Amazon Bedrock is a fully managed service that makes foundation models (FMs) from Amazon and leading AI startups available through an API so you can choose from various FMs to find the model that's best suited for your use case. With the Amazon Bedrock serverless experience, you can quickly get started, easily experiment with FMs, privately customize FMs with your own data, and seamlessly integrate and deploy them into your applications using AWS tools and capabilities. Agents for Amazon Bedrock is a fully managed capability that makes it easier for developers to create generative-AI applications that can deliver up-to-date answers based on proprietary knowledge sources and complete tasks for a wide range of use cases.
Amazon SageMaker is a fully managed service that helps data scientists and developers build, train, and deploy machine learning models at scale. It provides a range of features and tools to simplify the machine learning workflow, from data preprocessing and model training to model deployment and monitoring.
So in a nutshell, Bedrock is the easiest way to build and scale generative AI applications with foundation models (FMs); whereas SageMake is a managed machine learning service in general. Answer from AWS-User-alantam on repost.aws
AWS
docs.aws.amazon.com › aws decision guides › aws decision guide › amazon bedrock or amazon sagemaker ai?
Amazon Bedrock or Amazon SageMaker AI? - Amazon Bedrock or Amazon SageMaker AI?
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. ... Amazon Bedrock and Amazon SageMaker AI are optimized for different levels of machine learning expertise.
Reddit
reddit.com › r/aws › sagemaker vs bedrock
r/aws on Reddit: Sagemaker vs Bedrock
March 10, 2025 -
What are your pros to using Sagemaker? Seems to me that it’s a little dead whereas bedrock is the future due to it’s ease of use and flexibility specially for getting to use something that’s already “built”
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They are totally different products? SageMaker is a data science environment and has labeling tools like ground truth and model hosting cababilities. Bedrock is an LLM environment where you can build Knowledge bases and agent pipelines and such.
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You are comparing apples to oranges. Sagemaker lets you build the models, bedrock provides interaction with already built models. Obviously an oversimplification but these are in two different categories
Videos
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What is the Difference between Amazon Sagemaker and Amazon Bedrock?
Caylent
caylent.com › blog › bedrock-vs-sage-maker-whats-the-difference
Amazon Bedrock vs. Amazon SageMaker AI - What's The Difference? | Caylent
Regarding setup, in a nutshell, Bedrock is easier to setup than SageMaker AI. Being fully managed by AWS, Bedrock requires less effort compared to SageMaker AI considering that users and developers select one of the provided pre-trained models, ...
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Amazon Bedrock is a fully managed service that makes foundation models (FMs) from Amazon and leading AI startups available through an API so you can choose from various FMs to find the model that's best suited for your use case. With the Amazon Bedrock serverless experience, you can quickly get started, easily experiment with FMs, privately customize FMs with your own data, and seamlessly integrate and deploy them into your applications using AWS tools and capabilities. Agents for Amazon Bedrock is a fully managed capability that makes it easier for developers to create generative-AI applications that can deliver up-to-date answers based on proprietary knowledge sources and complete tasks for a wide range of use cases.
Amazon SageMaker is a fully managed service that helps data scientists and developers build, train, and deploy machine learning models at scale. It provides a range of features and tools to simplify the machine learning workflow, from data preprocessing and model training to model deployment and monitoring.
So in a nutshell, Bedrock is the easiest way to build and scale generative AI applications with foundation models (FMs); whereas SageMake is a managed machine learning service in general.
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Sagemaker Foundation models allows you more flexibility and choice than bedrock. In Sagemaker infrastrucuture is being provisioned on your behalf and you can train from scratch a new model, pick for a large list of models supported by Jumpstart (including Hugginface), and if the model can be fine-tuned you can do this on sagemaker. Note many foundational models cannot be finetuned and others are only available for Research and cannot be used in commercial applications. Sagemaker will give you the most flexibility but involves more work in setting up and you are charged for endpoints when they are running.
Bedrock is focused on offering an API driven and serverless experience. It offers a curated list of foundational models. You are only charged for what you use (there is no infrastructure costs involved). Only a subset of model on Bedrock will allow fine-tuning but again this will be a very simple api driven process.
