As businesses increasingly embrace cloud services, the rising concern of escalating cloud expenses becomes more apparent. Numerous factors contribute to these costs, including inefficient management practices, expanding data volumes, and neglecting storage-related expenditures.

Various strategies and tools are used to curb cloud expenses, like employing cloud visibility tools and forecasting resources. However, these solutions often present their own challenges. Therefore, it's crucial for organizations relying on cloud services to leverage cloud cost automation for getting maximum return from the cloud services.

In this article, we delve into the necessity for cloud cost automation to manage cloud expenses and explore solutions that can offer substantial assistance.

Introduction To Cloud Cost Automation

Effectively managing cloud expenses often involves a delicate balancing act, where optimizing costs without compromising performance presents a challenge. According to Flexera's report, 82% of organizations grapple with cloud cost management issues. This is precisely where automation emerges as a key player in cost reduction.

Automation holds the potential to address the challenges associated with cloud cost management by offering real-time adaptability and precision. Dynamic fluctuations in cloud workloads are effectively handled through cloudcost automation solutions, enabling the scaling of resources in line with actual usage. Features like automated rightsizing of resources, precise resorce tagging, and policy enforcement empower businesses to make data-driven decisions, identify cost-saving opportunities, and strike a balance between resource allocation and financial prudence.

Factors Leading To Increased Cloud Cost

The adoption of cloud services continues to surge, offering a myriad of advantages like increased profitability, enhanced security, operational flexibility, improved performance, and accelerated time to market for businesses. 

However, this substantial growth in cloud usage has sparked concerns regarding escalating costs. Studies show that 6 out of 10 organizations have experienced higher-than-anticipated cloud expenses. An Anodot report also revealed that nearly half of businesses struggle with controlling these costs. Additionally, a survey by StormForge unveiled alarming figures, suggesting that cloud wastage could soar up to 47%, as observed in a study of 131 IT professionals.

Let's delve into a detailed exploration of the factors contributing to this steep rise in cloud expenditures.

• Lack of efficient cloud management: The primary reason behind this significant wastage is the organization's lack of ability to manage cloud costs. To effectively manage cloud expenses, it is crucial to strike a precise balance between fulfilling resource demands and economizing expenditures. This entails comprehending the true needs of resources, enhancing configurations, and enforcing cost control measures. Manual management, lack of monitoring, ineffective scaling, etc can lead to high cloud costs. Companies must find a middle ground by allocating adequate resources to support their operations while avoiding extraneous expenditure. 
• Escalating data: The amount of data produced and managed by businesses is constantly on the rise. According to the International Data Corporation (IDC), the world is set to witness an extraordinary surge in global data, with estimates predicting a surpassing 175 zettabytes by 2025. This projection indicates an astonishing triple rise compared to the quantity of stored data documented in 2020. This growth can be attributed to various factors, including the surge in online transactions, the adoption of Internet of Things (IoT) devices, and the expansion of digital services. 
  As data emerges as a vital asset for organizations, there is an ongoing requirement for increased storage capacity, processing capabilities, and additional resources to handle and analyze this data efficiently. With organizations' growth in data production, an accompanying necessity arises for extra storage capacity. Cloud service providers usually impose fees based on the quantity of data stored, and extensive datasets incur elevated expenses for storage.

• Ease of scaling resources: Another crucial reason companies lose track of their cloud cost is the ease of scaling cloud resources. Cloud computing platforms offer a versatile and expandable setting, allowing businesses to boost their computing resources to meet escalating data requirements effortlessly. The scalability inherent in cloud services is a significant benefit. Nonetheless, it is crucial to note that scaling up in the cloud may result in companies swiftly overprovisioning resources without fully comprehending the connected expenses.

The ease of expanding cloud resources to meet increasing demand has presented a series of complex engineering difficulties. These challenges, characterized by their laborious nature and high resource requirements, make it difficult to keep up with the fast growth of data. Consequently, overprovisioning arises, resulting in the allocation of excessive resources. 

