How do I estimate the cost of my planned AWS resource configurations?
Last updated: 2021-11-12
I want to provision AWS resources. How can I estimate how much my AWS resources will cost?
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Understand the principles of AWS cloud pricing
Most AWS services offer pay-as-you-go pricing, so you pay for only what you use each month. For more information, see AWS pricing.
Each AWS service has its own pricing model. For more information, see Cloud Services pricing.
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Establish cloud budgets and forecasts: Customers use the cloud for efficiency, speed and agility, which creates a highly variable amount of cost and usage. Costs can decrease with increases in workload efficiency, or as new workloads and features are deployed. Or, workloads will scale to serve more of your customers, which increases cloud usage and costs. Existing organizational budgeting processes must be modified to incorporate this variability.
Adjust existing budgeting and forecasting processes to become more dynamic using either a trend-based algorithm [using historical costs as inputs], or using business driver based algorithms [for example, new product launches or regional expansion], or a combination of both trend and business drivers.
You can use AWS Cost Explorer to forecast daily [up to 3 months] or monthly [up to 12 months] cloud costs based on machine learning algorithms applied to your historical costs [trend based].
With Amazon Forecast, you pay only for what you use; there are no minimum fees and no upfront commitments. There are four different types of costs to consider when using Amazon Forecast:
- Imported data: Cost for each GB of data imported into Amazon Forecast for training and forecasting.
- Training a predictor: Cost for each hour of infrastructure use required for building a custom predictor based on your input data or for monitoring predictor performance. Training time includes time taken to clean your data, train multiple algorithms in parallel, find the best combination of algorithms, calculate accuracy metrics, generate explainability insights, monitor predictor performance, and infrastructure use of forecast creation. Note that costs are based on the number of instance hours used, not the actual clock time it takes to train a predictor. Because Amazon Forecast deploys multiple instances in parallel to train a predictor, the number of hours used will exceed the actual clock time observed.
- Generated forecast data points: Cost for number of unique forecast values generated across all time series [items and dimensions] combinations. Forecast data points are the combination of number of unique time series [e.g., SKU x stores], number of quantiles and the time points within the forecast horizon. Forecasted data points include those created by generating forecasts, and those produced through what-if analyses.
- Forecast explanations: Cost for explaining the impact of attributes or related data on your forecasts for each item and time point. Explainability helps you better understand how the attributes in your datasets impact your forecast values. The cost is based on the number of forecast data points and the number of attributes [e.g., price, holidays, weather index] being explained.
Free Tier
For the first two months of using forecast, customers receive up to 100,000 forecast data points per
month; up to 10 GB of data storage per month; and up to 10 hours of training per month.
Pricing tables
*Table 1: Generated Forecasts Data Points tiered pricing table
Note: Customers generating forecasts using a predictor which has been trained with the legacy CreatePredictor API will continue to get charged $0.60 per 1,000 time series, which is the combination of items and dimensions, for reach quantile. Forecasts are rounded up to the nearest thousand.
* *Table 2: Forecast Explanations tiered pricing table
Pricing examples
Pricing example 1 - Product Demand Forecasting
Let’s say you own a clothing company and have 1,000 items selling in 50 stores around the world and are forecasting for product demand for the next 7 days at 1 quantile. Each combination of
an item and store location equates to one time series, so you’ll have 50K [1000 items x 50 stores] time series to forecast. Since you are forecasting at 1 quantile, you are forecasting for a total of 50K forecasts [50K time series x 1 quantile]. At 7 days ahead forecasts with a weekly forecasting frequency, you are forecasting for 1 data point in the future with a total forecast data points of 50K [50K forecasts x 1 data point].
Now let’s assume the following change: You are now forecasting 7 days ahead forecasts with a daily forecasting frequency. This translates to forecasting for 7 data points in the future with a total forecast data points of 350K [50K forecasts x 7 data points].
Pricing example above is based on a single forecasting job in a month
Pricing example 2 - Capacity Planning
Let’s say you own an energy company. You have 5K resident customers who use both gas and
electricity. Each combination of resident customer and types of energy equates to one time series, so you’ll have 10K [2 energy types x 5K resident customers] time series. Let’s assume you need to plan 24 hours ahead with an hourly forecast at 1 quantile, so you are forecasting a total data points of 240K forecast data points [10K time series X 1 quantile x 24 hours].
You are adding a Price attribute and have selected to add the Holidays and the Amazon Forecast Weather Index built-in
datasets for predictor training. Let’s say that you are interested in learning what attributes are driving forecasts for your top 100 gas customers. The cost for forecast explainability will be as follows.
Pricing example above is based on a single forecasting job in a month
Additional pricing resources
Learn more about Amazon Forecast
Refer to developer guide for instructions on using Amazon Forecast.
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Get started building with Amazon Forecast in the AWS console.
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