Table Of Contents
The Need For Anomaly Detection Explained   What Is AWS Anomaly Detection? The Benefits And Limitations Of AWS Cost Anomaly Detection  Enter CloudZero Cost Anomaly Detection 

To explain how cost anomaly detection works in AWS, let’s first look at an analogy. Imagine you strategically propagate, cultivate, harvest, and replenish trees for a thriving forest products company. 

In keeping with your eco-friendly policy, you only harvest the trees with straight and tall trunks. You leave irregularly shaped trees alone. Your company doesn’t harvest trees you won’t use. 

You lead a team of foresters through a maze of trees and underbrush in search of quality trees to mark, while the rest of your team harvests the trees you mark. It’s impossible to use a car or horse in the woods. And a drone won’t work either, because all trees look the same from above. So deciding what trees meet your standards is a manual task. 

This process is slow and prone to errors. As time passes, the logging team always catches up with your markers. But that’s not all. Scaling operations over the years have magnified this problem. The budget does not allow you to hire more scouts. Plus, you would not want the loggers harvesting trees indiscriminately.

With this guide, we’ll explain why manual anomaly detection is as difficult as trying to spot crooked trees in a forest and what the AWS Anomaly Detection service does to help. Afterwards, we will show you a better way to detect, monitor, and report abnormal metrics in AWS without all the manual tagging.

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The Need For Anomaly Detection Explained  

This is the dilemma many organizations face today as the digital world progresses rapidly. With cloud computing, you can run many operations concurrently across multiple distributed systems and auto-scale. 

However, the technologies produce forests of data, and there is no way to tell which datasets are straight and tall. Trying to sift through the vast forests for a few crooked datasets takes time and money. As your company scales, it becomes harder and harder to analyze data fast enough to get distinctive, relevant, and valuable findings before the next C-Suite meeting.

In addition, it’s difficult to explain to finance what and why you spent your engineering budget on — let alone explain why you went over budget. You may have left your engineers on their own to innovate, test, and deploy numerous experiments. 

Yet without understanding how engineering activities impact the bottom line, they couldn’t keep costs under control, so you went over your monthly budget in half the time. Analyzing your AWS bill does not give you the answers you are looking for, either. 

Your AWS cost report only gives you a bird’s-eye view of your cloud costs, like a drone over a forest. It does not provide more detailed, insightful unit cost analytics, such as costs per customer, deployment, development project, and cost of new features over a given period. 

You do not also get a clear picture of the specific activity that led you to go over budget. The cost overrun didn’t raise any alarms before it occurred. Had you received a timely alert, you might have taken action to prevent the surprise billing from AWS.     

This is what happens when manual processes are used to support complex and dynamic automated systems. The solution: anomaly detection. 

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What Is AWS Anomaly Detection?

The AWS Anomaly Detection service uses Machine Learning (ML) to identify abnormal metrics, events, and other unusual dataset behavior within the Amazon Web Services ecosystem. 

AWS Anomaly Detection analyzes historical data for a specific metric, identifies patterns that repeat hourly, daily, or weekly, and uses this information to build models of predictable outcomes. Anomaly Detection flags the data instances that wander off from the rest as anomalies. 

Example:

AWS Cost Anomaly

The blue line represents the actual metrics movement over time. Grey shading around it shows the high and low baselines of normal behavior for the specific metric. If actual use (blue line) spikes up or drops below either side of the grey area, the activity triggers Anomaly Detection to report an anomaly (in this case, the red spike).        

The Benefits And Limitations Of AWS Cost Anomaly Detection 

An important benefit of anomaly detection is that it helps engineers and finance teams that use AWS to identify, monitor, and analyze root causes of interesting system changes so they can take proactive action to prevent adverse outcomes. 

As an example, an undesirable outcome could be a deployment that took your engineering team longer than expected, resulting in a huge IT expenditure within hours. 

Another undesirable scenario is forgetting to lower the limits for your EC2 instances. Without limits, your AWS system can use up resources indefinitely, causing your monthly AWS budget to disappear in hours or days.

Take, for example, AWS Cost Anomaly Detection, one of the most frequently requested Cloud Financial Management capabilities. 

With Cost Anomaly Detection, AWS helps its users reduce costly surprises by identifying unexpected costs and their root causes in advance, so they can take action to avoid exceeding their budgets.

You’ll receive alerts to any sign of an anomaly as soon as it appears. AWS customers have access to more anomaly detection benefits, including:

  • Deciding what a normal baseline should look like for your company’s performance, costs, security, etc. This service defines anomalies for you based on percentages, dollar amounts, or various possible events and metrics, so you don’t have to do the work yourself.
  • Anomaly detection does not require you to project into the future, overthinking all the possible ways anomalies might arise. Using Machine Learning, it sifts through mountains of data to find the really useful metrics for a particular purpose, like detecting cost anomalies.
  • Being able to detect anomalous data with minimal user intervention.
  • Examining application and infrastructure metrics regularly for both expected and unexpected patterns.
  • Identifing the root cause of unexpected changes.
  • Receiving alerts when your metrics behavior changes unexpectedly.
  • Being able to able to reduce false alarms (noise), its advanced ML technologies enable you to address anomalies that actually require your attention.  

Nevertheless, AWS Anomaly Detection is not flawless. 

What are the limitations of AWS Cost Anomaly Detection? 

There are some limitations to the AWS service, including:

  • It still takes way too much manual tagging, foresight, and configuration to get anomaly detection and alerting right.
  • You must define multiple segments for the specific metric you wish to assess, including cost allocation tags, alert preferences, member accounts, and what AWS services to apply the anomaly detection to. 
  • While AWS Cost Anomaly Detection may identify which resources are over or underutilized, it does not map costs to unit costs, such as cost per project, customer, or team costs.
  • AWS Cost Anomaly Detection also collects and analyzes a limited amount of data. The results may not be all that accurate, timely, or helpful without additional data enrichment. 
  • Although it offers decent performance, there are more robust real-time AWS anomaly detection tools available.

Now, what can you do to empower your engineering team to prevent cost overruns before they occur?

Enter CloudZero Cost Anomaly Detection 

CloudZero collects data from multiple AWS services, including CloudWatch, CloudTrail, AWS Cost Explorer, and AWS Budgets. It then uses relevant insights from other sources, like Kubernetes and Snowflake, to enrich the dataset with enough context and evidence to clarify an anomaly.

CloudZero’s advanced Machine Learning technology then analyzes that data quickly, in real-time, and without the need for endless tagging. These capabilities enable CloudZero to correlate cost changes to specific business activities, such as delivering a new feature release or deploying code to production.

CloudZero Deployment

The black bar lines show when CloudZero detected that a deployment led to a significant cost spike. 

It can also break down costs by team, so you can empower your engineers to make cost-conscious decisions and help finance understand what, when, and how you spend your budget. You can also use it to help C-Suite executives understand the company’s COGS and unit economics, such as cost per customer, project, team, and product.

Cost Per Customer Report

Furthermore, CloudZero’s cost anomaly detection works continuously, which means it detects anomalies as they happen and reports them to the most qualified members of your team via Slack.

Slack Anomaly Alert

Thus, they can take timely action to avoid going over their cloud budget. 

CloudZero’s cost anomaly alerts are designed to limit noise so you won’t miss out on any important anomalies. Request a demo today to see how CloudZero helps organizations like yours reduce AWS surprise billing.