AWS Cost Optimization Checklist

Amazon Web Services (AWS) offers over 200 services known for their simplicity and cost-effectiveness. However, their elasticity and scalability, the features that attract users, can significantly elevate cloud expenditures, potentially leading to financial repercussions.

Implementing a robust system capable of identifying and analyzing expenditure sources is imperative to tackle these challenges. Fortunately, numerous tools and best practices exist to efficiently manage and optimize AWS costs.

We have compiled a list of AWS cost optimization checklists to simplify the often complex cost optimization process. You will understand your AWS environment comprehensively by following these guides, which offer a systematic approach to allocating every penny appropriately.

Introduction To AWS Cost Optimization

AWS cost optimization implements best practices and techniques to maximize your investment in the AWS cloud. It is a continuous process that encourages conscious spending aligned with business objectives. 

As organizations gain insight into how their cloud spending aligns with these metrics, they are empowered to make informed decisions. This decision-making enhances profitability and helps organizations maximize the value of their cloud environments.

Importance Of Cost Optimization In AWS

Many organizations opt for the cloud due to its plethora of benefits. A Gartner press release on cloud spending forecasts revealed that the cloud will be responsible for 14% of the total enterprise spending in 2025, compared to 9% in 2020. One of the primary reasons behind this escalation is the merging of technologies like containerization, edge computing, and virtualization. This emergence of new technologies combined with the growing inclination towards cloud has made implementing strategies that help in AWS cost optimization mandatory.

Before we proceed with our AWS cost optimization checklist, we must understand what makes AWS expensive and how AWS cost optimization strategies can prove instrumental in ensuring the robust financial stability of your organization. 

AWS resource costs are intricately tied to usage patterns, comprising factors like compute resources (reserved instances, on-demand instances), data transfer, backups, and storage. While optimizing compute resources, organizations often overlook storage resource optimization, resulting in overprovisioning and increased costs.

While organizations put a lot of effort into optimizing their compute resources, they ignore storage resource optimization and overprovision the storage resources to ensure uptime and that the applications run smoothly. 

In our research to explore the significant contributory factors to the cloud bill, we came across a Virtana report, State of the Hybrid Cloud Storage, which mentions that out of 350 cloud decision makers that were interviewed, 94% of them said that their storage cost is rising, with over 54% confirming that their storage cost is growing at a relatively faster rate than the overall cloud bill. 

Going one step further, we performed a storage audit for some industry leaders. We found that, on average, these organizations were spending 40% of their cloud cost due to storage spending. 

We also did an independent study and found that cloud storage accounts for a significant portion of the total cloud bill. Moreover, there was a mere 25% disk utilization, and despite overprovisioning, organizations were facing downtimes.

Upon further investigation into what inflates this cost, we found out that:

• Organizations were overestimating the data growth: As a result of overestimating growth, you may provision more resources (such as EC2 instances and storage) than you need. This can result in higher infrastructure costs. 
  Moreover, overestimated growth will result in underutilized or unused resources, increasing costs, and not adhering to cost efficiency and optimization principles.
  If growth is overestimated, overestimation may lead to higher data transfer requirements between AWS services or regions, resulting in unnecessary expenses.

• Two times, the disk was overprovisioned to manage peak load effectively: When organizations overprovision and fail to utilize their resources fully, they pay for resources that don't contribute to the workload. This results in higher infrastructure costs because organizations end up paying for capacity that is not being used.

• Manual buffer estimation to prevent downtime: Buffering adds extra capacity or resources beyond what is required. Adding a buffer and manually trying to improve the 65% buffer means using three different tools and intensive DevOps efforts to implement the tools. Furthermore, disk upgrade results in a 30% latency increase, and shrinkage of 1 TB demands a downtime of 4 hours which acts as a hurdle in frequent scaling. 
  Moreover, manual buffers may require more than scaling down resources. Many organizations may be reluctant to reduce capacity, fearing performance issues or disruptions since it would require a gap of 6 hours between subsequent scaling.

Reiterating the significance of optimizing storage cost as equally crucial as compute resource optimization, let us look at the impact of overlooking storage resource usage and cost.

