We are no longer updating the Amazon Machine Learning service or accepting new users for it. This documentation is available for existing users, but we are no longer updating it. For more information, see What is Amazon Machine Learning.
Pricing for Amazon ML
With AWS services, you pay only for what you use. There are no minimum fees and no upfront commitments.
Amazon Machine Learning (Amazon ML) charges an hourly rate for the compute time used to compute data statistics and train and evaluate models, and then you pay for the number of predictions generated for your application. For real-time predictions, you also pay an hourly reserved capacity charge based on the size of your model.
Amazon ML estimates the costs for predictions only in the Amazon ML console
For more information about Amazon ML pricing, see Amazon Machine Learning
Pricing
Estimating Batch Prediction Cost
When you request batch predictions from an Amazon ML model using the Create Batch Prediction wizard, Amazon ML estimates the cost of these predictions. The method to compute the estimate varies based on the type of data that is available.
Estimating Batch Prediction Cost When Data Statistics Are Available
The most accurate cost estimate is obtained when Amazon ML has already computed summary
statistics on the datasource used to request predictions. These statistics are always
computed for datasources that have been created using the Amazon ML console. API users must set
the ComputeStatistics flag to True when creating datasources
programmatically using the CreateDataSourceFromS3, CreateDataSourceFromRedshift, or the CreateDataSourceFromRDS APIs. The datasource must be in the READY
state for the statistics to be available.
One of the statistics that Amazon ML computes is the number of data records. When the number
of data records is available, the Amazon ML Create Batch Prediction wizard estimates the number
of predictions by multiplying the number of data records by the fee for batch
predictions
Your actual cost may vary from this estimate for the following reasons:
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Some of the data records might fail processing. You are not billed for predictions from failed data records.
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The estimate doesn't take into account pre-existing credits or other adjustments that are applied by AWS.
Estimating Batch Prediction Cost When Only Data Size Is Available
When you request a batch prediction and the data statistics for the request datasource are not available, Amazon ML estimates the cost based on the following:
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The total data size that is computed and persisted during datasource validation
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The average data record size, which Amazon ML estimates by reading and parsing the first 100 MB of your data file
To estimate the cost of your batch prediction, Amazon ML divides the total data size by the average data record size. This method of cost prediction is less precise than the method used when the number of data records is available because the first records of your data file might not accurately represent the average record size.
Estimating Batch Prediction Cost When Neither Data Statistics nor Data Size Are Available
When neither data statistics nor the data size are available, Amazon ML cannot estimate the cost of your batch predictions. This is commonly the case when the data source you are using to request batch predictions has not yet been validated by Amazon ML. This can happen when you have created a datasource that is based on an Amazon Redshift (Amazon Redshift) or Amazon Relational Database Service (Amazon RDS) query, and the data transfer has not yet completed, or when datasource creation is queued up behind other operations in your account. In this case, the Amazon ML console informs you about the fees for batch prediction. You can choose to proceed with the batch prediction request without an estimate, or to cancel the wizard and return after the datasource used for predictions is in the INPROGRESS or READY state.
Estimating Real-Time Prediction Cost
When you create a real-time prediction endpoint using the Amazon ML console, you will be shown
the estimated reserve capacity charge, which is an ongoing charge for reserving the endpoint
for prediction processing. This charge varies based on the size of the model, as explained on
the service pricing
page
