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Read customer storyBuilding machine learning models can be expensive when you don’t know how Amazon SageMaker pricing works. This guide will cover Sagemaker costs in detail.
Amazon SageMaker makes it easy to prepare data for machine learning (ML) and then train, deploy, and modify ML models. SageMaker is a fully managed service that automates much of the ML lifecycle. So, if you want a single partner to help you through all stages of your Artificial Intelligence (AI) lifecycle, SageMaker might be the answer.
Perhaps more important for this post is the promise that Amazon SageMaker can reduce your machine learning model costs. But does SageMaker pricing reflect this?
We've put together a snackable guide to explain how Amazon SageMaker pricing works.
Table Of Contents
SageMaker billing is based on a pay-as-you-go model. You pay only for the resources you use. There are no upfront fees or long-term commitments required. Instead, you can use the service on-demand to meet your dynamic needs.
If you are not sure if the service suits your needs, you can use the Amazon SageMaker Free Tier to test it before committing long-term. The free tier provides a limited amount of resources each month for experimenting with each SageMaker feature.
Credit: TechCrunch
SageMaker claims it will reduce your total cost of ownership (TCO) by 54-90%, depending on the size of your team, compared to building and maintaining your own machine learning services using Amazon EC2.
Credit: Amazon SageMaker Total Cost of Ownership Analysis — Amazon Web Services
But there's more to an Amazon SageMaker bill than the dollar price; here's what you need to know.
Pricing for Amazon SageMaker is available in two billing options; Amazon SageMaker On-Demand or SageMaker Machine Learning Savings Plans. You can test the service for free in either case.
The Amazon SageMaker Free Tier includes the following benefits for each SageMaker component:
The Amazon SageMaker On-Demand pricing approach charges per second, without a minimum charge, upfront payment, or contract. SageMaker On-Demand billing applies to 12 features:
With Amazon SageMaker Machine Learning Savings Plans, you get flexible usage-based billing when you commit to a certain amount of usage (in $/hour) for one or three years. The Savings Plan rate can save you up to 64% off SageMaker ML On-Demand pricing. The On-Demand rate applies if you exceed your agreed commitment.
In addition, SageMaker ML Savings Plan rates are valid across multiple SageMaker ML usage instances, regardless of their size, region, or instance family. Those usage instances include:
Also, SageMaker ML SPs come with flexible payment plans. Those plans are:
Ultimately, the amount you pay with a SageMaker Savings Plan depends on the SageMaker component, payment plan, AWS region, and your commitment period (1 or 3 years).
You can see how SageMaker calculates your bill in the next section.
The SageMaker On-Demand pricing is based on your requirements; the SageMaker features you use, the ML instance type, size, and region you choose, and the duration of use.
The following table shows SageMaker Studio Notebooks and RStudio on SageMaker prices in the US East (Ohio) region using mid-size instance sizes:
Amazon SageMaker feature |
Instance class |
Machine Learning Instance type |
vCPU |
Memory |
Price per hour |
Studio Notebooks |
Standard |
ml.t3.large ml.m5.large ml.m5d.large |
2 2 2 |
8GiB 8GiB 8GiB |
$0.10 $0.115 $0.136 |
Compute-optimized |
ml.c5.large |
2 |
4GiB |
$102 |
|
Memory-optimized |
ml.r5.large |
2 |
16GiB |
$0.151 |
|
Inference accelerated |
ml.p3.2xlarge ml.g4dn.xlarge |
8 4 |
61GiB 16GiB |
$3.825 $0.7364 |
|
RStudio on SageMaker |
Standard |
ml.t3.large ml.m5.large ml.m5d.large |
2 2 2 |
8GiB 8GiB 8GiB |
$0.10 $0.115 $0.136 |
Compute-optimized |
ml.c5.large |
2 |
4GiB |
$102 |
|
Memory-optimized |
ml.r5.large |
2 |
16GiB |
$0.151 |
|
Accelerated computing |
ml.p3.2xlarge ml.g4dn.xlarge |
8 4 |
61GiB 16GiB |
$3.825 $0.736 |
Instance details and exact RStudioServerPro App pricing are subject to change, so check the Amazon SageMaker pricing page before purchasing.
Further, SageMaker offers 12 components, four instance classes, and dozens of combinations of instance types and sizes. Although these options increase flexibility, they also complicate cost visibility and optimization efforts (complexity).
Besides, SageMaker has some endpoints and service quotas you need to know about.
Also, it’s challenging to choose the right SageMaker ML instance for your specific workload because instances vary in performance and price.
Now what?
SageMaker attempts to fully manage the process of building and maintaining suitable machine learning models on your team's behalf, but rightsizing instances to meet your workload requirements can be difficult.
Also, researching, choosing, and configuring the ML instances manually is not just time-consuming, but also error-prone.
To overcome these challenges, you can use two solutions in one, even if you aren't sure how much computational power a workload will require.
CloudZero Advisor is a free tool that delivers recommendations to help you choose the right instances and sizes for your workload based on factors like AWS service (like SageMaker or EC2), pricing, region, network performance, storage needs, and more.
For Amazon SageMaker specifically, CloudZero Advisor will let you select suitable machine learning instances by 10 resource types:
Check this out:
CloudZero Advisor for Amazon SageMaker
Now here's the thing. You might be setting up a new machine-learning model. Or, maybe your existing setup is costing you too much.
Yet, it's hard to tell who, when, and how your Amazon SageMaker workloads drive your cloud costs when you receive your AWS bill every month.
Without that visibility, it's hard to pinpoint where to cut costs and where to invest more to maximize your returns.
With CloudZero, you can tell who, what, and why your cloud costs are changing in the way they do. With CloudZero's cloud cost intelligence approach, you can analyze, understand, and act on granular cost insights regardless of how messy your cost allocation tags are.
Using CloudZero, you can view your costs by customer, team, project, product feature, environment, product, deployment, etc.
In addition, CloudZero continuously analyzes your spend to detect cost anomalies in real-time.
Using smart alerts, CloudZero will alert you to any trending costs that could lead to overspending on SageMaker or other AWS services.
to see CloudZero for yourself!
The following are answers to frequently asked questions about Amazon SageMaker pricing.
Yes. It's a paid service, but you can try it out for free with an AWS Free Tier subscription. The free tier begins the very first month you create a SageMaker resource.
AWS developed Sagemaker based on the Jupyter project. SageMaker enables you to run Jupyter notebooks machine learning (ML) models for training and inference using AWS infrastructure.
Considering all the machine learning resource configurations available, SageMaker pricing is especially confusing. You'll still incur charges if you shut down a notebook on an instance but don't shut down the instance as well. Shutting down Studio notebooks does not delete any additional resources built with Studio, such as SageMaker endpoints, Amazon EMR clusters, and Amazon S3 buckets.
SageMaker has different quotas depending on the scenario. If you're interested in specific limitations for a particular use case, we recommend you review them here.
CloudZero is the only solution that enables you to allocate 100% of your spend in hours — so you can align everyone around cost dimensions that matter to your business.