Ultimate Guide To AWS Cost Optimization

‍Cloud computing is the new norm, with AWS leading the way. However, with an increasing number of organizations adopting AWS for their data storage needs, the pressing question persists: how can you effectively manage cost without impacting the performance of your business? Tackling this concern came AWS cost optimization. 

Aside from helping with cost-savings, implementing cost-controlling strategies proves instrumental in improving ROI, enhancing scalability, and giving the business a competitive edge.

This blog will answer all the questions that you may have regarding AWS cost optimization, from what makes AWS expensive, cost components, and pricing plans to AWS cost optimization strategies. 

Boasting more than 1 million active users, AWS needs no introduction. It offers more than 175 fully functional services that allow users to deploy application workloads with just one click. Its increasing adoption by businesses from different industries has made it the number one IaaS player. However, this increasing reliance on AWS has made it essential to effectively balance profitability and operations efficiency. This can be achieved with effective AWS cost optimization.

AWS cost optimization involves:

• Monitoring and analyzing usage
• Identifying the possibility of reducing cost
• Implementing strategies that can help save money

Cost optimization ensures businesses can use AWS resources efficiently, avoiding wasteful spending on idle or underutilized resources. Lack of proper cost optimization could lead to an organization overspending on cloud resources, leading to budget overruns and decreased ROI. Let us discuss the benefits of AWS cost optimization in detail.

• Cost-savings: By implementing AWS cost optimization strategies, businesses can save significant money in the long run. These strategies help identify and rectify mistakes and eliminate inefficiencies, which can help companies save money and redirect those savings to critical tasks or innovation.
• Improved ROI: AWS cost optimization strategies help businesses use resources more efficiently and cost-effectively. This empowers organizations to use their resources in a more desired manner without overspending.
• Scalability: A comprehensive AWS cost optimization strategies involve different methods such as auto-scaling, rightsizing resources, or adopting flexible pricing models. This empowers organizations ot adapt to the changing workloads and scale their AWS infrastructure without incurring additional expense. 
• Enhanced Performance: Effective cost optimization strategies empower organizations to allocate resources intelligently. This, in turn, optimizes configurations, eliminates bottlenecks, and strengthens workflows, thus improving the performance and responsiveness of the AWS services.

What Makes AWS Expensive?

To effectively manage AWS expenses and maximize the value of the cloud investment, it is essential to understand what makes AWS expensive. There are several reasons AWS can appear costly, and for effective AWS cost optimization, it is crucial to understand these reasons mentioned below. 

• Pricing model complexity: While it is helpful that AWS offers a wide range of pricing models, a lack of understanding and the growing complexity of these models can make cost allocation difficult, leading to overspending when not managed effectively.
• Unused resources: It is not uncommon for businesses to inadvertently allocate more resources than necessary. There is also a strong possibility that the unused resources will run without supervision. These circumstances can lead to increased costs. 
• Data transfer costs: Transferring data between the internet, region, and AWS services can incur costs. This cost can further add up if there are heavy data transfer volumes, especially across regions or external networks, and if left unsupervised.
• Storage costs: Storage services such as Amazon S3 or EBS offer high durability and accessibility; however, if the data is stored inefficiently or if the data that is infrequently accessed is left unmanaged, it can quickly increase the cost.
• Lack of cost optimization: Understanding and implementing cost optimization strategies to actively manage and optimize AWS resources to eliminate the possibility of incurring additional costs is essential.

Cost Components Of AWS

By now, you know what makes AWS expensive, necessitating AWS cost optimization. Before we dive head first into the different AWS cost optimization strategies, it is essential to understand the AWS cost components because having a comprehensive knowledge of the cost components will help you find the best fit for your organization. There are 5 AWS pricing models that you can choose from.

• On-demand: You can use EC2 instances and pay by the hour or second. This pricing model allows you to launch instances quickly without any upfront costs. You can terminate these instances promptly as required and only pay for the resources consumed. It is ideal for businesses testing their environment or having unpredictable workloads. However, it is essential to remember that on-demand can be expensive and accumulate costs if left unchecked.
• Spot Instances: One of the most exciting pricing models, Spot Instances, allows you to request a spare computing capacity and use it for free. Since they are 90% cheaper than the on-demand pricing, they can significantly help reduce costs. It suits users who require computing resources for large-scale dynamic workloads since spot instances offer flexible start and end times. However, the model is complex, as your instance could be terminated unexpectedly. You will receive a notification about the termination two minutes before it occurs.
• Dedicated Hosts: Under this pricing model, you pay for every dedicated host, regardless of the quantity or the size of the instances launched. This model enables businesses to leverage their application licenses, whether socket-based, core-based, or virtual machine-based, making it ideal for hosting workloads requiring specific licensing metrics like those of SQL Server.
• Reserved Instances: You utilize a reserved instance like with dedicated hosts. However, instead of requesting the instance on demand, with AWS pricing, you reserve it for a long-term period (1-3 years) and are billed for it regardless of utilization.

