Amazon SageMaker Canvas expands access to machine learning (ML) by providing business analysts with a visual interface that allows them to generate accurate ML predictions on their own—without requiring any ML experience or having to write a single line of code. With Amazon SageMaker Canvas, you can interactively prepare, explore, and analyze data, and access ready-to-use models including support for Amazon Bedrock foundation models (FMs) and public FMs from SageMaker JumpStart or create custom models to extract information and generate predictions.
With SageMaker Canvas, you pay for what you use. There are three factors that determine your bill: charges for the time you are logged into the SageMaker Canvas workspace instance, usage of ready-to-use models, and resources used to create custom models and generate predictions. Workspace instance (Session-Hrs) charges are based on the amount of time you are logged into SageMaker Canvas, when resources are dedicated for you. Common tasks when you are logged into SageMaker Canvas include ingesting, preparing, and exploring data, experimenting with ML models, and viewing prediction and explainability results. AWS AI services are used when you select a ready-to-use model and you are charged for your use of the particular AWS AI service. For publicly available SageMaker JumpStart FMs, you are charged for the usage of the Amazon SageMaker instance on which the FM is deployed. When you create a custom model and generate predictions, you are charged depending on the type of custom model.
Workspace instance (Session-Hrs)
A workspace instance is dedicated for your use when you are logged into SageMaker Canvas. You pay based on the number of hours for which SageMaker Canvas is used or logged into. The time starts when you launch the SageMaker Canvas application, and ends either when you log out from the SageMaker Canvas interface or when your administrator ends your SageMaker Canvas application from the AWS management console. There are no recurring charges when you log out of SageMaker Canvas.
Workspace instance (Session-Hrs) charges
$1.9/hour
Ready-to-use Models
SageMaker Canvas offers ready to use models from Amazon Bedrock, Amazon Rekognition, Amazon Comprehend, and Amazon Textract for NLP and CV tasks. When you utilize a ready-to-use model, you will be charged directly from the respective service and their pricing terms and conditions apply.
Generation, extraction, and summarization of content using foundation models (FMs) from Amazon Bedrock is charged based on the volume of input tokens and output tokens. For more information, see Amazon Bedrock pricing. SageMaker JumpStart FMs are deployed on SageMaker instances. You are charged for the duration of usage based on the instance type used. For more information, see the Amazon SageMaker Pricing for Hosting: Real-Time Inference.
Requests for object detection in images and text detection in images are charged based on the number of images in your dataset. For further pricing information, please see Amazon Rekognition pricing.
Requests for sentiment analysis, entity extraction, language detection, and personal information detection are measured in units of 100 characters and you are charged based on the number of units in your dataset. For further pricing information, please see Amazon Comprehend pricing.
Requests for expense analysis, document analysis, and identity document analysis are measured in units of 1,000 pages and you are charged based on the number of units in your dataset. For further pricing information, please see Amazon Textract pricing.
Custom Models
For custom models, you are charged for SageMaker resources that are used to train your model and generate predictions.
Training charges
For custom models, you are charged for the resources that are used to train your model.
Custom tabular models
SageMaker Canvas supports 2 category prediction (binary classification), 3+ category prediction (multi-class classification), and time-series forecasting for tabular models. The training charge for custom tabular models is based on the numbers of cells in the dataset used to train the 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. The pricing for training custom tabular models is as follows:
| 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 |
If you were 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.
Custom NLP and CV models
SageMaker Canvas supports 2 and 3+ category prediction (binary and multiclass text classification and image classification) for custom NLP and CV models. The training charge for custom NLP and CV models is based on the amount of time it takes to train the model. SageMaker training instances are used to render the model training service and you will be charged directly from SageMaker. Based on the instances used from SageMaker Canvas, the training price will range from $2.03 - $4.89 per hour of training time. For further pricing information, please see SageMaker Pricing.
The following table provides estimated custom CV model training charges based on image resolution of 640 x 480 pixels. The estimates use a SageMaker instance price of $4.89/h.
| Number of images | Estimate training charge |
| 100 | $1.62 |
| 250 | $1.63 |
| 500 | $1.65 |
| 1,000 | $1.68 |
| 5,000 | $1.97 |
| 10,000 | $2.33 |
| 50,000 | $5.19 |
The following table provides estimated custom NLP model training charges based on an average of 240 unicode characters per cell. The estimates use a SageMaker instance price of $4.89/h.
| Number of cells | Estimate training charge |
| 100 | $3.01 |
| 500 | $3.11 |
| 1,000 | $3.24 |
| 5,000 | $4.22 |
| 10,000 | $9.98 |
| 50,000 | $15.25 |
Note: Training times and charges are subject to variance based on a number of factors including image resolution for CV, number of characters per sequence for NLP, and number of categories.
Prediction charges
For custom CV, NLP, and time-series forecasting models, you are charged for the resources that are used to generate your predictions. For other custom tabular models, there are no additional charges for predictions.
Custom tabular models
There is no additional charge for making predictions with numeric prediction, 2 category prediction, and 3+ category prediction custom tabular models. For predictions with time-series forecasting, charges apply for Amazon SageMaker Asynchronous Inference, Amazon SageMaker Batch Transform, or both.
Time-series forecasting models
With time-series forecasting models, you can generate either single or batch predictions.
For single prediction, SageMaker Asynchronous Inference charges apply, with a minimum of 2 hours. Depending on your region, charges may range from $0.408 to $0.533 per hour. The charges stop automatically after two idle hours.
