Cloud Cost Anomalies are important indicators that can signify issues from a financial perspective, but primarily infrastructure-related.
What is meant by anomalies
Cloud cost anomalies are unexpected discrepancies between forecasted costs and actual costs resulting from the use of cloud resources. These deviations can be caused by various factors, including overutilization of resources, improper configurations, or sudden spikes in activity.
To understand if the multi-cloud cost data indicates anomalies, a full understanding of multi-cloud costs is needed to create a forecast for multi-cloud costs. Only after these two steps, which conclude with the creation of a budget and a plan for cloud costs, can sufficient data be obtained to understand when an expense exceeds thresholds and should therefore be considered anomalous.
However, these signals do not only concern financial aspects due to unexpected costs and potential difficulties in adhering to the budget but can also be indicators of problems in IT infrastructure, especially in terms of services and applications.
Therefore, it is essential to identify them promptly, both to prevent expenses from excessively exceeding (jeopardizing the entire financial planning) and to resolve any service disruptions before they cause further damage.
Careful and constant monitoring, along with timely management of these discrepancies, are key factors in preventing unexpected charges and ensuring better management of cloud resources, minimizing negative financial impacts on company finances.
Let's start with the types
As anticipated, anomalies can indicate technical or business-related issues. For instance, an incorrect configuration of the system that manages resource allocation automatically could cause a significant increase or decrease in costs. While a decrease in costs might seem positive, it could indicate interruptions in various services or applications, causing slowdowns or halts in business.
Mainly, anomalies can be divided into:
Anomalous Peaks in Total Costs: a substantial increase in the costs of a service within a specific (usually brief) period that deviates from the normal trend.
Anomalous Increases in Usage Costs: indicating an increase in the price per unit of use, such as a considerable increase in hourly processing costs.
Anomalous Reduction in Unit Economics: represents a decrease in the relationship between revenues derived from the cloud environment and the relative management cost. This decrease can indicate inefficiencies in resource utilization.
Dealing with and managing these types of anomalies is essential to keep cloud costs under control and ensure efficient use of resources. The proper identification and resolution of these discrepancies are crucial steps in improving overall cost management and ensuring long-term financial sustainability.
The 'Cost Anomalies' widget in the Xautomata Cloud Cost module [MP3] allows real-time visualization of automatically detected anomalies, providing the reasons why that expense is considered anomalous. The types shown are:
Peak: significantly high cost compared to transactions from the previous 3 months;
Forgotten: a cost steadily increasing over time without any change in the previous 3 months;
Geographic: an abrupt cost in a geographic area without costs attributed to it in the 3-month history;
Triple: when the total expense value in a category in a month has tripled compared to two months prior.
A cost can be considered anomalous when, in recent transactions, the amount is unexpected. However, this discrepancy does not necessarily imply an already occurred problem but indicates a potential issue. Hence, it's essential to predict and manage these.
Managing Anomalies
By management, it means the ability to detect, identify, define, alert promptly, and ensure that unexpected events in cloud costs are resolved, thereby minimizing the negative impact on the company.
This process provides a more detailed view of any irregularities associated with cloud resources.
This task primarily falls to the FinOps professional, responsible for defining the expenses the company will bear for that period and comparing them with the actual expenses, thereby having an understanding of operational trends.
Fortunately, not all cost variations constitute one or more anomalies. It is essential to set thresholds in the definition phase that allow a quick understanding if the infrastructure cloud cost should not be so high. However, these thresholds are not the same for all companies and can vary significantly based on the company's size, industry, and the extent of cloud usage.
The Importance of Timing
To avoid disruptions in financial management, timing is vital. As seen earlier, irregularities can indicate various issues such as infrastructure malfunctions, software defects, or even cyber attacks.
Promptly managing these anomalies is crucial to ensure costs remain within planned limits or at least mitigate damages resulting from unexpected events.
Without proper management, organizations risk not detecting unusual expense increases, jeopardizing forecasts and the budget. For instance, an anomaly might indicate intense usage of specific resources, potentially leading to reduced performance, negatively affecting service levels and the company's reputation.
Continual anomaly detection algorithms are employed by organizations to identify potential irregularities in spending and budget models. However, these algorithms need constant updating to avoid harmful inaccuracies to the organization's growth.
The accuracy of mathematical models directly depends on data precision, which directly impacts the quality of forecasts and insights.
Therefore, organizations must ensure a constant update of anomaly detection algorithms, thereby ensuring better financial management and preventing economic instability.
Finding a balance between cost reduction strategies and maintaining business operations allows long-term budget planning, continually integrating anomalies into anomaly detection models for ongoing situational evaluation. This process allows businesses to receive timely indications if an anomaly goes undetected, reducing the risk of budget overruns or financial instability. Continuous monitoring and integration of anomalies into financial models prevent last-minute surprises in the business growth and planning process.
The Solution to Eliminate Manual Steps
Executing all the steps manually would require an excessive amount of time, not to mention that by the end of the process, certain values might have changed, rendering the strategy obsolete.
To address this issue, we've developed a tailored platform [MP4] that allows you to collect data from various cloud providers directly on a single dashboard, predict future cloud expenses using advanced mathematical algorithms, and, most importantly, promptly alert you of any anomalies, correlating them with their causes and automatically engaging (where possible) the various teams or individuals dedicated to resolving that specific problem. Simply put, Xautomata.