As mentioned above, organizations overprovision the resources as a safe measure to avoid the complexities associated with optimizing storage. A specialized tool is required to optimize storage as CSPs have limited depth features. This, therefore, demands a lot more intensive use of DevOps and more time allocation in this pursuit. On the contrary, adhering merely to CSP-provided tools could lead to laborious procedures that are hard to implement every day and not practicable for day-to-day procedures.

As a result, organizations tend to "over-provision" storage so as not to interrupt the availability of their applications since downtimes can significantly influence day-to-day business processes. However, overprovisioning is not a good practice as it leads to the following issues.

This is why we suggest optimizing storage resource usage instead of overprovisioning the resources. Optimizing storage enables organizations to avoid unnecessary expenses associated with unused and underutilized resources.

Organizations can implement effective AWS cost optimization strategies by adopting a holistic approach that addresses both compute and storage resources. 

The significance of AWS cost optimization lies in its ability to enable organizations to navigate the complexities of cloud economics to innovate, grow, and maximize the value of their cloud infrastructure.

• Cost savings: Efficient resource optimization, instance sizing, and leveraging pricing models substantially reduce overall AWS expenses. By aligning resources with actual needs, companies avoid unnecessary spending.
• Resource efficiency: Strategic AWS resource selection and right-sizing ensure optimal resource utilization. This involves leveraging features like auto-scaling and efficient storage methods to minimize wastage and maximize efficiency.
• Scalability: Cost optimization ensures that AWS scalability remains cost-effective. Efficient provisioning and de-provisioning of resources meet fluctuating workloads without incurring unnecessary costs, ensuring scalability without overspending.
• Budget management: By gaining control and understanding of costs, organizations can effectively anticipate and plan for the financial aspects of their cloud infrastructure. This proactive approach helps avoid unexpected expenses and maintains financial stability.
• Maximized value: Beyond cost-cutting, cost optimization directs investments toward achieving maximum returns in the cloud. Purposeful resource allocation aligns every dollar spent with organizational goals, fostering efficiency, performance, and innovation, resulting in significant ROI.
• Better visibility: Utilizing tools like AWS Cost Explorer and cost allocation tags provides insights into cloud expenditures. This transparency identifies areas of excessive spending, enabling continuous cost control and optimization.
• Agility and flexibility: Cost optimization fosters organizational agility by allowing dynamic resource allocation according to demand. Cloud services adapt resource levels as needed, minimizing redundant expenses during downtimes and enabling quick responses to market changes.

Hence, AWS cost optimization is necessary for organizations to curtail unnecessary spending and channel their cloud investments effectively, aligning with business objectives for enhanced performance and agility

AWS Cost Optimization Checklist

1. Monitoring And Analyzing AWS Costs

Effective cloud management requires continuous monitoring and analysis of AWS costs. The insights gained from these practices help organizations optimize resources, enhance financial efficiency, and align cloud spending with business objectives. 

Using AWS Cost Explorer

AWS Cost Explorer helps you visually and intuitively explore resource consumption and its associated costs in the AWS public cloud. This tool facilitates cloud cost management within the expansive AWS ecosystem as a complementary service. Users can easily navigate and understand their AWS spending patterns visually with AWS Cost Explorer, which provides valuable insight into resource allocation and associated expenditures with its user-friendly interface. 

The primary appeal of AWS Cost Explorer lies in its ability to present data in the form of easily digestible bar or line graphs. Besides simplifying data sharing, this functionality also proves invaluable when justifying expenditures to other departments, such as finance and upper management. Using AWS Cost Explorer, your organization can collaborate and make decisions more effectively, thanks to the visual clarity offered by the tool.

Drawbacks of AWS Cost Explorer

However, AWS has its share of disadvantages, forcing organizations to look for a better cost-monitoring and analysis solution. Some of the issues associated with AWS Cost Explorer are

• It lacks the adaptability to changing conditions to incorporate potential changes into forecasts generated by the system.
• Return on Investment (ROI) data is unavailable, which limits the ability to provide comprehensive financial insights.
• It falls short of providing proactive cost reduction suggestions and lacks detailed explanations for the business rationale behind specific expenditures.
• It's not designed for real-time monitoring. The most recent cost and usage data is usually not reflected for a few hours after it is collected.

