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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. Answer from AWS-User-alantam on repost.aws

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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 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 ...

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”

Top answer

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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

Discussions

LLM model inference deployment models and pricing comparison

Hi, IHAC who is looking for model inference pricing considerations and comparison of the different deployment options for an open source LLM like Llama: 1/ accessed through Amazon Bedrock 2/ deployed and accessed through SageMaker 3/ self hosted in EC2. More on repost.aws

repost.aws

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March 18, 2025

Sagemaker vs Bedrock

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. More on reddit.com

r/aws

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March 10, 2025

Advice Needed: SageMaker vs Bedrock for Fine-Tuned Llama Models (Cost & Serverless Options)

I’ve been seeing a lot of questions about choosing between SageMaker and Bedrock for ML deployments, so let me break down what I’ve learned after working with both. The main thing to understand is that they serve different needs. SageMaker Serverless is your friend if you’re cost-conscious and working with smaller models. It’s basically pay-per-use with no minimum fees, scales to zero when idle, and while it has some limits (6GB RAM, 200 concurrent invocations), it’s great for testing and development. Bedrock, on the other hand, is more of a commitment. For fine-tuned models, you’re looking at $21-50 per hour per model unit with a mandatory 1 or 6-month commitment. This can add up to around $15.5K monthly plus storage fees. No serverless option for fine-tuned models. From my experience, SageMaker is best when you want control. You get to play with different instance types, really dig into model training, and optimize costs. Bedrock’s strength is simplicity - clean API integration, managed infrastructure, and it’s great for quick prototyping and scaling production workloads. For testing, I’d strongly recommend SageMaker. You’ll learn more about the fine-tuning process, have better cost control, and more room to experiment. Plus, here’s a pro tip: consider a hybrid approach. Use SageMaker for fine-tuning but leverage Bedrock’s base models for inference where it makes sense. If you’re building a web application, think about implementing a queue-based architecture - it’ll help manage costs while keeping response times reasonable. My two cents… More on reddit.com

r/aws

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January 20, 2025

Bedrock vs SageMaker for LLM

Start with Bedrock, because you pay per API call. Using a SageMaker Jumpstart to get Llama2 up and running is easy, but you're paying for every second the underlying server is running, even if you're not inferencing. At some point, there will be a cost crossover, where it makes sense to self-host your infrastructure. More on reddit.com

r/aws

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November 5, 2023

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