CostExplorer / Client / get_usage_forecast

get_usage_forecast

CostExplorer.Client.get_usage_forecast(**kwargs)

Retrieves a forecast for how much Amazon Web Services predicts that you will use over the forecast time period that you select, based on your past usage.

See also: AWS API Documentation

Request Syntax

response = client.get_usage_forecast(
    TimePeriod={
        'Start': 'string',
        'End': 'string'
    },
    Metric='BLENDED_COST'|'UNBLENDED_COST'|'AMORTIZED_COST'|'NET_UNBLENDED_COST'|'NET_AMORTIZED_COST'|'USAGE_QUANTITY'|'NORMALIZED_USAGE_AMOUNT',
    Granularity='DAILY'|'MONTHLY'|'HOURLY',
    Filter={
        'Or': [
            {'... recursive ...'},
        ],
        'And': [
            {'... recursive ...'},
        ],
        'Not': {'... recursive ...'},
        'Dimensions': {
            'Key': 'AZ'|'INSTANCE_TYPE'|'LINKED_ACCOUNT'|'PAYER_ACCOUNT'|'LINKED_ACCOUNT_NAME'|'OPERATION'|'PURCHASE_TYPE'|'REGION'|'SERVICE'|'SERVICE_CODE'|'USAGE_TYPE'|'USAGE_TYPE_GROUP'|'RECORD_TYPE'|'OPERATING_SYSTEM'|'TENANCY'|'SCOPE'|'PLATFORM'|'SUBSCRIPTION_ID'|'LEGAL_ENTITY_NAME'|'DEPLOYMENT_OPTION'|'DATABASE_ENGINE'|'CACHE_ENGINE'|'INSTANCE_TYPE_FAMILY'|'BILLING_ENTITY'|'RESERVATION_ID'|'RESOURCE_ID'|'RIGHTSIZING_TYPE'|'SAVINGS_PLANS_TYPE'|'SAVINGS_PLAN_ARN'|'PAYMENT_OPTION'|'AGREEMENT_END_DATE_TIME_AFTER'|'AGREEMENT_END_DATE_TIME_BEFORE'|'INVOICING_ENTITY'|'ANOMALY_TOTAL_IMPACT_ABSOLUTE'|'ANOMALY_TOTAL_IMPACT_PERCENTAGE',
            'Values': [
                'string',
            ],
            'MatchOptions': [
                'EQUALS'|'ABSENT'|'STARTS_WITH'|'ENDS_WITH'|'CONTAINS'|'CASE_SENSITIVE'|'CASE_INSENSITIVE'|'GREATER_THAN_OR_EQUAL',
            ]
        },
        'Tags': {
            'Key': 'string',
            'Values': [
                'string',
            ],
            'MatchOptions': [
                'EQUALS'|'ABSENT'|'STARTS_WITH'|'ENDS_WITH'|'CONTAINS'|'CASE_SENSITIVE'|'CASE_INSENSITIVE'|'GREATER_THAN_OR_EQUAL',
            ]
        },
        'CostCategories': {
            'Key': 'string',
            'Values': [
                'string',
            ],
            'MatchOptions': [
                'EQUALS'|'ABSENT'|'STARTS_WITH'|'ENDS_WITH'|'CONTAINS'|'CASE_SENSITIVE'|'CASE_INSENSITIVE'|'GREATER_THAN_OR_EQUAL',
            ]
        }
    },
    BillingViewArn='string',
    PredictionIntervalLevel=123
)
Parameters:

  • TimePeriod (dict) –

    [REQUIRED]

    The start and end dates of the period that you want to retrieve usage forecast for. The start date is included in the period, but the end date isn’t included in the period. For example, if start is 2017-01-01 and end is 2017-05-01, then the cost and usage data is retrieved from 2017-01-01 up to and including 2017-04-30 but not including 2017-05-01. The start date must be equal to or later than the current date to avoid a validation error.

    • Start (string) – [REQUIRED]

      The beginning of the time period. The start date is inclusive. For example, if start is 2017-01-01, Amazon Web Services retrieves cost and usage data starting at 2017-01-01 up to the end date. The start date must be equal to or no later than the current date to avoid a validation error.

