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AWS Cost Optimization Tools Compared

18 February 2026 · 7 min read

The AWS Cost Problem

Spinning up resources on AWS takes a few clicks. New EC2 instance, RDS database, a whole EKS cluster, done. The part AWS leaves to you is the boring part: knowing what any of it costs, and remembering to turn it off when nobody is using it.

Most organizations overspend on cloud by 20 to 35 percent, and that number has barely moved in years. So the question worth asking about any tool on this list is not whether it shows you the waste. Plenty do. It is whether the tool actually gets rid of the waste, or just hands you a to-do list.

AWS Cost Explorer

Cost Explorer is where you start, because it is already in your account and it answers the first question you have: where is the money going? Visualize the trailing 12 months, slice by service, account, region, or tag, and let it forecast forward off your history. Basic use is free; hourly granularity costs extra.

It is genuinely good at the macro view. You will see which three services dominate the bill and whether the line is going the wrong way. What it will not do is change anything. Cost Explorer reports. The acting is on you.

AWS Trusted Advisor

Trusted Advisor scans the account and flags idle EC2 instances, underutilized EBS volumes, unassociated Elastic IPs, sleepy load balancers, RDS you are barely touching. The free tier covers a thin slice of checks; the full cost set is locked behind Business and Enterprise support.

It is fine for a once-a-quarter look. It tells you what is wrong and then leaves the actual fixing to you, which is most of the work. And it has nothing to say about the larger issue, that half your fleet is running at 3am for no reason.

AWS Compute Optimizer

This is the AWS free tool I would actually keep around. Compute Optimizer reads your CloudWatch metrics (CPU, memory, network) and tells you when an instance is over-provisioned, with the estimated monthly saving attached. It covers EC2, EBS volumes, and Lambda, and there is nothing to set up.

When it says your m5.xlarge should be an m5.large, that is not a guess, it is your own usage talking. The catch is the same one that follows AWS everywhere: it recommends, you implement.

Third-Party Platforms

CloudHealth (VMware), Spot.io (NetApp), and Kubecost sit a tier above the native tools. You get centralized dashboards, deep analytics, multi-cloud coverage across AWS, Azure, and GCP, governance with budget policies and approval workflows, plus automated right-sizing and reserved instance management.

They are also expensive, often thousands a month. For a large enterprise drowning in accounts and clouds, that price buys back real management time and is easy to justify. For everyone else it is a platform you will underuse. Be honest about which one you are before signing.

Resource Scheduling Tools

Scheduling solves a problem none of the above touch: stopping and starting EC2 and RDS on a clock so they only run when someone needs them. Right-sizing makes each instance cheaper. It does not make a dev box that idles every night and weekend stop billing you for those hours. That is what scheduling is for.

  • ParkMyAWS: a managed scheduling service. Connect your account, pick resources, set a schedule, and start saving in minutes
  • AWS Instance Scheduler: AWS's open-source solution deployed via CloudFormation with Lambda, DynamoDB, and EventBridge
  • DIY with Lambda: build your own custom scheduling solution for maximum control

For non-production environments, scheduling is usually the highest-leverage thing you can do. Switching instances off overnight and on weekends takes roughly 65% off their runtime cost, and that compounds over every resource you put on a schedule.

Building Your Cost Optimization Stack

You do not need everything here. Start cheap, add layers when scale forces your hand.

  • Start with the free AWS tools: Cost Explorer for visibility, Compute Optimizer to surface over-provisioned resources
  • Schedule non-production next: this kills the idle-time bill that right-sizing physically cannot reach
  • Right-size from data: apply Compute Optimizer's recommendations to your biggest instances first, where the savings are
  • Commit once you know your baseline: lock in Reserved Instances or Savings Plans for the production workloads that run no matter what
  • Reach for third-party platforms when you outgrow the rest: many accounts, many teams, many clouds

The whole game is pairing visibility, knowing where the money goes, with automation, acting on it without a human in the loop every time. Take the quick wins first, watch the bill move, and keep going.