You're right to notice the changes in Amazon SageMaker, as AWS has indeed been evolving its offerings in the data and AI space. Let me clarify the relationship between SageMaker AI and SageMaker Unified Studio to help you understand the direction.
Amazon SageMaker Unified Studio is actually the next generation of Amazon SageMaker, representing a more comprehensive and integrated approach to data, analytics, and AI development. It's designed to bring together various AWS tools and services into a single, collaborative environment.
SageMaker Unified Studio is built on top of the existing SageMaker capabilities and integrates functionality from multiple AWS services, including Amazon EMR, AWS Glue, Amazon Athena, Amazon Redshift, Amazon Bedrock, and Amazon SageMaker AI. This means that SageMaker AI is now a part of the broader SageMaker Unified Studio ecosystem.
The key difference is in the scope and integration:
1. SageMaker AI focuses specifically on machine learning and AI capabilities.
2. SageMaker Unified Studio is a more comprehensive platform that includes SageMaker AI, but also incorporates data processing, SQL analytics, and generative AI application development tools.
Regarding domains, in the context of SageMaker Unified Studio, a domain is the organizing entity that connects assets, users, and their projects. It's managed through the SageMaker AI management console, where domain administrators can configure user management, account associations, project profiles, blueprints, Amazon Bedrock models, Git connections, and Amazon Q usage.
The concept of domains in SageMaker Unified Studio is more expansive and central to the organization of resources and collaboration across teams. It's designed to provide a unified experience for data scientists, analysts, and developers working on various aspects of data and AI projects.
In essence, SageMaker Unified Studio represents AWS's vision for a more integrated, collaborative, and comprehensive data and AI development environment, with SageMaker AI being a crucial component within this larger ecosystem. This new approach aims to streamline workflows, improve collaboration, and provide a single environment for a wide range of data and AI tasks.
**Sources**
The next generation of Amazon SageMaker: The center for all your data, analytics, and AI | AWS Big Data Blog
A single data and AI development environment - Amazon SageMaker Unified Studio - AWS
Announcing the preview of Amazon SageMaker Unified Studio - AWS
Amazon SageMaker Unified Studio terminology and concepts - Amazon SageMaker Unified Studio Answer from re:Post Agent on repost.aws
Top answer 1 of 2
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You're right to notice the changes in Amazon SageMaker, as AWS has indeed been evolving its offerings in the data and AI space. Let me clarify the relationship between SageMaker AI and SageMaker Unified Studio to help you understand the direction.
Amazon SageMaker Unified Studio is actually the next generation of Amazon SageMaker, representing a more comprehensive and integrated approach to data, analytics, and AI development. It's designed to bring together various AWS tools and services into a single, collaborative environment.
SageMaker Unified Studio is built on top of the existing SageMaker capabilities and integrates functionality from multiple AWS services, including Amazon EMR, AWS Glue, Amazon Athena, Amazon Redshift, Amazon Bedrock, and Amazon SageMaker AI. This means that SageMaker AI is now a part of the broader SageMaker Unified Studio ecosystem.
The key difference is in the scope and integration:
1. SageMaker AI focuses specifically on machine learning and AI capabilities.
2. SageMaker Unified Studio is a more comprehensive platform that includes SageMaker AI, but also incorporates data processing, SQL analytics, and generative AI application development tools.
Regarding domains, in the context of SageMaker Unified Studio, a domain is the organizing entity that connects assets, users, and their projects. It's managed through the SageMaker AI management console, where domain administrators can configure user management, account associations, project profiles, blueprints, Amazon Bedrock models, Git connections, and Amazon Q usage.
The concept of domains in SageMaker Unified Studio is more expansive and central to the organization of resources and collaboration across teams. It's designed to provide a unified experience for data scientists, analysts, and developers working on various aspects of data and AI projects.
In essence, SageMaker Unified Studio represents AWS's vision for a more integrated, collaborative, and comprehensive data and AI development environment, with SageMaker AI being a crucial component within this larger ecosystem. This new approach aims to streamline workflows, improve collaboration, and provide a single environment for a wide range of data and AI tasks.
**Sources**
The next generation of Amazon SageMaker: The center for all your data, analytics, and AI | AWS Big Data Blog
A single data and AI development environment - Amazon SageMaker Unified Studio - AWS
Announcing the preview of Amazon SageMaker Unified Studio - AWS
Amazon SageMaker Unified Studio terminology and concepts - Amazon SageMaker Unified Studio
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Hi! We did a livestream this week on Twitch where we covered your question in greater depth. You can check out a recording of the show.