Overprovisioning has become a common problem among organizations battling high cloud costs. This is because organizations overlook storage resources while focusing on compute resource optimization. This is because developing a specialized tool is essential for efficiently optimizing storage demands due to the limited capabilities provided by cloud service providers (CSPs). 

However, creating a custom tool requires significantly increasing DevOps efforts and time investment. On the other hand, relying solely on CSPs' tools may result in suboptimal outcomes characterized by labor-intensive manual processes that are not feasible for day-to-day operations.

As a result, organizations tend to over-provision resources to ensure uninterrupted application uptime, considering the significant impact any disruptions can have on daily business operations. This trade-off between tool development complexities and the crucial need for operational stability highlights organizations' challenges in balancing efficiency and practicality in their storage optimization efforts.

In our audits to help organizations identify their disk spending and cost-saving opportunities, we found out that, on average, 82% of the disk was underutilized, and 16% of the storage resources were idle. Moreover, despite significant overprovisioning, organizations were facing at least one downtime per quarter.

This overprovisioning storage resource has severe cost-related impacts, such as:

• Excessive expenditure: When storage resources are overprovisioned, organizations spend more than necessary, paying for an unused surplus storage capacity. This escalates costs and prevents the funds from being allocated effectively.
• Reduced cost-effectiveness: Overprovisioning undermines cost-effectiveness by funneling resources toward underutilized storage. Since cloud providers typically charge based on the amount of provisioned storage, paying for unused capacity hampers the overall cost-effectiveness of the storage infrastructure.
• Impaired return on investment (ROI): Overprovisioning directly impacts the return on investment obtained from cloud storage solutions. Organizations make investments in cloud services with the expectation of meeting their storage needs efficiently. However, overprovisioning results in an inefficient utilization of financial resources, thereby diminishing the overall ROI.
• Impact on budget allocations: Overprovisioning can significantly affect budget allocation, as it may result in funds being allocated towards surplus storage capacity rather than prioritizing more critical areas. This misalignment of resources can impede the organization's strategic financial planning and hinder the effective distribution of resources.

This inefficiency leads to unnecessary expenses for organizations as they invest financial resources in infrastructure surpassing their requirements. It emphasizes the critical need to tackle these engineering challenges to optimize the utilization of cloud resources.

Overlooking storage-related cost impact: Organizations often underestimate the financial implications of storage expenses, particularly in light of the vast amount of data they generate and store. It is common to prioritize compute resources and network usage, inadvertently neglecting the significant costs incurred through storage. Consequently, organizations may find themselves burdened with inflated bills. Given the continuous expansion of data, both in terms of quantity and significance, it becomes imperative for organizations to consider their storage needs diligently.

Virtana did a study, "State of Hybrid Cloud Storage 2023," which uncovered significant findings by interviewing 350 individuals responsible for cloud decision-making. An astonishing 94% of those surveyed admitted to a surge in storage costs. Notably, 54% confirmed that their storage expenses were growing faster than their overall increase in cloud bills. These revelations emphasize the considerable influence that storage costs have on the financial aspect of organizations. It underscores the critical necessity for strategic management and optimization of storage resources in cloud environments.

Our extensive examination of storage practices within the top-performing organizations in the market made a significant discovery: storage costs accounted for 40% of their total expenditure on cloud services. 

To better understand the impact of storage resources on cloud cost, we conducted an independent analysis and found that cloud storage represented a noteworthy portion of their overall cloud spending. Interestingly, our research revealed that disk utilization was only 35%, implying that 65% of disk space had been needlessly overprovisioned. It is striking that even with this surplus allocation, these organizations faced downtime at least once every quarter, highlighting the challenges associated with effectively aligning storage resources to operational requirements. This finding emphasizes the critical importance of optimizing storage configurations to enhance cost-effectiveness and operational reliability.

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The aforementioned has led to an urgent need for a cloud cost optimization solution that addresses data, storage, and scaling issues, leading to efficient cloud cost management.