Following are the types of Reserved Instances model:

• Standard RI: By utilizing Standard Reserved Instances (RIs), clients can secure capacity within a designated Availability Zone, guaranteeing they can deploy instances promptly, even during peak usage times. Standard RI is an ideal choice for workloads with consistent performance requirements for an extended period.
• Convertible RI: The convertible Reserved Instance option offers increased flexibility by allowing customers to adjust instance attributes throughout the reservation term. This includes changing the instance family, operating system, or tenancy. Convertible RIs are ideal for workloads adapting to evolving requirements or technological advancements.

• Savings plan:  You can choose the computing capacity you need on a fixed price per hour basis, keeping in mind that you are committing for 1-3 years. You can choose from one of the two types of savings plans. There are two types of savings plans.

• Compute Savings Plan: It includes charges for Amazon EC2 instance usage, regardless of factors such as instance family, availability zone, tenancy, and operating system. If you have varying compute usage patterns or need more resource usage flexibility, you should opt for a compute savings plan. 
• ECS Instance Savings Plan: For customers with predictable or stable computing usage requirements, an EC2 instance savings plan might be the ideal choice, only if they are willing to commit to a specific EC2 instance family for a more extended period.

AWS Pricing Plans

It is crucial to grasp pricing plans for successful AWS cost optimization. Doing so allows for precise cost prediction, the identification of optimization openings, informed resource allocation choices, strategic planning support, prevention of overpayment, and facilitation of ongoing optimization endeavors. Let us now look at AWS's pricing model:

• Free Usage Tier. This is suitable for those still unsure about the applicability of the AWS solution. Amazon offers businesses the chance to try the service for free for a year. Although you may be taking advantage of a free starting option, you will still need to invest in specialists to configure and assist you in transitioning your operations to the cloud.
• Pay as you go: The pricing plan utilizes an on-demand model, as its name shows. AWS Lambda and Amazon Lightsail are two instances of this pricing structure. Amazon Lightsail proves to be an excellent option for containerized applications, whereas AWS Lambda operates by charging based on the volume of requests made.
• Pay less by using more: The AWS pricing model incentivizes users to utilize additional resources, opening up more optimization opportunities. This approach is especially advantageous for experienced cloud storage users, as scaling solutions and cost optimization are closely linked. Users can establish an economy of scale by effectively controlling data movement across various storage levels and taking advantage of AWS Snowball and Amazon Glacier services. This ensures efficient resource utilization and cost reduction.
• Save as you commit: The pricing strategy of AWS is influenced by Spot and Reserved Instances, offering affordable options for computing resources. Amazon EC2 and RDS also provide tariff plans for reserving storage capacity, aligning with this model. Users can ensure predictable pricing and cost optimization for their storage requirements by planning and reserving storage, leading to improved financial planning and resource allocation.

Understanding Cost Drivers Of AWS

To optimize costs effectively, businesses must comprehend the factors influencing AWS expenses. This understanding enables identifying cost centers and exploring optimization opportunities, efficient resource allocation, accurate budgeting, performance enhancement, improved efficiency, effective usage monitoring, and continuous optimization efforts.

Compute

• Amazon EC2 (Elastic Compute Cloud): The pricing of EC2 instances relies on multiple variables, including instance type, operating system, region, and usage duration. Each instance type has varying compute power, memory, and storage options, all priced differently.
• AWS Lambda: Charges for Lambda are based on the volume of requests and execution time. It is especially beneficial for event-triggered workloads and can result in cost efficiency for sporadic or low-traffic applications.
• Elastic Beanstalk, ECS, and EKS: These managed services for container deployment and orchestration may result in costs associated with the underlying EC2 instances and additional fees for the management layer.

