Vertex AI Reviews & Product Details

Vertex AI makes it easy to try out the latest GenAI models, integrate them into applications, build our own models and expose them as endpoints. I've been using Vertex AI for more than 5 years now for variety of applications such as mobile apps that have image recognition, chat capabilities to web apps that summarize and extract meaningful content.

Vertex AI acts as a command center for all AI applications and is always updated with latest progress in the field of AI, especially Gen AI Review collected by and hosted on G2.com.

Learning vertex AI was a bit tough when I got started. Billing costs with features and the usage was tricky to estimate beforehand. Luckily over the years they have made it easier to try out the features and with help of Google Cloud Skill boost, we are able to implement and learn the new features without worrying to much about the costs. Review collected by and hosted on G2.com.

What I like most about Vertex AI is its unified ecosystem. It brings data preparation, model training, and deployment together in a single, cohesive workflow, which makes the overall process feel smooth and well connected. The Model Garden is a real highlight for me, offering easy access to over 150 foundation models such as Gemini and Claude, and it noticeably speeds up building and delivering production-grade AI solutions. Review collected by and hosted on G2.com.

I’m not a fan of the complex pricing structure, especially since there’s no “scale-to-zero” option for endpoints. That can leave you paying higher costs even when services are idle. On top of that, the learning curve feels steep, and the documentation is fragmented, which makes it harder for smaller teams—or anyone new to the Google Cloud ecosystem—to get up to speed and use it confidently. Review collected by and hosted on G2.com.

I like Vertex AI's easy infrastructure, which makes deploying production-grade software very straightforward and allows you to start quickly. The community support is great; if you run into trouble, you can search on Google and find help easily. It’s also very easy to use, considering the complexity of tasks it can handle. Review collected by and hosted on G2.com.

There's a slight issue when giving prompts; it's hard to understand whether I'm giving the system prompt for the product or for my own use case, leading to confusion. I think there's a misunderstanding there. Also, there are multiple APIs to configure, and it's unclear whether they are being charged or not, so I think API management could be better. Review collected by and hosted on G2.com.

The all in one ecosystem integration, we can make data pipelines as well as train data models in the same system without having to move them to another one. and we can also get access to other open source models along with google's gemini and foundational models Review collected by and hosted on G2.com.

If compute is not configured correctly, it can tun endlessly incurring hight costs, also its billing is very complex. And also, for my individual projects, it is very costly. Review collected by and hosted on G2.com.

I used Vertex AI for image recognition and generation, and I really like that it provides everything in one place for data preparation, training models, and deployment. It's the best tool for ML projects, and it works smoothly. We use it mainly for deployment and training models, and I love that aspect as it makes our work fast and easy. As Google developers, our work is in Google Cloud, so Vertex AI is a major tool for us to train models. Its updated GPT makes the work fast and easy, and we feel comfortable using it. It's also user-friendly, and even a beginner can learn the setup easily, which makes it feel unique. Review collected by and hosted on G2.com.

We use Vertex AI only in Google Cloud, and while it works fine there, when connecting to some other things, we feel that we don't get proper output. It would be better if there was more interaction with the AI to make it more user-friendly. Review collected by and hosted on G2.com.

I like that Vertex AI allows me to build, train, and deploy machine learning models without dealing with heavy infrastructure. It efficiently handles infrastructure and environment setup, saving me from the hassle of managing servers or configurations. I appreciate that all things are in one place, including data prep, model training, experimentation, deployment, and monitoring, which are all connected. Experimentation feels smoother because I can explore data, test models, and iterate quickly in a managed notebook environment without worrying about setup or performance issues. It's reliable and saves a lot of time when moving back and forth between analysis and modeling. Review collected by and hosted on G2.com.

There are a lot of features packed in, which is powerful, but it also means simple tasks sometimes take longer to find or configure than expected. A more streamlined experience for common workflows would help. Review collected by and hosted on G2.com.

