AWS Cost Optimization: 6 Best Practices For 2025

Cutting down your AWS bill starts with understanding the services and resources you’re paying for. The next step is the actual optimization process. The goal is to achieve AWS cost optimization without sacrificing the necessary resources. This involves reducing your cloud bill while maintaining performance and efficiency.

Here are six best practices for AWS cost optimization to help you achieve cost savings without a drop in performance or availability. 

1. Start with cost visibility

There’s only so much digging you can do by looking at your AWS bill. The AWS Cost Management console is a feature integrated into the billing console in AWS. When you use these tools together, you can view your costs and billing with a more holistic approach.

The console consists of several free-to-use features that enable you to better understand, organize, and detect anomalies within your cloud billing. Here’s a selection:

1. AWS Cost Explorer 
2. AWS Budgets
3. Cost Anomaly Detection

AWS Cost Explorer 

Cost Explorer allows you to visualize cost data for deeper analysis. For example, you can filter graphs to include cost allocation tags, availability zones, regions, and other purchase and service options. You can also forecast future expenses based on previous data to help you better understand your spending and how you can optimize your cloud costs further.

AWSbudgets

Setting budgets on AWS ensures you don’t go over your forecasted amount. Or, at the very least, you’ll receive a notification so you can take immediate action to prevent your spending from spiraling out of control. 

The tool uses AWS Cost Explorer to show a visual representation of your budgets, including forecasts for estimated costs, to help you better visualize your spending. You can also set notifications to alert your team when you reach certain budget levels – allowing you to stay on track without any nasty surprises.

Cost Anomaly Detection

The Cost Anomaly Detection tool alerts you when a potential irregularity is detected (for example, a rapid cost increase). This allows you to quickly identify the problem, keeping your cloud spending on budget without any unnecessary hiccups. 

One of Adobe’s development teams once burned $80,000 per day because of a computing job left running on Azure. Before someone discovered it and pulled the plug, the bill snowballed to more than half a million dollars. One simple alert would be enough to prevent this.

Cost reporting is important but it’s just the first step

Cost reporting is incredibly important, but it’s just one side of cost optimization.

You need more than cost reports to make a real difference in your setup. After all, what cost monitoring and reporting tools give you are static recommendations that require a human to implement them.

If you have a small environment, a human engineer can make it work. But think about the scale of a mid-sized company or enterprise.

Applying all the recommendations manually and regularly translates into time, which translates into costs (not to mention all the lost optimization opportunities for sudden traffic spikes and such).

2. Choose the right compute resources

Selecting the right compute instance can make or break your cloud bill. AWS offers more than 500 different instances today, which makes the task somewhat challenging.

Moreover, AWS offers variable performance for similar instance types, and a more expensive instance doesn’t always translate into better performance.

So, how do you pick the best compute instance for your workload?

Here are a few steps:

Create your base requirements

Ensure that all computational aspects are covered, such as memory, SSD, network connectivity, CPU (architecture, count, and type of processor), and memory.

Choose the right instance type 

A variety of CPU, memory, storage, and networking configurations are available, each packed in instance types that are tailored for a specific capability (for example, machine learning workloads such as model training or inference).

configure your instance’s size

Keep in mind that the instance needs to be large enough to support the demands of your workload and have options like bursting available if needed.

Analyze various pricing models

AWS offers various pricing models: on-demand (pay-as-you-go), reservations, Savings Plans, spot instances, and dedicated hosts. Each of them comes with unique offers, benefits and drawbacks.

3. Scale resources to match real-time demand

Most cloud-based applications experience changes in usage, but finding a balance between costs and performance is still a challenge.

If you leave your tab open, traffic spikes can result in a large, unexpected cloud bill. If you place stringent limits on your app’s resources, a large number of new visitors can cause your app to fail.

There are cloud cost management systems that keep an eye on your usage and instantly notify you if it deviates from predetermined limits or exhibits unusual patterns. These tools might offer you helpful suggestions on how to modify your cloud resources to meet your present needs.

However, increasing your cloud’s capacity manually takes effort and time.In addition to monitoring everything that occurs within the system, you typically have to:

• Adapt quickly to fluctuations in traffic volume and resource usage for every virtual machine across all of your services 
• Make sure that modifications made to one workload don’t negatively impact other workloads
• Create and oversee auto-scaling groups to ensure they are filled with resources appropriate for your tasks.

