Amazon Elastic Compute Cloud (EC2) vs. Amazon SageMaker

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Amazon Elastic Compute Cloud (EC2)
Score 8.8 out of 10
N/A
Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides secure, resizable compute capacity in the cloud. Users can launch instances with a variety of OSs, load them with custom application environments, manage network access permissions, and run images on multiple systems.
$0.01
per IP address with a running instance per hour on a pro rata basis
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
Pricing
Amazon Elastic Compute Cloud (EC2)Amazon SageMaker
Editions & Modules
Data Transfer
$0.00 - $0.09
per GB
On-Demand
$0.0042 - $6.528
per Hour
EBS-Optimized Instances
$0.005
per IP address with a running instance per hour on a pro rata basis
Carrier IP Addresses
$0.005 - $0.10
T4g Instances
$0.04
per vCPU-Hour Linux, RHEL, & SLES
T2, T3 Instances
$0.05 ($0.096)
per vCPU-Hour Linux, RHEL, & SLES (Windows)
No answers on this topic
Offerings
Pricing Offerings
Amazon Elastic Compute Cloud (EC2)Amazon SageMaker
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Amazon Elastic Compute Cloud (EC2)Amazon SageMaker
Considered Both Products
Amazon Elastic Compute Cloud (EC2)

No answer on this topic

Amazon SageMaker
Chose Amazon SageMaker
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 …
Top Pros
Top Cons
Features
Amazon Elastic Compute Cloud (EC2)Amazon SageMaker
Infrastructure-as-a-Service (IaaS)
Comparison of Infrastructure-as-a-Service (IaaS) features of Product A and Product B
Amazon Elastic Compute Cloud (EC2)
9.1
18 Ratings
11% above category average
Amazon SageMaker
-
Ratings
Service-level Agreement (SLA) uptime8.617 Ratings00 Ratings
Dynamic scaling9.317 Ratings00 Ratings
Elastic load balancing9.217 Ratings00 Ratings
Pre-configured templates9.517 Ratings00 Ratings
Monitoring tools8.117 Ratings00 Ratings
Pre-defined machine images9.817 Ratings00 Ratings
Operating system support9.617 Ratings00 Ratings
Security controls9.617 Ratings00 Ratings
Automation8.47 Ratings00 Ratings
Best Alternatives
Amazon Elastic Compute Cloud (EC2)Amazon SageMaker
Small Businesses
Akamai Cloud Computing
Akamai Cloud Computing
Score 9.0 out of 10
Google Cloud AI
Google Cloud AI
Score 8.3 out of 10
Medium-sized Companies
SAP on IBM Cloud
SAP on IBM Cloud
Score 9.1 out of 10
Google Cloud AI
Google Cloud AI
Score 8.3 out of 10
Enterprises
SAP on IBM Cloud
SAP on IBM Cloud
Score 9.1 out of 10
Dataiku
Dataiku
Score 7.9 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Amazon Elastic Compute Cloud (EC2)Amazon SageMaker
Likelihood to Recommend
8.5
(65 ratings)
9.0
(6 ratings)
Usability
8.5
(3 ratings)
-
(0 ratings)
Support Rating
8.5
(12 ratings)
-
(0 ratings)
User Testimonials
Amazon Elastic Compute Cloud (EC2)Amazon SageMaker
Likelihood to Recommend
Amazon AWS
I think nowadays, Amazon EC2 is best-suited for most app development and deployment use cases, especially if your resource requirements are not fixed over a long period of time. The flexibility provided by the on-demand pricing and rescaling option makes Amazon EC2 a great service, especially if your tech stack already runs on AWS. On the other hand, I think Amazon EC2 is not the best option if your tech infrastructure runs on another public cloud.
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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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Pros
Amazon AWS
  • A great variety of choices in Amazon Machine Image (AMI) types. Users can select a more basic type to run generic workloads, but also have the choice to pick an AMI pre-installed with specific services in the AWS Marketplace.
  • The range of instance types can support the usage from a student's exploration (inexpensive general-purpose nano instances) to an enterprise's most intense workloads (memory or storage-optimized instances with terabytes of memory and ultra-fast network connection).
  • The pricing options, from regular instances, reserved instances to spot instances allow users to get the job done and make smart choices about how much they want to pay and when they want to pay.
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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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Cons
Amazon AWS
  • This service is a bit difficult to consume. New users need a big learning curve to use this service effectively.
  • UI for EC2 service is a little complex and at many places, it misses detailed explanation.
  • Sometimes it takes too long to create images of EC2 instances. This keeps your EC2 up for that extra time. When instances are heavy, it penalizes a lot of money.
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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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Usability
Amazon AWS
Amazon Elastic Compute Cloud (EC2) allows various ways of gaining incidents, such as slow growth, money, and the reserved ones, mostly depend entirely on the necessity, because it makes highly intelligent choices possible at these times, which enable considerable cost savings whilst addressing the situation as best I like.
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Amazon AWS
No answers on this topic
Support Rating
Amazon AWS
AWS's support is good overall. Not outstanding, but better than average. We have had very little reason to engage with AWS support but in our limited experience, the staff has been knowledgeable, timely and helpful. The only negative is actually initiating a service request can be a bit of a pain.
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Amazon AWS
No answers on this topic
Alternatives Considered
Amazon AWS
Azure VM and Google Compute Engine are alternatives to EC2. AWS EC2 is most matures and advanced of the 3. All these provide easy-to-deploy and automatically configured third-party applications, including single virtual machine or multiple virtual machine solutions.
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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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Return on Investment
Amazon AWS
  • AWS has had a very positive return on investment for every client we have that uses it. They are saving money in the long run.
  • AWS includes the underlying operating system licenses with their EC2 instances so no longer do we have to navigate through Microsoft licensing headache.
  • EC2 allows us to easily create a golden image of servers and store them as AMIs. This makes spinning up new servers that need a particular set of software in the future extremely easy and cost-effective.
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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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