Understand the differences and pick the one that's right for you
Purpose |
Understand the differences between Amazon Bedrock and Amazon SageMaker AI, and determine which service is the best fit for your needs. |
Last updated |
February 14, 2025 |
Covered services |
Introduction
Amazon Web Services (AWS) offers a suite of services to help you build machine learning (ML) and generative AI applications. It’s helpful to understand how these services work together to form a generative AI stack, including:
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Generative AI-powered services such as Amazon Q Business and Amazon Q Developer, which leverage large language models (LLMs) and other foundation models (FMs) to boost productivity.
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Models and tools for building generative AI applications, including Amazon Bedrock.
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Infrastructure to build and train AI models, such as Amazon SageMaker AI and specialized hardware.

When considering which generative AI services you want to use, two services are often considered alongside one another:
Amazon Bedrock
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Choose Amazon Bedrock if you primarily need to use pre-trained foundation models for inference, and want to select the foundation model that best fits your use case. Amazon Bedrock is a fully managed service for building generative AI applications, with support for popular foundation models, including Amazon Nova, Amazon Titan, Anthropic Claude
, DeepSeek-R1 , Cohere Command & Embed , AI21 Labs Jurassic , Meta Llama , Mistral AI , and Stable Diffusion XL . Supported FMs are updated on a regular basis. -
Use Amazon Bedrock Marketplace to discover, test, and use over 100 popular, emerging, and specialized foundation models (FMs).
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Use Amazon Bedrock IDE, part of the new Amazon SageMaker Unified Studio, to discover Amazon Bedrock models and build generative AI apps that use Amazon Bedrock models and features.
Amazon SageMaker AI
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Amazon SageMaker AI (formerly Amazon SageMaker) is a fully managed service designed to help you build, train, and deploy machine learning models at scale. This includes building FMs from scratch, using tools like notebooks, debuggers, profilers, pipelines, and MLOps. Consider SageMaker AI when you have use cases that can benefit from extensive training, fine-tuning, and customization of foundation models. It can also help you through the potentially challenging task of evaluating which FM is the best fit for your use case.
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Amazon SageMaker AI is part of the next generation of Amazon SageMaker, a unified platform for data, analytics, and AI. Amazon SageMaker includes Amazon SageMaker Unified Studio, a unified development experience that brings together AWS data, analytics, AI, and ML services.
This guide is focused on understanding the differences between Amazon SageMaker AI and Amazon Bedrock. For more information about how Amazon Bedrock and SageMaker AI fit into Amazon’s generative AI services and solutions, see the generative AI decision guide.
While both Amazon Bedrock and Amazon SageMaker AI enable the development of ML and generative AI applications, they serve different purposes. This guide will help you understand which of these services is the best fit for your needs, including scenarios in which both services can be used together to build generative AI applications.
Here's a high-level view of the key differences between these services to get you started.
Category |
![]() Amazon Bedrock |
![]() Amazon SageMaker AI |
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Use Cases | Ideal for integration of AI capabilities into applications without investing heavily in custom model development | Optimized for unique or specialized AI/ML needs that may require custom models |
Target Users | Optimized for developers and businesses without deep machine learning expertise | Optimized for data scientists, machine learning engineers, and developers |
Customization | You'll primarily use pre-trained models, but can fine-tune as needed | You have full control, and can customize or create models according to your needs |
Pricing | Pay-as-you-go pricing based on the number of API calls made to the service | Charges based on the usage of compute resources, storage, and other services |
Integration | Integrate pre-trained models into applications through API calls | Integrate custom models into applications, with more customization options |
Expertise Required | Basic level of machine learning expertise needed to use pre-trained models | Working knowledge of data science and machine learning skills are helpful for building and optimizing models |
Differences between Amazon Bedrock and SageMaker AI
Let's examine and compare the capabilities of Amazon Bedrock and Amazon SageMaker AI.
Amazon Bedrock and Amazon SageMaker AI address different use cases based on your specific requirements and resources.
Amazon Bedrock
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Amazon Bedrock is designed for use cases where you want to build generative AI applications without investing heavily in custom model development. For example, a content moderation system for a social media platform could use Amazon Bedrock's pre-trained models to automatically identify and flag inappropriate text or images. Similarly, a customer support chatbot could use Amazon Bedrock's natural language processing capabilities to understand and respond to user inquiries. Amazon Bedrock is particularly useful if you have limited machine learning expertise or resources, as it helps you to benefit from AI without the need for extensive in-house development.
Amazon SageMaker AI
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SageMaker AI is a good choice for unique or specialized AI/ML needs that require custom-built models. It is ideal for scenarios where off-the-shelf solutions are not sufficient, and you have a need for fine-grained control over the model architecture, training process, and deployment. One example of a scenario that would benefit from using SageMaker AI would be a healthcare company developing a model to predict patient outcomes based on specific biomarkers. Another example would be a financial institution creating a fraud detection system tailored to their unique data and risk factors. Additionally, SageMaker AI is suitable for research and development purposes, where data scientists and machine learning engineers can experiment with different algorithms, hyperparameters, and model architectures.
The choice between Amazon Bedrock and Amazon SageMaker AI is not always mutually exclusive. In some cases,
you may benefit from using both services together. For example, you can use Amazon Bedrock to quickly
prototype and deploy a foundation model, and then use SageMaker AI to further refine and optimize the
model for better performance.
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Ultimately, the decision between Amazon Bedrock and Amazon SageMaker AI depends on your specific requirements. Evaluating these factors can help you make an informed decision and choose the service that is most suitable for your needs.
For more information about Amazon’s generative AI services and solutions, see the generative AI decision guide.
Use
Now that you've read about the criteria for choosing between Amazon Bedrock and Amazon SageMaker AI, you can select the service that meets your needs, and use the following information to help you get started using each of them.
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What is Amazon Bedrock?
Use this fully managed service to make foundation models (FMs) from Amazon and third parties available for your use through a unified API.
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Frequently asked questions about Amazon Bedrock
Get answers to the most commonly-asked questions about Amazon Bedrock. These include how to use agents, security considerations, details about Amazon Bedrock software development kits (SDKs), retrieval augmented generation, how to use model evaluation, and billing.
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Guidance for generating product descriptions with Amazon Bedrock
Use Amazon Bedrock in your solution to automate your product review and approval process for an e-commerce marketplace or retail website.