Vertex AI Reviews & Product Details

What I like most about Vertex AI is how it unifies the entire machine learning workflow — from data preparation and training to deployment and monitoring. We’ve used it to streamline our ML pipeline, and the integration with BigQuery and Google Cloud Storage makes data handling incredibly efficient. The UI is intuitive, and it’s easy to move between no-code experimentation and full-scale custom model development. Review collected by and hosted on G2.com.

Some advanced configurations can be complex at first, especially for setting up custom training jobs or tuning hyperparameters. Pricing can also become high with frequent model retraining. Documentation is thorough but sometimes fragmented across different Google Cloud sections, which can slow down setup for new users. Review collected by and hosted on G2.com.

What I like the most is how easy it is to manage the full machine learning workflow in one place. From training to deployment, everything is well integrated with other Google Cloud tools. The interface is simple, and automation features save a lot of time when handling multiple models. Review collected by and hosted on G2.com.

Sometimes the pricing can be a bit confusing, especially when working with large datasets or long training jobs. Also, documentation could go deeper in some areas for beginners. It’s powerful, but new users might need some time to get used to it. Review collected by and hosted on G2.com.

Vertex AI makes it surprisingly simple to move from experimentation to production. I really value the integration with the rest of Google Cloud services—BigQuery, Dataflow, and Cloud Storage connect seamlessly, which saves a lot of time when preparing and deploying models. As a cloud enthusiast, I also appreciate how Vertex AI offers managed Jupyter notebooks, AutoML, and pre-trained APIs that lower the barrier for teams of different technical levels. It feels like a true end-to-end ecosystem for machine learning and AI. Review collected by and hosted on G2.com.

The learning curve can be steep at the beginning, especially for those new to Google Cloud’s way of organizing resources. Pricing transparency could also improve; costs can ramp up quickly if you don’t set up quotas or monitoring. Some features, like advanced pipeline orchestration or custom training jobs, feel a bit overwhelming without strong documentation or prior ML Ops experience. Review collected by and hosted on G2.com.

I like its seamless integration across Google Cloud’s ecosystem, which makes the entire machine learning lifecycle—data prep, training, deployment, and monitoring—feel unified and efficient.

Whether you're a beginner using AutoML or an experienced data scientist deploying custom models, Vertex AI supports both without forcing you into one workflow.

Overall, it simplifies complex ML workflows without sacrificing flexibility or performance. Review collected by and hosted on G2.com.

What I don’t like about Vertex AI is that it can feel a bit overwhelming at first, especially if you’re new to Google Cloud or machine learning platforms in general.

There are a lot of tools, settings, and options—sometimes it’s hard to know where to start or what the “right” way to do something is. The documentation is good, but not always beginner-friendly, and some features feel hidden or not well-explained.

Also, the pricing can be a bit tricky to estimate upfront. You have to really pay attention to what resources you’re using (like training jobs, storage, notebooks, etc.) or you might end up surprised by the bill. Review collected by and hosted on G2.com.

The best part for me is how everything is integrated. I can train, test, and deploy models all inside the same platform. It saves time switching between tools. The dashboard is clean, and the connection with Google Cloud services makes the workflow smoother. Review collected by and hosted on G2.com.

Sometimes the pricing can be confusing, especially when you’re still learning how the resources are used. Also, some features need a bit of technical background to set up properly, which can be a bit frustrating at first. Review collected by and hosted on G2.com.

What I like best about Vertex AI is how it centralizes everything related to machine learning in one place. It’s easy to train, deploy, and monitor models without jumping between different tools. The integrations with Google Cloud services also make it very convenient to scale and automate workflows. Review collected by and hosted on G2.com.

Sometimes the interface feels a bit complex at first, especially when setting up pipelines or permissions. The pricing can also be tricky to estimate accurately for larger projects, which might be a concern for smaller teams. Review collected by and hosted on G2.com.

I appreciate the configuration and accessibility of models in Vertex AI. It is particularly beneficial when working with multiple models, as it provides a common URL for managing them, which simplifies deployment. This feature makes managing model URLs very convenient for deployment. Additionally, the initial setup of Vertex AI was very easy and efficient, which is a significant advantage. Review collected by and hosted on G2.com.

I am frustrated with the guard features in Vertex AI because they do not work well, particularly when dealing with certain types of content like armors and guards. I have encountered numerous issues related to this. Additionally, I am troubled by its inaccurate detection of PII (Personally Identifiable Information). Despite there being no PII data present, the system still flags it, which causes unnecessary complications and is a significant concern for me. Review collected by and hosted on G2.com.

I find Vertex AI really easy to use, which is a big plus for me, especially when I am navigating through AI development tasks. The simplicity in its design attracted me to choose it over other vendors, and it lived up to my expectations. I also appreciate the generous 300 free credits, which allowed me to explore its features without a financial commitment initially. This is a very good tool for AI development, and the availability of numerous AI models and tools, including many open models, adds to its appeal. Furthermore, I value the integration with OpenAI models, which is a vital component for my work. These aspects collectively make Vertex AI a favored choice for my AI projects. Review collected by and hosted on G2.com.

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Vertex AI make building and developing machine learning models feel almost effortless, I can easily go from experimenting in notebooks to production pipelines without juggling multiple tools, Everything from dataset versioning to model evaluaation, lives under one roof, and that simplicity keeps my workflow clean and fast. The mpdel fit so well with our existing tools.. Review collected by and hosted on G2.com.

The learning curve at the start is a bit casual, especially around setting up environments and understanding pricing. Review collected by and hosted on G2.com.

Google's vertex aí positions itself as a comprehensive platform for machine learning lifecycle and data training and preparation to its core strength across google cloud and as it has a very user friendly interface with lots of features available to make our work easy as we can easily implement our work effortlessly and make out awhile learning more easy and continent by the usage of 4-5 times a day with easy integration and great customer support. Review collected by and hosted on G2.com.

its learning curve is complex and doesn't give simplified workflow and as it is very costly it unpredicts pay as you go pricing and its documentation fragmentation is different across various google cloud sections which can slow down initial setup and troubleshooting. Review collected by and hosted on G2.com.