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Compare Amazon SageMaker and IBM Cloud Pak for Data 
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At a Glance
Market Segments
Small-Business (33.3% of reviews)
Enterprise (50.0% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
Amazon SageMaker
Star Rating
(45)4.3 out of 5
Market Segments
Small-Business (33.3% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about Amazon SageMakerIBM Cloud Pak for Data
Star Rating
(88)4.3 out of 5
Market Segments
Enterprise (50.0% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about IBM Cloud Pak for DataSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Amazon SageMaker excels in its Managed Service capabilities, scoring 9.5, which indicates a seamless experience for users looking for a fully managed solution. In contrast, IBM Cloud Pak for Data, while also strong at 8.5, does not match the level of ease and efficiency that SageMaker provides.
- Reviewers mention that SageMaker's Model Training feature is highly rated at 8.9, showcasing its robust capabilities in training machine learning models. On the other hand, IBM Cloud Pak for Data's model training score of 8.5 suggests it is competent but may not offer the same level of performance or user satisfaction.
- G2 users highlight that SageMaker's Data Querying feature is rated at 9.1, making it a preferred choice for users needing efficient data retrieval. In comparison, IBM Cloud Pak for Data's score of 8.9 indicates it is still effective but may not provide the same speed or ease of use.
- Users on G2 report that SageMaker's Scalability is impressive, with a score of 9.7, allowing businesses to grow without worrying about performance issues. Conversely, IBM Cloud Pak for Data, with a score of 9.1, is also scalable but may not handle large-scale operations as efficiently as SageMaker.
- Reviewers mention that SageMaker's Ease of Setup is rated at 8.4, which is significantly higher than IBM Cloud Pak for Data's score of 7.2. This suggests that users find SageMaker more straightforward to implement, which can be a crucial factor for teams with limited technical resources.
- Users say that SageMaker's Quality of Support is rated at 8.7, indicating a strong support system for users. In contrast, IBM Cloud Pak for Data's score of 8.3, while still good, suggests that there may be room for improvement in customer service and support responsiveness.
Pricing
Entry-Level Pricing
Amazon SageMaker
No pricing availableIBM Cloud Pak for Data
No pricing availableFree Trial
Amazon SageMaker
No trial information availableIBM Cloud Pak for Data
No trial information availableRatings
Meets Requirements
8.6
37
8.5
47
Ease of Use
8.4
38
8.1
47
Ease of Setup
8.5
25
7.2
26
Ease of Admin
8.4
20
7.6
27
Quality of Support
8.6
33
8.3
42
Has the product been a good partner in doing business?
9.2
20
8.1
25
Product Direction (% positive)
9.0
36
8.8
47
Features by Category
Customization
Not enough data
8.0
5
Not enough data
Not enough data
Not enough data
Not enough data
Infrastructure
Not enough data
Not enough data
Not enough data
9.0
5
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Management
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Deployment
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Deployment
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Management
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Operations
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Management
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Infrastructure Provision
Not enough data
7.7
8
Not enough data
8.1
7
Not enough data
7.9
7
Not enough data
8.3
6
Not enough data
8.5
8
Not enough data
7.9
8
Not enough data
8.3
8
Not enough data
8.3
7
Management
Not enough data
8.6
7
Not enough data
8.3
7
Not enough data
8.1
8
Functionality
Not enough data
8.3
8
System
8.2
19
9.5
11
Model Development
8.7
29
8.3
8
8.2
28
8.5
8
8.3
33
9.1
9
8.9
33
8.8
8
Model Development
8.4
19
8.5
12
Machine/Deep Learning Services
8.9
26
8.7
9
9.1
28
9.4
8
8.9
25
9.6
8
9.0
28
9.0
7
Machine/Deep Learning Services
9.2
17
9.2
8
9.2
18
9.3
7
Deployment
8.6
32
9.3
9
8.6
32
9.3
9
9.0
31
8.9
9
Generative AI
8.6
6
8.3
5
9.2
6
8.3
5
8.3
5
8.3
5
Agentic AI - Data Science and Machine Learning Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Data Management
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
8.3
5
Analytics
Not enough data
Not enough data
Security
Not enough data
9.0
5
Not enough data
Not enough data
Not enough data
Not enough data
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Agentic AI - Data Fabric
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Scalability and Performance - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Cost and Efficiency - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Integration and Extensibility - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Security and Compliance - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Usability and Support - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Data Ingestion & Preparation - Low-Code Machine Learning Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Model Construction & Automation - Low-Code Machine Learning Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Statistical Tool
Not enough data
8.3
5
Not enough data
8.8
8
Not enough data
8.6
7
Data Analysis
Not enough data
8.7
9
Not enough data
9.0
7
Decision Making
Not enough data
8.3
6
Not enough data
8.8
7
Not enough data
8.8
8
Not enough data
8.3
8
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Data Transformation
Not enough data
8.5
27
|
Verified
Not enough data
9.1
15
|
Verified
Connectivity
Not enough data
8.0
23
|
Verified
Not enough data
8.6
22
|
Verified
Not enough data
8.1
25
|
Verified
Not enough data
8.7
24
|
Verified
Operations
Not enough data
8.7
26
|
Verified
Not enough data
8.9
25
|
Verified
Not enough data
8.4
23
|
Verified
Not enough data
8.8
24
|
Verified
Not enough data
8.7
13
|
Verified
Building Reports
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Platform
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Categories
Categories
Shared Categories
Amazon SageMaker and IBM Cloud Pak for Data are categorized as Data Science and Machine Learning Platforms and Generative AI Infrastructure
Unique Categories
Amazon SageMaker is categorized as MLOps Platforms and Low-Code Machine Learning Platforms
IBM Cloud Pak for Data is categorized as Data Fabric, Virtual Private Cloud (VPC), Big Data Analytics, Predictive Analytics, and Infrastructure as a Service (IaaS)
Reviews
Reviewers' Company Size
Amazon SageMaker
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
IBM Cloud Pak for Data
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Small-Business
(50 or fewer emp.)
Mid-Market
(51-1000 emp.)
Enterprise
(> 1000 emp.)
Reviewers' Industry
Information Technology and Services
19.0%
Computer Software
16.7%
Marketing and Advertising
4.8%
Internet
4.8%
Hospital & Health Care
4.8%
Other
50.0%
Computer Software
12.9%
Information Technology and Services
8.6%
Banking
8.6%
Financial Services
5.7%
Education Management
5.7%
Other
58.6%
Alternatives
Discussions
What is the best way to integrate Sagemaker models with Kubernetes?
1 Comment
VJ
https://aws.amazon.com/blogs/machine-learning/introducing-amazon-sagemaker-operators-for-kubernetes/Read more
How do i make this platform reach to most of my developers?
1 Comment
VJ
you can manage the access via IAM users and roles and give them access as per their need, Sagemaker by default has all the basic AWS feature and you can...Read more
Amazon SageMaker has no more discussions with answers
IBM Cloud Pak for Data has no discussions with answers
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