Question 1

What's the difference between SageMaker and Amazon Bedrock?

Accepted Answer

SageMaker is for building, training, tuning, and deploying your own ML models (including classical ML and deep learning) and managing the full ML lifecycle. Amazon Bedrock is primarily for using and customizing foundation models (generative AI) via APIs without managing training infrastructure. In practice, teams often use Bedrock for LLM apps and SageMaker for custom training, fine-tuning workflows, and broader MLOps needs.

Question 2

When should I use SageMaker?

Accepted Answer

Use SageMaker when you need a managed, end-to-end ML platform on AWS—especially if you want to train models at scale, run hyperparameter tuning, deploy real-time or batch inference, manage features, track experiments, and automate CI/CD-style ML workflows. It’s a good fit when your team wants to focus on modeling and deployment rather than building and operating ML infrastructure from scratch.

Question 3

How much does SageMaker cost?

Accepted Answer

SageMaker pricing is usage-based. Common cost drivers include: (1) compute instances for notebooks, training jobs, and inference endpoints (instance type and hours), (2) storage (for data, model artifacts, and logs), (3) data processing jobs, (4) optional components like Feature Store, Model Monitor, and Pipelines, and (5) network/data transfer depending on architecture. Costs vary widely by workload; the main levers are instance sizing, autoscaling, endpoint uptime (24/7 vs on-demand), and how often you retrain.

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