Question 1

What's the difference between Vertex AI and AutoML?

Accepted Answer

AutoML is a capability for training models with minimal manual feature engineering and model selection. Vertex AI is the broader platform that includes AutoML plus custom training (your own code), pipelines, feature store, model registry, deployment endpoints, monitoring, and MLOps tooling.

Question 2

When should I use Vertex AI?

Accepted Answer

Use Vertex AI when you want a managed way to take models from experimentation to production—especially if you need repeatable training (pipelines), scalable training infrastructure (GPUs/TPUs), governed model deployment (endpoints, IAM), and monitoring for drift and performance. If you only need simple predictions and already have a model hosted elsewhere, Vertex AI may be more than you need.

Question 3

How much does Vertex AI cost?

Accepted Answer

Pricing is usage-based and depends on what you run: training compute (CPU/GPU/TPU hours), prediction (online endpoint instance hours and request volume, or batch prediction compute), data processing in pipelines, storage (artifacts, datasets), and optional components like feature store. Costs vary widely by model size, traffic, and hardware; the best estimate comes from sizing training jobs and endpoint capacity and reviewing the current Vertex AI pricing page for your region.

Vertex AI

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