Storage 

• Amazon S3 (Simple Storage Service): S3 provides scalable object storage with storage classes such as Standard, Infrequent Access, and Glacier. Pricing is based on storage volume, data transfer, and retrieval frequency.
• Amazon EBS (Elastic Block Store): EBS offers block-level storage volumes for EC2 instances, charged according to volume size and provisioned IOPS (Input/Output Operations Per Second).
• Amazon EFS (Elastic File System): EFS provides scalable file storage for EC2 instances and is billed based on the amount of data stored and accessed.

Data Transfer 

• Data Transfer OUT: Costs are applied to data transfer from AWS to the internet or other AWS regions. Pricing is dependent on the amount of data being transferred and the destination.
•  Data Transfer IN: Charges apply to data transferred into AWS services. This is typically free or at a lower rate compared to outbound data transfers.
• Data Transfer between AWS Services: Certain services, such as EC2 and S3, offer free data transfer within the same AWS region. However, transfers between different regions will incur additional costs.

Networking

• Amazon VPC (Virtual Private Cloud): VPC enables the creation of isolated virtual networks within AWS. Expenses may include fees for NAT gateways, VPC endpoints, VPN connections, and Direct Connect.
• Elastic Load Balancing: ELB disperses incoming application traffic among various targets to enhance high availability and fault tolerance. Costs are determined by the load balancer hours and data processed.

Database Services

• Amazon RDS: Amazon RDS offers managed relational databases such as MySQL, PostgreSQL, and SQL Server. Pricing for these databases is determined by instance types, storage, and provisioned IOPS.
• Amazon DynamoDB: This fully managed NoSQL database service is priced based on provisioned capacity, indexed data storage, and read/write throughput.

AWS Cost Optimization Strategies

By now, you have a fair understanding of how costs work in AWS and why it is urgently necessary to implement AWS cost optimization strategies. 

1. Using AWS Cost Management Tools

The first step to effective AWS cost optimization is using the right AWS cost management tool. Using AWS cost management tools allows businesses to enhance control over their AWS expenses, efficiently allocate resources, ensure governance and compliance, foster transparency and accountability, and encourage improvement in cost management strategies. 

There are a plethora of tools available in the market, with each catering to different aspects of cost optimization, broadly classified into

• Native tools: AWS Cost Explorer, AWS Trusted Advisor, AWS Budgets, AWS CloudWatch
• Tools for computing resource optimization: CloudCheckr, Densify, Granulate
• Tools for storage resources optimization: Lucidity, ProsperOps, Zesty
• Tools that enhance visibility and monitoring: Apptio, CloudZero, FinOut

2. Reduce EC2 Cost With Spot Instances

You can use Spot instances to reduce EC2 costs if you have a fault-tolerant workload. With Amazon, you get a Spot fleet feature that lets you run both on-demand and spot instances in the same Auto-scaling groups. This way, you can reserve some on-demand spot instances for crucial components. 

Utilizing Spot instances can result in significant cost savings, reducing expenses by as much as 90% for fault-tolerant workloads such as big data, CI/CD, and web servers. If Spot instances are disrupted, autoscaling will ensure that the target capacity is maintained by automatically procuring additional instances.

3. Reduce Costs Associated With Fargate, Lambda, And More With Compute Savings Plan.

Aside from EC2, compute savings plans is automatically applied to the Fargate and Lambda usage. By enrolling in Savings Plans, your compute usage will be automatically billed at the discounted Savings Plans rates. Any additional usage exceeding your initial commitment will be charged at standard On-Demand rates.

Utilize the one-year Compute Savings Plans with no upfront cost to receive a discount of up to 54% compared to On-Demand pricing. Review the suggestions in AWS Cost Explorer and select the compute, one-year, no upfront payment options to optimize your savings.

Aside from EC2, compute savings plans are automatically applied to the Fargate and Lambda usage. By enrolling in Savings Plans, your compute usage will be automatically billed at the discounted Savings Plans rates. Any additional usage exceeding your initial commitment will be charged at standard On-Demand rates.

Utilize the one-year Compute Savings Plans with no upfront cost to receive a discount of up to 54% compared to On-Demand pricing. Review the suggestions in AWS Cost Explorer and select the compute, one-year, no upfront payment options to optimize your savings.

