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Google Vertex AI Reviews

Vendor: Google
4.2 out of 5
Badge Ranked 1
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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 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.
Buyer's Guide
Google Vertex AI
September 2025
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Google Vertex AI mindshare

Product category:
AI Development Platforms
As of September 2025, the mindshare of Google Vertex AI in the AI Development Platforms category stands at 10.6%, down from 20.5% compared to the previous year, according to calculations based on PeerSpot user engagement data.
AI Development Platforms Market Share Distribution
ProductMarket Share (%)
Google Vertex AI10.6%
Hugging Face12.1%
Azure OpenAI9.9%
Other67.4%
AI Development Platforms

PeerResearch reports based on Google Vertex AI reviews

TypeTitleDate
CategoryAI Development PlatformsSep 18, 2025Download
ProductReviews, tips, and advice from real usersSep 18, 2025Download
ComparisonGoogle Vertex AI vs Azure OpenAISep 18, 2025Download
ComparisonGoogle Vertex AI vs Hugging FaceSep 18, 2025Download
ComparisonGoogle Vertex AI vs Microsoft Azure Machine Learning StudioSep 18, 2025Download
Suggested products
TitleRatingMindshareRecommending
Azure OpenAI3.99.9%93%34 interviewsAdd to research
Hugging Face4.112.1%100%13 interviewsAdd to research
 
 
Key learnings from peers

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.

Review data by company size

By reviewers
Company SizeCount
Small Business4
Midsize Enterprise3
Large Enterprise5
By reviewers
By visitors reading reviews
Company SizeCount
Small Business306
Midsize Enterprise171
Large Enterprise762
By visitors reading reviews

Top industries

By visitors reading reviews
Computer Software Company
14%
Financial Services Firm
11%
Manufacturing Company
9%
Educational Organization
6%
Retailer
6%
Comms Service Provider
5%
University
5%
Government
5%
Healthcare Company
4%
Media Company
4%
Real Estate/Law Firm
3%
Insurance Company
3%
Performing Arts
2%
Construction Company
2%
Hospitality Company
2%
Energy/Utilities Company
2%
Non Profit
2%
Outsourcing Company
2%
Legal Firm
2%
Wholesaler/Distributor
2%
Recreational Facilities/Services Company
1%
Consumer Goods Company
1%
Transportation Company
1%
Marketing Services Firm
1%
Pharma/Biotech Company
1%

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Product Categories

Product Categories
AI Development Platforms
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Google Vertex AI Reviews Summary
Author infoRatingReview Summary
Technology Consultant at Beta Information Technology4.5We utilize Google Vertex AI for diverse use cases, leveraging its customization and easy integration. Despite needing user interface improvements, it's superior in deployment and integration compared to other platforms like Quantum AI, requiring minimal infrastructure setup.
IT Senior Manager at a tech services company with 10,001+ employees4.0We use Google Vertex AI for chatbots and AI projects, valuing its LLM and AutoML features. However, customer support needs improvement, and managing multiple indexes is challenging. We transitioned from Oracle to Azure and Google Cloud for AI solutions.
Trainee Decision Scientist at a tech services company with 1,001-5,000 employees4.5In my research on Google Vertex AI, I found that it effectively supports Retrieval-Augmented Generation (RAG) use cases with integrated GCP data storage and ChromaDB support. Its features surpass Azure, and it offers a unified platform compared to H2O AI.
Chief Information Officer (CIO) at a tech services company with 51-200 employees4.0We explored Google Vertex AI as an alternative to Gurobi for machine learning optimization. Despite its user-friendly features, allowing non-specialists to use it effectively, we found it lacking in optimization compared to Gurobi, leading us to eventually discontinue its use.
Founder & CEO at JUMPSTARTNINJA TECHNOLOGIES LLP4.0I use Google Vertex AI for document summaries and keyword detection, finding its analytics more accurate than Azure OpenAI. Despite needing an improved knowledge base for chat solutions, it offers substantial ROI for enterprise-grade customers with targeted implementations.
Principal Data Architect at a computer software company with 201-500 employees4.5I find Google Vertex AI easy to use with strong integration across Google tools, though AutoML processing times and the UI could improve. Its performance with Gemini and end-to-end capabilities give it an edge over other platforms.
Senior Data Scientist at Breuninger4.5I use Google Vertex AI for testing and deploying machine learning models. Its valuable Feature Store simplifies collaboration, and its monitoring feature enhances reliability. Previously, I used Microsoft Azure, but I prefer the simplicity of Google’s offering.
CEO at Les Roches International School of Hotel Management4.5I primarily use Vertex AI for processing large documents using LLM models, as it offers multiple libraries and smooth deployment without extensive coding. Although previously using Azure OpenAI and AWS AI, I prefer Vertex AI due to its simplicity and payment flexibility.