Caylent
caylent.com › blog › bedrock-vs-sage-maker-whats-the-difference
Amazon Bedrock vs. Amazon SageMaker AI - What's The Difference? | Caylent
Amazon SageMaker AI is a fully managed machine learning (ML) platform designed to empower developers and data scientists, to rapidly create, train, and deploy ML models. SageMaker AI offers an extensive toolkit and features covering the entire ML lifecycle, from data preparation and feature engineering to model training and deployment.
Videos
03:30
AI on AWS: Ho do AWS Bedrock and AWS SageMaker Compare? - YouTube
57:35
Sagemaker Your All-in-One Data and AI Platform - Lets Talk About ...
09:01
Amazon SageMaker overview | Amazon Web Services - YouTube
01:55
Introduction to SageMaker AI | Amazon Web Services - YouTube
AWS
aws.amazon.com › blogs › aws › introducing-the-next-generation-of-amazon-sagemaker-the-center-for-all-your-data-analytics-and-ai
Introducing the next generation of Amazon SageMaker: The center for all your data, analytics, and AI | Amazon Web Services
December 3, 2024 - SageMaker AI is integrated within the next generation of SageMaker while also being available as a standalone service for those who wish to focus specifically on building, training, and deploying AI and ML models at scale.
The Register
theregister.com › 2024 › 12 › 06 › sagemaker_unified_studio_preview
AI and analytics converge in new generation Amazon SageMaker • The Register
December 6, 2024 - However the old SageMaker remains, ... version that is still available. The difference is that SageMaker AI has a narrower focus, on building and training ML models....
AWS
docs.aws.amazon.com › amazon sagemaker › developer guide › what is amazon sagemaker ai?
What is Amazon SageMaker AI? - Amazon SageMaker AI
Amazon SageMaker AI is a fully managed machine learning (ML) service. With SageMaker AI, data scientists and developers can quickly and confidently build, train, and deploy ML models into a production-ready hosted environment.
Reddit
reddit.com › r/aws › sagemaker is terrible - is using ec2 a better alternative?
r/aws on Reddit: SageMaker is Terrible - Is Using EC2 a Better Alternative?
March 7, 2025 -
I’ve been trying to use SageMaker, and honestly, it feels awful. The training and inference workflows force you to use the unnecessary SageMaker Python SDK, and the code editor is terrible, no support for Pylance or other Microsoft tools, making development incredibly difficult. I don’t see any real advantages.
The only thing that seems relatively easy is managing but overall, it feels extremely frustrating.
Would it make more sense to just spin up an EC2 instance, develop models there using VSCode + SSH, and handle deployment directly? Also, would setting up MLflow on EC2 work just as well?
Top answer 1 of 9
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What "code editor"? The current gen code editor SageMaker offers is basically VS Code which supports any extensions you want. SageMaker instances are "just EC2" plus few goodies. SageMaker has a ton of other features that you can use but don't have to. If you don't like SageMaker SDK you can just click through the web console or use CDK (if you know what it is). Doing all that properly using EC2 will end up being more labourous/tedious in the end of the day.
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The biggest and best companies are all using EKS for ML training and inferencing either with MLflow or their own custom pipelines. Sagemaker is bloated and expensive.
Saturn Cloud
saturncloud.io › blog › what-is-sagemaker-vs-sagemaker-studio
What is SageMaker vs. SageMaker Studio? | Saturn Cloud Blog
March 12, 2024 - SageMaker is a fully managed service that provides everything data scientists need to build, train, and deploy ML models. With SageMaker, data scientists can access pre-built ML algorithms, build custom ML models, and deploy models at scale.
Constellation Research
constellationr.com › blog-news › amazon-web-services-evolves-sagemaker-unified-data-platform-centered-ai
Amazon Web Services Evolves SageMaker into a Unified Data Platform Centered On AI | Constellation Research Inc.
July 8, 2025 - The ML and AI model-development environment formerly known as SageMaker has been renamed SageMaker AI, which becomes another of the component services accessible through the new, unified Amazon Sagemaker.
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?
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.
Medium
medium.com › data-science › sagemaker-vs-vertex-ai-for-model-inference-ef0d503cee76
SageMaker vs Vertex AI for Model Inference | by Julia Turc | TDS Archive | Medium
June 6, 2024 - If you’re starting from scratch and have no affinity for one cloud provider over another (because of free credits, existing lock-in, or strong familiarity with their tooling), just go for SageMaker. However, if GCP already has you enthralled, stay there: Vertex AI is putting up a good enough fight.
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 - Notebooks: Sagemaker AI provides a fully managed Jupyter notebook environment (JupyterLab), which is pre-configured with popular ML frameworks and libraries, allowing practitioners to start experimenting and developing ML models. The notebook instances can be shared in real time with collaboration features as well as supercharged with AI coding assistants (Amazon CodeWhisper/Amazon Q developer and Jupyter AI).
