Question 1

What’s the difference between Amazon Bedrock and Amazon SageMaker?

Accepted Answer

Amazon Bedrock is primarily for using foundation models through managed APIs (prompting, agents, and retrieval features) without managing infrastructure. Amazon SageMaker is a broader machine learning platform for building, training, tuning, and deploying ML models (including custom models) with more control over the ML lifecycle. Use Bedrock when you want to quickly add generative AI features; use SageMaker when you need end-to-end ML development or custom model training and deployment.

Question 2

When should I use Amazon Bedrock?

Accepted Answer

Use Bedrock when you want to add generative AI features—like chat, summarization, content generation, or Q&A over your documents—using managed foundation models and AWS-native security and governance. It’s a good fit when you want to avoid hosting models yourself, need to choose among multiple model providers, and want built-in options for retrieval-augmented generation (RAG) and agent-style workflows.

Question 3

How much does Amazon Bedrock cost?

Accepted Answer

Bedrock pricing is typically usage-based. Costs depend on the model you choose and how you use it (for example, input/output tokens for text models, image generation requests, or other model-specific units). Additional costs may apply for related components you integrate, such as vector storage/knowledge bases, data storage, logging/monitoring, and networking. For accurate estimates, use AWS pricing pages and the AWS Pricing Calculator with your expected request volume and token sizes.

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