Aside from the drawbacks mentioned above, leveraging monitoring tools like AWS Cost Explorer in the DevOps space is often complex because of the laborious effort required or the additional costs involved with deployment. Managing the intricacies can quickly become overwhelming and challenging when storage environments become more complex. This is where Lucidty's Storage Audit can help you. 

With Lucidity Storage Audit, disk health and utilization analysis tasks are automated using an easy-to-use executable, user-friendly, ready-to-use tool. This tool lets users gain comprehensive insights into disk performance, optimize expenditures, and proactively prevent downtime without cumbersome manual work.

Setting Up Budget Alerts

With AWS Budgets, you can create tailored budget limits and set up a notification mechanism when these limits are exceeded. Budgets can be configured according to tags, accounts, and resource usage, providing a comprehensive way to manage and monitor AWS expenses.

Analyzing Cost and Usage Reports

With Cost and Usage Reports, you can explore your AWS expenditures in-depth, seeing the total costs for each service utilized, how many instances you use, and the average hourly cost for each resource. With AWS Cost and Usage Reports, you can gather detailed information about your costs and usage in one place. Organizations can easily share their AWS billing information by publishing these reports to an Amazon Simple Storage Service (Amazon S3) bucket.

2. Rightsizing and Resource Optimization

Right-sizing your AWS cloud infrastructure and optimizing your resources is vital to controlling your cloud costs and maximizing your investment. Through right-sizing, you can select the right resource type and size based on the actual needs of your workloads, thereby reducing unnecessary costs. Your right-sizing and resource optimization process for compute resources should include the following.

Identifying Overprovisioned Resources

• Utilize AWS CloudWatch to monitor key metrics such as CPU utilization, memory usage, and network traffic. Set up alerts when resource usage exceeds predefined thresholds, which will help you identify overprovisioned resources.
• Use AWS Trusted Advisor to optimize your AWS resources. It highlights overprovisioned resources, offers right-sizing recommendations, and provides insight into potential cost savings.
• You can use AWS Cost Explorer to analyze and visualize your AWS expenditures. You can use its Right-sizing Recommendations to identify overprovision resources and make more informed decisions about resizing or modifying them.
• Identify resources that consistently have low utilization in resource utilization reports. These resources may be overprovisioned, and you can take steps to right-size or terminate them.

Downsizing or Terminating Unused Resources

• Analyze your resources’s lifecycle, identify instances with low or no utilization over time, and consider downsizing or terminating them. AWS Management Console provides detailed information on instance usage.
• AWS Lambda and CloudWatch Events can automate resources' start and stop processes, ensuring that resources are only active when required.
• Track resource usage and allocate costs to specific projects or teams using cost allocation tags. This can help identify unused resources and accurately attribute costs.

Utilizing AWS Auto Scaling

Dynamically scale resources using AWS Auto Scaling based on actual demand. Scaling policies automatically add or remove instances based on changing workloads, ensuring optimal resource utilization.

With Auto Scaling Groups, your resources can be automatically scaled based on predefined policies, helping to maintain performance during peak times and reduce it during off-peak periods.

With AWS Auto Scaling's Predictive Scaling, you can predict future demand and adjust capacity proactively to minimize resource usage and cost before actual demand peaks.

Use AWS Auto Scaling to automatically scale your application based on specific metrics, optimizing resource utilization and maintaining performance as workloads vary.

Now that we have understood how to rightsize and optimize compute resources, let us dive into how we can do the same for storage resources.

• Assessment and Inventory: Perform a comprehensive evaluation of your existing storage environment. Generate an inventory that details all storage resources' types, sizes, and usage patterns.
• Understanding Usage Patterns: Analyze historical data and usage patterns to gain insight into storage utilization over time. Identify peak periods and recurring patterns for data access or storage needs.
• Utilizing AWS Monitoring Tools: Effectively employ AWS CloudWatch and other monitoring tools to collect real-time storage performance and usage data. Establish alarms and notifications for any unusual or unexpected spikes in usage.
• Evaluating Storage Types: Familiarize yourself with the features and costs of various AWS storage classes, such as S3 Standard, S3 Intelligent-Tiering, and Glacier. Select the appropriate storage class that aligns with your data's access patterns and performance requirements.
• Auto-Scaling Policies: Have auto-scaling implemented for applicable storage resources. Configure policies to automatically adjust capacity based on predefined thresholds or metrics.
• AWS Cost Explorer: Utilize AWS Cost Explorer to analyze historical AWS costs and identify trends. Delve into specific storage-related costs using AWS Cost Explorer to identify areas for optimization.
• Serverless Storage: Consider utilizing serverless storage options, such as Amazon S3, for scalable and cost-efficient object storage. Assess the potential of using Amazon Aurora Serverless for database storage.

