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Compare AWS Bedrock and Amazon SageMaker 
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At a Glance
Market Segments
Enterprise (38.6% of reviews)
Small-Business (33.3% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
AWS Bedrock
Star Rating
(44)4.4 out of 5
Market Segments
Enterprise (38.6% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about AWS BedrockAmazon 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 SageMakerSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Amazon SageMaker excels in its Model Training capabilities, with a score of 8.9, making it a preferred choice for those focused on developing and training machine learning models. In contrast, AWS Bedrock also scores 8.9 in this area, but reviewers mention that its Pre-Built Algorithms feature, rated at 8.6, is less comprehensive compared to SageMaker's offerings.
- Reviewers mention that AWS Bedrock shines in Scalability, achieving a score of 9.7, which is higher than SageMaker's 9.0. Users on G2 appreciate Bedrock's ability to handle large-scale deployments seamlessly, while SageMaker's scalability is noted but not as robust in high-demand scenarios.
- G2 users highlight that Amazon SageMaker's Data Ingestion & Wrangling feature, rated at 8.1, is user-friendly, but AWS Bedrock's integration capabilities, particularly in AI Data Pipeline Integration (8.3), are seen as more flexible and efficient for complex workflows.
- Users say that both platforms offer strong AI Model Training Scalability, with SageMaker scoring 8.8 and Bedrock matching that score. However, reviewers mention that SageMaker's Drag and Drop functionality (9.0) is more intuitive for users who prefer a visual approach to model building compared to Bedrock's 8.3 rating in the same category.
- Reviewers mention that AWS Bedrock's AI Cost per API Call is rated at 8.1, which some users find more economical for large-scale applications, while SageMaker's cost efficiency is perceived as less favorable, leading to concerns about budget management in extensive deployments.
- Users on G2 report that Amazon SageMaker's Quality of Support is rated at 8.7, which is slightly higher than Bedrock's 8.6. Reviewers appreciate SageMaker's responsive customer service, while some users of Bedrock express a desire for more comprehensive support resources.
Pricing
Entry-Level Pricing
AWS Bedrock
No pricing availableAmazon SageMaker
No pricing availableFree Trial
AWS Bedrock
No trial information availableAmazon SageMaker
No trial information availableRatings
Meets Requirements
8.6
44
8.6
37
Ease of Use
8.1
44
8.4
38
Ease of Setup
8.4
43
8.5
25
Ease of Admin
9.0
15
8.4
20
Quality of Support
8.5
43
8.6
33
Has the product been a good partner in doing business?
8.5
14
9.2
20
Product Direction (% positive)
9.5
43
9.0
36
Features by Category
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
System
Not enough data
8.2
19
Model Development
Not enough data
8.7
29
Not enough data
8.2
28
Not enough data
8.3
33
Not enough data
8.9
33
Model Development
Not enough data
8.4
19
Machine/Deep Learning Services
Not enough data
8.9
26
Not enough data
9.1
28
Not enough data
8.9
25
Not enough data
9.0
28
Machine/Deep Learning Services
Not enough data
9.2
17
Not enough data
9.2
18
Deployment
Not enough data
8.6
32
Not enough data
8.6
32
Not enough data
9.0
31
Generative AI
Not enough data
8.6
6
Not enough data
9.2
6
Not enough data
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
Scalability and Performance - Generative AI Infrastructure
8.9
14
Not enough data
8.5
13
Not enough data
8.2
14
Not enough data
Cost and Efficiency - Generative AI Infrastructure
8.1
14
Not enough data
8.5
14
Not enough data
7.9
14
Not enough data
Integration and Extensibility - Generative AI Infrastructure
8.6
14
Not enough data
8.8
14
Not enough data
8.2
14
Not enough data
Security and Compliance - Generative AI Infrastructure
8.6
14
Not enough data
8.7
14
Not enough data
8.5
14
Not enough data
Usability and Support - Generative AI Infrastructure
7.7
14
Not enough data
7.7
14
Not enough data
Prompt Engineering - Large Language Model Operationalization (LLMOps)
8.0
5
Not enough data
7.3
5
Not enough data
Inference Optimization - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Model Garden - Large Language Model Operationalization (LLMOps)
8.7
5
Not enough data
Custom Training - Large Language Model Operationalization (LLMOps)
7.7
5
Not enough data
Application Development - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Model Deployment - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
8.3
5
Not enough data
Guardrails - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Model Monitoring - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Security - Large Language Model Operationalization (LLMOps)
9.0
5
Not enough data
Not enough data
Not enough data
Gateways & Routers - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Customization - AI Agent Builders
7.9
7
Not enough data
7.6
7
Not enough data
7.7
8
Not enough data
Functionality - AI Agent Builders
7.5
8
Not enough data
7.4
7
Not enough data
7.4
7
Not enough data
8.1
7
Not enough data
Data and Analytics - AI Agent Builders
8.1
7
Not enough data
8.1
7
Not enough data
9.3
7
Not enough data
Integration - AI Agent Builders
8.3
7
Not enough data
8.3
7
Not enough data
7.9
7
Not enough data
7.5
6
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
Categories
Categories
Shared Categories
AWS Bedrock and Amazon SageMaker are categorized as Generative AI Infrastructure
Unique Categories
AWS Bedrock is categorized as Large Language Model Operationalization (LLMOps) and AI Agent Builders
Amazon SageMaker is categorized as MLOps Platforms, Data Science and Machine Learning Platforms, and Low-Code Machine Learning Platforms
Reviews
Reviewers' Company Size
AWS Bedrock
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Amazon SageMaker
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
22.7%
Computer Software
18.2%
Financial Services
6.8%
Consulting
4.5%
Retail
4.5%
Other
43.2%
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%
Alternatives
Discussions
AWS Bedrock has no discussions with answers
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
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