Compare Amazon SageMaker and Vertex AI 
At a Glance
Market Segments
Enterprise (34.9% of reviews)
Small-Business (42.0% of reviews)
Entry-Level Pricing
No pricing available
Pay As You Go Per Month
Amazon SageMaker
Star Rating
(48)4.2 out of 5
Market Segments
Enterprise (34.9% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about Amazon SageMakerVertex AI
Star Rating
(652)4.3 out of 5
Market Segments
Small-Business (42.0% of reviews)
Pros & Cons
Entry-Level Pricing
Pay As You Go Per Month
Free Trial is available
Learn more about Vertex AISummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- G2 reviewers report that Vertex AI excels in managing complex machine learning workflows, with users appreciating its ability to centralize the entire ML lifecycle. One user highlighted how it simplifies everything from data preparation to deployment, making it easier to handle intricate tasks.
- Users say that Amazon SageMaker offers a user-friendly experience, particularly for those working on deep learning models. Reviewers noted the straightforward login process and uncomplicated setup, which makes it accessible for users without advanced hardware.
- According to verified reviews, Vertex AI has a significantly higher overall satisfaction score, reflecting its strong performance in quality and user experience. Users frequently mention its seamless integration with Google Cloud, which enhances the overall usability of the platform.
- Reviewers mention that while Amazon SageMaker is effective for deploying applications quickly, it may not match the comprehensive capabilities of Vertex AI in terms of managing the full machine learning lifecycle. Users appreciate SageMaker's affordability, but some feel it lacks the depth of features found in Vertex AI.
- G2 reviewers highlight that Vertex AI's recent updates and features have been well-received, with many users praising its ability to reduce the effort needed for model training and deployment. This contrasts with Amazon SageMaker, which has fewer recent reviews, indicating less current user engagement.
- Users report that while both platforms have their strengths, Vertex AI's higher ratings in areas like model training and natural language processing demonstrate its advanced capabilities. In contrast, Amazon SageMaker is noted for its strong performance in specific tasks like object detection, but may not offer the same breadth of features.
Pricing
Entry-Level Pricing
Amazon SageMaker
No pricing availableVertex AI
Try Vertex AI Free
Pay As You Go
Per Month
New customers get $300 in free credits to spend on Vertex AI.
- A unified UI for the entire ML workflow
- Pre-trained APIs for vision, video, natural language, and more
- End-to-end integration for data and AI
Free Trial
Amazon SageMaker
No trial information availableVertex AI
Free Trial is available
Ratings
Meets Requirements
8.6
38
8.6
389
Ease of Use
8.4
39
8.2
400
Ease of Setup
8.5
26
8.1
322
Ease of Admin
8.4
20
7.9
149
Quality of Support
8.6
34
8.1
364
Has the product been a good partner in doing business?
9.2
20
8.3
143
Product Direction (% positive)
9.1
37
9.2
383
Features by Category
Deployment
Not enough data
8.4
76
Not enough data
8.1
78
Not enough data
8.3
76
Not enough data
8.4
76
Not enough data
8.8
75
Deployment
Not enough data
8.5
75
Not enough data
8.3
73
Not enough data
8.4
72
Not enough data
8.6
74
Not enough data
8.7
71
Management
Not enough data
8.2
71
Not enough data
8.5
73
Not enough data
8.0
71
Not enough data
8.1
70
Operations
Not enough data
8.2
70
Not enough data
8.5
71
Not enough data
8.3
71
Management
Not enough data
8.1
69
Not enough data
8.4
72
Not enough data
8.3
70
Generative AI
Not enough data
8.4
37
Not enough data
8.6
37
System
8.3
20
8.2
173
Model Development
8.7
29
8.5
208
8.2
28
7.9
181
8.3
33
8.4
206
8.9
33
8.5
209
Model Development
8.4
19
8.2
167
Machine/Deep Learning Services
8.9
26
8.3
203
9.1
28
8.5
202
8.9
25
8.2
200
9.0
28
8.3
181
Machine/Deep Learning Services
9.2
17
8.5
167
9.2
18
8.5
166
Deployment
8.7
33
8.3
213
8.7
33
8.3
203
9.0
