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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:
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.
Models and
tools
for building
generative AI
applications,
including Amazon Bedrock.
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 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.
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
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.
Use cases
Amazon Bedrock and Amazon SageMaker AI address different use cases based on your specific requirements
and resources.
Amazon Bedrock
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
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.
Target users
Amazon Bedrock and Amazon SageMaker AI support different targeted users based on their level of
expertise and knowledge of machine learning and artificial intelligence.
Amazon Bedrock
Amazon Bedrock offers a more accessible and straightforward way to integrate AI
functionality into your projects. It’s appropriate for a broad audience, which
includes developers and businesses, that has limited experience in building and
training machine learning models, but wants to use AI to enhance their applications or
workflows.
Amazon SageMaker AI
SageMaker AI is predominantly for data scientists, machine learning engineers, and
developers who possess the necessary skills and knowledge to build, train, and deploy
custom machine learning models. Use SageMaker AI if you are well-versed in data science and
machine learning concepts, and require a platform that provides you with the tools and
flexibility to create models tailored to your specific needs.
Choice of FMs
While both
Amazon Bedrock and Amazon SageMaker AI
offer a broad set
of FMs
for
your applications, there are differences in the set of FMs that each service
offers.
Amazon Bedrock
Amazon Bedrock
provides access
to FMs such as Anthropic's Claude, Meta's Llama 3, Amazon's Nova and Titan models,
Stability AI's models for image generation, and many others. See the list of
available FMs, which is updated frequently.
Use the Amazon Bedrock
Marketplace to rapidly test and incorporate over 100 publicly available and
proprietary FMs.
Amazon Bedrock provides access to certain proprietary models, including Claude and Jurassic,
that aren't available in Amazon SageMaker JumpStart.
Amazon SageMaker AI
Amazon SageMaker JumpStart
offers
built-in publicly available and proprietary foundation models to customize and
integrate into your generative AI workflows, with a wider selection of FMs than Amazon Bedrock,
including models optimized for specific use cases.
JumpStart offers publicly available FMs, including models from Hugging Face,
StabilityAI, Meta, and Amazon, and proprietary FMs from AI21 Labs, Cohere, and
LightOn. See the list of publicly
available and proprietary FMs, which is updated frequently.
Customization
Amazon Bedrock and Amazon SageMaker AI offer different levels of customization capabilities that you
can tailor to your specific needs and expertise.
Amazon Bedrock
Amazon Bedrock offers a set of models from leading providers that you can use to build generative AI applications,
with limited customization. You have access to a set of API calls that you use to
enter data and receive predictions from these pre-trained models. While this approach
drastically simplifies the process of incorporating AI capabilities into applications,
it also means that you have less control over the underlying models, unless you
customize a model, or import a custom model. Amazon Bedrock's pre-trained models are optimized
for common AI tasks and are designed to work well for a wide range of use cases, but
they may not be suitable for highly specialized or niche requirements.
Amazon Bedrock supports fine-tuning for foundation models (FMs), such as
Amazon Nova Micro, Lite, and Pro , Cohere Command R, Meta
Llama 2,
Anthropic
Claude 3 Haiku, Amazon Titan Text Lite, Amazon Titan Text Express,
Amazon Titan Multimodal Embeddings, and Amazon Titan Image Generator.
The
list of supported FMs is updated on an ongoing basis.
Customize models for specific tasks and use cases, including FM fine-tuning
and pre-training. Bring your own customized model with custom model
import.
Amazon SageMaker AI
Amazon SageMaker AI provides extensive customization options, giving you full control over
the entire machine learning workflow. With SageMaker AI, you can fine-tune every aspect of
your models, from data preprocessing and feature engineering to model architecture and
hyperparameter optimization. By using this level of customization, you can create
highly specialized models that are tailored to your unique business requirements. SageMaker AI
supports a wide range of popular machine learning frameworks, such as TensorFlow,
PyTorch, and Apache MXNet, allowing you to use your preferred tools and libraries for
building and training models.
Use
Amazon SageMaker JumpStart to evaluate, compare, and select FMs based on pre-defined quality and
responsibility.
Choose which FM to use with Amazon SageMaker AI
Clarify. Use SageMaker AI Clarify to create model evaluation jobs, that you use to
evaluate and compare model quality and responsibility metrics for text-based
foundation models from JumpStart.
Generate predictions using Amazon SageMaker AI Canvas, without needing to
write any code. Use SageMaker AI Canvas in collaboration with Amazon Bedrock to fine-tune and deploy
language models. This blog post describes how you can use them to optimize customer
interaction by working with your own datasets, such as your product FAQs, in Amazon Bedrock
and Amazon SageMaker JumpStart.
