With Amazon SageMaker Canvas, you pay based on usage. SageMaker Canvas enables you to interactively ingest, explore, and prepare your data from multiple sources, train highly accurate ML models with your data, and generate predictions. There are two components that determine your bill: session charges based on the number of hours for which SageMaker Canvas is used or logged into, and charges for training the model based on the size of the dataset used to build the model.
$1.9 per hour
You pay based on the number of hours for which SageMaker Canvas is used or logged into. A session starts when you launch the SageMaker Canvas application, and ends when you log out (user logs out from inside the interface).
You pay based on the number of cells of training data provided to train each model. Multiply your number of columns by your number of rows in your dataset and this will equal your number of cells. For example, if your dataset has 100 columns and 10,000 rows, your number of cells would equal (100 * 10,000), or 1 million.
|Number of cells||Price|
|First 10M cells||$30 per million cells|
|Next 90M cells||$15 per million cells|
|Over 100M cells||$7 per million cells|
For example, when running a modeling job with a dataset of 25.5 million cells, the training charges would be based on $30/M x first 10M cells ($300) and $15/M x next 16M cells ($240) for a total of $540.
AWS Free Tier summary
The Amazon SageMaker Canvas free tier provides a 2-month free tier. The free tier includes interactive session hours up to 750 hours/month, and up to 10 model creation requests/month, each with up to 1M cells/model creation requests.
|$1.9 per hour|
|First 10 Million cells||$30 per million cells|
|Next 90 Million cells||$15 per million cells|
|Over 100 Million cells||$7 per million cells|
Let’s say you have a team of 4 analysts who want to try SageMaker Canvas. Let’s say one of them builds a machine learning model to predict on time delivery of packages, for which they bring a dataset with 18 columns and 5000 rows, translating to 90,000 cells. Another analyst builds a propensity model using a training dataset with 20 columns and 40,000 rows, translating to 800,000 cells. Through this process, the team is logged into SageMaker Canvas for 10 hours/week/user. The time is spent exploring data, preparing datasets, and generating predictions, translating to 40 hours/month/user or 160 hours total usage. The bill at the end of the month would be calculated as follows:
Session charges under free tier up to 750 hours/month: $0.00
Model training charges under free tier including 10 models/month with up to 1 million cells/model: $0.00
Let’s say that after you consumed the free tier, your team continues to use SageMaker Canvas. You use a dataset that includes information about 500,000 customers, and contains information on 26 attributes about each customer, thereby translating to 13 million cells. Throughout this process the team is logged into SageMaker Canvas and spends 40 hours in SageMaker Canvas during one month to explore data, join datasets, and run predictions. The bill at the end of the month would be calculated as follows:
Session Charges: $1.9 x 40 = $76
Model training charges: $30/million x first 10M cells and $15/million x next 3 million cells for a total of $345
Visit the Amazon SageMaker Canvas features page.
Instantly get access to the AWS Free Tier.
Get started building with Amazon SageMaker Canvas in the AWS Management Console.