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Compare Amazon SageMaker and BigML 
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At a Glance
Market Segments
Small-Business (33.3% of reviews)
Small-Business (87.5% of reviews)
Entry-Level Pricing
No pricing available
$30 per month
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 SageMakerBigML
Star Rating
(24)4.7 out of 5
Market Segments
Small-Business (87.5% of reviews)
Pros & Cons
Not enough data
Entry-Level Pricing
$30 per month
Browse all 3 pricing plansSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that BigML excels in ease of use with a score of 9.0, making it particularly appealing for small businesses looking for a user-friendly interface. In contrast, Amazon SageMaker has a lower ease of use score of 8.4, which some users find less intuitive.
- Reviewers mention that BigML's quality of support is outstanding, scoring 9.5, which users appreciate for timely assistance and guidance. Conversely, Amazon SageMaker's support quality, rated at 8.7, has received feedback indicating that response times can be slower.
- G2 users highlight BigML's strong performance in deployment flexibility, particularly with its managed service scoring 9.5, which allows for easier scaling and management. Amazon SageMaker, while also effective, scored slightly lower at 9.0, indicating it may require more effort for similar deployment scenarios.
- Users on G2 report that BigML's data ingestion and wrangling capabilities are rated at 8.1, which some find limiting compared to Amazon SageMaker's higher score of 8.8, suggesting that SageMaker offers more robust tools for handling data preparation.
- Reviewers mention that BigML shines in its drag-and-drop functionality, scoring 9.0, which simplifies model development for users. In contrast, Amazon SageMaker's drag-and-drop feature scored lower at 8.3, indicating a less seamless experience for users looking to build models quickly.
- Users say that BigML's product direction is highly rated at 9.5, reflecting a strong commitment to innovation and user feedback. Amazon SageMaker, with a score of 8.8, is also seen as forward-thinking but may not align as closely with user needs as BigML does.
Pricing
Entry-Level Pricing
Amazon SageMaker
No pricing availableBigML
STANDARD
$30
per month
- UNLIMITED TASKS
- MAX. TASK SIZE: 64 MB
- MAX. PARALLEL TASKS: 2
Free Trial
Amazon SageMaker
No trial information availableBigML
No trial information availableRatings
Meets Requirements
8.6
37
9.2
24
Ease of Use
8.4
38
9.0
24
Ease of Setup
8.5
25
9.2
22
Ease of Admin
8.4
20
9.3
22
Quality of Support
8.6
33
9.5
22
Has the product been a good partner in doing business?
9.2
20
9.1
22
Product Direction (% positive)
9.0
36
9.5
24
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
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
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
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Data Analysis
Not enough data
Not enough data
Not enough data
Not enough data
Decision Making
Not enough data
Not enough data
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
Categories
Categories
Shared Categories
Amazon SageMaker and BigML are categorized as Data Science and Machine Learning Platforms and Low-Code Machine Learning Platforms
Unique Categories
Amazon SageMaker is categorized as MLOps Platforms and Generative AI Infrastructure
BigML is categorized as Predictive Analytics
Reviews
Reviewers' Company Size
Amazon SageMaker
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
BigML
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
83.3%
Management Consulting
4.2%
International Trade and Development
4.2%
Electrical/Electronic Manufacturing
4.2%
Alternative Dispute Resolution
4.2%
Other
0.0%
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
BigML has no discussions with answers
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