    • End (string) – [REQUIRED]

      The end of the time period. The end date is exclusive. For example, if end is 2017-05-01, Amazon Web Services retrieves cost and usage data from the start date up to, but not including, 2017-05-01.

  • Metric (string) –

    [REQUIRED]

    Which metric Cost Explorer uses to create your forecast.

    Valid values for a GetUsageForecast call are the following:

    • USAGE_QUANTITY

    • NORMALIZED_USAGE_AMOUNT

  • Granularity (string) –

    [REQUIRED]

    How granular you want the forecast to be. You can get 3 months of DAILY forecasts or 12 months of MONTHLY forecasts.

    The GetUsageForecast operation supports only DAILY and MONTHLY granularities.

  • Filter (dict) –

    The filters that you want to use to filter your forecast. The GetUsageForecast API supports filtering by the following dimensions:

    • AZ

    • INSTANCE_TYPE

    • LINKED_ACCOUNT

    • LINKED_ACCOUNT_NAME

    • OPERATION

    • PURCHASE_TYPE

    • REGION

    • SERVICE

    • USAGE_TYPE

    • USAGE_TYPE_GROUP

    • RECORD_TYPE

    • OPERATING_SYSTEM

    • TENANCY

    • SCOPE

    • PLATFORM

    • SUBSCRIPTION_ID

    • LEGAL_ENTITY_NAME

    • DEPLOYMENT_OPTION

    • DATABASE_ENGINE

    • INSTANCE_TYPE_FAMILY

    • BILLING_ENTITY

    • RESERVATION_ID

    • SAVINGS_PLAN_ARN

    • Or (list) –

      Return results that match either Dimension object.

      • (dict) –

        Use Expression to filter in various Cost Explorer APIs.

        Not all Expression types are supported in each API. Refer to the documentation for each specific API to see what is supported.

        There are two patterns:

        • Simple dimension values.

          • There are three types of simple dimension values: CostCategories, Tags, and Dimensions.

            • Specify the CostCategories field to define a filter that acts on Cost Categories.

            • Specify the Tags field to define a filter that acts on Cost Allocation Tags.

            • Specify the Dimensions field to define a filter that acts on the DimensionValues.

          • For each filter type, you can set the dimension name and values for the filters that you plan to use.

            • For example, you can filter for REGION==us-east-1 OR REGION==us-west-1. For GetRightsizingRecommendation, the Region is a full name (for example, REGION==US East (N. Virginia).

            • The corresponding Expression for this example is as follows: { "Dimensions": { "Key": "REGION", "Values": [ "us-east-1", "us-west-1" ] } }

            • As shown in the previous example, lists of dimension values are combined with OR when applying the filter.

          • You can also set different match options to further control how the filter behaves. Not all APIs support match options. Refer to the documentation for each specific API to see what is supported.

            • For example, you can filter for linked account names that start with “a”.

            • The corresponding Expression for this example is as follows: { "Dimensions": { "Key": "LINKED_ACCOUNT_NAME", "MatchOptions": [ "STARTS_WITH" ], "Values": [ "a" ] } }

        • Compound Expression types with logical operations.

          • You can use multiple Expression types and the logical operators AND/OR/NOT to create a list of one or more Expression objects. By doing this, you can filter by more advanced options.

          • For example, you can filter by ((REGION == us-east-1 OR REGION == us-west-1) OR (TAG.Type == Type1)) AND (USAGE_TYPE != DataTransfer).