However, using multiple tools for right sizing and resource optimizing storage resources can result in:

• DevOps time and effort: Changes or updates in various tools can take much time. It involves careful planning and testing to coordinate these changes and ensure they don't cause any issues. Moreover, using multiple tools to optimize storage requires monitoring and maintaining each tool individually. Unfortunately, this means that the operations team might have to deal with a heavier workload, as they have to stay updated on all the tool's updates, patches, and any possible issues that may arise.
• Downtime: If your organization chooses to switch or upgrade storage optimization tools, there might be a short period of downtime. Moving data, adjusting settings, and ensuring a seamless transition can be challenging and may lead to temporary service interruptions.

Moreover, while the storage resources can be expanded in AWS, it does not have any straightforward method to shrink the storage resources. The manual process is time-consuming, leading to performance issues and downtime. 

For EBS Shrinkage and Expansion: Lucidity EBS Auto-Scaler

In cloud environments, it is common to experience workload fluctuations. An automated shrinkage and expansion system should be implemented for storage resources to handle these fluctuations and maintain an optimal balance between performance, cost, and resource utilization. This system will ensure that the storage resources promptly respond to workload changes, preventing disruption and ensuring cost-efficient resource allocation.

Keeping this in mind, we at Lucidity have developed an autonomous storage orchestration solution- Lucidity EBS Auto-scaler for automated expansion and shrinkage. 

Our auto scaler is designed to handle unexpected increases in website traffic or help you save on storage costs during slow periods. It uses intelligent technology to adjust your real-time storage capacity, ensuring top-notch performance and cost efficiency. 

By simply clicking three times, you can reduce your cloud storage expenses by an impressive 70% without worrying about downtime or performance problems. This effortless solution allows you to strike the right balance between performance and expenses, aligning your cloud infrastructure perfectly with your needs, whether you're experiencing high-demand or low-demand periods.

How can Lucidity help you?

70% reduction in cost storage: By automating the shrinking and expanding process of EBS, you can save a significant amount, up to 70%, on storage costs. You'll see a remarkable boost in disk utilization, jumping from a mere 35% to an impressive 80%. 

With Lucidity seamlessly integrated into your system, you no longer have to worry about paying for unused or idle resources. This saves money and ensures efficient and optimized use of your disk resources. You'll only pay for what you need, maximizing the value of your storage investment.

No downtime: In the traditional way of managing resources, the DevOps team often struggles with the inefficiencies of navigating three separate tools. The complexity associated with manually navigating through these different tools increases the possibility of error, which demands a significant investment of time and effort, leading to downtime. Our automated resizing kicks in just minutes after you request it, so you can quickly address your storage needs. With this, you can say goodbye to manual interventions and minimize disruptions, making the process more streamlined. Lucidity offers a more agile and efficient resource management approach, boosting productivity and eliminating the complexities of traditional methods. 

Lucidity allows for customized policies, ensuring seamless operation and optimal efficiency. You can set utilization thresholds, minimum disk requirements, and buffer sizes according to your preferences, and Lucidity easily manages instances. It's important to mention that with Lucidity, you can create unlimited policies, enabling you to precisely adjust storage resources as your needs evolve.

Automated shrinkage/expansion: You can count on our EBS Auto-Scaler to effortlessly modify capacity whenever there is a sudden increase in demand or if usage drops below the ideal thresholds.

Wondering will it impact the instances performance?

Our Lucidity solution is specially designed to have minimal impact on your CPU and RAM usage. With our lightweight Lucidity agent, you can rest assured that it will consume only 2% or less of your CPU or RAM. This ensures that your workload continues running smoothly without affecting the performance of your system.

