Amazon SageMaker vs. AWS Fargate

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Amazon SageMaker
Score 8.3 out of 10
N/A
Amazon SageMaker enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.N/A
AWS Fargate
Score 9.0 out of 10
N/A
AWS Fargate is a compute engine for Amazon ECS that allows the user to run containers without having to manage servers or clusters. With AWS Fargate there is no need to provision, configure, and scale clusters of virtual machines to run containers.
$0
*per hour
Pricing
Amazon SageMakerAWS Fargate
Editions & Modules
No answers on this topic
Fargate Spot per GB
$0.00138679
*per hour
per GB
$0.004445
*per hour
Fargate Spot per vCPU
$0.01262932
*per hour
per vCPU
$0.04048
*per hour
Offerings
Pricing Offerings
Amazon SageMakerAWS Fargate
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details*based on US East rates. Price varies region to region.
More Pricing Information
Community Pulse
Amazon SageMakerAWS Fargate
Top Pros
Top Cons
Features
Amazon SageMakerAWS Fargate
Infrastructure-as-a-Service (IaaS)
Comparison of Infrastructure-as-a-Service (IaaS) features of Product A and Product B
Amazon SageMaker
-
Ratings
AWS Fargate
7.1
1 Ratings
13% below category average
Service-level Agreement (SLA) uptime00 Ratings9.01 Ratings
Dynamic scaling00 Ratings8.01 Ratings
Elastic load balancing00 Ratings9.01 Ratings
Pre-configured templates00 Ratings2.01 Ratings
Monitoring tools00 Ratings6.01 Ratings
Operating system support00 Ratings7.01 Ratings
Security controls00 Ratings9.01 Ratings
Automation00 Ratings7.01 Ratings
Best Alternatives
Amazon SageMakerAWS Fargate
Small Businesses
Google Cloud AI
Google Cloud AI
Score 8.3 out of 10
Akamai Cloud Computing
Akamai Cloud Computing
Score 9.0 out of 10
Medium-sized Companies
Google Cloud AI
Google Cloud AI
Score 8.3 out of 10
SAP on IBM Cloud
SAP on IBM Cloud
Score 9.1 out of 10
Enterprises
Dataiku
Dataiku
Score 7.9 out of 10
SAP on IBM Cloud
SAP on IBM Cloud
Score 9.1 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Amazon SageMakerAWS Fargate
Likelihood to Recommend
9.0
(6 ratings)
9.0
(1 ratings)
Usability
-
(0 ratings)
8.0
(1 ratings)
Support Rating
-
(0 ratings)
8.0
(1 ratings)
Contract Terms and Pricing Model
-
(0 ratings)
8.0
(1 ratings)
User Testimonials
Amazon SageMakerAWS Fargate
Likelihood to Recommend
Amazon AWS
Amazon SageMaker is a great tool for developing machine learning models that take more effort than just point-and-click type of analyses. The software works well with the other tools in the Amazon ecosystem, so if you use Amazon Web Services or are thinking about it, SageMaker would be a great addition. SageMaker is great for consumer insights, predictive analytics, and looking for gems of insight in the massive amounts of data we create. SageMaker is less suitable for analysts who do generally "small" data analyses, and "small" data analyses in today's world can be billions of records.
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Amazon AWS
If you need to deploy Docker containers, Amazon Fargate is a very good fit. It integrates very well with other AWS services like RDS, EFS, and Secrets manager. You can have a very robust application using those services. In case you have many containers to deploy, it is however more expensive
that if you use other services like ECS or EKS, since they allow you to
share the same infrastructure to deploy multiple containers.
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Pros
Amazon AWS
  • Provides enough freedom for experienced data scientists and also for those who just need things done without going much deeper into building models.
  • Customization and easy to alter and change.
  • If you already are an Amazon user, you do not need to transition over to another software.
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Amazon AWS
No answers on this topic
Cons
Amazon AWS
  • The UI can be eased up a bit for use by business analysts and non technical users
  • For huge amount of data pull from legacy solutions, the platform lags a bit
  • Considering ML is an emerging topic and would be used by most of the organizations in future, the pipeline integrations can be optimized
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Amazon AWS
No answers on this topic
Usability
Amazon AWS
No answers on this topic
Amazon AWS
It's a very practical service to use. If you need to deploy any application with a Database, disk storage, you're pretty much set.
Everything around that can be taken care of using other AWS services. Like secrets manager, certificate manager, RDS ...
And the CI/CD part is also very easy to setup, you only need on AWS CLI command to trigger a deployment, and done !
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Support Rating
Amazon AWS
No answers on this topic
Amazon AWS
AWS provides different support tiers. They are usually very reactive and are able to help solve the issues very quickly.
As for everything, the higher the support tier you get, the better and faster support you get.
If you're also a part of big company, you probably have solution architects at your disposal to help you with any inqueries.
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Alternatives Considered
Amazon AWS
Amazon SageMaker comes with other supportive services like S3, SQS, and a vast variety of servers on EC2. It's very comfortable to manage the process and also support the end application by one click hosting option. Also, it charges on the base of what you use and how long you use it, so it becomes less costly compared to others.
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Amazon AWS
No answers on this topic
Contract Terms and Pricing Model
Amazon AWS
No answers on this topic
Amazon AWS
Pricing and billing of AWS Fargate is loosely tied to your exisiting AWS billing. You're unlikely to only use Fargate in your AWS subscription, so you get billed for everything alltoghter.
Fargate is naturally a bit more expensive that usuel docker services, but with careful planning and architecturing, you can have a very manageable cost.
You can also rely on Saving plans to reduce your bill.
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Return on Investment
Amazon AWS
  • We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers.
  • We can prototype more rapidly because it is easy to configure notebooks to access AWS resources.
  • For our use-cases, serving models is less expensive with SageMaker than bespoke servers.
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Amazon AWS
No answers on this topic
ScreenShots