Google Vertex AI Reviews

Name: Google Vertex AI
Brand: Google
Rating: 4.2 (11 reviews)

4.2 out of 5

11 reviews
100% willing to recommend

What is Google Vertex AI?

Build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified artificial intelligence platform.

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Google Vertex AI is the #1 ranked solution in top AI Infrastructure solutions and #2 ranked solution in top AI Development Platforms. PeerSpot users give Google Vertex AI an average rating of 8.4 out of 10. Google Vertex AI is most commonly compared to Microsoft Azure Machine Learning Studio: Google Vertex AI vs Microsoft Azure Machine Learning Studio. Google Vertex AI is popular among the large enterprise segment, accounting for 63% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a computer software company, accounting for 13% of all views.

Helped 859,579 peers since 2012

Featured Google Vertex AI reviews

Hamada Farag

Technology Consultant at Beta Information Technology

We are familiar with most Google Cloud services, particularly infrastructure services, storage, compute, AI tools, containerization, GCP containerization, and cloud SQL. We are familiar with approximately eighty percent of Google's services, primarily related to infrastructure, AI, containers, backup, storage, and compute. We are familiar with Gemini AI and Google Vertex AI, and we have completed some exercises and cases with our customers for Google AI. We use automation in machine learning. I work with a team where everyone has specific responsibilities. We have design and development processes in place. Based on my experience, I would rate Google Vertex AI a 9 out of 10.

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Rajeev-Dave

IT Senior Manager at a tech services company with 10,001+ employees

We are using Google Vertex AI for various purposes, especially various chatbots for customer engineers and customers. Additionally, we are working on projects involving AI and machine learning Features like the LLM model are valuable, and we are using Google Vertex AI for these purposes.…

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Serge Dahdouh

CEO at Les Roches International School of Hotel Management

We work with clients who request the implementation of a certain document into a chatbot. Because of the limited knowledge of AI, our task is to link that file to the ML and provide a platform that can work as a customer service. We previously used LangChain Phython, but now it is done through Vertex AI.

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Google Vertex AI mindshare

Product category:

As of July 2025, the mindshare of Google Vertex AI in the AI Development Platforms category stands at 11.8%, down from 21.3% compared to the previous year, according to calculations based on PeerSpot user engagement data.

AI Development Platforms

PeerResearch reports based on Google Vertex AI reviews

Type	Title	Date
Category	AI Development Platforms	Jul 1, 2025	Download
Product	Reviews, tips, and advice from real users	Jul 1, 2025	Download
Comparison	Google Vertex AI vs Azure OpenAI	Jul 1, 2025	Download
Comparison	Google Vertex AI vs Hugging Face	Jul 1, 2025	Download
Comparison	Google Vertex AI vs Microsoft Azure Machine Learning Studio	Jul 1, 2025	Download

Title	Rating	Mindshare	Recommending
Microsoft Azure Machine Learning Studio	3.9	5.8%	93%	62 interviews Add to research
Amazon SageMaker	3.9	5.3%	91%	37 interviews Add to research

Valuable Features

Google Vertex AI offers valuable features including model garden for deploying models through endpoints, flexible no-code and low-code services, multi-model capabilities, integration with GCP and diverse databases, a customizable interface, and strong CRM integration. Key tools like Feature Store and monitoring enhance machine-learning workflows. Integration of AutoML features and the LLM model simplifies processes. An intuitive UI allows non-experts to manage tasks efficiently, leveraging libraries for reduced coding, and supports comprehensive external analytics.

"The support is perfect and fantastic."
"Google Vertex AI is better for deployment, configuration, delivery, licensing, and integration compared to other AI platforms."
"Vertex comes with inbuilt integration with GCP for data storage."

Room for Improvement

Google Vertex AI could benefit from integrating features like image generators, text-to-speech, and improved chat functions. More comprehensive and accessible documentation is needed, alongside enhanced model training efficiency and customizable settings. The interface should be more intuitive for non-technical users and should support a seamless workflow with BigQuery. Customer support and platform stability require refinement. Users highlight that Vertex AI excels in machine learning and AI but necessitates further optimization and functional advancements.

"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."
"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."
"I'm not sure if I have suggestions for improvement."

Pricing

Enterprise experiences with Google Vertex AI pricing highlight a clear, structured approach offering benefits such as pre-trained models for rapid machine learning development. Users note the platform is relatively inexpensive, with moderate costs compared to competitors like Azure. Various pricing models, including pay-as-you-go and subscription options, provide flexibility. While some find the pricing attractive and beneficial for business value, others suggest it could be cheaper or more flexible, reflecting a mix of opinions.

"I think almost every tool offers a decent discount. In terms of credits or other stuff, every cloud provider provides a good number of incentives to onboard new clients."
"The solution's pricing is moderate."
"The Versa AI offers attractive pricing. With this pricing structure, I can leverage various opportunities to bring value to my business. It's a positive aspect worth considering."

Popular Use Cases

Google Vertex AI is primarily utilized by companies for various machine learning tasks, including deploying models, creating workflows, and integrating diverse data types such as EDI and Salesforce. It is also used for generating document summaries, keyword detection, and leveraging image augmentation. Additionally, businesses focus on GenAI capabilities and RAG use cases, deploying chatbot and AI applications for customer support, contract management, and fraud detection across different sectors.

Service and Support

Google Vertex AI’s customer service is described as generally good but sometimes slow in response. Some users find the support to be highly responsive and impressed with their assistance, while others suggest quicker responses are needed. A direct connection with Google representatives and availability during downtime are appreciated, but delays and the need for faster ticket resolution are noted. The technical support is often recognized for professional and knowledgeable assistance.

Deployment

Many users found Google Vertex AI's initial setup user-friendly and straightforward, requiring only basic knowledge and a Google account. It was praised for being intuitive with abundant documentation. Some noted that deploying outside Google Cloud may need additional expertise. Users appreciated the ease of executing initial configurations, though some recognized technical complexities that could be simplified for better user experience. A small team can efficiently manage the setup process.

Scalability

Google Vertex AI offers both scalability and flexibility, leveraging Google’s infrastructure to enable seamless machine learning processes. Users find it easy to scale both horizontally and vertically. Despite some concerns about training time for large datasets, it can handle increased user volumes and large datasets efficiently. Scalability depends on configuration and pricing, with no inherent limits. Minor technical restrictions exist but do not significantly impact scalability for most.

Stability

Users express satisfaction with Google Vertex AI's stability, highlighting no concerns with stability and rating it highly, often as a nine. They praise its reliability in both business and technical areas, noting seamless integration with tools like Cloud Composer and Dataflow. The infrastructure is robust, ensuring no downtime. While a few mention its growth and complexities, it is generally found more stable than Azure, providing a cohesive experience without bugs.

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

Download our Google Vertex AI Buyer's Guide for additional reliable information.