FinOps Anomaly Detection

Detect and resolve cloud cost anomalies in real-time

Anodot detects anomalies as soon as cost data is available, alerts the appropriate teams only when a risk is significant, and enables fast response and resolution through in-depth root-cause analysis

Anomalies are a top priority for FinOps practitioners

Cloud cost anomalies have become a top priority for FinOps practitioners due to economic pressures. While the time to detect cloud cost anomalies has largely improved thanks to maturing native cloud tools and vendor solutions, many organizations are still unable to detect anomalies within 1-2 days of their occurence.

For those that can detect anomalies in near-real-time, quickly determining the cause and implementing a response is the key. For many, this might be less than a few days, but for others this can take many weeks.

Anodot’s AI automatically detects anomalies in near-real-time, alerts the appropriate teams only when a risk is significant, and enables fast response and resolution through in-depth root-cause analysis.

Automated anomaly detection that works

Anodot’s anomaly detection is fully autonomous, processing a pre-defined set of cost metrics that are automatically prioritized and optimized to reduce noise and surface critical anomalies. Anodot enables FinOps teams to manage anomalies efficiently and collaboratively by providing them with an interactive repository of their anomalies.

Search across all dimensions, add comments and labels, provide ML feedback, and mark anomalies as resolved or recognized. You can also streamline team workflows by integrating Jira to ensure efficient tracking and resolution.

Find the anomaly’s root cause quickly

Anodot goes beyond cloud cost anomaly detection with in-depth root-cause analysis that allows users to understand the factors driving anomalies—enabling smarter, more informed decisions, and faster resolution times.

A detailed chart shows anomaly behavior patterns for the past 3 months with explanations of the primary parameters, making it easy to investigate anomalies quickly.

Create custom alerts for your mission-critical resources

With Anodot, you can also monitor your mission-critical resources with custom alerts that can be linked to specific cost centers, linked accounts, and services. As opposed to anomalies, which are automatically detected by Anodot, custom alerts must be configured individually.

Alerts are displayed in their own tab and managed using a centralized rules list that shows all the custom alerts you’ve defined, and lets you edit, delete, or add new ones. You can configure alerts for specific email recipients, Slack channels, or Microsoft Teams groups.

Are you able to detect and fix cloud cost anomalies quickly?

AWS, Azure, and GCP all have native cloud tools that let you set up basic anomaly alerts for deviations from spending patterns. But these tools often fall short in several key areas: too many alerts, no prioritization, and no root-cause analysis.

Whenever anomalies are detected, they have to be investigated immediately to determine if they are legit changes in demand or misconfigurations. When you have too many alerts, alert fatigue sets in, critical anomalies are missed, and long resolution times result in wasted cloud resources.

Detecting and addressing cloud cost anomalies effectively requires innovative ML algorithms and automated workflows that reduce noise, prioritize anomalies based on risk, and pinpoint the root cause so FinOps teams can quickly respond.

Analyze your ability to manage cloud cost anomalies

Here are a few questions to get you started:

Can you detect cloud cost anomalies in near-real time (e.g., same day or 1-2 days later)? If not, how long do anomalies go unnoticed?
How long does it take to determine the root cause and implement a response?
Have you implemented automated workflows to identify and manage anomalies?
What is your signal-to-noise ratio? How often do you experience false positives and false negatives?
How do you account for seasonality and changes in usage patterns?
Do you track the count of anomalies within a given period?
Do you measure mean time to detection (e.g., occurrence to discovery/acknowledgement), time to root cause (e.g., time of investigation), and time to resolution (e.g., total duration of the anomaly)?

Managing cost anomalies is easy with Anodot

Find out how Anodot’s intelligent cost optimization platform can support your FinOps strategy.

Talk to us now