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”
“Leveraging Machine Learning Efforts: The Power of Data Science Tools”
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.
Company Details
Key Insights
A Snapshot of What Matters - Based on Validated User Reviews
User Sentiment About Vertex AI
Reviewer Insights for: Vertex AI
Deciding Factors: Vertex AI Vs. Market Average
Performance of Vertex AI Across Market Features
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.
Read Full ReviewVertex AI is easy to use and integrates well with Google Cloud Services, hence, preprocessing of data, and model training and deployment can happen in one place. This includes integration with BigQuery and Cloud Storage. We have access to many pre-trained models for AutoML and support for custom models as well. The custom training options provide great flexibility and granular fine-tuning. I was also impressed by the smooth production-grade MLOps capabilities like pipelines, versioning and experiment tracking. This made iterative testing and deployment at scale feasible.
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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.
Read Full ReviewThe learning curve can be hard for new users and the navigation in the interface is not the easiest one.
Read Full ReviewThe biggest issue is cost unpredictability. Deployed models reserve nodes, and as such, idle Vertex AI endpoints also incur costs. Since you pay for node-hours continuously, even low-traffic endpoints generate significant charges. Training jobs and endpoints also have their own billing meter, which is complicated by varied prices across tiers.
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Peer Discussions
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. - 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. - SENIOR SOFTWARE ENGINEER10B+ USDTravel and HospitalityReview Source
Seamless integration with a breadth of options with Google quality
Vertex AI is a solid AI platform with a unified workflow bringing together different disciplines (data engineering, data science, ML engineering). The progress in Generative AI shows that Google is constantly innovating and investing in this space; their models are consistently competitive in terms of intelligence, speed, and price. The number of built-in tools in the MLOps space makes it a fully-fledged platform that is enterprise ready.