Hosting model in AWS

Hosting a model in AWS

Amazon SageMaker Jumpstart allows users to host custom machine learning models with extensive customization and control over infrastructure, making it suitable for complex projects. It includes pre-trained models for various domains and offers tools for training and inference. Amazon Bedrock, on the other hand, is a fully managed service providing API access to pre-built AI models, aimed at rapid deployment and ease of use without needing infrastructure management. Bedrock is ideal for standard tasks and integrates seamlessly with AWS services but offers less customization and model choice compared to Jumpstart.

Criteria	Amazon SageMaker JumpStart	Amazon Bedrock
Use Case & Customization	Designed for comprehensive control over custom models with extensive customization options.	Simplified approach with seamless integration with hosted models, offering limited customization.
Development Time & Training	Requires a longer development cycle due to custom model creation and training, supporting user-provided data.	Accelerates development by leveraging pre-trained models, eliminating the need for custom training.
Scalability & Cost Control	Provides robust scalability options and granular cost control through resource allocation.	Scalability influenced by AWS-hosted models with less flexibility in managing costs.
Model & Integration Options	Allows selection from a wide array of models and frameworks with flexible integration options, requiring more configuration effort.	Restricted to pre-built models within Bedrock, offering seamless integration with AWS services.
Maintenance & Security	Users manage model versions, updates, and security settings, ensuring tailored control.	AWS handles updates and maintenance, providing robust security measures for hosted models.
Data & RAG Integration	Users manage data and training workflows independently, providing flexibility to integrate Retrieval-Augmented Generation (RAG) models as needed.	No additional training data required for pre-trained models; RAG integration depends on the availability of such models within Bedrock.