Vertex AI Reviews & Product Details

What I like most about Vertex AI is that it brings the entire machine learning workflow together in a single platform. From data preparation and training to deployment and ongoing monitoring, we can manage everything smoothly without having to juggle multiple tools. We’ve been using it for several years to build and deploy ML models in production, and its integration with other Google Cloud services, such as BigQuery and Cloud Storage, makes data handling and movement much easier. The AutoML features and pre-built pipelines also save a lot of time, so our team can spend more energy on experimentation and improving model performance instead of setting up and maintaining infrastructure. Review collected by and hosted on G2.com.

One thing I dislike about Vertex AI is that it can feel overwhelming for new users because of the sheer number of features and services it offers. Although it’s very powerful, setting up custom pipelines or debugging more complex workflows can sometimes require deep knowledge of Google Cloud and core ML concepts. On top of that, costs can add up quickly if resources aren’t managed carefully, especially when training large models or running multiple experiments in parallel. Review collected by and hosted on G2.com.

I use Vertex AI for building, training, and deploying machine learning models, and I love how it solves the problem of managing complex ML workflows. It reduces the effort needed to build, train, and deploy models, with everything centralized, making automation easier and scaling faster. This means I can focus more on building better models instead of worrying about infrastructure. What I like most is how it combines training, deployment, and monitoring in one place. The integration with Google Cloud services works really well, scaling is smooth, and managed pipelines save a lot of time. Overall, it makes ML development more efficient and reliable. Review collected by and hosted on G2.com.

The learning curve is steep, documentation can be confusing in places, and costs are not always clear. Better tutorials, simpler UI for common tasks, and more transparent pricing would improve the experience. Review collected by and hosted on G2.com.

What I like most about Vertex AI is the model garden and the ability to quickly and easily experiment and test out different generative models. Review collected by and hosted on G2.com.

I find the complexity of Vertex AI quite overwhelming. There's just so much unnecessary stuff bombarding you immediately when you open it up. There are too many options, which just become noise and take away energy and time to figure out their actual purpose. It feels like everything is just categorized under different names, making it problematic and overcomplicated. The initial setup also feels unnecessarily complicated. I like things to be simplified because, even as an advanced technical user, I often get lost in all the noise, and it takes away from my clear targets and goals. Review collected by and hosted on G2.com.

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.

I use Vertex AI Pipeline to predict delivery days for my ecommerce. What I appreciate is having everything as a one-stop shop, which has made my life easier as someone semi-technical. Since I have a very extensive relationship with Google products, it works well for me. I like the Google Cloud Platform and feel that the UI and general design philosophy make it easier for me to use, even without a heavy data science background. The integrated development, configuration, licensing, and integration in one spot is really convenient. Review collected by and hosted on G2.com.

I find the analytics and accuracy lacking, with a lot of hallucination happening, especially during my first trials with bigger data models. It's crucial to have extremely precise data to get better output. The user interface was definitely a challenge for me initially. Even though I like Google's design philosophy, I kept getting lost and had to frequently search online to figure things out. Moving from A to Z wasn't intuitive. Review collected by and hosted on G2.com.

What stands out most about Vertex AI is how it unifies the entire ML lifecycle in one managed environment. Data prep, training, hyperparameter tuning, model registry, deployment, monitoring, and now foundation model access through Gemini are all integrated. The tight coupling with BigQuery and Cloud Storage reduces data friction significantly.

I also appreciate the managed infrastructure. You get scalable training on GPUs and TPUs without wrestling with low level provisioning. Experiment tracking, model versioning, and endpoint autoscaling are built in, which makes it production friendly. For teams deploying LLM powered apps, the generative AI APIs and evaluation tooling are particularly strong. Review collected by and hosted on G2.com.

The learning curve can be steep. There are many moving parts across projects, service accounts, IAM roles, networking, and quotas. For smaller teams or solo developers, initial setup can feel heavy.

Cost visibility can also be challenging. Training jobs, prediction endpoints, storage, logging, and networking all accumulate charges separately. Without strong monitoring, it is easy to overspend. The UI is powerful but sometimes inconsistent across different services, and debugging distributed training jobs is not always straightforward. 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.

What I like most about Vertex AI is how it brings the entire machine learning lifecycle into one well-organized platform. It simplifies everything from data preparation and model training to deployment and monitoring, which makes even complex ML workflows easier to manage. The tight integration with Google Cloud services adds real value, especially when it comes to scalability, security, and performance. Overall, Vertex AI strikes a strong balance between the flexibility advanced users need and the ease of use teams want when building reliable, production-ready machine learning without a lot of extra overhead. Review collected by and hosted on G2.com.

What I don’t like about Vertex AI is that it can feel overwhelming at the beginning, especially for users who are new to Google Cloud or ML platforms. The learning curve is steep, and it takes time to understand how all the services, permissions, and pricing pieces fit together. The documentation can also feel a bit fragmented, which makes it harder to find clear, end-to-end guidance for specific use cases. On top of that, costs aren’t always easy to predict without close monitoring, which can be challenging for smaller teams or budget-conscious projects. Review collected by and hosted on G2.com.