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At a Glance
Market Segments
Small-Business (41.0% of reviews)
Enterprise (40.7% of reviews)
Entry-Level Pricing
Pay As You Go Per Month
No pricing available
Vertex AI
Star Rating
(593)4.3 out of 5
Market Segments
Small-Business (41.0% of reviews)
Entry-Level Pricing
Pay As You Go Per Month
Free Trial is available
Learn more about Vertex AIscikit-learn
Star Rating
(59)4.8 out of 5
Market Segments
Enterprise (40.7% of reviews)
Entry-Level Pricing
No pricing available
Learn more about scikit-learnSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Vertex AI excels in AI High Availability with a score of 9.2, which reviewers mention ensures consistent performance and reliability for production-level applications. In contrast, scikit-learn, while strong in model training, does not offer the same level of infrastructure support, leading to potential downtime during critical operations.
- Reviewers mention that scikit-learn shines in its ease of use, scoring 9.6 in this area, making it a favorite among data scientists for quick prototyping and experimentation. Vertex AI, with a score of 8.3, is perceived as more complex, which may require a steeper learning curve for new users.
- Users on G2 highlight that Vertex AI's integration capabilities, particularly with AI Data Pipeline Integration, score 8.2, allowing for seamless data flow across various platforms. In comparison, scikit-learn's integration options are more limited, which can hinder users looking for a comprehensive solution.
- Reviewers mention that scikit-learn's pre-built algorithms and feature engineering capabilities, both scoring 8.4, provide users with a robust toolkit for machine learning tasks. Vertex AI, while offering powerful tools, does not match the breadth of pre-built options available in scikit-learn, which can be a deciding factor for users focused on rapid development.
- G2 users report that Vertex AI's AI Cost per API Call is rated at 8.0, which some find to be a drawback in terms of budget management for extensive projects. In contrast, scikit-learn, being an open-source library, incurs no direct costs, making it a more economical choice for startups and small businesses.
- Users say that scikit-learn's community support is robust, with many resources available for troubleshooting and learning, contributing to its high G2 rating of 4.8. Vertex AI, while having good support, does not have the same level of community engagement, which can be a disadvantage for users seeking peer assistance.
Pricing
Entry-Level Pricing
Vertex 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
scikit-learn
No pricing availableFree Trial
Vertex AI
Free Trial is available
scikit-learn
No trial information availableRatings
Meets Requirements
8.6
359
9.6
52
Ease of Use
8.2
368
9.6
52
Ease of Setup
8.1
291
9.6
40
Ease of Admin
7.9
142
9.4
39
Quality of Support
8.1
335
9.4
48
Has the product been a good partner in doing business?
8.2
136
9.2
35
Product Direction (% positive)
9.2
353
9.3
52
Features by Category
Deployment
8.3
73
Not enough data
8.1
74
Not enough data
8.3
74
Not enough data
8.3
70
Not enough data
8.8
70
Not enough data
Deployment
8.4
73
Not enough data
8.3
72
Not enough data
8.4
71
Not enough data
8.5
71
Not enough data
8.7
69
Not enough data
Management
8.3
70
Not enough data
8.5
69
Not enough data
8.0
69
Not enough data
8.1
69
Not enough data
Operations
8.2
69
Not enough data
8.4
70
Not enough data
8.3
70
Not enough data
Management
8.1
68
Not enough data
8.4
69
Not enough data
8.3
68
Not enough data
Generative AI
8.2
34
Not enough data
8.4
34
Not enough data
System
8.2
170
Not enough data
Model Development
8.5
202
Not enough data
7.9
179
Not enough data
8.4
200
Not enough data
8.5
202
Not enough data
Model Development
8.3
165
Not enough data
Machine/Deep Learning Services
8.2
200
Not enough data
8.4
196
Not enough data
8.2
195
Not enough data
8.2
178
Not enough data
Machine/Deep Learning Services
8.5
165
Not enough data
8.5
163
Not enough data
Deployment
8.2
193
Not enough data
8.3
194
Not enough data
8.5
193
Not enough data
Generative AI
8.3
102
Not enough data
8.3
102
