Overview
Product Information on Vertex AI
What is Vertex AI?
Vertex AI Pricing
Overall experience with Vertex AI
“Managed Infrastructure and Google Cloud Integration Reduce Overhead in Vertex AI”
“Enterprise grade ML and AI tooling”
About Company
Company Description
Googlers is a company that creates products intended to create opportunities for an extensive audience, regardless of their location across the globe. The company values diverse perspectives, imaginations and non-conformity to predefined norms and impossibilities. The goal is to build products while incorporating uniqueness of each individual involved in this process, aiming to make their products accessible and useful to all.
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User Sentiment About Vertex AI
Reviewer Insights for: Vertex AI
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Vertex AI Likes & Dislikes
What I like most about Vertex AI is how well it brings the entire machine learning lifecycle together in one place. You can go from data preparation to training, deployment, and ongoing monitoring without having to stitch together a long list of separate tools. That end-to-end flow makes a real difference when youre trying to move beyond experiments and actually run models in production. I also appreciate how deeply its integrated with the rest of Google Cloud. Using BigQuery, Cloud Storage, and managed pipelines alongside Vertex AI feels natural and reduces a lot of operational overhead. On top of that, the managed infrastructure takes care of scaling, reliability, and updates, which lets teams focus more on model quality and business impact rather than platform maintenance.
As a member of the tech team, I like the reliability that comes from Google. It also has a good ML base with foundational models to guide you through AI app development
The easy access to top foundation models like Gemini and PALM via the model garden, combined with the ability to fine-tune them, is a game changer for rapid development. Also, i like the fact that we have feature stores, pipelines and model monitoring all under a single roof which significantly reduces our operational overhead for maintaining these models in production.
The biggest drawback for me is the complexity and learning curve, especially for teams that arent already familiar with Google Cloud. While Vertex AI is powerful, it can feel overwhelming at first, with many concepts, configurations, and GCP-specific patterns you need to understand before becoming productive. Cost visibility is another pain point. Its easy to spin up experiments, training jobs, or endpoints, but without careful monitoring, costs can add up faster than expected. I also find some parts of the UI and documentation inconsistentcertain advanced features are clearly built for experienced ML engineers, and the guidance isnt always as clear or practical as it could be for real-world use cases. Overall, none of these are deal-breakers, but they do mean Vertex AI rewards experienced teams more than beginners.
It is harder to use than some of its competitors and requires a steeper learning curve It is not app centric as it focusses mainly on ML platform so devs need additional hand holding on agentic deployments on app Service and suport can be improved
Complexity in pricing. It can be difficult to accurately estimate costs for complex training pipelines or generative AI usage beforehand. Billing metrics aren't always immediately understood and they need improvement. I would say that for new users, configuring the correct permissions and service agents for projects would be tricky. Would definitely lead to permission errors during the initial setup.
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Vertex AI Reviews and Ratings
- PLATFORM ENGINEER50M-1B USDIT ServicesReview Source
Managed Infrastructure and Google Cloud Integration Reduce Overhead in Vertex AI
Overall, my experience with Vertex AI has been very strong. The platform is mature, well-integrated with the broader Google Cloud ecosystem, and clearly designed for teams that want to move models from experimentation into production with minimal friction. Features like managed training, pipelines, model registry, and monitoring work reliably and save a lot of operational effort. That said, it does come with a learning curve, especially for teams new to GCP, and costs can escalate if usage isn’t carefully managed. Because of that, I wouldn’t call it “truly exceptional” in every scenario, but for organizations already invested in Google Cloud and serious about production-grade ML, Vertex AI delivers an outstanding overall experience. - Application Developer10B+ USDTransportationReview Source
My comprehensive toolkit for scaling Enterprise GenAI and MLOps
My overall experience with Vertex AI has been impressive so far. It effectively centralizes the entire machine learning lifecycle, making it much easier to transition from experimentation to production. The ability to access foundational models like Gemini alongside custom training tools in a single interface is a huge advantage to our team. While there is a learning curve, the platform's seems reliable and there is seamless integration with the rest of the Google Cloud ecosystem making it a powerful tool for scaling AI applications. And we have a lot of them :) - MACHINE LEARNING/ AI ENGINEER<50M USDSoftwareReview Source
Enhanced MLOps Features in Vertex AI Offset by Steady Costs From Idle Endpoints
Vertex AI allows a finer degree of control over the complete workflow from training to deployment and monitoring. Vertex AI integrates well with Google Cloud Storage and there is an improvement in the performance of most out-of-the-box models compared to the older AutoML models. Pricing is more complex as it varies by region and tiers (based on usage). Additionally, costs are computed on node hours, so charges are incurred even when the Vertex AI endpoints are idle. - CUSTOMER SERVICE & SUPPORT ASSOCIATE<50M USDServices (non-Government)Review Source
Powerful, enterprise-grade AI platform with deep GCP integration, but success depends on upfront planning and cost discipline
Vertex AI has strong integration with Google cloud ecosystem and its robust support for end to end ML workflows. What has worked well is the unified platform for training. deploying and monitoring models, along with seamless access to managed services, scalable infrastructure, and build-in MOLps capabilities. Auto ML and foundation model support are also valuable for accelerating deployment. However, the platform has a steep learning curve, especially for teams new to GCP, and costs can be difficult to predict without careful monitoring. Additionally, debugging and observability across complex pipelines can sometimes feel less intuitive than expected - Application Developer<50M USDSoftwareReview Source
A Scalable and Feature-Rich ML Platform Best Suited for Production-Ready Workloads
Vertex AI is a powerful, unified platform for building, training, deploying, and managing machine learning models.Its integration with other Google Cloud services makes infrastructure setup and scaling much easier. The tools for data preprocessing, model evaluation, and deployment pipelines are robust and well documented. On the downside - the learning curve can be steep for beginners and some features - especially around custom training, resource configuration, or advanced pipelines - require careful setup.Overall, Vertex AI significantly streamlines ML workflows if you invest time to learn it properly.