What is cloud cost anomaly detection?
Cloud cost anomaly detection is a perfect example of the saying “a penny saved is a penny earned.” Imagine your monthly cloud bill suddenly skyrocketing despite no new services being added to your app. After thorough analysis, your engineering team discovers the increase was caused by code issues that resulted in unnecessary file transfers, which in turn increased spending by an extra 20%, which is money that could have been put to better use had the anomaly been detected earlier.
Let’s dig into what cloud cost anomalies are, how anomaly detection works, and the features to look for in your anomaly detection tool.
What is a cloud cost anomaly?
Cloud cost anomalies are unpredicted fluctuations (spikes or dips) in cloud spending compared to historical cost data. If undetected, anomalies may result in cost surprises and cloud waste. As such, FinOps encourages enterprises to periodically forecast costs and monitor the relevant cloud cost metrics; this will enable companies to accurately predict daily spending as well as proactively detect and resolve outliers.
Why is cloud cost anomaly detection necessary?
Cloud cost anomaly detection empowers various stakeholders within an organization to minimize cost surprises.
DevOps and engineering teams
Cost anomalies may be indicative of architectural design challenges or misconfigurations, for example, update releases containing too many API calls per operation. Aside from wasting precious financial resources, such misconfigurations can also result in performance bottlenecks.
Let’s consider the previous example of an application with excessive API calls, which may lead to performance degradation. To prevent this, the API provider queues and throttles the calls. However, if, in addition to the API misconfiguration, the app experiences a spike in traffic and the provider further increases the API throttling, users may experience latency and job failures. Essentially, the anomaly may not only cost money but could also damage your reputation, sending paying customers to better-performing competitor apps.
When engineering teams spot cost anomalies caused by misconfigurations promptly, they can resolve them quickly to avert performance failures and revenue loss.
FinOps teams
FinOps teams are saddled with the responsibility of optimizing cloud spend for their organizations. With real-time cost anomaly detection, they can efficiently discover cost spikes that show noncompliance with FinOps cost optimization best practices.
For example, FinOps teams should be vigilant to delete idle instances, track unexpected cloud service provider (CSP) charges, and ensure the pricing model or tier chosen is ideal for each workload. By taking such actions, they can cut waste that’s driving up cloud costs over time.
C-suite executives
Anomalies can gobble up huge percentages of revenue, destroying products’ cost efficiency. Through timely anomaly detection and resolution, CEOs, CFOs, and other executives can keep budgets in check and drive profitability.
CXOs
With anomaly data, chief experience officers (CXOs) can keep track of changes in cloud costs and how they impact the user experience (UX). CXOs can also leverage anomaly data to push for cost optimization measures; saved costs can then be reinvested into innovative releases that further boost app performance and customer satisfaction.
Imagine code issues are causing excessive data transfers, which hike costs and result in latency. The CXO pushes for decentralized data storage where data is moved closer to all major customer bases. By doing so, the enterprise cuts wasted spending and invests the money saved into improving response times and, ultimately, the UX.
Types of cloud cost anomalies
Cloud cost anomalies vary according to granularity and scope. Let’s look at three types.
Anomalies in total spending over time
This form of anomaly is the easiest to derive. It refers to the total deviation in cost for an hour, day, or week when compared to historical patterns. Comparing cloud spend by period can give quick insights into whether anomalies exist or not—without being precise as to the reasons for or sources of the anomaly.
Anomalies per resource and workload
Anomalies per resource are identified by comparing the total variation in the amount spent on various components, such as volumes, instances, or load balancers. Enterprises can compare this across teams, workloads, and regions, or they can compare spending for an hour or day to historical spending for that hour or day to gain granular insights into the exact reason for the anomalous cost increase.
Say the cost per hour of a newly deployed instance is high when compared to existing ones, and there’s no corresponding increase in traffic or workload. This may indicate that the new instance belongs to a higher-priced tier, for example, on-demand versus commitment-based plans. If the workload on the instance is relatively long-term, then the enterprise may want to switch to an available savings plan or reserved instance.
