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Cost anomaly detection

Cost anomaly detection

Cost anomaly detection

A new application is available to customers that aims to identify cost deviations in the operation of public clouds. After carefully selecting customer feedback, we are proud to deliver an agile, transparent, searchable and very customizable application.

The application is available under section Costs -> Anomalies .

Main features

  • Real-time execution : After its creation, an Anomaly rule is now available for execution. It takes between a few seconds and a few minutes to populate the data;
  • Searches : it is quick and easy to find anomalies by value, percentage, provider, date, etc.;
  • Visualization : integrated with daily cost charts. By clicking on the graph icon, we redirect to Cost Analytics , where you can clearly see how costs varied. You can also export the chart for reporting purposes;
  • Cycle of agility : quickly create, execute and delete anomalies to simulate scenarios and model the best rules;
  • Works with all types of providers : child and individual accounts, organizations, business units and external integrations;
  • Works for all currencies and custom markups (MSP) : consistent across all data transformations, apportionments, etc.;
  • Methods : monitoring daily variations, every 7 days, monthly. Coming soon other methods such as weekly sums and monthly averages;
  • Parameterization : you can create several rules with percentage limits, costs, products and daily exceptions. Some examples:
    • I want to monitor anomalies in EC2, S3 services on account 1234 if it exceeds 5% with a minimum of USD 10.00 daily. I need to exclude the Friday-Saturday and Sunday-Monday transitions to avoid false positives;
    • I want to monitor variations in the monthly estimates of all my child accounts within the Organization (or Business Unit that I created) that exceed 10% with a minimum of R$5000.00 per month;

Some screenshots

Next steps

  • Integrate with notifications;
  • Negative anomalies: It is important to track optimization/reduction gains;
  • Documentation: add text and status to the anomaly for later reporting purposes;
  • Tag integration;
  • Main anomalies in the initial dashboard.

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