Amazon Bedrock vs. SageMaker JumpStart for AI apps
Amazon Bedrock and Amazon SageMaker JumpStart both provide AI models for applications. Bedrock offers readily available models, while JumpStart supports more customization.
To build an effective AI application, developers must find and optimize the right AI models for a specific use case. Because building an AI model from scratch can be a time-consuming and costly process, it's important to explore the available models an application can use.
Amazon Bedrock and Amazon SageMaker JumpStart simplify this process by providing development teams with a range of AI models that software applications can use in a scalable way. This article compares the two offerings from AWS, their features and their use cases in machine learning and AI development.
What is Amazon Bedrock?
Amazon Bedrock is a managed service to build generative AI applications. It can be used by beginner, intermediate or advanced AI professionals.
Bedrock provides a range of foundation models made available by third-party providers, such as Anthropic, AI21 Labs, Meta, Cohere, Stability AI and Mistral AI, as well as AWS itself. These models are executed via a serverless method. The available models in Bedrock support text and image inputs and outputs, as well as the following use cases:
- Chat interactions.
- Text and code generation.
- Text analysis.
- Image generation.
- Image analysis.
The Bedrock service also enables model customization by providing the ability to fine-tune available models using custom data. It's also possible to store the customized models for future use.
What is SageMaker JumpStart?
JumpStart is part of the Amazon SageMaker service and easily integrates with other SageMaker features. JumpStart helps facilitate the deployment, evaluation, customization and integration of available AI models with application components.
Once developers choose an AI model, they can use SageMaker-managed infrastructure, such as compute instances or Serverless Inference, to deploy it. JumpStart offers a wider range of models compared with Bedrock and supports similar use cases, as well as more specific ones.
Compare Amazon Bedrock vs. SageMaker JumpStart
Here is a breakdown of how Amazon Bedrock and Amazon SageMaker JumpStart compare.
| Area | Amazon Bedrock | Amazon SageMaker JumpStart |
| Model availability and use cases |
Supports retrieval-augmented generation (RAG) |
Supports RAG and has a higher number of available models |
| Model customization | Limited | Detailed customization |
| Application development | Models are ready to use via Bedrock API | Models require additional deployment steps before they're ready to use |
| Infrastructure | Serverless, ready to use | Compute instances managed by SageMaker Supports Serverless Inference |
| Performance and scalability | Managed by Bedrock | Managed by SageMaker, but developers have more control regarding compute capacity and configurations |
| DevOps | Managed by Bedrock | Requires more steps and configurations |
| Security | Managed by Bedrock | Managed by SageMaker, but developers can customize additional configurations |
| Cost model | Input and output usage to a particular model, such as tokens and generated images Optional Provisioned Throughput pricing (varies by model) |
Compute instance type, size, storage and data transfer Training, development and inference processes require compute infrastructure |
Integration
Developers can use a service like SageMaker Canvas early in the development process to easily access models in Bedrock or JumpStart. The Canvas no-code interface simplifies the early model evaluation stages and makes the process accessible to a wider range of team members within an organization.
SageMaker notebooks also integrate with both Bedrock and JumpStart. This is useful for evaluation and development tasks. After the initial evaluation stages, JumpStart requires more expertise than Bedrock to execute development, customization and operational tasks.
Cost
A cost comparison between Bedrock and SageMaker JumpStart isn't simple, as multiple factors affect cost during the development, testing and production stages.
Developers need to deploy JumpStart models to compute instances for training and inference tasks and incur storage and data transfer costs -- although there's no additional cost for the models themselves. Once the JumpStart models are properly trained, developers can make them available to Serverless Inference by adding them to SageMaker Model Registry.
In contrast, Bedrock costs are driven by input and output usage to a particular model, such as tokens and generated images. Bedrock also offers Provisioned Throughput pricing for workloads that require a predictable transaction volume or for customized models. Provisioned Throughput in Bedrock has a different cost per model. Customized models also incur a monthly storage cost that varies by model.
Application use cases
Determining whether Bedrock or SageMaker JumpStart is the best fit for a particular application is a complex task, which requires deep analysis of the following:
- Which models the application needs.
- Usage requirements.
- The potential need to use customized models.
Usage volume requirements and patterns might make a difference in costs. In some cases, deploying models to compute instances results in better performance and cost versus paying per token, per image usage or with Provisioned Throughput.
In general, Bedrock simplifies DevOps by making models available in a relatively simple way. Applications that require more specific behavior and significant model customizations might better benefit from JumpStart. Both Amazon Bedrock and SageMaker JumpStart greatly simplify the development of AI applications, however.
Ernesto Marquez is owner and project director at Concurrency Labs, where he helps startups launch and grow their applications on AWS. He particularly enjoys building serverless architectures, automating everything and helping customers cut their AWS costs.
