Compare Altair AI Studio and Gemini Enterprise Agent Platform 
At a Glance
Market Segments
Small-Business (42.4% of reviews)
Small-Business (42.3% of reviews)
Entry-Level Pricing
No pricing available
Pay As You Go Per Month
Altair AI Studio
Star Rating
(516)4.6 out of 5
Market Segments
Small-Business (42.4% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about Altair AI StudioGemini Enterprise Agent Platform
Star Rating
(651)4.3 out of 5
Market Segments
Small-Business (42.3% of reviews)
Pros & Cons
Entry-Level Pricing
Pay As You Go Per Month
Learn more about Gemini Enterprise Agent PlatformSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- G2 reviewers report that Altair AI Studio excels in its user-friendly interface, particularly its no-code, drag and drop functionality, which allows users to build and test machine learning models with ease. This feature is especially appreciated by those who may not have extensive coding experience.
- Users say that Vertex AI stands out for its comprehensive management of the entire machine learning lifecycle. Reviewers highlight its ability to centralize data preparation, model training, and deployment, making it a powerful tool for teams looking to streamline complex workflows.
- According to verified reviews, Altair AI Studio is praised for its quick implementation process, with users noting that it provides results in a timely manner, helping them to explore ideas from a broader perspective. This can be a significant advantage for small businesses needing to pivot quickly.
- Reviewers mention that Vertex AI's seamless integration with Google Cloud enhances its usability, allowing for efficient management of resources and simplifying the deployment of machine learning models. This integration is a key selling point for organizations already invested in the Google ecosystem.
- G2 reviewers highlight that while Altair AI Studio has a higher ease of setup score, indicating a smoother onboarding experience, Vertex AI still provides a solid foundation for users who require more advanced features, despite a slightly steeper learning curve.
- Users express mixed feelings about the support quality for both platforms. Altair AI Studio receives commendations for its responsive support team, while Vertex AI's support is seen as adequate but not as robust, which could be a consideration for teams that prioritize customer service.
Pricing
Entry-Level Pricing
Altair AI Studio
No pricing availableGemini Enterprise Agent Platform
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
Altair AI Studio
Free Trial is available
Gemini Enterprise Agent Platform
No trial information availableRatings
Meets Requirements
8.6
426
8.6
386
Ease of Use
8.8
425
8.1
397
Ease of Setup
9.0
128
8.1
319
Ease of Admin
8.4
104
7.9
150
Quality of Support
8.5
354
8.1
362
Has the product been a good partner in doing business?
8.9
93
8.3
144
Product Direction (% positive)
8.8
412
9.2
380
Features by Category
Reports
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
Data Preparation
Not enough data
Not enough data
Not enough data
Not enough data
Data Modeling and Blending
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
Agentic AI - Analytics 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
Self Service
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
Deployment & Integration - Analytics 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
Advanced Analytics
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Performance & Scalability - Analytics Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Advanced Analytics & Modeling - Analytics Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Agentic AI Capabilities - Analytics 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
Personalized Intelligence - Analytics Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Building Reports
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
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
8.5
52
8.2
169
Model Development
7.5
35
8.5
205
9.1
58
7.8
178
8.5
54
8.4
203
8.4
52
8.5
205
Model Development
8.4
47
8.2
163
Machine/Deep Learning Services
7.7
30
8.3
200
7.9
35
8.5
199
7.5
23
8.2
196
8.4
39
8.3
177
Machine/Deep Learning Services
7.6
32
8.5
163
8.3
35
8.5
162
Deployment
8.0
38
8.3