4. Selecting The Appropriate AWS Region

Choosing the appropriate AWS region is crucial for efficient cost control, as it influences pricing, data transfer rates, resource availability, performance, compliance, disaster recovery, and service uptime. You must consider the following factors while choosing the right AWS region:

• Cost: AWS regions may have different pricing models. By using the AWS cost calculator in conjunction with official pricing details, businesses can accurately estimate expenses based on their chosen region. This enables companies to strategically choose areas that provide optimal value without sacrificing performance or service availability.
• Availability: Utilizing resources across various AWS regions improves availability and resilience, allowing businesses to set up distinct disaster recovery centers. This distributed design minimizes downtime and data loss, guaranteeing continuous operations and protecting against unexpected interruptions.
• Latency: When considering latency optimization for improved application accessibility and responsiveness, choosing regions with lower latency is essential, especially for users in specific geographic locations. Lower latency enhances user experience and satisfaction and boosts engagement and retention rates.
• Service availability: When aligning service availability with AWS regions, it is essential to note that only some AWS services are available in some areas. Therefore, verifying the availability of required AWS services in the chosen region is essential to ensure smooth service integration and prevent potential limitations or compatibility issues.
• Compliance with data sovereignty: Compliance with local legal requirements related to data sovereignty is critical when storing data in specific geographical locations. Organizations must follow regulatory frameworks that govern data residency and privacy to remain compliant and reduce legal risks associated with data handling and storage. This becomes crucial to ensure alignment with legal obligations and industry standards, especially when dealing with sensitive or regulated data.

5. Identifying And Deleting Older Snapshots

Terminating an EC2 instance only deletes the associated EBS volume. However, the old snapshots are still there, taking up space and costing money every month. While they are inherently incremental, if you perform backups of EBS volume regularly and have a high retention period, this small incremental addition can add up over time. 

It is crucial to emphasize that most snapshots rely on data from the original snapshot of the entire EBS volume. Therefore, it is imperative to identify and remove the original snapshot if it is no longer necessary. Doing so can result in significant storage space savings compared to removing multiple incremental snapshots.

6. Identifying And Deleting Idle Load Balancers

It is important to check the load balancers that are not currently being used since every load balancer incurs costs. When a load balancer has no backend instances linked to it, or when network traffic is minimal, it is not optimally utilized and is inefficiently allocating resources. Use AWS Trusted Advisor to identify load balancers with few requests.

Another way to reduce costs associated with load balancing is by optimizing bandwidth usage. For reduced data transfer costs from EC2 to the public web, consider utilizing Amazon CloudFront. CloudFront functions as a Content Delivery Network (CDN), allowing you to store web content in various edge locations globally. By doing so, the bandwidth needed to handle sudden traffic increases can be notably minimized.

7. Automatic Monitoring Idle/Unused Or Overprovisioned Resources

The next step to reduce AWS costs is identifying and deleting idle/unused and overprovisioned resources. Let us explain how idle/unused and overprovisioned resources increase costs.

• Resource underutilization: Idle or unused resources can lead to ongoing costs, even when not in active use. For example, an EC2 instance running with no workload or storage volumes not actively storing data consumes computing power, storage space, and network bandwidth. This results in unnecessary expenses incurred.
• Capacity wastage: Overprovisioned resources, such as instances with higher specifications than necessary or storage volumes with excessive capacity, represent wasted capacity. This inefficiency leads to paying for resources that exceed actual requirements, ultimately resulting in inflated costs without corresponding value delivered.
• Complexity: Dealing with a vast infrastructure containing underutilized or overprovisioned resources introduces complexity to cloud management, challenging the optimization of resources. This inefficiency can result in missed chances for cost optimization and impede overall cost management endeavors.

While you can manually discover these resources or use monitoring tools for them, they would do more harm than good.

How so?

Manual discovery or over-reliance on monitoring tools may pose limitations due to the laborious tasks involved in DevOps activities or the added expenses related to deployment. Additionally, with the growing complexity of storage environments, manual management can result in inefficiencies and rapidly escalating difficulties.

This is why we suggest cloud cost automation with Lucidity Storage Audit.

The Lucidity Storage Audit provides a convenient solution for overcoming the obstacles presented by manual discovery or monitoring tools. Automating the entire process using a user-friendly executable tool simplifies the task of analyzing disk health and utilization. This efficient approach enhances cost-effectiveness and reduces the chances of downtime without requiring extensive DevOps efforts or extra deployment expenses.

With just one click, you gain insight into the following

Lucidity Storage Auidt offers the following benefits.