YouTube
youtube.com › watch
What Happened to Amazon SageMaker? Changes and New Services Explained in Plain English - YouTube
Amazon SageMaker has changed, and if you’re feeling a little confused by it all, then this video is for you!In this short explainer, I’ll break down the late...
Published February 24, 2025
Reddit
reddit.com › r/datascience › why not sagemaker?
r/datascience on Reddit: Why not Sagemaker?
April 5, 2023 -
Hi All,
I am looking into the easiest way to set up machine learning infrastructure for a team. It seems like Sagemaker does exactly that. However, almost nobody uses it when I ask other DS teams. Curious why that is. None of the people I talked to had a strong reason not to use it.
Is anybody here using it that wants to share the pros and cons?
Top answer 1 of 13
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Con: Bloody expensive
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It does a few things well, like spin up a notebook server or host a simple model without much fuss. However, I’ve found that as soon as you color outside the lines or customize anything, you start fighting against the framework. SageMaker hides a lot of stuff from you in an attempt to be “easy”, but can end up being a pain to figure out if anything goes wrong. AWS kind of cobbles things together really fast just to say they have Feature X in sales meetings. It’s almost never a better experience than their competitors though.
Venturebeat
venturebeat.com › data-infrastructure › aws-sagemaker-is-transforming-into-a-combined-data-and-ai-hub
AWS SageMaker is transforming into a combined data and AI hub
August 25, 2025 - This all allows for easier AI app development for customers — since native access to data can make AI much faster and more efficient. Microsoft has been perceived a leader here, and now Amazon is catching up. "Many customers already use combinations of our purpose-built analytics and ML tools (in isolation), such as Amazon SageMaker — the de facto standard for working with data and building ML models — Amazon EMR, Amazon Redshift, Amazon S3 data lakes and AWS Glue.
Wikipedia
en.wikipedia.org › wiki › Amazon_SageMaker
Amazon SageMaker - Wikipedia
October 12, 2025 - Amazon SageMaker AI is a cloud-based machine-learning platform that allows the creation, training, and deployment by developers of machine-learning (ML) models on the cloud. It can be used to deploy ML models on embedded systems and edge-devices. The platform was launched in November 2017.
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 - In contrast, Bedrock's customizability appears less flexible than SageMaker. While it allows users to customize foundation models with their data, Bedrock only comes with default Foundation Language Models, although with fine-tuning capabilities. Bedrock is ideal for scenarios where organizations need advanced AI capabilities quickly without having to deal with building models or managing infrastructure.
CloudOptimo
cloudoptimo.com › home › blog › sagemaker vs azure ml vs google ai platform: a comprehensive comparison
SageMaker vs Azure ML vs Google AI Platform: A Comprehensive Comparison
March 13, 2025 - When comparing the architectures of these platforms, several key differences emerge: ... SageMaker is deeply integrated with the AWS ecosystem, offering seamless connections to various AWS services.
TechTarget
techtarget.com › searchenterpriseai › tip › Compare-Google-Vertex-AI-vs-Amazon-SageMaker-vs-Azure-ML
Compare Google Vertex AI vs. Amazon SageMaker vs. Azure ML | TechTarget
AWS offers savings plans for SageMaker that the vendor says can reduce overall costs by up to 64%. ... Your project involves developing a custom model, rather than tweaking an existing foundation model. Ease of use is a greater priority than fine-grained infrastructure control. Azure ML is the Microsoft Azure cloud service designed for developing and running AI models.
Medium
medium.com › @tdhtp2016 › aws-machine-learning-and-generative-ai-sagemaker-and-bedr-256512b5514f
AWS Machine Learning and Generative AI — SageMaker and Bedrock | by Duy Hưng | Medium
May 30, 2024 - Amazon Bedrock is an AWS service that helps you choose from different pre-built AI models that other companies make. These models can help you with tasks like making predictions or recognizing patterns. With Bedrock, you can easily customize these models with your data and put them into your applications without worrying about managing any technical stuff, such as model training using SageMaker or figuring out how to use IT infrastructure to train the model at scale.
GeeksforGeeks
geeksforgeeks.org › machine learning › sagemaker-vs-vertex-ai-for-model-inference
SageMaker vs Vertex AI for Model Inference - GeeksforGeeks
July 23, 2025 - SageMaker is ideal for organizations that are already invested in the AWS ecosystem and require flexibility with multi-model deployments and custom containers. Vertex AI is well-suited for those who prioritize a user-friendly interface, streamlined ...