aws sagemaker vs bedrock

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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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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.

AWS re:Post

repost.aws › questions › QURQ0DJ5oPSUyyaLv0jjS4vw › bedrock-vs-sagemaker

Bedrock vs Sagemaker | AWS re:Post

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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.

Discussions

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

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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

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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

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November 4, 2023

May I use Sagemaker/Bedrock to build APIs to use LM and LLM?

Of course you can! That's the benefit of Bedrock :) Here's the python code to interface with Bedrock + Claude v2. It's up to you how you want to run it - it could be through a Lambda function, a SageMaker notebook, or even locally on your laptop. You just need the appropriate AWS permissions and credentials. import boto3 import json boto3_bedrock_client = boto3.client(service_name='bedrock-runtime', region_name="us-east-1") prompt_data = "Explain black holes to 8th graders" modelId = 'anthropic.claude-v2' accept = 'application/json' contentType = 'application/json' body = json.dumps({ "prompt" : f"\n\nHuman:{prompt_data}\n\nAssistant:", "max_tokens_to_sample" : 300, "temperature" : 0.1, "top_p" : 0.9, }) response = boto3_bedrock_client.invoke_model( body = body, modelId = modelId, accept = accept, contentType = contentType ) response_body = json.loads(response.get('body').read()) print(response_body.get('completion')) More on reddit.com

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February 13, 2024

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