• Assessment of disk spending: Understand your existing disk expenditure, determine the ideal cost savings target, and discover methods to decrease storage expenses by up to 70%.
• Identification of disk waste: Uncover the amount of unused or overprovisioned disk space within your system and implement strategies to eliminate inefficiencies.
• Mitigation of disk downtime risks: Anticipate the likelihood of downtime incidents in advance to proactively prevent any negative financial or reputational impacts.

The Lucidity Storage Audit offers several benefits:

• Streamlined auditing: Say goodbye to manual efforts and cumbersome monitoring tools. Lucidity Storage Audit provides an automated process that simplifies auditing with a user-friendly, pre-configured executable tool.
• Comprehensive insights: Easily understand the health and utilization of your disks. This tool offers valuable insights that help optimize spending and prevent downtime by providing clear visibility into your storage environment.
• Optimization of resources: Efficiently analyze storage utilization percentages and disk sizes with Lucidity Audit. This information empowers you to make well-informed decisions to improve resource allocation and maximize efficiency.
• Privacy and security ensured: Lucidity Audit always protects your data using Azure internal services for secure storage metadata collection. This safeguard guarantees the protection of customers' personally identifiable information (PII) and sensitive data from unauthorized access or compromise.
• Effortless operation: Trust in the authenticity of your cloud environment and assets with Lucidity Audit. Our system is designed to function seamlessly without disruptions or strain on your infrastructure, ensuring smooth operations during the auditing process.

8. Auto-Scaling Resources

The next step in the AWS cost optimization checklist is auto-scaling the resources. Auto-scaling automatically modifies computing resources, including virtual machines or containers, in response to specified conditions or metrics. The primary objective of auto-scaling is to enable applications or workloads to seamlessly adjust to fluctuations in demand, thereby ensuring optimal performance and resource efficiency while keeping costs to a minimum. Mentioned below are some of the ways you can auto-scale AWS resources.

• EC2 Auto-scaling: EC2 Auto-scaling guarantees that your application consistently maintains an appropriate number of EC2 instances to manage incoming traffic efficiently. By forming EC2 auto-scaling groups, a cluster of EC2 instances is established. The group's capacity can be controlled by setting minimum and maximum scaling values to prevent it from dropping below or exceeding a set size. 
• RDS Auto-scaling: RDS Auto Scaling offers automated storage scaling for MySQL, PostgreSQL, MariaDB, SQL Server, and Oracle databases. The service monitors database storage utilization and increases storage capacity when usage nears the provisioned size, ensuring optimal performance. Scaling events are seamlessly executed without downtime or disruption to ongoing database operations.
• ECS Auto-scaling:  To achieve this, utilize CloudWatch metrics such as CPU and memory usage for ECS containers. With AWS Auto Scaling, the capacity of ECS container tasks can be automatically adjusted based on these metrics. By leveraging CloudWatch metrics, additional tasks can be added to manage a high influx of requests, while tasks can be removed as loads decrease.
• EBS Auto-scaling resources: While we have gone through enough ways to auto-scale computing resources, we should also focus on storage-related aspects in AWS cost optimization best practices.

Why?

Because storage is one of the significant contributors to the overall cloud costs, according to research conducted by Virtana in the report "State of Hybrid Cloud Storage in 2023", the findings suggest that a significant majority, 94% of cloud decision-makers, acknowledge an increase in their storage costs. Additionally, 54% of respondents stated that the growth rate of storage costs outpaced that of overall cloud expenditures.

At Lucidity, we decided to look into how storage impacts the overall cloud bill. Our independent study encompassed over 100 organizations leveraging cloud service providers such as Azure, AWS, and GCP.

• The findings revealed that, on average, 40% of cloud costs were attributed to storage usage and inefficiencies. Specifically, Block Storage - including AWS EBS, emerged as a significant contributor to overall cloud expenditure.
• Furthermore, our investigation uncovered alarmingly low disk utilization rates for Block Storage across functions such as Root Volumes, application disks, and self-hosted databases. Organizations were overestimating their storage needs and overprovisioning resources.
• Despite these efforts to preemptively address storage requirements, a persistent challenge remained: organizations experienced at least one downtime incident per quarter.