Prevailing Strategies Employed for Cloud Cost Management

Organizations have been implementing strategies that come with the following features to reduce their cloud bill.

Cost Visibility

Cost visibility is crucial for organizations as it offers a comprehensive grasp of cloud expenses. This facilitates the identification of the primary contributors to the overall bill, aiding in informed decision-making and fostering awareness within the organization. Organizations use different tools and techniques to gain comprehensive cost visibility into their cloud services. Cloud monitoring services like AWS Cost Explorer, Azure Monitor, or Google Cloud Monitoring offer real-time insights into resource utilization, performance metrics, and operational data.

A famous example of how real-time monitoring and alerting could have saved costs is that of Adobe. One of the teams at Adobe unintentionally left a computational task running on Microsoft Azure, leading to an unexpected cloud bill exceeding $500,000. Significant expenses were incurred due to the extended duration of the computing job, which likely required substantial computational resources.

The main lesson from this incident is the need for a prompt alerting mechanism. By implementing robust monitoring and alerting systems, Adobe could have swiftly received a notification regarding the prolonged execution of the computing task. Such an alert would have triggered immediate intervention from the team, halting the job and preventing the accumulation of significant and unplanned costs.

Cost Forecasting

Organizations can enhance their understanding of the cost components of different cloud services through detailed breakdowns available in cost visibility tools. This comprehensive analysis provided through different cost monitoring tools helps create more precise forecasts as it considers specific service costs and their influence on the overall budget. 

Organizations can effectively strategize their budget and allocate financial resources per projected cloud expenses by comprehensively assessing historical performance and future requirements. Several tools are available in the market, such as AWS Cost Explorer, Azure Cost Management and Billing, Google Cloud Management Tools, etc, that organizations use to predict, analyze, and manage their cloud expenditure. While this might sound easy, it is anything but that. There are several challenges shadowing cost forecasting. 

Cloud workloads frequently exhibit dynamic behavior and may undergo fluctuations in resource utilization. Accurately predicting these changes poses a significant challenge, particularly when managing applications that scale in response to demand. Moreover, predicting the future growth path of an organization, particularly for rapidly growing businesses, is inherently unpredictable. Striking a balance between the requirement for adaptability and the imperative of precise cost projections poses a significant challenge.

For instance, Pinterest originally projected its cloud usage for the season and entered a significant $170 million agreement with AWS. However, the surge in user activity during the holidays exceeded these expectations by a large margin. The platform encountered an unparalleled usage level, resulting in an unforeseen rise in cloud resource consumption. Since Pinterest had committed to a fixed contract, it found itself in a situation where the initially estimated capacity was insufficient, requiring acquiring additional resources at a higher pricing tier.

This scenario highlights common challenges in cost forecasting, such as unpredictable workload spikes, user behavior variability, and budget overruns.

Legacy Cost Optimization

Traditional tools for optimizing costs play a vital role in assisting organizations in thoroughly comprehending their cloud expenses. Tools such as Cloudability and CloudHealth by VMWare collect and analyze different aspects of usage, resource allocation, and cost information to present a comprehensive and detailed overview of cloud expenditure. By utilizing historical usage patterns, cost breakdowns, and pertinent data, these tools generate valuable insights to identify potential optimization opportunities for organizations.

The main challenge lies in the static nature of the suggestions and the subsequent timeline for implementation. Cloud environments, on the other hand, are dynamic, with workloads that constantly change, resource demands that fluctuate, and evolving service offerings from cloud providers. As a result, static recommendations may quickly become outdated or fail to adapt to real-time changes. This ultimately limits their effectiveness in achieving meaningful optimization.

Additionally, the conventional method typically overlooks cloud environments' dynamic and constantly evolving aspects when making recommendations. Consequently, engineers are responsible for implementing these recommendations, which can be demanding regarding time and resources. 

