Overview
Product Information on Vertex AI
What is Vertex AI?
Vertex AI Pricing
Overall experience with Vertex AI
“Enhanced MLOps Features in Vertex AI Offset by Steady Costs From Idle Endpoints”
“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.
Company Details
Do You Manage Peer Insights at Google?
Access Vendor Portal to update and manage your profile.
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
Vertex 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.
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 integration with the rest of the GCP is the biggest strength. Managing models and endpoints works well for conversational and agent-based use cases. You have a lot of flexibility to mix managed models with your own logic, combining different LLMs as well (Gemini and 3rd parties). In addition, the managed infrastructure saves time and money.
The 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.
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
Costs can grow faster than expected if not managed carefully, especially when testing voice agents based on end-points. Not the best user experience for not advanced players
Top Vertex AI Alternatives
Peer Discussions
Vertex AI Reviews and Ratings
- 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. - Manager Of Data And Analytics1B-10B USDEnergy and UtilitiesReview Source
Vertex AI Excels In Agent-Based Use Cases But Learning Curve Remains Steep
We have used Vertex AI to test AI agents including voice, customer service agents rather than classsic ML projects. Overall I believe it is a solid and flexible platform, specially if you are already working on GCP. Not simple to master though. - Project Lead<50M USDServices (non-Government)Review Source
Integrated Machine Learning Workflow Streamlined by Vertex AI With Google Cloud Synergy
I used Vertex AI for machine learning projects and it is a very good and reliable platform. It makes it easier to build and deploy models all in one place. It supports both coding and low code options so depending on the project we can write custom code or use autoML Overall i believe it is a powerful platform for ML projects, especially if you are already working with Google Cloud - DIRECTOR OF INFORMATION MANAGEMENT50M-1B USDIT ServicesReview Source
Unified Data Workflow Benefits Offset by Outdated Documentation and Endpoint Limitations
Vertex AI has the advanced level of security which provides me a peace of mind also i can connect it with other services and it has scalable training and compute so it depends on the workload. - Principal Product Manager50M-1B USDHealthcare and BiotechReview Source
Enterprise grade ML and AI tooling
A good ML/ML Ops platform from Google that offer unified application development tool that is enterprise grade with good governance