Rightsizing tools for cloud resources read usage data and recommend a smaller or larger size for a virtual machine, database or container. The phrase also names software-licence products and staff planning, but this guide covers neither. It is for IT heads, cloud and platform owners and engineering managers at mid-size companies in India and the GCC who run workloads on AWS, Azure or both. It compares the kinds of tool, from AWS Compute Optimizer and Azure Advisor to platforms and Kubernetes tools, by what they read, what they cover and what they may change. The guide ranks no product and gives no savings figure.
Last reviewed 2026-11-01 by Manish Sharma.
Rightsizing Tools: The Short Answer
Start with the native recommenders from AWS and Microsoft, since they come with the platform and only recommend. Add a platform or a Kubernetes-specific tool when you can name the reason, such as one view across two clouds or tuning for containers. Choose by three things: what a tool reads, which resources it covers and what it may change. Our guide to cloud cost management tools compares the wider group of cost tools, while this one is only about sizing. One vendor’s guide describes mature teams as combining two or three kinds of tool, which is one author’s view and not a measured rule. This guide also treats cloud rightsizing tools by type, not by product name.
Rightsizing means matching a resource’s size to what the workload really uses. A recommender is a tool that suggests a size but does not change anything itself.
What a Rightsizing Tool Reads and What It May Change
Four things separate one tool from another. Signals come first: CPU, memory, network, disk and, in some tools, application data. Next is the look-back window, which is the period of history the tool analyzes. Then comes the set of resources it covers. Last is the control model, meaning what the tool may do with a recommendation.
There are three control models. A recommend-only tool produces a list and stops, whereas a workflow tool adds an owner and an approval step before anyone changes a resource. Finally, an automatic tool changes capacity by itself within limits you set. One vendor’s guide uses the word “execution” for the last of these, and it means changing capacity, not exporting a list. Ask which of the three a tool does before you compare anything else.
The signals matter more than the dashboard. A recommendation built on CPU alone can shrink a memory-bound database, for example, and a short window can miss a month-end peak. Our explainer on why cloud costs keep rising lists the default windows of the native tools, including the options to extend them.
Key Takeaway Compare tools on what they read, how far back they look, what they cover and what they may change.
Rightsizing Tools, Type 1: Native Recommenders From AWS and Microsoft
The native recommenders are built into each provider’s console and recommend only. Each one covers only its own cloud, so an estate on both clouds ends up with two lists in two places.
What AWS Offers
AWS Compute Optimizer covers EC2 instances, Auto Scaling groups, EBS volumes, Lambda functions, ECS services on Fargate, and RDS and Aurora databases, along with other resource types such as DynamoDB, ElastiCache and SageMaker. You opt in first. By default, it analyzes 14 days of metrics, and a paid feature called enhanced infrastructure metrics extends that to 93 days for selected resources. Rightsizing preferences let you adjust CPU and memory headroom, thresholds and the look-back period. For EC2, it can also ingest memory metrics from external observability products.
AWS Cost Explorer has its own rightsizing recommendations, but they cover EC2 instances only. Cost Explorer’s rightsizing recommendations page recommends AWS Cost Optimization Hub for finding cost optimization opportunities. The Hub consolidates recommendations across accounts and Regions, and its rightsizing and idle-resource recommendations come from Compute Optimizer.
What Azure Advisor Offers
Azure Advisor gives resize and shut-down recommendations for virtual machines and scale sets. Resize recommendations also use memory, but shut-down recommendations use CPU and outbound network and not memory. The look-back period in Azure Advisor is 7 days by default, but you can change it to 7, 14, 21, 30, 60 or 90 days. The page also lists cases in which to dismiss or postpone a recommendation. Facts about all three tools are as of November 2026.
Before you rely on a list, check that the tool covers your resource types. A resource type it does not cover does not appear as a problem, because it does not appear at all.
Advantages
Limitations
Key Takeaway The native recommenders are a built-in place to start, and a person makes every change.
Rightsizing Tools, Type 2: Multi-Cloud Platforms
Add a platform when you need sizing recommendations across clouds in one view, an ownership and approval workflow, or deeper models that use memory and policy. A platform adds a layer on top of the providers’ data, but it does not create an owner for each resource, and it is still only as good as the signals it reads.
Put three questions to any platform. What does it read, and over what window? Which resource types does it cover? Does it apply changes, or only list them? Our guide to cloud cost management tools lists the kinds of platform and the signals for adding one, so this guide does not repeat them.
