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Get Cost Forecast

costexplorer_get_cost_forecast R Documentation

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

Description

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

Usage

costexplorer_get_cost_forecast(TimePeriod, Metric, Granularity, Filter,
  PredictionIntervalLevel)

Arguments

TimePeriod

[required] The period of time that you want the forecast to cover. The start date must be equal to or no later than the current date to avoid a validation error.

Metric

[required] Which metric Cost Explorer uses to create your forecast. For more information about blended and unblended rates, see Why does the "blended" annotation appear on some line items in my bill?.

Valid values for a get_cost_forecast call are the following:

  • AMORTIZED_COST

  • BLENDED_COST

  • NET_AMORTIZED_COST

  • NET_UNBLENDED_COST

  • UNBLENDED_COST

Granularity

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

The get_cost_forecast operation supports only DAILY and MONTHLY granularities.

Filter

The filters that you want to use to filter your forecast. The get_cost_forecast 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

PredictionIntervalLevel

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.

Value

A list with the following syntax: 

list(
  Total = list(
    Amount = "string",
    Unit = "string"
  ),
  ForecastResultsByTime = list(
    list(
      TimePeriod = list(
        Start = "string",
        End = "string"
      ),
      MeanValue = "string",
      PredictionIntervalLowerBound = "string",
      PredictionIntervalUpperBound = "string"
    )
  )
)

Request syntax

svc$get_cost_forecast(
  TimePeriod = list(
    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 = list(
    Or = list(
      list()
    ),
    And = list(
      list()
    ),
    Not = list(),
    Dimensions = list(
      Key = "AZ"|"INSTANCE_TYPE"|"LINKED_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 = list(
        "string"
      ),
      MatchOptions = list(
        "EQUALS"|"ABSENT"|"STARTS_WITH"|"ENDS_WITH"|"CONTAINS"|"CASE_SENSITIVE"|"CASE_INSENSITIVE"|"GREATER_THAN_OR_EQUAL"
      )
    ),
    Tags = list(
      Key = "string",
      Values = list(
        "string"
      ),
      MatchOptions = list(
        "EQUALS"|"ABSENT"|"STARTS_WITH"|"ENDS_WITH"|"CONTAINS"|"CASE_SENSITIVE"|"CASE_INSENSITIVE"|"GREATER_THAN_OR_EQUAL"
      )
    ),
    CostCategories = list(
      Key = "string",
      Values = list(
        "string"
      ),
      MatchOptions = list(
        "EQUALS"|"ABSENT"|"STARTS_WITH"|"ENDS_WITH"|"CONTAINS"|"CASE_SENSITIVE"|"CASE_INSENSITIVE"|"GREATER_THAN_OR_EQUAL"
      )
    )
  ),
  PredictionIntervalLevel = 123
)