Google Cloud Vertex AI

Vertex AI integrates tightly with Google's ecosystem—BigQuery, Google Kubernetes Engine, and TensorFlow—while SageMaker is built around AWS services like S3, EC2, and IAM. Vertex AI provides access to Google's Model Garden, which includes Gemini foundation models and Google's AutoML pipeline. SageMaker offers more granular notebook and endpoint configuration and has a larger established community and third-party ecosystem.

Yes, Vertex AI's Model Garden provides access to Gemini model endpoints, and Google supports fine-tuning of certain Gemini variants through the platform. You can also access models from other providers like Anthropic's Claude and Meta's Llama through the same interface. Fine-tuning availability depends on the specific model version and Google's current API support.

Yes, Vertex AI offers AutoML for tabular data supporting classification, regression, and time-series forecasting use cases. You upload a dataset from BigQuery or Cloud Storage and Google's AutoML pipeline handles feature engineering, model selection, and hyperparameter tuning automatically. The trade-off is less control over model architecture and higher per-training cost compared to custom training jobs.

Training costs are billed per compute-hour based on the machine type (e.g., n1-standard-8) and any attached GPUs or TPUs you select, while model deployment (endpoints) is billed per node-hour the endpoint stays active. AutoML training commands a premium over custom training. Google provides a pricing calculator in the Google Cloud Console for estimating costs before launching jobs.