Furthermore, the time required to implement these recommendations can reduce the overall impact on gross margin. In the ever-evolving cloud landscapes, any delays in implementing optimization measures can result in missed opportunities for immediate cost savings and operational efficiencies. The longer the implementation process takes, the longer organizations have to wait before they can start benefiting from cost optimization.

Cloud-Native Cost Optimization

This involves incorporating cost management strategies that align with cloud platforms'distinct features and capabilities like AWS, Azure, Google Cloud, etc. Cloud-native cost optimization follows the principles of DevOps, automation, and scalability inherent in cloud-native architectures. By seamlessly integrating cost optimization into the framework of cloud-native development and operations, businesses can effectively regulate expenses, enhance resource efficiency, and maximize the economic value of their cloud investments.

The optimization initiatives encounter limitations due to their reliance on individual Cloud Service Providers (CSPs) and their specialized optimization tools aimed at addressing specific issues.

Various factors constrain these solutions. Primarily, they are closely integrated with the characteristics and limitations of a specific CSP, thus making them less flexible in a multi-cloud environment or situations where organizations utilize services from multiple providers. 

Additionally, implementing and managing these solutions often necessitates significant technical expertise, which can prove challenging for organizations with limited resources or specialized knowledge.

For instance, while the cloud-native solutions, be it AWS or Azure, offer storage resource expansion, there is no straightforward method for shrinking the same storage resources. AWS does not provide any native shrinkage feature for its EBS volume. Similarly, Azure has no such method to shrink disk resources. You can implement various workaround for AWS or Azure, but those involve resizing, data movement, file system adjustment, and more, leading to temporary service disruptions during the preparation and execution phase.

Keeping the aforementioned into consideration, there is an urgent need for an automated cloud cost management solution that will:

• Provide real-time monitoring and alerting mechanisms that offer timely notifications, thus preventing any unexpected expenses.
• Utilize historical data and advanced analytics to effectively navigate challenges, enabling organizations to obtain precise forecasts and facilitating strategic budget planning.
• Include dynamic and adaptive optimization strategies to ensure real-time responsiveness to changes in cloud environments, resulting in faster implementation and immediate cost savings.
• Follow an agnostic approach, allowing comprehensive optimization across multi-cloud environments without solely relying on individual CSP tools.
• Offer streamlined and efficient processes for storage management, thereby reducing the potential for disruptions during execution.

Manual Approach To Cloud Cost Management

The primary reason contemporary cloud cost management techniques fail to control costs is that organizations tend to invest their time in attaining the aforementioned features through a manual approach. Let us look at how taking the manual approach might not be the best cloud cost management solution. To help you understand it better, we will take the example of reducing cloud costs through storage optimization. 

Forecasting

The manual approach would involve thoroughly analyzing past data, user behavior, and application requirements to anticipate future storage necessities. However, the unpredictable nature of workloads and fluctuating resource utilization challenge accurately predicting the precise storage requirements.

Storage Selection

Decision-makers will be required to manually select the appropriate cloud storage option (such as standard, premium, or cold) based on their perceived requirements and cost factors. However, these choices may not be optimal due to the lack of timely information. The decisions made may not accurately align with the actual demand for storage.

Regular Demand Monitoring

Manually predicting demand can be challenging, considering the user behavior or unexpected workload. This, more often than not, leaves the team with the only choice to overprovision the resources. However, manually provisioning storage resources leads to static allocation, resulting in a lack of ability to scale in response to the fluctuating workload dynamically. This leads to either underutilization or performance issues during peak loads.

Monitoring And Alerts

Going the manual route, you can monitor the storage usage and occasionally make adjustments based on the observed patterns or changes in the demand. However, this is resource-intensive, and the reactive adjustment leads to delayed responses and potential disruptions. 