3. Choosing The Right Pricing Model

To optimize your costs, you should choose the right pricing model in AWS. AWS offers a variety of pricing models, so you should choose the one that works best for your workflow, usage patterns, and business requirements. Gain insights into your costs, create budget alerts, and get cost-saving recommendations using AWS Cost Explorer, AWS Budgets, and AWS Trusted Advisor. The pricing models offered by Amazon Web Services (AWS) include On-Demand, Reserved Instances (RIs), Spot Instances, and Savings Plans.

• On-Demand: No commitment, pay as you go. It can be used on unpredictable workloads or small periods of duration projects.
• Reserved Instances (RI): Significant savings over On-demand pricing over a year or three-year terms. Ideal for stable, predictable workloads.
• Spot Instances: Bid for unused EC2 capacity, thus helping you access available spare capacity at a low expense. Great for flexible and fault-tolerant workloads.
• Savings Plans: Provide substantial savings compared to On-Demand pricing for usage measured in dollars per hour on a consistent plan commitment basis over one or three years.

Analyzing Usage Patterns To Choose The Most Cost-Effective Model

You can optimize costs by analyzing usage patterns and selecting appropriate pricing models for different periods while ensuring your application has the resources it needs during peak demand times and scales down during low activity periods. For example, instances on-demand may be appropriate during business hours when resource demand is consistent. On the other hand, a spot instance might be used outside of business hours when traffic is low, and applications are tolerant of interruptions.

4. Utilizing Cost Optimization Tools And Services

AWS offers numerous cloud-native tools that can be used individually or in combination with one another to gain a holistic view of cost, receive optimization recommendations, and proactively detect and address any anomaly in spending patterns.

• AWS Cost Explorer: With the AWS Cost Explorer, you can visualize, understand, and manage your AWS costs and usage. It enables you to analyze historical data while forecasting future costs. It boasts many features, such as cost visualization, grouping, forecasting, right-sizing recommendations, etc. 
  AWS Cost Explorer identifies potential cost savings opportunities by analyzing your spending patterns. For instance, these could include recommendations to use Reserved Instances for certain workloads, optimize storage costs, or take advantage of certain pricing models.

•  AWS Trusted Advisor: The AWS Trusted Advisor provides real-time guidance on AWS best practices. It provides recommendations in many categories, such as cost optimization, performance, security, and fault tolerance.
  Using Trusted Advisor, you can identify idle or underutilized AWS resources, such as instances, volumes, and other resources that are not actively contributing to your workload and can be terminated or downsized.
  Moreover, users can use this service to identify unused or underutilized resources, such as Amazon Elastic Load Balancers, Elastic IPs, and Amazon RDS instances, so that they may be eliminated or downsized to reduce costs.

• AWS Cost Anomaly Detection: The AWS Cost Anomaly Detection tool allows you to detect unexpected changes in your costs in your AWS account so you can investigate and correct them. Aside from anomaly detection, it offers root cause analysis and customizable alerts.
  Cost Anomaly Detection provides insight into potential causes of anomalies. Understanding the root cause allows you to investigate and take appropriate actions, including cost optimization.
  When AWS Cost Anomaly Detection detects unusual spending patterns, it highlights areas for cost optimization. Identifying unexpected increases in resource usage, flagging underutilized resources during specific periods and highlighting places where reserved capacity might be more cost-effective are some of the examples.

5. Implementing Cost Optimization Best Practices

Implement the following robust AWS cloud optimization strategies to ensure that your organization maximizes the value of AWS while keeping the cloud in check. 

Tagging Resources for Better Cost Allocation and Tracking

AWS allows you to tag resources with metadata that provides additional information about their purpose, owner, or other relevant characteristics. In your environment, tags are composed of key-value pairs so that you can categorize resources flexibly and customize them. Tagging plays a crucial role in reducing AWS bills in the following ways.

• By categorizing resources with tags, you can allocate costs to specific departments, projects, or teams, clearly showing how your AWS budget is used.
• You can gain insights into which areas of your organization drive costs by associating resources with business units or projects. This promotes accountability and informs decision-making.
• Using tags as a foundation, you can implement targeted cost-saving measures. Using accurate resource categorization, you can identify inefficiencies and right-size instances and apply optimization recommendations precisely, ensuring your infrastructure is not unnecessarily disrupted.