209
8.3
37
8.3
199
8.5
34
8.6
204
Generative AI
6.7
6
8.3
105
6.7
6
8.3
102
6.4
6
8.1
101
Agentic AI - Data Science and Machine Learning Platforms
Not enough data
8.0
34
Not enough data
7.8
33
Not enough data
7.6
35
Not enough data
7.8
31
Not enough data
8.3
33
Not enough data
7.3
32
Not enough data
7.5
32
Setup
8.0
5
Not enough data
7.7
5
Not enough data
8.3
5
Not enough data
Data
8.0
5
Not enough data
8.3
5
Not enough data
Analysis
8.3
5
Not enough data
7.3
5
Not enough data
7.3
5
Not enough data
7.0
5
Not enough data
7.0
5
Not enough data
7.0
5
Not enough data
6.0
5
Not enough data
Customization
8.3
5
Not enough data
8.7
5
Not enough data
8.0
5
Not enough data
Generative AI
Feature Not Available
Not enough data
Feature Not Available
Not enough data
Scalability and Performance - Generative AI Infrastructure
Not enough data
9.1
30
Not enough data
8.7
31
Not enough data
8.7
30
Cost and Efficiency - Generative AI Infrastructure
Not enough data
8.0
33
Not enough data
7.7
30
Not enough data
8.0
29
Integration and Extensibility - Generative AI Infrastructure
Not enough data
8.5
29
Not enough data
8.3
31
Not enough data
8.6
30
Security and Compliance - Generative AI Infrastructure
Not enough data
8.7
29
Not enough data
8.3
31
Not enough data
9.0
29
Usability and Support - Generative AI Infrastructure
Not enough data
8.2
30
Not enough data
8.5
30
Integration - Machine Learning
Not enough data
8.5
66
Learning - Machine Learning
Not enough data
8.5
64
Not enough data
8.3
63
Not enough data
8.8
64
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
27
Not enough data
7.7
26
Not enough data
8.4
25
Functionality - AI Agent Builders
Not enough data
8.1
26
Not enough data
7.3
26
Not enough data
8.2
25
Not enough data
7.1
26
Data and Analytics - AI Agent Builders
Not enough data
7.7
25
Not enough data
7.9
26
Not enough data
8.1
27
Integration - AI Agent Builders
Not enough data
8.8
27
Not enough data
8.2
29
Not enough data
8.1
27
Not enough data
7.4
26
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
7.9
176
Not enough data
8.9
249
Not enough data
8.6
266
Not enough data
Data Analysis
8.8
284
Not enough data
8.6
269
Not enough data
Decision Making
8.9
275
Not enough data
8.0
251
Not enough data
7.9
254
Not enough data
8.2
226
Not enough data
Generative AI
7.2
6
Not enough data
6.9
6
Not enough data
Categories
Categories
Shared Categories
Altair AI Studio and Gemini Enterprise Agent Platform are categorized as Data Science and Machine Learning Platforms, Generative AI Infrastructure, and Low-Code Machine Learning Platforms
Unique Categories
Gemini Enterprise Agent Platform is categorized as Large Language Model Operationalization (LLMOps), MLOps Platforms, Machine Learning, and AI Agent Builders
Reviews
Reviewers' Company Size
Altair AI Studio
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Gemini Enterprise Agent Platform
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
Higher Education
18.3%
Education Management
15.2%
Information Technology and Services
9.1%
Research
7.0%
Computer Software
6.8%
Other
43.6%
Computer Software
17.5%
Information Technology and Services
14.2%
Financial Services
6.9%
Retail
3.6%
Hospital & Health Care
3.4%
Other
54.3%
Alternatives
Discussions
How to predict 8 test values using 12 other test values. 500 data points available for each tests. Also what should I do to find a relation between parameters.
3 Comments
DS
if I understand you well, you want to predict 8 variables using a set of 12 columns. This sounds like a simple regression. Basically build 8 regression...Read more
What is RapidMiner used for?
1 Comment
MR
RapidMiner is majorly used for data preparation, machine learning, and predictive analysis of development. It includes the interactive data preparation,...Read more
before using they should have knowledge of data science and machine learning with R/python experience
1 Comment
DR
It will help to learn rapidminer easily and fastlyRead more
What is Google Cloud AI Platform used for?
3 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?
3 Comments
SA
Google Cloud ML Engine supports many software libraries, including TensorFlow, scikit-learn, XGBoost, Keras, etc....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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