Bedrock and Sagemaker offer different characteristics and what is the right choice will depend on your use case, your foundational model choice (or even train your own), the need for fine-tuning for the model, do you have a data science team or are you more developer oriented.
CloudOptimo
cloudoptimo.com › home › blog › amazon bedrock vs amazon sagemaker: a comprehensive comparison
Amazon Bedrock vs Amazon SageMaker: A Comprehensive Comparison
March 13, 2025 - Although both platforms are part ... SageMaker offers a comprehensive suite for custom model creation and training, whereas Bedrock streamlines the experience by focusing on pre-trained models for rapid deployment...
DEV Community
dev.to › aws-builders › amazon-bedrock-vs-amazon-sagemaker-understanding-the-difference-between-awss-aiml-ecosystem-5364
Amazon Bedrock vs Amazon SageMaker: Understanding the difference between AWS's AI/ML ecosystem - DEV Community
October 2, 2023 - They have the ability to encrypt data both at rest and in transit, manage data access through Identity and Access Management (IAM) roles, and comply with regulations through AWS’s robust compliance offerings. Users can also use SageMaker in their Virtual Private Cloud (VPC) to have network level control. For customers with stringent data security requirements, this level of control is paramount. On the other hand, Amazon Bedrock, being a managed service, processes data within the confines of the AWS environment.
Deepchecks
deepchecks.com › blog › amazon bedrock vs sagemaker ai: when to use each one
Amazon Bedrock vs SageMaker AI: When to Use Each One
April 24, 2025 - For Bedrock, secure using AWS Identity and Access Management (IAM) tightly and monitor usage to reduce costs. For SageMaker, right-size resources by selecting proper instance types, leverage CI/CD using SageMaker Pipelines for streamlined workflows, and implement model monitoring for performance and reliability.
Medium
medium.com › @jsathiskumar › aws-sagemaker-vs-8bb24b9d6455
AWS SageMaker vs. AWS Bedrock for Generative AI – Which to Choose? Plus, a Guide to Building RAG Applications: | by Dr. Sathiskumar Jothi | Medium
August 7, 2025 - AWS Bedrock: Key Differences · Amazon SageMaker: A comprehensive ML platform for building, training, and deploying custom models. It’s ideal for data scientists who need full control over the ML lifecycle, including custom algorithms, distributed training, and MLOps.
Caylent
caylent.com › blog › amazon-bedrock-vs-sage-maker-jumpstart
Amazon Bedrock vs SageMaker JumpStart | Caylent
When ranking each solution on ease of use, Bedrock stands atop, SageMaker positions itself in the middle, and EC2 remains reliable but cumbersome. The way customers take advantage of each solution will depend on the entities, the access patterns, ...
Zircon Tech
zircon.tech › home › amazon bedrock vs. amazon sagemaker: a guide to choosing the right tool for your ai needs
Amazon Bedrock vs. Amazon SageMaker: A Guide to Choosing the Right Tool for Your AI Needs
November 5, 2024 - When working on an API-based, serverless application where cost control and agility are important. Amazon SageMaker is a more comprehensive machine learning platform that supports the entire ML lifecycle.
CloudTern Solutions
cloudtern.com › home › differentiating aws’s ai/ml ecosystem: amazon bedrock vs amazon sagemaker
Differentiating AWS's AI/ML Ecosystem: Amazon Bedrock vs Amazon SageMaker - CloudTern Solutions
July 4, 2025 - Within the AWS ecosystem, Amazon ... to different needs. Bedrock excels in quickly integrating advanced AI features with minimal customization, thanks to its pre-configured models and streamlined workflows....