Ask a vendor to show a recommendation for a resource you know well. If you can explain it, the tool reads the right signals. If not, find out why before you extend it to the rest.
Rightsizing Tools, Type 3: Kubernetes Pod and Cluster Tools
Containers need a different analysis from a virtual machine, because each container has requests and limits to tune. A request is the CPU and memory reserved for a container, and a limit is the ceiling it may reach. Sizing a pod means setting both, whether by hand or through a tool.
Three kinds of tool work at this level:
- Recommend-only tools, such as open-source tools and the vertical pod autoscaler in Off mode, show suggested requests and limits without applying them.
- Automatic, policy-driven tools apply changes within bounds and approvals.
- Cost-visibility tools show cost by cluster, namespace and workload but do not change anything.
In Off mode, the Kubernetes documentation says the vertical pod autoscaler’s recommendations are not applied to pods automatically. The vertical and horizontal pod autoscalers can both act on CPU or memory, so decide which one owns each signal. Lower requests free capacity on a node, but the cost falls only when the number of nodes does.
Do not let two autoscalers act on the same signal. Pick one to own CPU and memory for a workload, then record the choice.
Rightsizing Tools Side by Side
The four kinds differ in what they read, what they cover, what they may change and where they stop.
| Type | Signals read | Resources covered | Changes allowed | Where it stops |
|---|---|---|---|---|
| Native recommenders | Usage metrics from the provider, with memory for some resources | Resources in their own cloud, if the documentation lists the type | Nothing, since they recommend only | If the resource type is not supported, no recommendation appears |
| Multi-cloud platforms | Provider data from several clouds, plus memory and policy when connected | Both clouds in one view, if the resource types are connected | A list, an approval workflow or, in some tools, the resource itself | Without an owner for each resource, a recommendation has no one to act on it |
| Kubernetes recommend-only tools | Container requests against actual use | Pods and workloads in a cluster | Nothing, while in recommendation mode | Node count stays the same, so the cost may not fall |
| Automatic tools | Live usage, while staying within the bounds you set | Whatever the policy includes | Capacity, within limits and approvals | For stateful or peak-sensitive systems, a wrong change can cause an outage |
Key Takeaway The real decision is the control model, so decide first between recommend-only, a workflow with approval and automatic change.
Rightsizing Tools: How to Choose
Answer five questions in order. A single-cloud estate can start with that cloud’s native recommender, and a platform or Kubernetes tool comes in only when a question below needs it.
One cloud may need only its native recommender, while two clouds raise the question of a single view.
List the resource types that carry the cost, and check each tool’s coverage against them.
Decide whether a list is enough, or whether you want a workflow or automatic change.
Name an owner for each resource, since a tool does not supply one.
A test environment can accept automatic change, but a payment system may not.
Automatic Change Needs Guardrails
Automatic change needs a guardrail for each way it can go wrong. A guardrail is a limit that stops a tool from doing harm. Set an approval step, a rollback path, a maintenance window and bounds on how far a size may move, since each one limits the damage of a wrong change. Start with a small canary group, such as a first release to a few resources. Exclude stateful and peak-sensitive systems, meaning those that hold data or carry a known busy period. One vendor’s guide advises starting read-only and widening only after you check the results.
Our guide to cloud cost management tools lists the questions to ask before you buy anything, including a test on one difficult month, so they are not repeated here.
Do not apply an unreviewed recommendation to a database or a system that keeps state. Test it on a copy before you apply it.
What This Article Does Not Cover
This guide compares kinds of tool for sizing recommendations. It does not compare cost management tools, which is the job of the guide linked above, and it leaves out the outside review. For the scan that finds candidates, use our cloud cost optimization checklist. The series also covers the method for changing a resource and the checklist to run first. This guide ranks no product, and no template file comes with the page. For what counts as waste in the first place, read our explainer on idle and oversized resources.
To put the tools to work, read how Signisys can help you right-size cloud resources and cut waste. The same topic sits within cloud cost optimization and FinOps, and Signisys delivers the work through cloud services and platform engineering. See also how to reduce cloud costs without losing performance.
Rightsizing Tools FAQ
What are rightsizing tools?
Are the native AWS and Azure tools enough?
Do rightsizing tools change resources by themselves?
Which resources does AWS Compute Optimizer cover?
Why does a tool miss a resource that looks oversized?
When do I need a Kubernetes-specific tool?
Security Insights, Delivered.
Practical, vendor-neutral analysis of threats, tools, and architecture decisions.