Despite the importance of storage optimization, many organizations overlook it, focusing on overprovisioning resources. However, we recognize the rationale behind this decision, which includes the following factors:

• Custom tool development for storage optimization: Due to the limited capabilities provided by Cloud Service Providers (CSPs) for storage optimization, organizations often find it necessary to develop custom tools. These tailored solutions are designed to meet the specific storage optimization needs of the organization.
• Increased DevOps efforts and time commitment: Creating and maintaining custom storage optimization tools require significant DevOps resources and time. These tools must undergo continuous development, rigorous testing, and ongoing maintenance, adding complexity and additional workload to the DevOps workflow.
• Limited capabilities: Relying solely on tools provided by cloud service providers (CSPs) for storage optimization can lead to inefficiencies, as these tools often lack the sophistication needed for comprehensive optimization tasks.
• Manual and resource-intensive work: Depending solely on CSP-provided tools, labor-intensive manual processes may be required to meet optimization requirements. These hands-on procedures can be taxing and impractical for routine tasks, resulting in the depletion of valuable resources and workforce.
• Lack of live shrinkage: While leading CSPs offer expansion options for storage, live shrinkage of EBS volume is not supported. Achieving shrinkage requires manual steps like creating new volumes, taking snapshots, stopping instances, and more, leading to potential downtime.

However, overprovisioning reflects operational inefficiency and indicates a financial drain since you are paying for the resources you are not using. 

Focusing on EBS, Lucidity came up with the right solution to prevent overprovisioning or underprovisioning of the resources by automating the scaling process. Traditional methods for scaling storage resources can often result in inefficient allocation. This may involve over-provisioning, causing wastage of resources, or under-provisioning, resulting in performance bottlenecks.

Lucidity has developed the industry's first autonomous storage orchestration solution, Lucidity Block Storage Auto-Scaler. It shrinks and expands the storage resources per fluctuating requirements without any performance lags or downtime. 

The Lucidity Block Storage Auto-Scaler boasts the following features:

• Effortless deployment: With just three clicks, you can deploy the Lucidity Block Storage Auto-Scaler and experience a revolution in your storage management process.
• Storage optimization: The Auto-Scaler immediately optimizes storage capacity to maintain a utilization rate of 70-80%, leading to significant cost reductions.
• Highly responsive: In the face of sudden traffic or workload spikes, the Auto-Scaler swiftly responds by providing additional storage capacity. With expansion capabilities that only take one minute, you can efficiently handle unexpected surges without disruption, ensuring you always have the resources to manage high demands effectively.
• Minimal performance impact: The Lucidity solution is meticulously engineered to reduce its impact on your instance's resources, such as CPU and RAM, while onboarded. Our optimized Lucidity agent is crafted to utilize only a tiny fraction of your resources, consistently running at under 2% CPU and RAM usage. This deliberate design choice guarantees that your workload in the instance remains undisturbed, allowing you to focus on your tasks without any interruptions.

With Lucidity Block Storage Auto-Scaler, you get the following benefits:

• Automated shrinkage and expansion of storage resources: The Lucidity Auto-Scaler is engineered with precision to automatically adjust disk scaling in just 90 seconds, enabling efficient management of large amounts of data. Unlike traditional EBS volumes, such as Standard EBS, which have a limit of around 8GB per minute (125MB/sec), the Auto-Scaler maintains a solid buffer to handle sudden increases in data without exceeding the EBS throughput limit.
• Reduce storage costs by up to 70% with Lucidity Block Storage Auto-Scaler: Cloud service providers typically charge based on provisioned resources. However, our auto-scaling feature eliminates the need for overprovisioning. This means you only pay for the storage you use, leading to significant savings on storage expenses.

To get an estimate of your potential savings, utilize our ROI Calculator. Simply select your preferred cloud provider (e.g., Azure or AWS), input your current monthly or annual spending information and details on disk usage and growth rate. Save money efficiently with Lucidity Block Storage Auto-Scaler.

• Minimize Downtime with Lucidity Block Storage Auto-Scaler: Due to lengthy processes, traditional provisioning methods often result in downtime. However, Lucidity's Block Storage Auto-Scaler ensures zero downtime by adjusting storage capacity in real time based on fluctuating needs.

Additionally, our platform offers a customizable "Create Policy" feature, allowing users to tailor scaling policies to specific use cases and performance requirements. The Block Storage Auto-Scaler automatically adjusts storage resources according to the defined policies, ensuring seamless operations without interruptions.

We hope you have gained sufficient knowledge from our comprehensive blog on AWS cost optimization. If you lose sleep over bringing down your storage cost, contact Lucidity for a demo, and we will help you understand how automation can make your cloud infrastructure cost-efficient without impacting performance.