Google Vertex AI Reviews

Name: Google Vertex AI
Brand: Google
Rating: 4.2 (12 reviews)

4.2 out of 5

12 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 Development Platforms and top AI Infrastructure solutions. PeerSpot users give Google Vertex AI an average rating of 8.4 out of 10. Google Vertex AI is most commonly compared to Azure OpenAI: Google Vertex AI vs Azure OpenAI. Google Vertex AI is popular among the large enterprise segment, accounting for 62% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a computer software company, accounting for 14% of all views.

Helped 865,295 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 August 2025, the mindshare of Google Vertex AI in the AI Development Platforms category stands at 11.1%, down from 20.9% 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	Aug 20, 2025	Download
Product	Reviews, tips, and advice from real users	Aug 20, 2025	Download
Comparison	Google Vertex AI vs Azure OpenAI	Aug 20, 2025	Download
Comparison	Google Vertex AI vs Hugging Face	Aug 20, 2025	Download
Comparison	Google Vertex AI vs Microsoft Azure Machine Learning Studio	Aug 20, 2025	Download

Title	Rating	Mindshare	Recommending
Azure OpenAI	3.9	10.3%	93%	34 interviews Add to research
Amazon SageMaker	3.9	5.0%	91%	38 interviews Add to research

Valuable Features

Google Vertex AI offers various valuable features such as Feature Store for centralized feature management, monitoring for model performance, integration with the Google ecosystem, and flexible deployment options. Users praise its ease of use, low-code capabilities, integration with CRM systems, and support for multiple libraries to reduce coding needs. It provides valuable external analytics, model registry, AutoML features, and seamless GCP data storage integration. Its multi-model capabilities and Gemini advancements enhance speed and performance.

"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 requires improvements in processing time, user interface, and feature inclusion like image generators and text-to-speech. Users find the platform challenging due to technical complexity and insufficient documentation. There is feedback for a more integrated experience through BigQuery UI and enhanced model efficiency. Improved customer support and a streamlined knowledge base are needed. Although it ranks highly, further development and additional functionalities are needed for a more user-friendly and mature offering.

"It takes a considerable amount of time to process, and I understand the technology behind why it takes this long, but this is something that could be reduced."
"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."

Pricing

Enterprise buyers find Google Vertex AI offers a transparent pricing structure with a range of pre-trained models for accelerated development. Users note various pricing options, including pay-as-you-go and subscription models, along with new client incentives. Some wish for lower costs, rating it moderately expensive, yet find it cheaper than Azure. The service provides valuable opportunities without hidden fees, although flexibility in pricing models could be improved.

"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

Companies primarily use Google Vertex AI for tasks such as managing machine learning workflows, deploying models, and leveraging AI chatbots. They utilize it for document summarization, keyword detection, image augmentation, and advanced language analytics. Google Vertex AI is essential for building operational applications with generative AI capabilities. They engage in contract management, customer support, and fraud detection, integrating with CRM systems and optimizing workflows, often focusing on AutoML and agentic AI use cases.

Service and Support

Customer service for Google Vertex AI is praised for its professionalism and responsiveness, although response times can sometimes be slower due to escalation needs. Some users find the support impressive and highly knowledgeable. However, others suggest that the support could benefit from faster resolutions and improved access, such as live chat options. Direct connections with Google representatives enhance user experiences, but there is a call for quicker and more accurate responses.

Deployment

Google Vertex AI's initial setup is user-friendly and straightforward, requiring only a Google account and Cloud. Documentation supports the process, making it accessible even to beginners. Deployment options include Google, Microsoft, or AWS Cloud. Technical expertise may be needed for customization or when choosing non-Google clouds. Users appreciate the convenience but note complexities in parameter configuration. The setup efficiently integrates with Google Cloud services for a seamless experience.

Scalability

Google Vertex AI offers robust scalability and flexibility for machine learning projects. It allows easy scaling and handles increased user volumes automatically. Users benefit from ample resources and efficient infrastructure, enabling seamless process execution. Although training times may vary, Vertex AI supports large datasets and is limited by provisioning and configuration rather than inherent constraints. Some technical restrictions exist, yet users find its capability to scale from small to large data quite satisfactory.

Stability

Users express satisfaction with Google Vertex AI's stability, noting its high reliability. It supports business and technical needs, enabling effective machine learning model implementation and robust infrastructure. Integration with tools like Cloud Composer and Dataflow ensures efficient workflows without downtime. Ratings of nine suggest strong dependability, although some complexities exist due to its growth. Compared to Azure, users consider Vertex more stable. No reports of bugs further reinforce its stable reputation.

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.