Making The Storage Live

Taking up the storage to live is an exhaustive process that involves multiple touch points in the following steps:

• Deployment Process: The first step is manually deploying storage volumes within the cloud environment. This can be accomplished by accessing the cloud provider's console or utilizing command-line interfaces. By doing so, storage resources such as AWS's Elastic Block Store (EBS) volumes, Azure's Managed Disks, or Google Cloud's Persistent Disks can be created.
• Establishment Of Volume Monitoring: Once the storage volume has been successfully deployed, it is vital to establish effective monitoring techniques to gain valuable insights regarding its performance, utilization, and overall health. This process involves configuring monitoring tools provided by the cloud provider or using reputable third-party monitoring solutions.
• Configuration Of Alerting And Rules: Alongside monitoring, configuring appropriate alerting systems and rules is crucial to ensure proactive notification of any potential issues or deviations in performance. During this phase, specific conditions are defined, notification channels are identified, and the necessary actions are configured accordingly.

Resizing The Storage Resources

Once the storage is live, the next concern is ensuring it has enough space to accommodate the fluctuating demands. Take the example of AWS, where we want to expand the EBS volume on an EC2 instance. The manual approach will include the following steps.

• Log in: Log in to your AWS Management Console and navigate to the EC2 dashboard.
• Locate EBS Volume: In the EC2 Dashboard, select "Volume" from the left navigation panel, where you will find all the EBS volume associated with your account. Take note of the Volume ID that you want to expand.
• Stop EC2 Instance: If the EBS Volume is currently attached to any EC2 instance, you must stop the instance before making any changes to the volume. This not only prevents data corruption but also ensures data consistency and integrity.
• Select EBS Volume: Click on the EBS Volume that you want to expand and then click on Actions.
• Specify New Size: In the Modify Volume dialogues box, specify the new volume size and enter the size you want to expand.
• Start EC2 instance: Start the EC2 instance to apply the changes.
• Verify changes: Check the volume detail in the AWS Management Console.

Now that we know how to expand EBS volume size let us move on to the shrinkage process. It is important to note that AWS does not support live shrinkage of the EBS volume. However, an alternate method includes multiple steps, such as follows.

• Snapshot: Take a snapshot of the volume
• New Volume: Stop the instance and create a new smaller volume. Attach, format, and mount the new volume.
• Copy data: Copy the data from the old to the new volume.
• Prepare new volume: Dteach the old volume and then start the instance.
• Verify: Check the newly acquired volume size.

Expanding and shrinking EBS volume manually is a tedious process. When increasing or decreasing the size of an EBS volume, performing an offline resizing operation for specific volume types or configurations may be necessary. This operation can cause downtime during the resizing process, thus affecting applications or services that depend on the volume. 

Furthermore, the expansion or shrinkage process requires data movement and adjustments within the storage infrastructure. As a result, it can consume a significant amount of resources, leading to longer processing times and potentially impacting performance. 

This is why, to ensure application uptime, organizations overprovision their storage resources. However, overprovisioning has severe cost-related impacts. Since you allocate more storage resources than you need, you end up paying for underutilized or unused resources. So, you are paying a high cloud bill without any corresponding enhancement in the performance or functionality. 

The Ultimate Solution- Cloud Cost Automation

In today's complex cloud landscape, manual approaches to cost management are inefficient. Automated solutions have emerged as essential tools, automating various facets of cloud cost optimization, from continuous monitoring to dynamically resizing storage resources. Lucidity, for instance, provides two powerful solutions: a Storage Audit and an Auto-Scaler for cloud cost automation.

The automated Storage Audit meticulously analyzes usage patterns, identifying underutilized or idle resources. This continuous monitoring empowers organizations with proactive cost management solutions. By pinpointing such resources, informed decisions can be made, whether it's downsizing or eliminating unnecessary storage capacity.

For instance, Lucidity's Storage Audit offers invaluable insights, allowing organizations to:

• Optimized Disk Spend: Gain visibility into your overall disk expenditure, set optimized billing targets, and achieve up to 70% reduction in costs.
• Identifying Disk Wastage: Pinpoint reasons for cloud wastage, whether it's overprovisioning or idle resources.
• Mitigating Downtime Risks: Prevent potential downtime, averting financial and reputational risks for your organization.

