Rightsizing

What is rightsizing?

Rightsizing is the process of matching cloud resource allocation to actual workload requirements. It means making sure the resources, including compute, memory, and storage, assigned to a workload are not more than it needs, without cutting things so tight that performance suffers. The same principle extends to any infrastructure (on-premises servers, databases, and hybrid environments) where resources can be overprovisioned.

Most overspend doesn't come from forgotten resources. It comes from instances that are running fine and doing their job—just on more hardware than the workload calls for. Nobody flags the overspending because everything is working as it should.

Why rightsizing matters

Cloud providers make it easy to provision large. Defaulting to a bigger instance feels safe during setup, and revisiting it later rarely feels urgent. Over time, overprovisioning adds up.

A workload running at 15% CPU utilization on a large instance costs roughly the same as one using it fully. However, you're paying for the provisioned capacity regardless of how much of it you actually use. Rightsizing is how organizations work through provisioning gaps systematically.

How does rightsizing work?

The starting point of rightsizing is utilization data. CPU, memory, network, and storage metrics are collected over a meaningful period, at least 30 days, to account for normal traffic patterns and usage spikes.

From that data, the cloud provider's native tooling or cloud cost management tools generate recommendations. Here are some examples:

• Downsize instances where utilization consistently sits well below capacity.
• Upsize instances where workloads are regularly hitting resource limits.
• Change instance families where a different configuration is a better fit for the workload type.

The recommendations are reviewed, tested where possible, and applied.

Rightsizing in practice: A real-world example

Let's say a development team runs a batch processing pipeline on 12 EC2 instances. The instances were provisioned during a period of heavy load that has since passed.

These are the numbers after a rightsizing review:

After rightsizing, a smaller instance type handled the workload with headroom to spare. The pipeline ran exactly as before.

The challenge of rightsizing

Analyzing your numbers is usually the easy part. Here are a few things that tend to complicate rightsizing in practice:

• Teams are cautious about touching production workloads, especially if the original sizing decision was made by someone who has since left.
• Utilization data collected over a short window can miss seasonal spikes or month-end processing peaks, leading to recommendations that look right on paper but cause problems in real life.
• In multi-cloud environments, instance types and sizing logic differ across AWS, Azure, and GCP, so recommendations need to be evaluated per provider rather than applied uniformly.
• Some workloads have memory or network requirements that do not show up in CPU utilization alone, which means surface-level metrics can point you in the wrong direction.

Collecting data over a longer window, typically 30—60 days, and looping in the team that owns the workload before making changes catch most of these complications before they become problems.

Rightsizing vs. other optimization approaches

There are two things that rightsizing often gets confused with:

• Idle resource cleanup removes resources that are not being used at all. Rightsizing addresses resources that are in use but overprovisioned.
• Reserved Instances and Savings Plans reduce the cost of running resources at their current size. Rightsizing reduces the size itself.

These approaches work well together, but the order matters. For instance, applying commitment-based discounts before rightsizing locks in savings on the wrong baseline.

Rightsizing across AWS, Azure, and GCP

Each provider has native tooling for rightsizing, but coverage and methodology vary:

• AWS Compute Optimizer analyzes EC2, Lambda, EBS, and ECS workloads.
• Azure Advisor provides recommendations for virtual machines based on CPU and memory telemetry.
• GCP surfaces rightsizing recommendations for Compute Engine through Active Assist.

In multi-cloud environments, consolidating recommendations from each provider in one place makes it easier to prioritize where to start.

Best practices for rightsizing

Getting rightsizing implemented consistently is where most teams run into friction. Here are a few things that tend to make the difference:

• Collect utilization data for at least 30 days, and longer for workloads with irregular patterns like month-end processing or seasonal traffic.
• Have the team that owns the workload review the recommendation, not just the platform or FinOps team, because the owning team knows the edge cases.
• Test changes in a non-production environment before applying them to live workloads.
• Revisit provisioning regularly. Workloads shift over time, and what was right six months ago may no longer be.

Rightsizing is one of the fastest ways to reduce cloud spend without changing what you're running. The work is mostly in building the habit: reviewing regularly, involving the right people, and not letting utilization data sit unread.