Cost Policies

Gathers AWS CloudWatch CPU and Burst Credit data for instances on 30 day intervals.

Sends email notifications before AWS Reserved Instances expire.

Checks for inefficient instance utilization using provided CPU and Memory thresholds. Instances matching the criteria can be resized after user approval.

Checks for snapshots older than specified number of days and, optionally, deletes them.

Sends email notifications when AWS RI Recommendations are identified.

Note:These RI Purchase Recommendations are generated by AWS.

Checks for Inefficient database services that are inside or outside the CPU threshold for the last 30 days and resizes them after approval.

Monitoring your Savings Plans is an important part of Cloud Financial Management, as tracking the utilization of Savings Plans will inform you of wasted commitments and help you distribute your savings more effectively.

This policy is built on AWS Savings Plan utilization metrics, which shows you the percentage of your Savings Plans commitment you are using across your On-Demand usage.

Applying this policy requires you to select a lookback period (number of days of past usage to analyze), the Amazon Resource Name (ARN) of the Savings Plan to analyze, and a Utilization threshold to alert on if below the threshold.

Checks AWS for unused IP Addresses and deletes them.

Checks for unused volumes and if no read/write operations performed within a specified number of days and, optionally, deletes them.

Sends email notifications when an Azure Reserved Instance are about to expire.

Identifies SQL instances eligible for Azure Hybrid Use Benefit.

Checks for instances that are idle for the last 30 days and terminates them after approval.

Sends email notifications when Azure RI Recommendations are identified for MCA customers.

Note:These RI Purchase Recommendations are generated by Microsoft Azure.

Sends email notifications when Azure RI Recommendations are identified.

Note:These RI Purchase Recommendations are generated by Microsoft Azure.

Checks for Inefficient Azure SQL single database services that are inside or outside the CPU threshold for the last 30 days and resizes them after approval.

A key metric when managing Commitments in the cloud is savings realized from purchasing and using reservations. This allows you to understand the effectiveness of your savings strategy.

Flexera One’s Azure Savings Realized from Reservations policy will help you to identify trends in your savings from Compute commitments, and importantly tie this back to total Compute spend as a percentage.

Applying this policy only requires a start date, end date, and specific billing centers for the period you wish to view your savings realized for, as well as the chart type to view the data by.

Checks for unused IP addresses in the given account and, optionally deletes them.

Checks for unused volumes older than specified number of days and, optionally, deletes them.

Creates a Monthly Budget Alert for a Billing Center or for the entire Organization.

Specifies which regions have cheaper alternatives by specifying the expensive region name and the cheaper region name for analysis.

Predicting what you may spend on cloud is a challenging task given the variable nature of cloud consumption and the effects of optimization activities such as Reservations and Rightsizing. Our Cloud Spend Forecast policies provide several ways to gain some insight into what your future spend may look like.

The following policies can be applied:

This policy uses the Moving Average method to forecast your spend. You can choose to forecast at the Billing Center or entire Organization level, specify how many months of historical data should be considered, and specify how many months should be forecasted.

Predicting what you may spend on cloud is a challenging task given the variable nature of cloud consumption and the effects of optimization activities such as Reservations and Rightsizing. Our Cloud Spend Forecast policies provide a number of ways to gain some insight into what your future spend may look like.

The following policies can be applied:

This policy uses the Straight-Line method using a Simple model which uses a more basic calculation than the Linear Regression model to forecast your spend.

You can choose to forecast at the Billing Center or entire Organization level, specify how many months of historical data should be considered, and specify how many months should be forecasted. In addition, you can choose to breakdown forecasted costs by the following dimensions: category, region, service, and vendor account (for example, AWS account, Azure subscription, and so on).

Downsizes instances.

Sends email notifications for all Google CUDs.

Sends email notifications when Google CUDs are about to expire.

Finds Idle IP addresses and reports when it finds them. You can then delete the idle IP Addresses.

Finds Idle Virtual Machine Recommendations and reports when it finds them. You can then delete the idle instances.

Checks Google Storage objects for last updated time and moves the object to “nearline” or “coldline” or delete (enable delete action as mentioned in README.md) after user approval.

Checks Google CloudSQL instances based on provided CPU threshold and Resize them after approval.

Checks for unused Google Cloud SQL instances using DB connections over 30 day period.

Checks Google for Unutilized IP Addresses and deletes them.

Checks cooldown time tag that the Instance Utilization policy sets and if time has expired, it adds back the tag to allow the instance to be resized.

Analyzes all service usage and determines recommend consolidation or deletion.

Analyzes bills for new service usage and notify.

Sends email notifications on reserved instance coverage.

Starts and stops instances based on a schedule.

Automatically supersedes instances based on user-defined standards.

Terminates instances based on tag.

Checks for unattached volumes older than specified number of days and, optionally, deletes them.