewer resources, you'll end up faster and cheaper.

We take you through the most important considerations. No vendor talk, absolutely no technical hot air. Instead: insights that will help you make better decisions today about your cloud capacity.

Sizing vs scaling: two sides of the same coin

Anyone who wants to get a grip on cloud performance and costs must keep two concepts well apart: sizing and scaling. They overlap, but are governed at a different level. Understanding the difference means much more targeted optimization.

Sizing is about choosing the right amount of resources: CPU, memory, storage, IOPS. You determine based on known workloads what an application needs as a minimum to function properly without overcapacity, without bottlenecks. You do this in advance, using a combination of measurements, experience and assumptions.

Scaling is the process of adjusting capacity to changing conditions. This can be done automatically (autoscaling), but also manually, scheduled or seasonally. Think of scaling an environment for a marketing campaign, expanding storage when data grows, or scaling back compute in quiet periods. Scaling means: moving with your business, both up and down and both short and long term.

Whereas sizing is mainly about the initial configuration, scaling focuses on the evolution of that configuration over time. The two affect each other directly: if you size incorrectly, you have to scale unnecessarily often. And if you can't scale, you still fall short despite good sizing.

Smart IT decision makers deploy both: strategically, deliberately and aligned with the realities of their organization.

Pay-as-you-go or reserve? Choose consciously, pay less

In the cloud, you don't pay for hardware, you pay for usage. That seems like an advantage until you find that wrong choices cost you a lot of money. The key consideration: do you go for pay-as-you-go or reserve capacity in advance? I put it in a nutshell:

Pay-as-you-go means maximum flexibility. You pay only for what you use, by the second or minute. Ideal for:

1. Unpredictable workloads - you never pay for hot air.
2. Experiments and pilots – you're not entering long term commitments.
3. Quick scaling up and down - you adjust your capacity in real time.

But that flexibility comes at a price: you pay full price. With long-term or constant load, costs can add up considerably.

Reserving is the exact opposite: you commit to certain capacity for one or three years. By doing so, you buy:

1. Up to 70% cost benefit for consistent workloads.
2. Budget certainty - you know exactly where you stand.
3. Capacity allocation - especially valuable during scarcity.

But beware: reserved capacity is exclusively yours and therefore your cost, even if you don't use that capacity temporarily. Unlike on-premises infrastructure, you can't simply redeploy excess capacity in the cloud to other workloads. It sits idle, it costs money and it doesn't deliver anything.

The moral? Analyze your consumption patterns. Combine flexibility with predictability. And reserve only what you really need. The cloud is forgiving, but not cheap.

Autoscaling: smart scaling starts with the right trigger

Autoscaling is one of the biggest promises of the cloud: you let your environment automatically grow with the load and shrink again as soon as it can. Less manual work, less waste. But how you set up autoscaling makes the difference between efficiency and a (common) unnecessarily expensive cloud bill.

There are two main forms of autoscaling:
🔹 Horizontal schaling means: Deploy more copies of a resource. Think additional Web servers or containers at peak load times. This works especially well for stateless, unified workloads. Containers are ideally suited for this. In an orchestrator like Kubernetes, you can easily distribute workloads and scale up or down per pod. Virtual Machine Scale Sets in Azure also work on this principle.
🔹 Vertical schaling means: provision the same instance with more CPU, memory or storage. This is more often done manually or scheduled (e.g., for monthly shutdowns or peak times), as it risks instability or downtime on restart.

Other types of autoscaling include:
– Scheduled scaling: time-based (e.g., scale up every day at 9 a.m.).
– Predictive scaling: based on trends or machine learning.
– Custom triggers: based on application-specific signals.

Which trigger you choose for scaling is crucial. Common triggers are CPU usage, memory pressure, network traffic or queue waiting time. But scaling too aggressively (a new instance at every peak) leads to flapping and actually higher costs. Scaling too conservatively means slow-responding systems.

In short: autoscaling is powerful, but only if you deploy it thoughtfully.

Measuring is knowing: stay sharp on your cloud capacity

The most common mistake in cloud capacity management? Thinking you can set it up right once. Sizing and scaling are not set-and-forget actions. What seems optimal today may grow skewed tomorrow: due to changing usage, growing data sets or simply faulty assumptions.

Initial sizing is all about a good assessment of what an application needs. But even with the best analysis, you can be wrong. And once you're live, the real work begins.

You have to keep measuring: is your application actually using what you've allocated? Or are you paying for air? Does your autoscaling coincide with actual load? Or are you scaling based on noise?

You may now be wondering as you read: how? At Sciante, we help organizations keep exactly that in focus. We make visible what you often don't see yourself:

• underutilized resources that drive up your costs,

• workloads that spike at unexpected times,

• scale settings that don't do what you think.

With our tooling and experience, we keep our finger on the pulse. Continuously. Optimization is our business. No superfluous dashboards, but clear insights that lead to concrete actions. That way, you keep control of your cloud capacity and your budget.

Want to know how we do that for your organization and what your cost savings will be? Schedule a no-obligation appointment with me. I will gladly show you where your biggest profit lies. For free, without obligations. Just: honest advice, techie to techie.

You will always and immediately receive insight into your three biggest optimization opportunities.

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