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Compare Amazon SageMaker and SuperAnnotate 
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At a Glance
Market Segments
Small-Business (33.3% of reviews)
Small-Business (63.5% of reviews)
Entry-Level Pricing
No pricing available
Contact Us
Amazon SageMaker
Star Rating
(45)4.3 out of 5
Market Segments
Small-Business (33.3% of reviews)
Entry-Level Pricing
No pricing available
Learn more about Amazon SageMakerSuperAnnotate
Star Rating
(172)4.9 out of 5
Market Segments
Small-Business (63.5% of reviews)
Entry-Level Pricing
Contact Us
Browse all 3 pricing plansSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Amazon SageMaker excels in its Managed Service capabilities, with a score of 8.8, making it a reliable choice for businesses looking for a robust cloud-based solution. In contrast, SuperAnnotate shines with a higher score of 9.5, indicating a more comprehensive managed service experience that users find easier to navigate.
- Reviewers mention that SuperAnnotate's Quality of Support is exceptional, scoring 9.9, which is significantly higher than Amazon SageMaker's score of 8.7. Users appreciate the responsiveness and helpfulness of SuperAnnotate's support team, often highlighting their quick resolution of issues.
- G2 users indicate that SuperAnnotate offers superior Ease of Use with a score of 9.6 compared to Amazon SageMaker's 8.4. Reviewers frequently comment on SuperAnnotate's intuitive interface and user-friendly features, making it accessible for users with varying levels of technical expertise.
- Users on G2 report that Amazon SageMaker provides excellent Scalability with a perfect score of 10.0, which is crucial for businesses anticipating growth. However, SuperAnnotate also performs well in this area with a score of 9.7, indicating that both platforms can handle increasing workloads effectively.
- Reviewers mention that SuperAnnotate's Data Quality is highly rated at 9.8, with users praising its ability to maintain high standards in data annotation. In comparison, Amazon SageMaker's score of 9.8 in Labeler Quality shows it also delivers quality results, but users often find SuperAnnotate's tools more efficient for their specific needs.
- Users say that Amazon SageMaker's Model Training capabilities are robust, scoring 8.9, which is on par with SuperAnnotate's score of 8.9. However, reviewers highlight that SuperAnnotate's Feature Engineering tools, scoring 8.6, provide a more streamlined experience for users looking to customize their models effectively.
Pricing
Entry-Level Pricing
Amazon SageMaker
No pricing availableSuperAnnotate
Pro
Contact Us
Get ready to scale your most sophisticated AI projects and MLOps needs
- Fully customizable multimodal editor
- Image, video, text, and audio editors
- Data curation and exploration
Free Trial
Amazon SageMaker
No trial information availableSuperAnnotate
Free Trial is available
Ratings
Meets Requirements
8.6
37
9.6
113
Ease of Use
8.4
38
9.6
118
Ease of Setup
8.5
25
9.6
78
Ease of Admin
8.4
20
9.6
46
Quality of Support
8.6
33
9.7
115
Has the product been a good partner in doing business?
9.2
20
9.7
47
Product Direction (% positive)
9.0
36
9.5
99
Features by Category
Deployment
Not enough data
9.8
10
Not enough data
9.2
11
Not enough data
9.3
9
Not enough data
9.7
10
Not enough data
9.5
10
Deployment
Not enough data
9.4
11
Not enough data
9.5
11
Not enough data
9.5
10
Not enough data
9.4
11
Not enough data
9.3
12
Management
Not enough data
9.3
9
Not enough data
9.0
10
Not enough data
9.2
8
Not enough data
9.6
8
Operations
Not enough data
9.3
9
Not enough data
9.2
8
Not enough data
9.2
8
Management
Not enough data
9.6
8
Not enough data
9.4
8
Not enough data
9.3
7
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
System
8.2
19
Not enough data
Model Development
8.7
29
Not enough data
8.2
28
Not enough data
8.3
33
Not enough data
8.9
33
Not enough data
Model Development
8.4
19
Not enough data
Machine/Deep Learning Services
8.9
26
Not enough data
9.1
28
Not enough data
8.9
25
Not enough data
9.0
28
Not enough data
Machine/Deep Learning Services
9.2
17
Not enough data
9.2
18
Not enough data
Deployment
8.6
32
Not enough data
8.6
32
Not enough data
9.0
31
Not enough data
Generative AI
8.6
6
Not enough data
9.2
6
Not enough data
8.3
5
Not enough data
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
Quality
Not enough data
9.7
76
Not enough data
9.6
74
Not enough data
9.7
74
Not enough data
9.7
68
Automation
Not enough data
9.3
57
Not enough data
9.5
47
Image Annotation
Not enough data
9.3
70
Not enough data
9.4
67
Not enough data
9.3
59
Not enough data
9.5
61
Natural Language Annotation
Not enough data
9.3
46
Not enough data
9.2
39
Not enough data
9.6
43
Speech Annotation
Not enough data
9.2
40
Not enough data
9.0
38
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
Large Language Model Operationalization (LLMOps)Hide 15 FeaturesShow 15 Features
Not enough data
9.5
13
Prompt Engineering - Large Language Model Operationalization (LLMOps)
Not enough data
9.6
13
Not enough data
9.6
13
Inference Optimization - Large Language Model Operationalization (LLMOps)
Not enough data
9.4
12
Model Garden - Large Language Model Operationalization (LLMOps)
Not enough data
9.6
13
Custom Training - Large Language Model Operationalization (LLMOps)
Not enough data
9.5
13
Application Development - Large Language Model Operationalization (LLMOps)
Not enough data
9.4
13
Model Deployment - Large Language Model Operationalization (LLMOps)
Not enough data
9.5
11
Not enough data
9.4
13
Guardrails - Large Language Model Operationalization (LLMOps)
Not enough data
9.5
13
Not enough data
9.6
12
Model Monitoring - Large Language Model Operationalization (LLMOps)
Not enough data
9.4
12
Not enough data
9.5
11
Security - Large Language Model Operationalization (LLMOps)
Not enough data
9.5
11
Not enough data
9.4
11
Gateways & Routers - Large Language Model Operationalization (LLMOps)
Not enough data
9.4
12
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 SuperAnnotate are categorized as MLOps Platforms
Unique Categories
Amazon SageMaker is categorized as Data Science and Machine Learning Platforms, Generative AI Infrastructure, and Low-Code Machine Learning Platforms
SuperAnnotate is categorized as Data Labeling and Large Language Model Operationalization (LLMOps)
Reviews
Reviewers' Company Size
Amazon SageMaker
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
SuperAnnotate
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
22.6%
Computer Software
16.4%
Research
6.9%
Higher Education
5.0%
Health, Wellness and Fitness
3.8%
Other
45.3%
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 SuperAnnotate?
1 Comment
MM
SuperAnnotate is an end-to-end platform to annotate, version, and manage ground truth data for your AI.Read more
What is your experience with SuperAnnotate for data annotation, and what would you like to see improved?
1 Comment
ST
I have been invited to skill test for a few projects with superannotate over the past year or so, each resulting in the platform being buggy and not allowing...Read more
SuperAnnotate has no more discussions with answers
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