Question 1

What's the difference between Vertex AI and Google AutoML?

Accepted Answer

AutoML is a set of tools that helps you train models with minimal code (for example, for vision, tabular, or text tasks). Vertex AI is the broader platform that includes AutoML plus custom training, managed endpoints for deployment, pipelines for automation, model registry, monitoring, and other MLOps features. In practice, AutoML is a capability you can use inside Vertex AI.

Question 2

When should I use Vertex AI?

Accepted Answer

Use Vertex AI when you want a managed, end-to-end way to build and run ML on Google Cloud—especially if you need repeatable training pipelines, scalable training on GPUs/TPUs, managed online prediction endpoints, model monitoring, or a central place (registry) to manage models. It’s a good fit when you’re moving from experiments to production or when multiple teams need consistent MLOps practices.

Question 3

How much does Vertex AI cost?

Accepted Answer

Vertex AI pricing is usage-based and depends on what you run. Common cost drivers include: training compute (CPU/GPU/TPU type and hours), hyperparameter tuning trials, online prediction endpoint uptime and autoscaled replicas, batch prediction resources, data processing in pipelines, storage for artifacts/models, and any connected services (like BigQuery, Cloud Storage, or networking). Costs vary widely by model size and traffic, so estimating usually starts with expected training hours, endpoint instance types, and prediction volume.

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