Try our new research platform with insights from 80,000+ expert users
Google Vertex AI Logo

Google Vertex AI pros and cons

Vendor: Google
4.2 out of 5
Badge Ranked 1

Pros & Cons summary

Buyer's Guide

Get pricing advice, tips, use cases and valuable features from real users of this product.
Get the report

Prominent pros & cons

PROS

Vertex AI eliminates the need for extensive coding with multiple libraries.
Vertex AI monitoring feature is essential for model performance tracking and understanding results.
Model garden and endpoints in Vertex AI allow easy deployment and usage of models.
Vertex AI integrates AutoML features for seamless machine-learning workflows.
Vertex AI offers superior deployment, configuration, and integration compared to other AI platforms.

CONS

Google Vertex AI would benefit from the inclusion of image generators and text-to-speech functionalities in the future.
There is a need to improve the training time of Google Vertex AI, especially when handling large datasets.
Enhancing the knowledge base could provide better insights, requiring an expansion of information sources.
Utilizing Google Vertex AI effectively demands significant domain knowledge and proficiency in machine learning frameworks and Python programming.
The technical documentation for Google Vertex AI is difficult to access and utilize.
 

Google Vertex AI Pros review quotes

TuPhan - PeerSpot reviewer
Nov 28, 2023
We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for training machine learning models. The AI model registry in Vertex AI is crucial for cataloging and managing various versions of the models we develop. When it comes to deploying models, we rely on Google Cloud's AI Prediction service, seamlessly integrating it into our workflow for real-time predictions or streaming. For monitoring and tracking the outcomes of model development, we employ Vertex AI Monitoring, ensuring a comprehensive understanding of the model's performance and results. This integrated approach within Vertex AI provides a unified platform for managing, deploying, and monitoring machine learning models efficiently.
reviewer1285629 - PeerSpot reviewer
Aug 1, 2024
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.
Hamada Farag - PeerSpot reviewer
Jun 23, 2025
Google Vertex AI is better for deployment, configuration, delivery, licensing, and integration compared to other AI platforms.
Learn what your peers think about Google Vertex AI. Get advice and tips from experienced pros sharing their opinions. Updated: July 2025.
861,490 professionals have used our research since 2012.
Ivy W. - PeerSpot reviewer
Aug 31, 2023
The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten.
Serge Dahdouh - PeerSpot reviewer
Aug 16, 2023
Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding.
Srikar Kumar - PeerSpot reviewer
Nov 9, 2023
It provides the most valuable external analytics.
Kashif Yaseen - PeerSpot reviewer
Nov 11, 2024
Vertex comes with inbuilt integration with GCP for data storage.
Suresh Ayyavoo - PeerSpot reviewer
Nov 24, 2023
Google Vertex AI is an out-of-the-box and very easy-to-use solution.
Rajeev-Dave - PeerSpot reviewer
Sep 13, 2024
The integration of AutoML features streamlines our machine-learning workflows.
Kartik Singh - PeerSpot reviewer
Jul 19, 2024
The most valuable feature we've found is the model garden, which allows us to deploy and use various models through the provided endpoints easily.
 

Google Vertex AI Cons review quotes

TuPhan - PeerSpot reviewer
Nov 28, 2023
I believe that Vertex AI is a robust platform, but its effectiveness depends significantly on the domain knowledge of the developer using it. While Vertex AI does offer support through the console UI in the Google Cloud environment, it is better suited for technical members who have a deeper understanding of machine learning concepts. The platform may be challenging for business process developers (BPDUs) who lack extensive technical knowledge, as it involves intricate customization and handling numerous parameters. Effectively utilizing Vertex AI requires not only familiarity with machine learning frameworks like TensorFlow or PyTorch but also a proficiency in Python programming. The complexity of these requirements might pose challenges for less technically oriented users, making it crucial to have a solid foundation in both machine learning principles and Python coding to extract the full value from Vertex AI. It would be beneficial to have a streamlined process where we can leverage the capabilities of Vertex AI directly through the BigQuery UI. This could involve functionalities such as creating machine learning models within the BigQuery UI, providing a more user-friendly and integrated experience. This would allow users to access and analyze data from BigQuery while simultaneously utilizing Vertex AI to build machine learning models, fostering a more cohesive and efficient workflow.
reviewer1285629 - PeerSpot reviewer
Aug 1, 2024
I think the technical documentation is not readily available in the tool.
Hamada Farag - PeerSpot reviewer
Jun 23, 2025
It is not completely mature and needs some features and functions. The interface needs to be more user-friendly.
Learn what your peers think about Google Vertex AI. Get advice and tips from experienced pros sharing their opinions. Updated: July 2025.
861,490 professionals have used our research since 2012.
Ivy W. - PeerSpot reviewer
Aug 31, 2023
The solution is stable, but it is quite slow. Maybe my data is too large, but I think that Google could improve Vertex AI's training time.
Serge Dahdouh - PeerSpot reviewer
Aug 16, 2023
It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions.
Srikar Kumar - PeerSpot reviewer
Nov 9, 2023
I've noticed that using chat activity often presents a broader range of options and insights for a well-constructed question. Improving the knowledge base could be a key aspect for enhancement—expanding the information sources to enhance the generation process.
reviewer2145921 - PeerSpot reviewer
Jul 9, 2025
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.
Kashif Yaseen - PeerSpot reviewer
Nov 11, 2024
I'm not sure if I have suggestions for improvement.
Suresh Ayyavoo - PeerSpot reviewer
Nov 24, 2023
Google Vertex AI is good in machine learning and AI, but it lacks optimization.
Rajeev-Dave - PeerSpot reviewer
Sep 13, 2024
Both major systems, Azure and Google, are not yet stabilized, especially their customer support.