Anomalies in cost vs. revenue
Revenue breakdowns per unit and day can be very useful for anomaly detection. Where there’s predicted, usual cloud revenue, benchmarking it against the cloud cost of running the specific service or environment can provide insights into spend inefficiencies and how to resolve them.
Challenges with cloud cost anomaly detection
Cloud cost anomaly detection is not a straightforward look at yesterday’s cost compared to today’s. Cloud cost data can change rapidly due to several factors. Below, we discuss some of the hurdles to accurately detecting cloud cost anomalies.
Visibility
Sharing resources between teams, microservices, and enterprises may impact visibility into which services are causing anomalies. For instance, multi-tenant cloud networks involve multiple enterprise customers sharing the same databases, making it hard to separate costs per customer and identify cost anomalies on the fly.
Billing variations
CSPs may jack up prices over time, or teams may migrate workloads to costlier instance types as usage needs change. This also means monthly bills will increase, and if the price differences are not properly factored in, they may be mistaken for anomalies.
Data and volume inconsistencies
Cloud data evolves rapidly, and changes may be indicative of positive (e.g., customer growth) or negative (e.g., idle testing environments) trends. Considering factors such as an increase in traffic and deployed resources (e.g., new microservices, databases, load balancers) makes cost anomaly detection even more complex.
False positives and negatives
Detecting anomalies by comparing current to historical data without adding context may make teams believe there are anomalies where none exists or vice versa. False positives and negatives are usually caused by incorrect predictions or false interpretations of baseline behavior.
Features to seek in anomaly detection solutions
The best way to sidestep the challenges listed above is to invest in a powerful cost anomaly detection solution. The tool should be able to walk you through the entire process of detecting, alerting on, analyzing, and resolving anomalies. Below are the top features to look out for.
ML capabilities
Unlike traditional models that depend fully on thresholds and historical data, your ideal tool should incorporate machine learning (ML) capabilities, including the ability to self-train, adjust to ever-changing cloud data, and deploy seasonal awareness. With such features, the solution will accurately detect anomalies with less false positives and negatives.
Contextual information
After detecting anomalies, you will need to conduct a root cause analysis to better understand the source or cause of the anomaly. Consider a solution that offers granular anomaly data across regions, teams, workloads, resource types, and features. The tool should break down the cost per project, data transfer, service, department, customer, or team to help you spot anomalies faster.
Real-time alerts and notifications
Choose a tool that sends email or Slack notifications in real time for proactive anomaly resolution. It should also alert engineering teams once spending is approaching the preconfigured budget, as this may help catch anomalies early. For example, an early warning should notify engineering teams that 75% of the budget has been spent in over half the expected time.
Intuitive and interactive dashboards
Invest in a solution that is easy to implement, lets you set up your own policies and thresholds, and generates comprehensive and graphical cost reports. It should display anomalies across hybrid, multi-cloud, development, testing, and production environments as well as offer plausible recommendations.
Exploring cost anomalies with CloudSpend
ManageEngine CloudSpend is a powerful ML-based, self-training cost anomaly detection tool and cloud cost management solution rolled into one. It not only tracks metrics such as hourly costs, CSP prices, and traffic variations to continuously refine historical data, but it also tailors anomaly detection to your specific business context, abstracting false positives and negatives.
It detects both sudden and gradual cost hikes in real time and automatically alerts relevant stakeholders via your preferred notification channels when anomalies are detected or preset thresholds are reached.
CloudSpend has out-of-the-box dashboards for setting up budgets, forecasts, and cost allocations. The dashboards also offer granular insights through their Resource Explorer and Business Units components, which can help you break down data based on various factors. By offering complete visibility into and control over every instance in your cloud, CloudSpend helps ensure you don’t have any surprises on your cloud bill and prevents cloud waste. To learn more, refer to our documentation and schedule a demo of CloudSpend or get a free trial to see CloudSpend in action today.
