Question 1

What's the difference between SageMaker and AWS Bedrock?

Accepted Answer

Amazon SageMaker is a platform for building, training, tuning, and deploying your own machine learning models (including classical ML and deep learning). AWS Bedrock is focused on using and customizing foundation models (generative AI) via APIs, without managing the underlying model training infrastructure. Use SageMaker when you need full control over training and MLOps for your models; use Bedrock when you primarily want to consume or lightly customize foundation models for generative AI apps.

Question 2

When should I use SageMaker?

Accepted Answer

Use SageMaker when you need an end-to-end workflow for ML: preparing data, training models at scale, tracking experiments, automating pipelines, and deploying to real-time or batch inference. It’s a good fit when you want managed infrastructure (instead of building everything on EC2/Kubernetes yourself), need repeatable MLOps processes, or must deploy models reliably with monitoring and controlled rollouts.

Question 3

How much does SageMaker cost?

Accepted Answer

SageMaker pricing is usage-based and depends on what you run: notebook/IDE instances, training jobs (instance type and duration), hosted endpoints for real-time inference (instance type and uptime), batch transform jobs, data processing jobs, and optional features (for example, certain monitoring or governance capabilities). Costs are driven mainly by compute hours, instance size (CPU/GPU), storage, and how long endpoints stay running. A common cost-control approach is to use ephemeral training jobs, right-size instances, use auto-scaling for endpoints, and shut down idle development environments.

SageMaker

Definition

Real-World Example

Related Terms

Cloud Provider Equivalencies

Explore More Cloud Computing Terms