I like Vertex AI's unified and production-ready approach to machine learning. It brings model training, deployment, monitoring, and access to foundation models into a single platform, significantly reducing operational overhead. The scalability, tight integration with Google Cloud services, and support for both custom models and generative AI make transitioning from experimentation to real-world deployment easy. I also appreciate how it solves the challenge of managing the end-to-end machine learning lifecycle in production, reducing the complexity of model training, deployment, scaling, and monitoring. Its unified platform also addresses infrastructure management, experiment tracking, and collaboration between data science and engineering teams, enabling faster and more reliable delivery of AI solutions. Plus, the initial setup was pretty smooth and easy. Review collected by and hosted on G2.com.

While Vertex AI is powerful, it can feel complex for new users due to the number of services and configuration options involved. Costs can also be difficult to predict, especially when running experiments or scaling models. I’d like to see improvements in onboarding and usability, especially clearer guidance for transitioning from experimentation to production. More intuitive cost visibility and real-time spend alerts would help teams manage budgets more effectively. Review collected by and hosted on G2.com.

I use Vertex AI as an all-in-one place to handle our machine learning work from start to finish, which lets us prepare data, build and train models, test different ideas, and put models into production in a more organized way. It makes teamwork smoother as data scientists and engineers can work from the same workflows without a lot of back-and-forth. Vertex AI solves the problem of fragmented and time-consuming machine learning workflows by bringing these steps together in one place, reducing manual effort and confusion. It keeps experiments, versions, and results organized, which makes collaboration easier. I appreciate how it simplifies monitoring and managing models once they’re in production with built-in tools for tracking model performance, detecting drift, and analyzing predictions. The ability to run multiple experiments in parallel is extremely helpful, and the platform’s collaborative features allow team members to work together, share insights, and maintain consistency. Vertex AI feels reliable and well-structured, helping the team work faster and with more confidence. Review collected by and hosted on G2.com.

While I really value what Vertex AI offers, there are a few areas where it could be smoother. Sometimes the interface and navigation feel a bit overwhelming, especially when juggling multiple complex projects or if you’re just getting started. Some of the advanced features, like customizing pipelines or tweaking deployment settings, have a steep learning curve and require digging through documentation, which can slow things down. Certain resource-heavy workflows can also feel a bit slow unless you optimize carefully. The reporting and visualization tools are useful, but I wish they were a bit more intuitive and flexible so I could get insights faster without relying on external tools. Overall, making navigation simpler, guiding users more through advanced features, and improving performance and reporting flexibility would make Vertex AI even easier and more efficient to use. Review collected by and hosted on G2.com.

I really like Vertex AI for its JupyterLab environment, which is incredibly useful for development. Terminal access and the real-time file manager provide great flexibility, especially with their upload and download options. The cloud platform is convenient for connecting and using various resources needed for machine learning and deep learning model development. I find the different RAM, memory, GPU, and CPU compute options essential for my tasks. I appreciate the built-in editors for JSON and xlsx files because I don't need to manually install different extensions for viewing and editing these formats. Overall, the simplicity of the initial setup stood out to me. Review collected by and hosted on G2.com.

The UI can be improved. It does not support autocomplete or code suggestions. It lacks features available in VS Code IDE like multi-cursor support and rich code editing, which could enhance productivity. The UI only offers a white color option, and it could benefit from a variety of color options. Extensions for editing Vertex AI hosted codebases directly from VS Code would be helpful. Review collected by and hosted on G2.com.

I really enjoy the image and video models available in Vertex AI, which I think are the best and have no comparison due to their attention to detail. The platform helps automate daily tasks, like email sorting, sending, and replying in my tone, which is a significant convenience. I love how it generates faceless videos with great attention to detail. Using it with GCP cloud architecture is a game-changer for projects, especially when working with custom models and productionizing them effortlessly through the MLOps Pipeline. The collaboration with multiple tools to scale and productionize applications is quite unique, and integrating Vertex AI with tools like BigQuery enhances its power. Review collected by and hosted on G2.com.

I usually face issues with the pricing, and at many places, it feels overpriced or costly compared to competitors like Bedrock. I would suggest making billing easier to follow, so it's clear where the costs are coming from. It's currently calculated on multiple dimensions, which makes it super complex. Review collected by and hosted on G2.com.