Medium
medium.com › @sisodiyapradeep › key-differences-between-amazon-bedrock-amazon-sagemaker-jumpstart-amazon-q-0a2776db4efd
Key Differences between Amazon Bedrock, Amazon Sagemaker Jumpstart & Amazon Q | by Pradeep Singh Sisodiya | Medium
February 15, 2024 - With Amazon Bedrock, users have the option to customize foundation models through continued pre-training (unlabeled data) and fine-tuning (labeled data). This allows users to adapt the models to their specific needs and use cases. ... Sagemaker Jumpstart offers extensive customization options. Users can use their own algorithms, built-in ones, or select from a wide range of models available on the AWS Marketplace or from third-party sources.
TechTarget
techtarget.com › searchcloudcomputing › tip › Amazon-Bedrock-vs-SageMaker-JumpStart-for-AI-apps
Amazon Bedrock vs. SageMaker JumpStart for AI apps | TechTarget
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.
GitSelect
gitselect.com › post › aws-bedrock-vs-sagemaker
AWS Bedrock vs Sagemaker
August 18, 2025 - For those who may lack specific technical know-how or for businesses looking for a more hands-off approach, AWS Bedrock's high degree of automation can be a key advantage. This feature makes it possible to take full advantage of machine learning technologies without being an expert in every aspect of the machine learning workflow. Its automation capability is its strongest appeal, presenting a straightforward pathway to harness the potential of machine learning. Transitioning our focus to AWS Sagemaker, this fully managed platform empowers developers and data scientists to expedite the process of creating, training, and deploying machine learning models at any scale.
Leanware
leanware.co › insights › amazon-sagemaker-vs-amazon-bedrock-what-s-the-difference
Amazon SageMaker vs Amazon Bedrock: What's the Difference?
We trust their judgment because they are extremely reliable
Two services that often generate confusion among teams evaluating AWS's AI offerings are Amazon SageMaker and Amazon Bedrock. While both enable AI capabilities, they serve fundamentally different purposes and cater to distinct use cases. Understanding their differences is crucial for making informed Since beginning their engagement with Leanware, the client has been able to unblock their frontend development. The service provider pushes the client to be more productive and expand their product. Leanware is highly dedicated to the project and ensuring they provide value for the client. Compare
AWS
docs.aws.amazon.com › pdfs › decision-guides › latest › bedrock-or-sagemaker › bedrock-or-sagemaker.pdf pdf
AWS Decision guide Amazon Bedrock or Amazon SageMaker AI?
Let's examine and compare the capabilities of Amazon Bedrock and Amazon SageMaker AI.
Medium
medium.com › @yashwanths_29644 › llm-series-06-aws-bedrock-vs-3bb3a8aa2af8
LLM Series 07:-AWS Bedrock vs. AWS SageMaker vs. AWS EC2 for LLM Use Cases: A Cost-Effectiveness Analysis | by Yashwanth S | Medium
June 16, 2025 - As large language models (LLMs) continue to shape AI-driven applications, choosing the right AWS service for deployment is crucial. AWS provides multiple solutions for hosting and running LLMs, including AWS Bedrock, AWS SageMaker, and AWS EC2. Each has its unique advantages, trade-offs, and cost implications.
G2
g2.com › compare › aws-bedrock-vs-amazon-sagemaker
Compare AWS Bedrock vs. Amazon SageMaker | G2
Users on G2 appreciate Bedrock's ability to handle large-scale deployments seamlessly, while SageMaker's scalability is noted but not as robust in high-demand scenarios. G2 users highlight that Amazon SageMaker's Data Ingestion & Wrangling feature, ...
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SaaSworthy
saasworthy.com › home › new saas software › bedrock vs amazon sagemaker
Bedrock vs Amazon SageMaker Comparison | SaaSworthy.com
Which product offers a wider range of features for machine learning development? Amazon SageMaker offers a more comprehensive set of features for machine learning development, including data preparation, model training, deployment, and monitoring.
Bacancy Technology
bacancytechnology.com › blog › amazon-bedrock-vs-sagemaker
Amazon Bedrock vs SageMaker | Key Differences Explained
July 19, 2025 - The main goal of Amazon Bedrock is to simplify access to cutting-edge AI. Amazon SageMaker, launched in 2017, is a fully managed service from AWS that helps developers and data scientists build, train, and deploy machine learning models at scale.