Implementing an auto-scaler in your cloud system dynamically adjusts storage resources to match changing demands efficiently. Here's how it optimizes storage:

• A Dynamic Capacity Expansion: Automatically allocates additional storage space when a specific storage volume utilization threshold is reached, accommodating data growth seamlessly.
• Automated Storage Tiering: Transfers data between different storage tiers based on usage patterns and cost factors. Frequently accessed data resides in high-performance, pricier tiers, while less accessed data is moved to lower-cost tiers, optimizing both performance and cost.
• Efficient Snapshot Management: Establishes policies for creating, retaining, and deleting snapshots, ensuring only necessary snapshots are kept while removing older or redundant ones, optimizing storage costs.
• Automated Surveillance and Notifications: Tracks storage utilization, anticipating issues or changes in behavior. This prompts timely adjustments to storage settings, preventing unexpected expenses and optimizing cost management.

Lucidity's auto-scaler seamlessly integrates with major cloud service providers (Azure, AWS, GCP) and block storage. It automatically adjusts storage resources in response to demand fluctuations, efficiently scaling resources during high activity and scaling down during low-activity periods.

Automate Cost Optimization With Lucidity

At Lucidity, we recognize the often-overlooked impact of storage resources, particularly block storage, on cloud bills. Our automated storage audit and auto-scaler solution are designed to identify cost-saving opportunities and seamlessly adapt to changing storage needs.

Let's explore how our Lucidity Auto-scaler helped Bobble AI streamline their AWS system, saving both their DevOps team's efforts and cloud costs.

Bobble AI, a leading technology firm, sought assistance optimizing their AWS Auto Scaling Groups (ASGs) due to Elastic Block Storage (EBS) limitations. Insufficient EBS volume provisioning led to operational complexities and excessive expenses, prompting their collaboration with Lucidity.

Our auto-scaler is a cutting-edge storage orchestration solution, automatically adjusting storage resources based on demand. Deploying Lucidity's Auto-scaler offers several benefits:

• No Downtime: Effortlessly handling increased demand, our EBS Auto-Scaler smoothly expands disk capacity without system interruption. Its exceptional resizing capability ensures optimal performance during expansion or contraction, guaranteeing continuous operations.
• Cost Savings: Our storage solutions redefine cost efficiency, delivering exceptional savings of up to 70%. Unlike traditional solutions requiring large-scale storage, Lucidity's EBS Auto-Scaler provides meaningful ROI even with minimal storage, setting new standards for cost optimization.
• Automated Expansion and Shrinkage: Our solution prioritizes automation, simplifying Elastic Block Storage (EBS) management without manual intervention. With customizable policies, it accurately scales volumes based on unique requirements, ensuring efficiency and precision.

Deploying Lucidity's Auto-scaler within their ASG and integrating with Bobble's Amazon Machine Image (AMI) transformed their operations. Our integration dynamically scales volumes, maintaining a healthy utilization range without coding or AMI refresh cycles.

The outcomes for Bobble were remarkable:

• Reduced storage costs by 48%
• Saved 3 to 4 hours per week on DevOps efforts.

Lucidity transformed its ASG management by streamlining and enhancing the automation and optimization of storage resources. Such advancements enabled Bobble to effectively reduce operational expenses and associated overheads while maintaining exceptional performance standards.

It Is Time For Cloud Cost Automation

The rise in data volumes and operational complexities highlights the necessity of an automated cloud cost optimization solution. Bobble AI's success using Lucidity's Autoscaler showcases the advantages of automating and optimizing storage resources through auto-scaling, significantly reducing overall cloud costs.

Dynamic adjustment of compute and storage resources in real-time, aligned with demand, boosts operational efficiency and mitigates risks associated with overprovisioning. As cloud services evolve and data's significance grows, embracing automated solutions like auto-scaling becomes essential for effective cloud cost management.

If storage-related issues have been a challenge in controlling your cloud costs, connect with Lucidity for a demo. Discover how our automation solutions can significantly reduce your cloud bill.

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