Autoscaling to the rescue

All of these tasks can be automatically completed via autoscaling, which also lowers cloud expenses. 

Autoscaling is an excellent technique for optimizing cloud costs when used with dynamic cloud-native systems such as Kubernetes. Your optimization efforts will be more impactful and there will be less waste in running the application the tighter your scaling methods are designed.

Real-time autoscaling solutions use business metrics to determine the ideal number of instances needed. If there is no more work to be done, they can scale up, down, or to zero. Such systems make sure that the resources being used constantly meet the needs of the application.

Check out this graph to see how requested vs. provided resources (CPU and memory) differ. Observe how it decreased (labeled “Start of optimization”) as soon as the team utilized the CAST AI autoscaling feature:

4. Remove orphaned resources

The cloud makes it simple to start an instance and then forget to shut it down.

Because of this, many teams deal with orphaned instances, that is, instances that don’t belong to anyone but still end up on your AWS bill. This issue is especially severe in large businesses where there is no centralized resource visibility and multiple programs underway at the same time.

How can you find and shut down unnecessary instances? This is another situation where automated AWS cost optimization proves useful.

Automated cloud optimization solutions can continuously monitor your consumption for inefficiencies and reduce resources whenever it makes sense. To save your cloud expenses, they can also terminate inactive instances and processes. 

5. Increase savings by using Spot instances

Buying idle capacity from Amazon Web Services makes sense because Spot instances cost as little as 90% less than on-demand ones. There’s a catch, though: the vendor can take these resources back at any time. 

Before joining the Spot club, ensure that your workloads are Spot-ready:

Assess your workload

Can your workload tolerate disruptions? How much time will it take to finish the task? Is this workload essential to your application? Questions such as these and others help qualify a workload for Spot instances.

Review AWS offering

Examining less well-known compute instances is a good idea because they are less likely to experience interruptions and have longer operating lifespans. Always check an instance’s frequency of interruption before choosing one.

Place a Spot instance bid

Decide on the maximum price you’re willing to spend for the Spot instance of your choice. Keep in mind that it will only last for as long as your offer is met by the market price. The general rule in this case is to set the maximum price at the level of on-demand pricing.

Control group Spot instances

Group your Spot instances to request multiple instance types simultaneously, improving your chances of snatching an instance.

Expect to invest a lot of time in the configuration, setup, and maintenance tasks above. That is unless you choose to automate Spot instance provisioning as Branch did.

6. Choose the right tooling

To take charge of their cloud expenses, companies use a combination of cloud cost optimization tools:

• Cost visibility and allocation: You can determine the source of your spending by using a range of cost allocation, monitoring, and reporting tools. Since real-time expense monitoring promptly notifies you when you surpass a certain threshold, it’s helpful in this situation.
• Cost forecasting and budgeting: If you’ve crunched enough historical data and have a good sense of your future requirements, you can plan your budget and predict the number of resources your teams will need. Sounds easy enough? Cloud financial management is riddled with challenges.
• Legacy AWS cost optimization solutions: To get a full view of your cloud spend and identify possible areas for improvement, you will need to compile the data from the above Numerous products on the market, such as VMware’s CloudHeath and Cloudability, can help with that. However, the majority of the time, all they offer you are static suggestions that engineers must manually execute.
• Automated, cloud-native cost optimization: Even if you have been carrying out excellent manual optimization, automation brings even better results without requiring more work from teams. The ideal strategy in this case is a completely autonomous, fully managed system that can respond rapidly to shifts in resource demand or cost effectively. 

Wrap up

AWS cost optimization isn’t a one-time exercise but a continuous effort. You need to constantly keep an eye on our cloud resource usage, match it with your bill, forecast future requirements, and provision resources that deliver the best cost-performance ratio. 

Or you can have an automation solution do that for you.

If you’re interested in exploring not only AWS cost optimization but also EKS-specific solutions, check out how we used our technology to reduce our Amazon EKS costs by 66% for one of our clusters: 8 Tips for Amazon EKS Cost Optimization.