With Sagemaker pricing plans, there are many features being offered that make it easy for data scientists and developers to build, train, and deploy Machine Learning (ML) models easily and quickly.
What Is Sagemaker?
SageMaker is an AWS solution that integrates algorithms and pre-packaged Machine Learning (ML) libraries with domain knowledge to enable users to build sophisticated models. In SageMaker, you can work with deep learning, which is a predictive metric-based form of ML. SageMaker allows data scientists and business analysts to prepare and label datasets, build machine learning models, track iterations to ML models, tune ML models based on parameter configurations, and deploy multiple ML models simultaneously.
The software is used by both small and large companies across a wide range of industries. This is probably because it follows the same pay-per-use pricing model as most AWS services. Among the industries that use it most frequently are tech, finance, and healthcare.
Source: https://aws.amazon.com/sagemaker/
Sagemaker Overview
Sagemaker Pricing
SageMaker pricing as mentioned earlier follows the pay-as-you-go model. Your costs are based on how much you use the resources. You do not need to make any upfront payments or commit to a long-term contract. The service can instead be used on-demand to meet your changing needs. To test the service before committing long-term, you can use the Amazon SageMaker Free Tier. For testing each SageMaker feature, a limited number of resources are available each month on the free tier.
Sagemaker Pricing Plans
Amazon’s SageMaker pricing is available in two payable options. You can test the service for free in either case.
- Amazon SageMaker On-Demand – Pay per second, no minimum charge, no upfront payment, no contract.
- SageMaker Machine Learning Savings Plans – The flexible usage-based billing method involves committing to an amount of usage for a certain period of time (in terms of dollars per hour). Using the SageMaker ML Savings Plan can save you up to 64% off the On-Demand price. If you exceed your agreed commitment, you will be charged at the On-Demand rate.
Sagemaker Product Comparison
Your SageMaker pricing for the On-Demand Plan depends on your SageMaker features, the ML instance type, size, region, and duration of use whereas SageMaker Savings Plan pricing varies with SageMaker component, payment plan, and region (1-3 years).
Sagemaker Enterprise Pricing
Ideally, you should contact AWS for information on Sagemaker’s Enterprise pricing charges. The SageMaker products on AWS are all based on a usage-based pricing model. A price scale is determined by the type of instance used and the number of users.
How Much Does Sagemaker Cost?
A Sagemaker pricing example is provided on the website so you can get an idea of the charges. In this case, SageMaker geospatial capabilities notebook and geospatial operations are being used. Among the team members are a data scientist and a geospatial analyst. Suppose that they use the notebook for 10 hours a month, and geospatial operations are used as jobs for 20 hours, generating 30 GB of data, which is stored for a month.
Here is how the bill is calculated at the end of the month:
Subscriptions for two users per month are $150 x 2 = $300
The compute charges for ml.geospatial.interactive are $1.20 * 10 = $12.00
The compute charges for ml.geospatial.jobs are $0.40 x 20 = $8.00.
The storage charges are $0.023 * 30 = $0.69
The total is $300 + $12.00 + $8.00 + $0.69 = $320.69
If Sagemaker Pricing is Too High, Check Out These Sagemaker Alternatives
Here are the top alternatives that you can try out
- Databricks – With Databricks, businesses can unify their analytics, data, and AI, storing them all in one place with security. Prices start at $0.07/DBU billed per second for Standard SKU and $0.22/DBU billed per second for Premium SKU.
- Dataiku – Dataiku creates business impact by bringing people together through data science and machine learning. Has a quotation-based pricing model.
- MLflow – The MLflow is an open-source platform that facilitates the efficient management of the entire machine learning lifecycle.
