Compare TensorFlow and Vertex AI 
Featured Products
At a Glance
Market Segments
Small-Business (51.9% of reviews)
Small-Business (42.0% of reviews)
Entry-Level Pricing
No pricing available
Pay As You Go Per Month
TensorFlow
Star Rating
(137)4.5 out of 5
Market Segments
Small-Business (51.9% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about TensorFlowVertex 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
Free Trial is available
Learn more about Vertex AISummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- G2 reviewers report that Vertex AI excels in managing complex machine learning workflows, with users appreciating its ability to centralize the entire ML lifecycle. One user noted, "It simplifies everything from data preparation and model training to deployment and monitoring," making it a strong choice for those looking for an integrated solution.
- Users say TensorFlow offers a user-friendly interface that is particularly beneficial for newcomers to machine learning. Reviewers highlighted that "anyone new to this platform can easily grasp the concepts," which can be a significant advantage for teams just starting their ML journey.
- According to verified reviews, Vertex AI has a higher overall satisfaction score, reflecting its strong performance in user experience and support. Users have praised its seamless integration with Google Cloud, which enhances the ease of managing ML projects, a feature that many find invaluable.
- Reviewers mention that while TensorFlow has a solid reputation for model training capabilities, it faces challenges in terms of setup and configuration. Some users have expressed that they encountered difficulties when transitioning from other platforms, indicating that the learning curve can be steeper compared to Vertex AI.
- Users highlight that Vertex AI's support quality is commendable, with a score that reflects its effectiveness in assisting users. One user remarked on the platform's ability to reduce the effort needed to build and deploy models, showcasing the value of responsive support in enhancing user satisfaction.
- G2 reviewers note that TensorFlow shines in its advanced capabilities for deep learning and neural networks, with many users praising its efficiency in handling complex tasks. A reviewer mentioned its effectiveness for developing applications like face detection, which underscores its strength in specialized ML applications.
Pricing
Entry-Level Pricing
TensorFlow
No pricing availableVertex 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
TensorFlow
No trial information availableVertex AI
Free Trial is available
Ratings
Meets Requirements
9.1
120
8.6
389
Ease of Use
8.0
124
8.2
400
Ease of Setup
8.3
102
8.1
322
Ease of Admin
7.9
39
7.9
149
Quality of Support
8.7
106
8.1
364
Has the product been a good partner in doing business?
8.3
36
8.3
143
Product Direction (% positive)
9.2
118
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
8.6
70
8.2
173
Model Development
8.9
97
8.5
208
7.2
83
7.9
181
8.8
97
8.4
206
9.2
96
8.5
209
Model Development
9.0
69
8.2
167
Machine/Deep Learning Services
9.1
95
8.3
203
9.0
89
8.5
202
8.9
87
8.2
200
9.4
97
8.3
181
Machine/Deep Learning Services
8.7
64
8.5
167
9.3
69
8.5
166
Deployment
8.5
80
8.3
213
8.7
90
8.3
203
9.0
89
8.6
207
Generative AI
8.0
11
8.3
110
7.7
11
8.3
106
8.0
11
8.1
105
Agentic AI - Data Science and Machine Learning Platforms
7.3
5
8.1
38
7.3
5
7.8
37
7.5
6
7.7
38
8.7
5
7.9
35
8.3
5
8.5
37
7.0
5
7.5
36
7.7
5
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
Categories
Categories
Shared Categories
TensorFlow and Vertex AI are categorized as Data Science and Machine Learning Platforms
Unique Categories
TensorFlow has no unique categories
Vertex AI is categorized as Large Language Model Operationalization (LLMOps), Generative AI Infrastructure, MLOps Platforms, Machine Learning, AI Agent Builders, and Low-Code Machine Learning Platforms
Reviews
Reviewers' Company Size
TensorFlow
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
27.1%
Information Technology and Services
19.5%
Research
7.5%
Computer & Network Security
4.5%
Higher Education
3.0%
Other
38.3%
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
What is TensorFlow and why it is used?
2 Comments
PS
TensorFlow is an open-source library that lets you generate various models AI/ML/DL models.Read more
TensorFlow has no more 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
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