Not enough data
8.1
103
Not enough data
Agentic AI - Data Science and Machine Learning Platforms
8.1
34
Not enough data
7.9
34
Not enough data
7.7
34
Not enough data
7.9
34
Not enough data
8.5
34
Not enough data
7.8
34
Not enough data
8.0
34
Not enough data
Scalability and Performance - Generative AI Infrastructure
8.9
28
Not enough data
8.6
28
Not enough data
8.5
28
Not enough data
Cost and Efficiency - Generative AI Infrastructure
8.2
28
Not enough data
7.8
28
Not enough data
7.9
28
Not enough data
Integration and Extensibility - Generative AI Infrastructure
8.4
28
Not enough data
8.1
28
Not enough data
8.3
28
Not enough data
Security and Compliance - Generative AI Infrastructure
8.6
28
Not enough data
8.5
28
Not enough data
8.9
28
Not enough data
Usability and Support - Generative AI Infrastructure
8.2
28
Not enough data
8.3
28
Not enough data
Integration - Machine Learning
8.5
67
Not enough data
Learning - Machine Learning
8.5
66
Not enough data
8.3
65
Not enough data
8.8
66
Not enough data
Large Language Model Operationalization (LLMOps)Hide 15 FeaturesShow 15 Features
9.0
23
Not enough data
Prompt Engineering - Large Language Model Operationalization (LLMOps)
8.8
23
Not enough data
9.0
23
Not enough data
Inference Optimization - Large Language Model Operationalization (LLMOps)
8.8
23
Not enough data
Model Garden - Large Language Model Operationalization (LLMOps)
9.3
23
Not enough data
Custom Training - Large Language Model Operationalization (LLMOps)
9.1
23
Not enough data
Application Development - Large Language Model Operationalization (LLMOps)
9.2
22
Not enough data
Model Deployment - Large Language Model Operationalization (LLMOps)
9.1
23
Not enough data
8.7
22
Not enough data
Guardrails - Large Language Model Operationalization (LLMOps)
9.0
22
Not enough data
8.9
22
Not enough data
Model Monitoring - Large Language Model Operationalization (LLMOps)
8.8
22
Not enough data
9.1
22
Not enough data
Security - Large Language Model Operationalization (LLMOps)
9.1
23
Not enough data
9.0
23
Not enough data
Gateways & Routers - Large Language Model Operationalization (LLMOps)
8.9
23
Not enough data
Customization - AI Agent Builders
8.5
27
Not enough data
7.6
27
Not enough data
8.3
26
Not enough data
Functionality - AI Agent Builders
8.1
27
Not enough data
7.3
27
Not enough data
8.2
26
Not enough data
7.2
27
Not enough data
Data and Analytics - AI Agent Builders
7.7
25
Not enough data
7.9
27
Not enough data
8.0
27
Not enough data
Integration - AI Agent Builders
8.7
27
Not enough data
8.0
27
Not enough data
8.0
27
Not enough data
7.5
27
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
Vertex AI and scikit-learn are categorized as Machine Learning
Unique Categories
scikit-learn has no unique categories
Reviews
Reviewers' Company Size
Vertex AI
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
scikit-learn
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
Computer Software
17.7%
Information Technology and Services
13.9%
Financial Services
7.0%
Retail
3.8%
Hospital & Health Care
3.4%
Other
54.2%
Computer Software
35.6%
Information Technology and Services
16.9%
Higher Education
10.2%
Computer & Network Security
6.8%
Hospital & Health Care
5.1%
Other
25.4%
Alternatives
Discussions
What is Google Cloud AI Platform used for?
2 Comments
KS
Google cloud AI Platform enables us to build Machine learning models, that works on any type and any size of data. Read more
What software libraries does cloud ML engine support?
2 Comments
JP
It's supporting approx all trending libraries.Read more
What is Google AI platform?
1 Comment
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
What is scikit-learn used for?
2 Comments
MS
Scikit-learn is a powerful library, well-integrated with other Python libraries such as pandas, NumPy, Matplotlib, and Seaborn. It supports creating machine...Read more
What is Python Scikit learn?
1 Comment
RA
It is a library used to implement machine-learning models. Provides vast range of methods to perform data preprocessing, feature selection, and popularly...Read more
scikit-learn has no more discussions with answers
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