          • The corresponding Expression for this example is as follows: { "And": [ {"Or": [ {"Dimensions": { "Key": "REGION", "Values": [ "us-east-1", "us-west-1" ] }}, {"Tags": { "Key": "TagName", "Values": ["Value1"] } } ]}, {"Not": {"Dimensions": { "Key": "USAGE_TYPE", "Values": ["DataTransfer"] }}} ] }

        Note

        Because each Expression can have only one operator, the service returns an error if more than one is specified. The following example shows an Expression object that creates an error: { "And": [ ... ], "Dimensions": { "Key": "USAGE_TYPE", "Values": [ "DataTransfer" ] } }

        The following is an example of the corresponding error message: "Expression has more than one roots. Only one root operator is allowed for each expression: And, Or, Not, Dimensions, Tags, CostCategories"

        Note

        For the GetRightsizingRecommendation action, a combination of OR and NOT isn’t supported. OR isn’t supported between different dimensions, or dimensions and tags. NOT operators aren’t supported. Dimensions are also limited to LINKED_ACCOUNT, REGION, or RIGHTSIZING_TYPE.

        For the GetReservationPurchaseRecommendation action, only NOT is supported. AND and OR aren’t supported. Dimensions are limited to LINKED_ACCOUNT.

    • And (list) –

      Return results that match both Dimension objects.

      • (dict) –

        Use Expression to filter in various Cost Explorer APIs.

        Not all Expression types are supported in each API. Refer to the documentation for each specific API to see what is supported.

        There are two patterns:

        • Simple dimension values.

          • There are three types of simple dimension values: CostCategories, Tags, and Dimensions.

            • Specify the CostCategories field to define a filter that acts on Cost Categories.

            • Specify the Tags field to define a filter that acts on Cost Allocation Tags.

            • Specify the Dimensions field to define a filter that acts on the DimensionValues.

          • For each filter type, you can set the dimension name and values for the filters that you plan to use.

            • For example, you can filter for REGION==us-east-1 OR REGION==us-west-1. For GetRightsizingRecommendation, the Region is a full name (for example, REGION==US East (N. Virginia).

            • The corresponding Expression for this example is as follows: { "Dimensions": { "Key": "REGION", "Values": [ "us-east-1", "us-west-1" ] } }

            • As shown in the previous example, lists of dimension values are combined with OR when applying the filter.

          • You can also set different match options to further control how the filter behaves. Not all APIs support match options. Refer to the documentation for each specific API to see what is supported.

            • For example, you can filter for linked account names that start with “a”.

            • The corresponding Expression for this example is as follows: { "Dimensions": { "Key": "LINKED_ACCOUNT_NAME", "MatchOptions": [ "STARTS_WITH" ], "Values": [ "a" ] } }

        • Compound Expression types with logical operations.

          • You can use multiple Expression types and the logical operators AND/OR/NOT to create a list of one or more Expression objects. By doing this, you can filter by more advanced options.

          • For example, you can filter by ((REGION == us-east-1 OR REGION == us-west-1) OR (TAG.Type == Type1)) AND (USAGE_TYPE != DataTransfer).

          • The corresponding Expression for this example is as follows: { "And": [ {"Or": [ {"Dimensions": { "Key": "REGION", "Values": [ "us-east-1", "us-west-1" ] }}, {"Tags": { "Key": "TagName", "Values": ["Value1"] } } ]}, {"Not": {"Dimensions": { "Key": "USAGE_TYPE", "Values": ["DataTransfer"] }}} ] }

        Note

        Because each Expression can have only one operator, the service returns an error if more than one is specified. The following example shows an Expression object that creates an error: { "And": [ ... ], "Dimensions": { "Key": "USAGE_TYPE", "Values": [ "DataTransfer" ] } }

        The following is an example of the corresponding error message: "Expression has more than one roots. Only one root operator is allowed for each expression: And, Or, Not, Dimensions, Tags, CostCategories"

        Note

        For the GetRightsizingRecommendation action, a combination of OR and NOT isn’t supported. OR isn’t supported between different dimensions, or dimensions and tags. NOT operators aren’t supported. Dimensions are also limited to LINKED_ACCOUNT, REGION, or RIGHTSIZING_TYPE.

        For the GetReservationPurchaseRecommendation action, only NOT is supported. AND and OR aren’t supported. Dimensions are limited to LINKED_ACCOUNT.

    • Not (dict) –

      Return results that don’t match a Dimension object.

    • Dimensions (dict) –

      The specific Dimension to use for Expression.

