China (Beijing) region

Beijing

China (Ningxia) region

Ningxia

Pricing Examples

Pricing Example #1: Processing

Amazon SageMaker Processing only charges you for the instances used while your jobs are executing. When you provide the input data for processing in Amazon S3, Amazon SageMaker downloads the data from Amazon S3 to local file storage at the start of a processing job in the China (Beijing) region.The data analyst runs a Processing job to preprocess and validate data on two ml.m5.4xlarge instances for a job duration of 10 minutes. She uploads a dataset of 100 GB in S3 as input for the processing job, and the output data which is roughly the same size is stored back in S3.

Hours  Processing Instances Cost per hour Total
1 * 2 * 0.167 = 0.334 ml.m5.4xlarge ¥ 8.917 ¥ 2.978
General Purpose (SSD) Storage (GB)  Cost per hour Total
100 GB * 2 = 200 ¥ 1.044

¥ 0.0242

The sub-total for Amazon SageMaker Processing job = ¥ 2.978;
The sub-total for 200 GB of general purpose SSD storage = ¥ 0.0242;
The total price for this example would be ¥ 3.0022.

Pricing Example #2: Data Wrangler

As a data scientist, you spend three days using Amazon SageMaker Data Wrangler in the China (Beijing) region to cleanse, explore, and visualize your data for 6 hours per day. To execute your data preparation pipeline, you then initiate a SageMaker Data Wrangler job that is scheduled to run weekly.

The table below summarizes your total usage for the month and the associated charges for using Amazon SageMaker Data Wrangler.

Application SageMaker Studio Instance Days Duration Total duration Cost per hour  Cost sub-total
SageMaker Data Wrangler ml.m5.4xlarge 3 6 hours 18 hours ¥ 10.133 ¥ 182.394
SageMaker Data Wrangler job ml.m5.4xlarge - 40 minutes 2.67 hours ¥ 10.133 ¥ 27.055

From the table, you use Amazon SageMaker Data Wrangler for a total of 18 hours over 3 days to prepare your data. Additionally, you create a SageMaker Data Wrangler job to prepare updated data on a weekly basis. Each job lasts 40 minutes, and the job runs weekly for one month.

Total monthly charges for using Data Wrangler = ¥ 182.394 + ¥ 27.055 = ¥ 209.449

Pricing Example #3: Feature Store

You have a web application which issues reads and writes of 25 KB each to the Amazon SageMaker Feature Store in the China (Beijing) region. For the first 10 days of a month, you receive little traffic to your application, resulting in 10,000 writes and 10,000 reads each day to the SageMaker Feature Store. On day 11 of the month, your application gains attention on social media and application traffic spikes to 200,000 writes and 200,000 reads that day. Your application then settles into a more regular traffic pattern, averaging 80,000 writes and 80,000 reads each day through the end of the month.

The table below summarizes your total usage for the month and the associated charges for using Amazon SageMaker Feature Store.

Day of the month Total Writes Total Write Units Total Reads Total Read Units
Days 1 to 10 100,000 writes 
(10,000 writes * 10 days)
2500000 
(100,000 * 25KB )
100000 
(10,000 * 10 days)
700,000++ 
(100,000 * 25/4 KB )
         
Day 11 200,000 writes 5000000 
(200,000* 25KB)
200,000 reads 1,400,000++ 
(200,000* 25/4KB)
         
Days 12 to 30 1,520,000 writes 
(80,000 * 19 days)
38000000 
(1,520,000 * 25KB)
1,520,000 writes
(80,000 * 19 days)
10,640,000++
(1,520,000 * 25/4KB)
         
Total chargeable units   45,500,000 write units   12,740,000 read units
Monthly charges for writes and reads  

¥ 507.5
(45.5 million write units * ¥ 11.1538 per million writes)

  ¥ 24.8204
(12.74M read units * ¥ 2.2308 per million reads)

All fractional read units are rounded to the next whole number

Data storage
Total data stored = 31.5 GB
Monthly charges for data storage = 31.5 GB * ¥ 3.960= ¥ 124.74

Total monthly charges for Amazon SageMaker Feature Store = ¥ 507.5+¥ 24.8204+¥ 124.74 = ¥ 657.06

Pricing Example #4: Training

A data scientist has spent a week working on a model for a new idea in the China (Beijing) region. She trains the model 4 times on an ml.m4.4xlarge for 30 minutes per training run with Amazon SageMaker Debugger enabled using 2 built-in rules and 1 custom rule that she wrote. For the custom rule, she specified ml.m5.xlarge instance. She trains using 3 GB of training data in Amazon S3, and pushes 1 GB model output into Amazon S3. SageMaker creates General Purpose SSD (gp2) Volumes for each Training instance. SageMaker also creates General Purpose SSD (gp2) Volumes for each rule specified. In this example a total of 4 General Purpose SSD (gp2) Volumes will be created. SageMaker Debugger emits 1 GB of debug data to customer’s Amazon S3 bucket.

 

Hours Training Instance Debug Instance Cost per hour  Sub-total
4 * 0.5 = 2.00 ml.m4.4xlarge n/a ¥ 12.373 ¥ 24.746
4 * 0.5 * 2 = 4 n/a No additional charges for built-in rule instances ¥ 0 ¥ 0
4 * 0.5 = 2 ml.m5.xlarge n/a ¥ 2.229 ¥ 4.458
        -------
        ¥ 29.204
  General Purpose (SSD) Storage for Training (GB)  General Purpose (SSD) Storage for Debugger built-in rules (GB) General Purpose (SSD) Storage for Debugger custom rules (GB) Cost per GB-Month Sub- total
Capacity used 3 2 1    
Cost ¥ 0 No additional charges for built-in rule storage volumes ¥ 0 ¥ 1.044 ¥ 0

The total charges for training and debugging in this example are ¥ 30.248. The compute instances and general purpose storage volumes used by Amazon SageMaker Debugger built-in rules do not incur additional charges.