DJames617 Service Comparison
Bedrock vs SageMaker AI vs Managed AI Services
Foundation model app, custom ML lifecycle, and prebuilt AI APIs are different levels of abstraction.
What They Have in Common
Each option can solve part of the scenario. The exam expects you to choose the one that satisfies the stated constraints with the right operational burden, security boundary, availability model, and cost behavior.
Key Differences
| Option | Primary model | Best use | Security | Availability | Operations and cost |
|---|---|---|---|---|---|
| Bedrock | Foundation models and GenAI app tools | Prompts, RAG, agents, guardrails | IAM, KMS, guardrails, data-access controls | Managed model access with service quotas | Pay per input/output or provisioned throughput; lowest model ops |
| SageMaker AI | Custom ML lifecycle | Training, tuning, deployment, registry, monitoring | IAM, VPC, KMS, endpoint and artifact controls | Deploy across AZs when the endpoint design requires it | Training/endpoint/storage cost; highest ML operations |
| Managed AI services | Prebuilt APIs | Translate, Comprehend, Rekognition, Textract use cases | IAM, KMS, data privacy and service policies | Managed regional service; verify feature availability | Per API-use pricing; least custom model management |
Typical Exam Clues
Practice After Studying
Return to the certification guide that includes this comparison and launch domain training from there.