For batch prediction, SageMaker Batch Transform charges apply based on the amount of time it takes to generate your predictions. The table below estimates charges based on the number of time-series observed in your data.
| Number of time-series | Estimated prediction charge |
| 1,000 | $0.60 |
| 10,000 | $9 |
| 100,000 | $25 |
For detailed SageMaker pricing, please see SageMaker Pricing.
Custom NLP and CV models
The prediction charge for custom NLP and CV models is based on the amount of time it takes to generate your predictions. SageMaker instances, priced at $0.408 per hour of prediction generation time, are used to render the model prediction and you will be charged directly from SageMaker. For further pricing information, please see SageMaker Pricing.
For example, the estimated charge to generate a prediction for 1,000 images of resolution 640 x 480 is $0.03. Similarly, the estimated charge to generate a prediction for 1,000 sequences of 520 unicode characters per sequence is $0.01.
AWS Free Tier summary
Amazon SageMaker Canvas provides a 2-month free tier. The free tier includes workspace instance (Session-Hrs) usage up to 160 hours/month for using the SageMaker Canvas application.
For custom tabular models, the free tier includes up to 10 model creation requests/month limited up to 1M cells/model creation request. The free tier does not support model training and predictions for custom NLP and CV models.
Ready-to-use NLP and CV models are rendered by Amazon Rekognition, Amazon Comprehend, and Amazon Textract. Each service offers a varying free tier duration and coverage. To learn more, please see the respective AWS service price pages: Amazon Rekognition, Amazon Comprehend, and Amazon Textract.
Pricing table
| Session | |
| $1.9 per hour |
| Model Training | |
| First 10 Million cells | $30 per million cells |
| Next 90 Million cells | $15 per million cells |
| Over 100 Million cells | $7 per million cells |
Pricing examples
Example 1:
Let’s say you have a team of 4 analysts who want to try SageMaker Canvas. Let’s say one of them builds a custom tabular model to predict on time delivery of packages, and they bring a dataset with 18 columns and 5,000 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 per week per user. The time is spent exploring data, preparing datasets, and generating predictions, translating to 40 hours per month per user, or 160 hours total usage. The bill at the end of the month would be calculated as follows:
Workspace instance (Session-Hrs) charges under free tier up to 160 hours/month: $0.00
Model training charges under free tier including 10 models/month with up to 1 million cells/model: $0.00
Total: $0.00
Example 2:
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, 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:
Workspace instance (Session-Hrs) 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
Total: $421.00
Example 3:
Let’s say after you consumed the free tier, you build a custom CV classification model to detect manufacturing defects in images and you use a training dataset of 1,000 images. Your training time is approximately 21 minutes and the price point is $4.89 per hour. During the process, you spend 4 hours in SageMaker Canvas to label the images in the training dataset, view the explainability heat map, and understand the model accuracy. You then run predictions which take approximately 12 minutes at a price point of $0.408 per hour. The bill would be calculated as follows:
Workspace instance (Session-Hrs) charges: $1.9*4 = $7.60
Model training charges: $4.89/hour x 21 mins x 1/60 = $1.68
Predictions: $0.408/hour x 12 mins x 1/60 = $0.08
Total: $9.36
Example 4:
Let’s say after you consumed the free tier, you build a custom NLP model to understand user sentiment in reviews and you use a training dataset of 6,700 reviews at an average of 120 characters per review and you use your model to generate predictions on 1,000 reviews. Your training time is approximately 31 minutes and the price point is $3.825 per hour, and the time to generate a prediction is 4.1 minutes and the price point is $0.408 per hour. During the process, you spend 2 hours in SageMaker Canvas to label the reviews in the training dataset and view the prediction results. The bill would be calculated as follows:
Workspace instance (Session-Hrs) charges: $1.9*2 = $3.80
Model training charges: $3.825/hour x 31 mins x 1/60 = $1.98
Predictions: $0.408/hour x 4.1 mins x 1/60 = $0.03
Total: $5.81
Example 5:
Let’s say after you consumed the free tier, you want to extract information from 50 identity documents. During the process, you spend 1.5 hours in SageMaker Canvas to import your documents and view your results. The bill would be calculated as follows:
Workspace instance (Session-Hrs) charges: $1.9*1.5 = $2.85
Ready-to-use model charges (as per Amazon Textract pricing): The pricing per page in the US West (Oregon) Region for the first 100,000 pages is $0.025 per page. The charge would be $0.025 x 50 = $1.25
Total: $4.10
Example 6:
Let’s say after you consumed the free tier, you build a custom time-series forecasting model to predict product demand. 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 12 weeks. Your dataset includes weekly sales data from the past year and provides information on two additional attributes: price and marketing spend. This amounts to 5.2 million cells in your dataset. After building the model, you generate forecasts for the next 12 weeks, a process taking 3 hours. Additionally, you spend 30 minutes performing 'what-if' analysis using single predictions. Throughout this process, the team is logged into SageMaker Canvas, spending 10 hours during the month to explore data, join datasets, and run predictions. The bill at the end of the month would be calculated as follows:
Workspace instance (Session-Hrs) charges: $1.9 x 10 = $19
Model training charges: $30/million x 5.2 million cells = $180
Batch Predictions: $2.8/hour x 3 hrs = $8.4
Single Predictions: $0.408 x (30 mins of usage + 2 hrs idle time) = $1.02
Total = $208.42