Tips to Use Cost Allocation Tags to Reduce AWS Bill

• Ensure that relevant information is consistently captured within your organization by establishing consistent tagging standards.
• Automate tagging to ensure consistency and accuracy. Utilize AWS services like Lambda and Config Rules to apply tags automatically based on predefined rules, reducing human error.
• During each stage of a resource's lifecycle, from provisioning to decommissioning, tags ensure accurate and up-to-date cost allocation.

Implementing Cost Optimization Recommendations from AWS Trusted Advisor

The Amazon Web Services (AWS) Trusted Advisor service offers best practices and recommendations for optimizing the AWS environment in several ways, including cost optimization, security, performance, and fault tolerance. You can use it as a virtual cloud consultant to improve your AWS infrastructure, providing insights and guidance. The following tips will help reduce any unnecessary spending. 

• Keep reviewing the Trusted Advisor recommendations regularly to keep up with the latest insights. AWS environments and workloads can change rapidly, making staying current critical.
• Focus on high-impact recommendations aligned with your organization's goals and cost optimization strategy based on potential cost savings and the impact on your workload.
• Use automation tools like AWS Lambda functions to implement changes Trusted Advisor recommends. Automation helps ensure consistency and minimizes manual effort.
• To identify potential issues, you should test any changes in a staging or development environment before making them live. This will help you determine if any changes will impact live workloads.

6. Optimizing Data Transfer Costs

For effective cloud cost management, it is crucial to understand the impact of data transfer costs on the overall AWS bill. It charges for data transfers within and outside of its network, which vary according to the type of transfer (data transported within the same region, between regions, or to the Internet) and the AWS services used.

• Compared to transferring data across regions or to the internet, data transfers within an AWS region are frequently less expensive.
• There are typically higher costs when data is transferred between AWS regions than when data is transferred within a region.
• Outbound data transfer rates vary based on the region in which the AWS service is located. Outbound data transfer costs vary based on content delivered to end users or interactions with external APIs.

Utilize AWS Direct Connect for Reduced Data Transfer Costs

With AWS Direct Connect, you bypass the public internet by connecting your on-premises data center to an AWS Direct Connect location. If you have consistent and significant requirements for data transfer between your on-premises infrastructure and AWS, using Amazon Web Services Direct Connect can be a strategic move to reduce your data transfer costs.

The following steps will help you utilize AWS Direct Connect for reduced data transfer costs:

• Consider the nature and volume of data transfers between your on-premises infrastructure. and AWS. AWS Direct Connect is best used when data transfers are consistent and significant.
• The AWS Direct Connect location should be close to your on-premises data center. The AWS Direct Connect location is a physical data center in which AWS provides network connectivity to customers.
• The AWS Management Console allows you to provision a Direct Connect connection. You need to specify the Direct Connect location, port speed, and other relevant information.
• Establish a virtual interface between your on-premises network and your Virtual Private Cloud (VPC) in AWS after provisioning your Direct Connect connection.
• Ensure your on-premises routers are configured to route traffic towards and away from AWS through the Direct Connect connection instead of the public internet.
• Monitor your data transfer metrics regularly through AWS Direct Connect and optimize. your network configurations as needed. AWS provides tools for tracking usage, such as AWS Direct Connect monitors.

Optimizing Data Transfer between AWS Services

Optimizing data transfer between AWS services is crucial to improve performance, reduce latency, and manage costs effectively. Follow the steps mentioned below to optimize data transfer between AWS services.

