Google Vertex AI Reviews

Name: Google Vertex AI
Brand: Google
Rating: 4.2 (10 reviews)

4.2 out of 5

10 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 64% 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 851,604 peers since 2012

Featured Google Vertex AI reviews

Kashif Yaseen

Trainee Decision Scientist at a tech services company with 1,001-5,000 employees

My research was around how well they support RAG use cases. Do they have all the components required for building the RAG use case? As in, you need a data storage part, vector storage where you can store your vector embeddings, and the model layer itself Vertex comes with inbuilt integration with…

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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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Phillipe Ramos

Data Architect at Trinidad Systems Limited

It should improve the model efficiency, which is just training the model over and over again. Building an application on Agent Builder and grounding it with our internal documents and internal data does improve its accuracy when used in generative AI. Some of the tools should have more advanced settings available. Some are very locked into certain features and settings, and there is no customization. A more advanced button to fine-tune some of those settings would be good. Having it more available throughout all the other applications on GCP would be good.

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Google Vertex AI mindshare

Product category:

As of May 2025, the mindshare of Google Vertex AI in the AI Development Platforms category stands at 13.1%, 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	May 20, 2025	Download
Product	Reviews, tips, and advice from real users	May 20, 2025	Download
Comparison	Google Vertex AI vs Azure OpenAI	May 20, 2025	Download
Comparison	Google Vertex AI vs Microsoft Azure Machine Learning Studio	May 20, 2025	Download
Comparison	Google Vertex AI vs Hugging Face	May 20, 2025	Download

Title	Rating	Mindshare	Recommending
Microsoft Azure Machine Learning Studio	3.8	7.0%	93%	61 interviews Add to research
Amazon SageMaker	3.9	5.5%	91%	37 interviews Add to research

Valuable Features

Google Vertex AI offers a range of valuable features including the Feature Store for centralized data management, easy deployment of models via API, and monitoring capabilities that alert users to errors. Its integration with Google Cloud enhances real-time predictions and analytics. The platform supports flexible, low-code development and provides a model registry for version control. AutoML and seamless GCP integration further streamline workflows, although some services come with a higher cost.

"Vertex comes with inbuilt integration with GCP for data storage."
"The integration of AutoML features streamlines our machine-learning workflows."
"The most valuable features of the solution are that it is quite flexible, and some of the services are almost low-code, with no-code services, so it gives agents flexibility to build the use cases according to the operational needs."

Room for Improvement

Google Vertex AI requires improvements in documentation accessibility, model optimization, and broader feature offerings like image generation and text-to-speech. Users find the platform complex for those lacking technical knowledge, emphasizing the need for more user-friendly interfaces, especially for business process developers. Enhanced customer support and better integration with BigQuery UI are desired. The platform's chat activity and knowledge base need expansion for more comprehensive query handling. More advanced settings and customization options are also requested.

"I'm not sure if I have suggestions for improvement."
"Both major systems, Azure and Google, are not yet stabilized, especially their customer support."
"I think the technical documentation is not readily available in the tool."

Pricing

Google Vertex AI users appreciate the clear pricing structure and variety of models, enhancing machine-learning application development efficiency. While some find it moderately priced and economically beneficial, others feel it could be less costly, rating it an average of five to six out of ten in terms of expense. Differences in pricing models, such as pay-as-you-go or subscription, offer flexibility. Incentives and discounts make it competitively cheaper compared to Azure.

"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 machine learning and data analysis tasks. They create, test, deploy, and monitor models, generating document summaries, keyword detection, and image augmentation. It's utilized for building workflows, AI chatbots, advanced language analytics, and optimizing GenAI capabilities. Vertex AI also supports diverse data integrations and applications like machine learning model gardens, LLM models, and RAG use cases with vector storage and embeddings.

Service and Support

Some users find Google Vertex AI's customer service responsive and proactive, while others encounter delays, needing escalation to the product team for resolution. Comparisons highlight slower communication than competitors with live chat support. Users rate support variably, with calls for faster issue resolution and improved response times. Direct connections with Google personnel provide effective assistance, although some feel quicker and precise responses are needed for complex issues despite generally good support reliability.

Deployment

Users found Vertex AI's initial setup straightforward and user-friendly. It required minimal effort, with ample documentation aiding the process. While deploying on Google Cloud was seamless, other clouds like AWS or Microsoft needed a cloud engineer. The platform offered versatile deployment options and easy integration with Google Cloud, though managing technical parameters could be intricate. Setup time varied from days to months. Despite the potential complexities, users appreciated the simplicity and customization options available.

Scalability

Google Vertex AI is praised for its scalability and flexibility, allowing seamless adaptation for machine learning projects. Users appreciate its ability to scale with user demands, leveraging Google's robust infrastructure without limitations. Although stable, some find it slow for large datasets. Companies value its capacity to scale from single nodes to extensive configurations, depending on pricing tiers. Technical experts note its potential for managing increased user volumes smoothly, despite some restrictions with multiple indexes.

Stability

Google Vertex AI is regarded as stable by users who cite its robustness and lack of downtime. It integrates seamlessly with tools such as Cloud Composer and Dataflow, supporting efficient machine learning workflows. Users report it as reliable for both business and technical purposes, contributing to growth and analysis capabilities. Ratings for stability are high, typically around eight to nine, though some mention complexities. Users generally find it more stable compared to Azure.

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.