Pricing
Amazon Bedrock and Amazon SageMaker AI have different pricing models that reflect their target users
and the services they provide.
Amazon Bedrock
Amazon Bedrock employs a simple pricing
model based on the number of API calls made to the service. You pay a fixed
price per API call, which includes the cost of running the pre-trained models and any
associated data processing. This straightforward pricing structure makes it more
efficient for you to estimate and control your costs, as you pay only for the actual
usage of the service. Amazon Bedrock's pricing model is particularly well-suited for
applications with predictable workloads, or for cases where you want more transparency
in your AI-related expenses.
Amazon SageMaker AI
SageMaker AI follows a pay-as-you-go pricing model based on the usage of compute resources, storage, and other
services consumed during the machine learning process. You’re charged for the
instances that you use to build, train, and deploy you models, with prices varying
depending on the instance type and size. Additionally, you incur costs for data
storage, data transfer, and other associated services like data labeling and model
monitoring. This pricing model provides flexibility and allows you to optimize costs
based on your specific requirements. However, it also means that costs can vary and
may require careful management, especially for resource-intensive projects.
Integration
Amazon Bedrock and Amazon SageMaker AI offer different approaches to integrating machine learning
models into applications, catering to your specific needs and expertise.
Amazon Bedrock
Amazon Bedrock simplifies the integration process by providing pre-trained models that you
can access directly through API calls. Use the Amazon Bedrock SDK or REST API to send input data
and receive predictions from the models without needing to manage the underlying
infrastructure. This approach significantly reduces the complexity and time required
to integrate AI capabilities into applications, making it more accessible to
developers with limited machine learning expertise. However, this ease of integration
comes at the cost of limited customization options, as you’re restricted to the
pre-trained models and APIs provided by Amazon Bedrock.
Amazon SageMaker AI
SageMaker AI provides a comprehensive platform for building, training, and deploying
custom machine learning models. However, integrating these models into applications
requires more effort and technical expertise compared to Amazon Bedrock. You need to use the
SageMaker AI SDK or API to access the trained models and build the necessary infrastructure to
expose them as endpoints. This process involves creating and configuring API Gateway,
Lambda functions, and other AWS services to enable communication between the
application and the deployed model. While SageMaker AI provides tools and templates to
simplify this process, it still requires a deeper understanding of AWS services and
machine learning model deployment.
Expertise required
Amazon Bedrock and Amazon SageMaker AI are optimized for different levels of machine learning
expertise.
Amazon Bedrock
Amazon Bedrock is more accessible to a broader range of users, including developers and
businesses with limited machine learning expertise. By providing pre-trained models
that can be easily integrated into applications through API calls, Amazon Bedrock abstracts away
much of the complexity associated with building and deploying machine learning models.
You don't need to worry about data preprocessing, model selection, or infrastructure
management, as these aspects are handled by the Amazon Bedrock service. This allows you to focus
on integrating AI capabilities into your applications without needing to invest
significant time and resources in acquiring deep machine learning knowledge.
Amazon SageMaker AI
If you have deeper expertise in data science and machine learning, SageMaker AI provides a
powerful and flexible platform for building, training, and deploying custom models.
While SageMaker AI aims to simplify the machine learning workflow, it still requires a
significant level of technical expertise to take full advantage of its capabilities.
You’ll benefit from being proficient in programming languages like Python, along with
a deep understanding of machine learning concepts, such as data preprocessing, model
selection, and hyperparameter tuning. Additionally, you should be comfortable working
with various AWS services and managing the infrastructure required to deploy and
integrate their models. As a result, SageMaker AI may have a steeper learning curve if you’re
new to machine learning or have limited experience with AWS.
Amazon Bedrock and Amazon SageMaker AI address different use cases based on your specific requirements
and resources.
Amazon Bedrock
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
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.
This
blog post describes
how you can deploy models
from Amazon SageMaker JumpStart and register them with Amazon Bedrock, allowing you to access them through Amazon Bedrock
APIs.
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.
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.
Amazon Bedrock
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.
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.
Build
generative AI applications with Amazon Bedrock IDE
This blog post describes how you can build applications using a wide array of top
performing models. It then explains how to evaluate and share your generative AI apps
with Amazon Bedrock IDE.
Explore Amazon SageMaker JumpStart solution templates that set up infrastructure for common use
cases, and executable example notebooks for machine learning with SageMaker AI.
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.
Diagram showing the AWS generative AI stack. This diagram shows the infrastructure to build and train AI models at the bottom of the stack, models and tools to build generative AI apps in the middle, and applications that use LLMs and other FMs to boost productivity, at the top.