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Compare Amazon SageMaker and IBM watsonx.ai 
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At a Glance
Market Segments
Small-Business (33.3% of reviews)
Small-Business (40.5% 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 watsonx.ai
Star Rating
(122)4.4 out of 5
Market Segments
Small-Business (40.5% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Free Trial is available
Learn more about IBM watsonx.aiSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Amazon SageMaker excels in scalability with a score of 9.0, allowing businesses to efficiently handle increasing workloads, while IBM watsonx.ai, although strong, has a slightly lower scalability score of 8.5, which may impact larger deployments.
- Reviewers mention that the ease of use for IBM watsonx.ai is notably higher at 9.1 compared to Amazon SageMaker's 8.4, indicating that users find IBM's interface more intuitive and user-friendly, which can significantly reduce the learning curve for new users.
- G2 users highlight that Amazon SageMaker's drag and drop functionality is rated at 9.0, making it easier for users to build and deploy models without extensive coding knowledge, whereas IBM watsonx.ai's drag and drop feature is rated lower at 8.0, which may hinder some users looking for simplicity.
- Reviewers mention that both platforms offer strong data ingestion and wrangling capabilities, but Amazon SageMaker scores slightly lower at 8.1 compared to IBM watsonx.ai's 8.2, suggesting that IBM may provide a more robust solution for handling diverse data sources.
- Users on G2 report that Amazon SageMaker shines in model training with a score of 8.9, indicating a solid performance in developing machine learning models, while IBM watsonx.ai's score of 8.5 suggests it may not be as efficient in this area, potentially affecting users' ability to quickly iterate on models.
- Reviewers mention that IBM watsonx.ai has a strong focus on natural language processing with a score of 8.8, which is crucial for applications requiring advanced text analysis, while Amazon SageMaker's score of 9.0 in natural language understanding indicates it may have a slight edge in this specific functionality.
Pricing
Entry-Level Pricing
Amazon SageMaker
No pricing availableIBM watsonx.ai
No pricing availableFree Trial
Amazon SageMaker
No trial information availableIBM watsonx.ai
Free Trial is available
Ratings
Meets Requirements
8.6
37
8.8
77
Ease of Use
8.4
38
8.9
109
Ease of Setup
8.5
25
8.5
100
Ease of Admin
8.4
20
8.7
36
Quality of Support
8.6
33
8.8
76
Has the product been a good partner in doing business?
9.2
20
8.9
36
Product Direction (% positive)
9.0
36
9.9
79
Features by Category
Deployment
Not enough data
9.1
9
Not enough data
8.5
9
Not enough data
7.8
9
Not enough data
8.7
9
Not enough data
8.7
9
Deployment
Not enough data
9.3
9
Not enough data
8.7
9
Not enough data
8.3
9
Not enough data
8.9
9
Not enough data
9.1
9
Management
Not enough data
8.0
9
Not enough data
8.5
9
Not enough data
8.5
9
Not enough data
9.3
9
Operations
Not enough data
9.1
9
Not enough data
8.7
9
Not enough data
9.3
9
Management
Not enough data
8.5
9
Not enough data
9.0
8
Not enough data
8.5
8
Generative AI
Not enough data
9.1
9
Not enough data
9.3
9
System
8.2
19
8.2
31
Model Development
8.7
29
8.6
32
8.2
28
8.2
32
8.3
33
8.7
31
8.9
33
8.4
32
Model Development
8.4
19
8.5
32
Machine/Deep Learning Services
8.9
26
Feature Not Available
9.1
28
8.9
32
8.9
25
8.6
32
9.0
28
8.1
32
Machine/Deep Learning Services
9.2
17
8.5
32
9.2
18
8.8
32
Deployment
8.6
32
8.2
32
8.6
32
8.6
32
9.0
31
8.8
32
Generative AI
8.6
6
8.8
31
9.2
6
8.8
31
8.3
5
Feature Not Available
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 Type
Not enough data
8.8
13
Not enough data
Feature Not Available
Not enough data
8.5
12
Synthesis Type
Not enough data
9.0
12
Not enough data
9.2
12
Data Transformation
Not enough data
8.6
12
Not enough data
9.3
12
Not enough data
9.7
12
Not enough data
9.2
12
Not enough data
9.2
12
Scalability and Performance - Generative AI Infrastructure
Not enough data
9.3
7
Not enough data
8.8
7
Not enough data
9.3
7
Cost and Efficiency - Generative AI Infrastructure
Not enough data
8.3
7
Not enough data
8.6
7
Not enough data
8.3
7
Integration and Extensibility - Generative AI Infrastructure
Not enough data
9.5
7
Not enough data
8.6
7
Not enough data
8.8
7
Security and Compliance - Generative AI Infrastructure
Not enough data
8.3
7
Not enough data
8.8
7
Not enough data
8.6
7
Usability and Support - Generative AI Infrastructure
Not enough data
9.3
7
Not enough data
9.0
7
Content Generation - AI Content Creation Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Management - AI Content Creation Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Integration - Machine Learning
Not enough data
9.0
21
Learning - Machine Learning
Not enough data
9.2
22
Not enough data
9.1
22
Not enough data
9.0
21
Prompt Engineering - Large Language Model Operationalization (LLMOps)
Not enough data
9.2
6
Not enough data
8.1
6
Inference Optimization - Large Language Model Operationalization (LLMOps)
Not enough data
8.9
6
Model Garden - Large Language Model Operationalization (LLMOps)
Not enough data
8.9
6
Custom Training - Large Language Model Operationalization (LLMOps)
Not enough data
8.1
6
Application Development - Large Language Model Operationalization (LLMOps)
Not enough data
8.3
6
Model Deployment - Large Language Model Operationalization (LLMOps)
Not enough data
8.3
6
Not enough data
8.6
6
Guardrails - Large Language Model Operationalization (LLMOps)
Not enough data
9.4
6
Not enough data
8.6
6
Model Monitoring - Large Language Model Operationalization (LLMOps)
Not enough data
8.6
6
Not enough data
8.9
6
Security - Large Language Model Operationalization (LLMOps)
Not enough data
9.4
6
Not enough data
9.2
6
Gateways & Routers - Large Language Model Operationalization (LLMOps)
Not enough data
8.9
6
Customization - AI Agent Builders
Not enough data
8.8
7
Not enough data
9.0
7
Not enough data
9.0
7
Functionality - AI Agent Builders
Not enough data
8.6
7
Not enough data
9.0
7
Not enough data
9.3
7
Not enough data
8.8
7
Data and Analytics - AI Agent Builders
Not enough data
9.0
7
Not enough data
8.8
7
Not enough data
9.0
7
Integration - AI Agent Builders
Not enough data
9.0
7
Not enough data
9.0
7
Not enough data
9.0
7
Not enough data
8.6
7
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
Categories
Categories
Shared Categories
Amazon SageMaker and IBM watsonx.ai are categorized as MLOps Platforms, Data Science and Machine Learning Platforms, Generative AI Infrastructure, and Low-Code Machine Learning Platforms
Unique Categories
Amazon SageMaker has no unique categories
IBM watsonx.ai is categorized as Synthetic Data, Machine Learning, AI Content Creation Platforms, Large Language Model Operationalization (LLMOps), and AI Agent Builders
Reviews
Reviewers' Company Size
Amazon SageMaker
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
IBM watsonx.ai
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%
Information Technology and Services
18.9%
Computer Software
11.7%
Consulting
7.2%
Banking
6.3%
Marketing and Advertising
5.4%
Other
50.5%
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 watsonx.ai has no discussions with answers
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