Cloud spend optimization is a key pillar of a good architecture cloud environment. The benefits of using Amazon Web Services (AWS) for the modern-day enterprise are innumerable. But AWS can turn into a money pit if enterprises do not manage it well.
Luckily, workloads deployed in the public cloud are usually much easier to review and audit than those in a private data center. This is because of the visibility the public cloud affords enterprises. Even then, there’s always room for improvement.
Here are some of the tools and approaches organizations can use to improve AWS visibility and cost optimization.
AWS Cost Optimization Tools and Strategies
Organizations should use AWS Tags as the first step toward cost optimization. A tag is a label that you can assign to resources residing on AWS. Using tags can help organize resources, so it is easier to identify who uses a specific AWS resource and how that person is using it.
Organizations can use various AWS tagging strategies to suit their business context. For example, you can tag resources based on organizational unit, department, owner, cost center, application, region, project, etc.
AWS tags can also come in handy during cost allocation. They help organizations track and categorize AWS costs. You can easily break down AWS costs by tag.
AWS Cost Explorer
AWS Cost Explorer is an AWS cost optimization tool with an intuitive user interface. It allows users to visualize and manage AWS costs over time. The tool gives you a selection of options for filtering costs based on tags. The insights gathered from this tool can help organizations to make informed choices about the costs of their workloads.
AWS Budgets is a tool that allows organizations to set custom budgets for their deployments. Once you set a budget threshold, you receive alerts whenever you exceed this threshold. Setting a budget for workloads is critical for organizations as it can help them flag resource drains.
AWS Compute Optimizer
Using Compute Optimizer is the easiest way to achieve cost optimization on AWS. It allows you to review resource usage and stop those that aren’t currently in use. Compute Optimizer uses machine learning to analyze historical resource utilization metrics and help organizations choose optimal Amazon EC2 instance types.
Over-provisioning resources can lead to an unnecessary spike in cloud costs, while under-provisioning can lead to subpar application performances. AWS Cost Optimizer detects workload patterns and gives recommendations that help organizations to optimize compute resources.
Organizations are spoiled for choice when it comes to strategies for optimizing EC2 costs on AWS. Nonetheless, one of the best ways to go about it is to change the infrastructure. This involves a change in your mindset toward using Lambda functions and containers to achieve a serverless architecture.
In this model, organizations initiate compute infrastructure, so no resources are sitting in idle mode. There is also no need for right-size instances since resource utilization is at 100 percent.