Compare BigML and Vertex AI 
At a Glance
Market Segments
Small-Business (87.5% of reviews)
Small-Business (42.0% of reviews)
Entry-Level Pricing
$30 per month
Pay As You Go Per Month
BigML
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 plansVertex 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
Learn more about Vertex AISummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- G2 reviewers report that BigML excels in user experience, particularly for small businesses, with many praising its intuitive interface and ease of use. Users appreciate its support for various ML algorithms and data formats, making it a versatile tool for data classification and preprocessing.
- Users say that Vertex AI stands out for its comprehensive management of the machine learning lifecycle. Reviewers highlight its ability to centralize complex workflows, which significantly reduces the effort required for building, training, and deploying models, making it a strong choice for teams handling intricate projects.
- According to verified reviews, BigML's implementation process is notably quick and straightforward, with users mentioning the effective onboarding experience. This ease of setup is particularly beneficial for those new to machine learning, allowing them to get started without extensive technical knowledge.
- Reviewers mention that Vertex AI's integration with Google Cloud is a major advantage, providing seamless access to a wide range of tools and resources. This integration simplifies data preparation and model monitoring, which is a significant plus for organizations already invested in the Google ecosystem.
- G2 reviewers highlight BigML's exceptional customer support, with many users noting the responsiveness and helpfulness of the support team. This level of assistance is crucial for small businesses that may not have dedicated data science resources, ensuring they can effectively utilize the platform.
- Users report that while Vertex AI offers a robust platform, some find it less user-friendly compared to BigML, particularly for those who are not as experienced in machine learning. The complexity of its features can be overwhelming, which may hinder adoption for smaller teams or businesses.
Pricing
Entry-Level Pricing
BigML
STANDARD
$30
per month
- UNLIMITED TASKS
- MAX. TASK SIZE: 64 MB
- MAX. PARALLEL TASKS: 2
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
Free Trial
BigML
No trial information availableVertex AI
No trial information availableRatings
Meets Requirements
9.2
24
8.6
389
Ease of Use
9.0
24
8.2
400
Ease of Setup
9.2
22
8.1
322
Ease of Admin
9.3
22
7.9
149
Quality of Support
9.5
22
8.1
364
Has the product been a good partner in doing business?
9.1
22
8.3
143
Product Direction (% positive)
9.5
24
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
Not enough data
8.2
173
Model Development
Not enough data
8.5
208
Not enough data
7.9
181
Not enough data
8.4
206
Not enough data
8.5
209
Model Development
Not enough data
8.2
167
Machine/Deep Learning Services
Not enough data
8.3
203
Not enough data
8.5
202
Not enough data
8.2
200
Not enough data
8.3
181
Machine/Deep Learning Services
Not enough data
8.5
167
Not enough data
8.5
166
Deployment
Not enough data
8.3
213
Not enough data
8.3
203
Not enough data
8.6
207
Generative AI
Not enough data
8.3
110
Not enough data
8.3
106
Not enough data
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
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
BigML and Vertex AI are categorized as Data Science and Machine Learning Platforms and Low-Code Machine Learning Platforms
Unique Categories
BigML is categorized as Predictive Analytics
Vertex AI is categorized as Large Language Model Operationalization (LLMOps), Generative AI Infrastructure, MLOps Platforms, Machine Learning, and AI Agent Builders
Reviews
Reviewers' Company Size
BigML
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
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%
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
BigML has no 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
Hunting for software insights?
With over 3 million reviews, we can provide the specific details that help you make an informed software buying decision for your business. Finding the right product is important, let us help.
