AWS Generative AI Services Explained (SageMaker, Bedrock, Amazon Q & Cost Tradeoffs)

In this AWS Generative AI Services review, we recap the most important tools and concepts you need to know for the AWS Generative AI / AI Practitioner exam.

This video walks through the core AWS services used to build, deploy, and scale generative AI applications, including Amazon SageMaker, Amazon Bedrock, PartyRock, and Amazon Q. You’ll also learn how SageMaker JumpStart accelerates development with prebuilt models and how different inference options impact cost and performance.

We also review critical exam topics, such as:

Real-time vs batch inference tradeoffs

Token-based pricing models

Provisioned throughput vs on-demand usage

Availability, redundancy, and global deployment costs

When and why custom models increase compute spend

By the end of this lesson, you’ll understand how AWS balances performance, scalability, security, and cost, and how to evaluate these tradeoffs in exam scenarios and real-world architectures.

🎯 Perfect for:

AWS Generative AI exam prep

Cloud architects & engineers

Anyone building GenAI solutions on AWS

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#AmazonQ #AWSAI #CloudComputing #AICosts
#AWSExam #AWSCertification #MachineLearning
#GenAI #AIArchitecture #CloudAI

Видео AWS Generative AI Services Explained (SageMaker, Bedrock, Amazon Q & Cost Tradeoffs) канала Ai Cloud Path