31
8.6
207
Generative AI
8.6
6
8.3
110
9.2
6
8.3
106
8.3
5
8.1
105
Agentic AI - Data Science and Machine Learning Platforms
Not enough data
8.1
38
Not enough data
7.8
37
Not enough data
7.7
38
Not enough data
7.9
35
Not enough data
8.5
37
Not enough data
7.5
36
Not enough data
7.7
36
Scalability and Performance - Generative AI Infrastructure
Not enough data
9.0
31
Not enough data
8.7
32
Not enough data
8.6
31
Cost and Efficiency - Generative AI Infrastructure
Not enough data
8.0
34
Not enough data
7.7
31
Not enough data
8.1
30
Integration and Extensibility - Generative AI Infrastructure
Not enough data
8.5
30
Not enough data
8.3
32
Not enough data
8.5
31
Security and Compliance - Generative AI Infrastructure
Not enough data
8.7
30
Not enough data
8.3
32
Not enough data
8.9
30
Usability and Support - Generative AI Infrastructure
Not enough data
8.2
31
Not enough data
8.5
31
Integration - Machine Learning
Not enough data
8.5
68
Learning - Machine Learning
Not enough data
8.5
66
Not enough data
8.3
65
Not enough data
8.8
66
Large Language Model Operationalization (LLMOps)Hide 15 FeaturesShow 15 Features
Not enough data
9.0
26
Prompt Engineering - Large Language Model Operationalization (LLMOps)
Not enough data
8.8
24
Not enough data
9.0
24
Inference Optimization - Large Language Model Operationalization (LLMOps)
Not enough data
8.8
22
Model Garden - Large Language Model Operationalization (LLMOps)
Not enough data
9.3
25
Custom Training - Large Language Model Operationalization (LLMOps)
Not enough data
9.1
24
Application Development - Large Language Model Operationalization (LLMOps)
Not enough data
9.2
23
Model Deployment - Large Language Model Operationalization (LLMOps)
Not enough data
9.0
23
Not enough data
8.7
22
Guardrails - Large Language Model Operationalization (LLMOps)
Not enough data
8.7
23
Not enough data
8.9
22
Model Monitoring - Large Language Model Operationalization (LLMOps)
Not enough data
8.7
21
Not enough data
9.1
22
Security - Large Language Model Operationalization (LLMOps)
Not enough data
9.1
22
Not enough data
9.0
23
Gateways & Routers - Large Language Model Operationalization (LLMOps)
Not enough data
8.9
22
Customization - AI Agent Builders
Not enough data
8.6
28
Not enough data
7.6
27
Not enough data
8.3
26
Functionality - AI Agent Builders
Not enough data
8.1
27
Not enough data
7.3
27
Not enough data
8.2
26
Not enough data
7.2
27
Data and Analytics - AI Agent Builders
Not enough data
7.8
26
Not enough data
7.9
27
Not enough data
8.1
28
Integration - AI Agent Builders
Not enough data
8.8
28
Not enough data
8.2
30
Not enough data
8.1
28
Not enough data
7.5
27
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 Vertex 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
Vertex AI is categorized as Large Language Model Operationalization (LLMOps), Machine Learning, and AI Agent Builders
Reviews
Reviewers' Company Size
Amazon SageMaker
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Vertex 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
20.9%
Computer Software
16.3%
Marketing and Advertising
4.7%
Internet
4.7%
Education Management
4.7%
Other
48.8%
Computer Software
17.9%
Information Technology and Services
14.3%
Financial Services
6.8%
Retail
3.6%
Hospital & Health Care
3.3%
Other
54.1%
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
What is Google Cloud AI Platform used for?
3 Comments
AO
Vertex AI is Google Cloud’s managed machine learning platform. It’s used to build, train, and deploy ML models at scale. It unifies data, AutoML, and custom...Read more
What software libraries does cloud ML engine support?
3 Comments
AO
Cloud ML Engine (now part of Vertex AI) supports popular ML frameworks and libraries such as TensorFlow, PyTorch, Scikit‑learn, and XGBoost. This flexibility...Read more
What is Google AI platform?
2 Comments
ZM
The Google AI Platform is a comprehensive set of tools and services provided by Google Cloud to develop, deploy, and manage artificial intelligence. It...Read more
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