      • Key (string) –

        The names of the metadata types that you can use to filter and group your results. For example, AZ returns a list of Availability Zones.

        Not all dimensions are supported in each API. Refer to the documentation for each specific API to see what is supported.

        LINKED_ACCOUNT_NAME and SERVICE_CODE can only be used in CostCategoryRule.

        ANOMALY_TOTAL_IMPACT_ABSOLUTE and ANOMALY_TOTAL_IMPACT_PERCENTAGE can only be used in AnomalySubscriptions.

      • Values (list) –

        The metadata values that you can use to filter and group your results. You can use GetDimensionValues to find specific values.

        • (string) –

      • MatchOptions (list) –

        The match options that you can use to filter your results.

        MatchOptions is only applicable for actions related to Cost Category and Anomaly Subscriptions. Refer to the documentation for each specific API to see what is supported.

        The default values for MatchOptions are EQUALS and CASE_SENSITIVE.

        • (string) –

    • Tags (dict) –

      The specific Tag to use for Expression.

      • Key (string) –

        The key for the tag.

      • Values (list) –

        The specific value of the tag.

        • (string) –

      • MatchOptions (list) –

        The match options that you can use to filter your results. MatchOptions is only applicable for actions related to Cost Category. The default values for MatchOptions are EQUALS and CASE_SENSITIVE.

        • (string) –

    • CostCategories (dict) –

      The filter that’s based on CostCategory values.

      • Key (string) –

        The unique name of the Cost Category.

      • Values (list) –

        The specific value of the Cost Category.

        • (string) –

      • MatchOptions (list) –

        The match options that you can use to filter your results. MatchOptions is only applicable for actions related to cost category. The default values for MatchOptions is EQUALS and CASE_SENSITIVE.

        • (string) –

  • BillingViewArn (string) – The Amazon Resource Name (ARN) that uniquely identifies a specific billing view. The ARN is used to specify which particular billing view you want to interact with or retrieve information from when making API calls related to Amazon Web Services Billing and Cost Management features. The BillingViewArn can be retrieved by calling the ListBillingViews API.

  • PredictionIntervalLevel (integer) – Amazon Web Services Cost Explorer always returns the mean forecast as a single point. You can request a prediction interval around the mean by specifying a confidence level. The higher the confidence level, the more confident Cost Explorer is about the actual value falling in the prediction interval. Higher confidence levels result in wider prediction intervals.

Return type:

dict

Returns:

Response Syntax

{
    'Total': {
        'Amount': 'string',
        'Unit': 'string'
    },
    'ForecastResultsByTime': [
        {
            'TimePeriod': {
                'Start': 'string',
                'End': 'string'
            },
            'MeanValue': 'string',
            'PredictionIntervalLowerBound': 'string',
            'PredictionIntervalUpperBound': 'string'
        },
    ]
}

Response Structure

  • (dict) –

    • Total (dict) –

      How much you’re forecasted to use over the forecast period.

      • Amount (string) –

        The actual number that represents the metric.

      • Unit (string) –

        The unit that the metric is given in.

    • ForecastResultsByTime (list) –

      The forecasts for your query, in order. For DAILY forecasts, this is a list of days. For MONTHLY forecasts, this is a list of months.

      • (dict) –

        The forecast that’s created for your query.

        • TimePeriod (dict) –

          The period of time that the forecast covers.

          • Start (string) –

            The beginning of the time period. The start date is inclusive. For example, if start is 2017-01-01, Amazon Web Services retrieves cost and usage data starting at 2017-01-01 up to the end date. The start date must be equal to or no later than the current date to avoid a validation error.

          • End (string) –

            The end of the time period. The end date is exclusive. For example, if end is 2017-05-01, Amazon Web Services retrieves cost and usage data from the start date up to, but not including, 2017-05-01.

        • MeanValue (string) –

          The mean value of the forecast.

        • PredictionIntervalLowerBound (string) –

          The lower limit for the prediction interval.

        • PredictionIntervalUpperBound (string) –

          The upper limit for the prediction interval.

Exceptions