• Reduce data transfer latency and improve performance by choosing the right AWS region according to the location of your users or other services.
• Use regional endpoints to transfer data within the same region instead of public endpoints to keep data within the AWS network.
• By using AWS PrivateLink, you can secure access to services via the AWS network rather than the public internet. The service enables private connectivity between VPCs and services, increasing security and potentially speeding up data transfer.
• You can improve upload and download speeds with Amazon S3 Transfer Acceleration, which utilizes Amazon CloudFront's worldwide edge locations to accelerate transfers. In S3, automatic compression based on content type can reduce the amount of data transferred and reduce costs.
• Data replication across regions can be achieved with Amazon S3's Cross-Region Replication (CRR). This ensures data availability and durability across regions, facilitating efficient replication of objects.
• Choose the appropriate storage class based on your data access patterns. For example, Standard might fit the needs of frequently accessed data, but Glacier might fit the needs of archival data.
• Using CDNs, such as AWS CloudFront, can help reduce latency by delivering content closer to end-users.
• Consider batch processing and parallelization to maximize bandwidth usage when transferring large volumes of data. Split large files into smaller chunks, then transfer them concurrently.

7. Continuously Monitoring And Reviewing Cost Optimization Strategies

To remain competitive in a dynamic cloud environment, optimize costs as your business evolves, and ensure that your cloud spending aligns with your organizational goals, you must regularly monitor and review your cost optimization strategies in AWS.

Setting Up Regular Cost Optimization Reviews

Performing regular cost optimization reviews is crucial for maintaining efficiency, identifying opportunities for savings, and aligning your AWS environment with your budget.

• Establish periodic schedules for reviews of cost optimizations. Your AWS environment's size and level of sophistication determine the frequency of the review process. Monthly and quarterly are the most frequent review periods; however, a customized schedule should meet your company's goals.
• Define the cost review objectives clearly. Define more distinct objectives, like decreasing total expenses, maximizing employment of resources, and adhering to the budgeting frameworks.
• Use AWS Cost Explorer and billing dashboards to understand your cost pattern. These tools offer charts that show exactly how much of your cost is concentrated on a particular aspect and, thus, possible optimizations.
• Create Amazon Web Services (AWS) budgets for receiving notifications once the set thresholds for your spending are crossed. They ensure efficiency in managing costs by giving real-time alerts and quick responses on high-cost increments.
• Monitor AWS Trusted Advisor recommendations concerning cost optimization daily. Trusted Advisor provides information on possible savings, unused assets, and measures for improving the usability of your AWS environment.
• Check on your usage of Reservations Insights and Saving Plans from time to time. Determine if your commitments fit your workloads, and make changes if necessary to save more.
• Revisit your resource labeling processes. This means that resources must be appropriately marked and categorized so they can be readily allocated at actual cost. Your costs must come from somewhere consistent and well-defined for tagging to understand them.
• Record what was discovered during each cost optimization assessment and distribute them amongst concerned parties. It encourages openness between various teams towards a cost-conscious culture in firms.

8. Implementing Feedback Loops For Continuous Improvement

A feedback loop is a helpful process that involves constantly monitoring, analyzing, and fine-tuning your AWS resources and expenses. By gathering insights from this monitoring, you can optimize and gradually decrease your AWS costs. These feedback loops are essential for cost savings in the long run. The steps mentioned below can help contribute to AWS cost reduction.

• Assess the performance and efficiency of your AWS resources to uncover areas where improvements can be made.
• Regularly generate and review cost reports to stay aware of ongoing expenses and make optimization efforts.
• Establish a regular schedule for reviewing and optimizing your AWS resources, considering changes in workloads, business requirements, and new AWS offerings.
• Make monitoring and analysis an ongoing part of your processes to maintain a continuous feedback loop.
• Take action based on the insights gained from monitoring and analysis by adjusting resources, configurations, and strategies as needed.

To wrap it up, knowing how to optimize AWS costs is a smart financial move and a vital necessity for businesses using cloud services. The popularity of cloud computing is undeniable, as more and more companies are shifting to AWS, according to statistics. However, this switch has its challenges, and if AWS expenses are not handled properly, they can quickly spiral out of control.

By implementing a well-rounded plan for cutting costs, businesses can make the most of their resources, get the most value out of their cloud investments, have a clearer view of their expenses, and increase their ability to adapt and be flexible. 

The provided AWS cost optimization checklist is a handy guide to help navigate the intricacies of managing costs on AWS. It covers essential areas such as monitoring and analysis, adjusting resources to the correct size, choosing suitable pricing models, utilizing cost optimization tools, following best practices, and regularly reviewing the progress.

If you have a low disk utilization or your EBS cost is getting out of control, book a demo with Lucidity to enable automation and save plenty of time and money.