A Guide to Cloud Cost Optimization

Cloud cost optimization is a strategy or set of strategies you adopt to reduce your cloud spend and to achieve cost-efficiency when consuming cloud resources. It’s crucial to the success of your cloud adoption program, as cloud costs can have a huge impact on your organization’s bottom line. In this post, we take a look at the top four cloud cost optimization best practices and discuss how you can apply them to your cloud environment.

Resource Cleanup

One of the major advantages of cloud environments is the ease and speed of adding resources. The problem is that we often forget to remove/delete/terminate those resources after using them. For example, it’s very common to see IT staff spin up an Amazon Elastic Compute Cloud (EC2) instance and then leave it running, even after it has outlived its usefulness.

Sometimes, even if you do terminate the compute instance, you might forget to remove any storage volume attached to it. Until you terminate a resource, you’ll always be billed for it.

Another slightly similar practice that also results in overspending is leaving active but severely underutilized resources running. A compute instance that’s using less than 5% of its CPU or a 100 GB volume that’s only holding 1 GB of data will still cost you the equivalent amount of a maxed-out CPU or storage volume.

If you have, say, hundreds of such underutilized instances lying around, you could end up wasting hundreds of thousands of dollars per month for resources you’re not actually using. The practical thing to do is consolidate these into fewer instances to maximize resources while reducing costs. The same can be applied to storage and other resources.

Rightsizing Analysis

One way to determine whether you’re overspending on resources is by employing rightsizing analysis. It involves comparing usage and capacity to see whether a resource is underutilized and therefore needs tweaking. Once you’ve determined that a resource is indeed underutilized, you can then reduce its capacity to make it closer to the actual usage. This process is called rightsizing.

You don’t have to do this manually. There are tools that can help you, not only to see whether you’re running underutilized resources but also to recommend the right size. For example, let’s say you’re using an m4.xlarge (4 vCPU, 16 GB)—an Amazon EC2 instance size that, as of this writing, has an on-demand hourly rate of $0.20 for a Linux OS in the US East (Ohio) region.

You can then run a tool that will check metrics like CPU and memory utilization, and then recommend the right instance. Let’s say the tool eventually recommends a smaller m4.large (2 vCPU, 8 GB) instance, which is currently at $0.10 per hour for the same OS and region. That’s already a 50% savings for one instance.

Some of the tools you can use for right sizing in Amazon Web Services (AWS) include Compute Optimizer, Cost Explorer, and Trusted Advisor. In Azure, you can use Azure Advisor. There are also third-party solutions out there.


You can implement proactive measures such as monitoring to spot peaks and valleys in demand. This will help you identify specific hours or perhaps even days when you can turn off certain resources. If you see, for example, there’s zero computing demand from 9 p.m. to 6 a.m., you can switch off your instances over that nine-hour period. That’s already 37.5% of your total cost.

Monitoring will also help you identify which specific services are making up the bulk of your cloud spend. It’s in those places where you can start looking for areas to adjust, as any changes you make will have a significant impact on costs.

Monitoring can also help you determine a baseline for your fixed-cost, predictable workloads and identify your variable-cost workloads. You can assign long-term, predictable workloads to reserved instances, which are entitled to large discounts, and assign variable cost workloads to on-demand instances, which gives you flexible pricing. The idea is to match your workloads to the right pricing model to obtain cloud cost optimization.

Lastly, monitoring can help you spot outliers or abnormalities in usage quickly that, if left unchecked, will result in excessive charges.

Automated Cloud Cost Optimization

Cloud cost optimization is not something you can do effectively if you simply rely on manual methods. Considering how vast and diverse your cloud infrastructure is, it is extremely difficult to pinpoint precisely where costs are coming from, analyze and understand usage patterns, examine instances and other resources for underutilization and overprovisioning, implement changes, and so on.

The best way to go about cloud cost optimization is to employ automation at every opportunity. Not only will this save you time and money but also it will ensure that policies are enforced strictly. For example, let’s say, after monitoring your cloud, you decided to create a policy requiring users to switch off cloud resources before they leave the office.

Instead of relying on users to switch off resources themselves, you’ll have a better chance of achieving 100% enforcement of this policy if you simply employ a scheduler (your cloud provider should have a native tool for this) that switches off specific resources automatically at a specified time.

Another automation tool that can help in optimizing costs is autoscaling. This tool increases and decreases capacity dynamically as needed, thereby preventing overprovisioning and, in turn, reducing waste. While very impactful, autoscaling is an advanced feature that requires re-architecting and re-designing your applications, so you would need expert guidance to get it right. Please contact your cloud provider for assistance.

Make Parallels RAS Part of Your Cloud Cost Optimization Strategy

The surging popularity of remote and hybrid work environments has resulted in increased adoption of cloud, virtual desktop infrastructure (VDI), and Desktop as a Service (DaaS) solutions. A recent survey about VDI and cloud computing confirmed this growing trend.

As a leader in the VDI space and an enabler of DaaS, Parallels® Remote Application Server (RAS) can make a big difference in your cost optimization efforts. Parallels RAS is built on a simplified architecture that enables businesses to achieve a low total cost of ownership (TCO) when embarking on a virtual desktop/application delivery program—one of the key initiatives being undertaken by various organizations to support remote and hybrid work environments.

Parallels RAS can be deployed on all major public clouds, i.e., AWS, Azure, and Google Cloud Platform. Unlike other VDI solutions, Parallels RAS is delivered under an all-inclusive single-license model that already includes a wide range of enterprise-grade features such as load balancing, multi-cloud support, Universal Printing, and 24/7 support, among others.

Experience the cloud cost optimization benefits of a cloud-ready, all-in-one VDI solution!

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