Amazon Bedrock vs SageMaker AI: When to Use Each One

Brain John Aboze

| April 24, 2025 |

Introduction

The demand for artificial intelligence (AI) solutions is expected to skyrocket in the coming years, with the global AI market expected to reach $1.81 trillion by 2030 and 9 out of 10 organizations believing that AI can give them a competitive edge. The cloud plays an important role in the growth, scalability, and on-demand infrastructure that makes AI solutions (predictive or generative) accessible to organizations of all sizes. The cloud provides various AI tools from different providers, and choosing the right one for your organization is critical. The wrong choice can lead to wasted efforts and resources, missed opportunities, and suboptimal results. With this in mind, we will focus on AI offerings on Amazon Web Services (AWS), the largest cloud service provider, boosting over 31% of the market share. We will be looking into Amazon Bedrock and Amazon SageMaker, two standout AI services from AWS. While both enjoy a strong following, both serve two vastly different sets of requirements and skill sets. In this article, we will dissect their strengths, use cases, and when to use one over the other, and then make a well-informed determination for your business.

Cloud Service Market Share, Author

Overview of Amazon Bedrock

Amazon Bedrock is a fully managed AWS service that lets you combine popular generative AI foundational models (FM) with your application, cutting across but not limited to chatbots, automation, RAG pipeline, and agents. This offering is easily accessed via an API. A quick refresher: FMs are trained on prodigiously unlabeled datasets and are considerably bigger than traditional machine learning (ML) models. These FMs can be utilized as a baseline for developing and creating models that can be adapted for different use cases, such as stable diffusion of images and GPT series for natural language. So, Amazon Bedrock eliminates the need to manage FM workloads with its serverless infrastructure for your organization application.

Amazon Bedrock Overview, Author

Amazon Bedrock allows developers to fine-tune and customize FMs with their data. The fine-tuning( instruct-tuning) can be achieved by pointing Amazon Bedrock to label examples stored in an Amazon S3 instance. The service then uses these examples to fine-tune the selected FMs within a Virtual Private Cloud (VPC), ensuring that the original FMs remain unchanged. This approach keeps the base models isolated from customer data, which remains encrypted and secure within the customer’s VPC. These data encryption, private VPC connection, and access control provide robust security and compliance measures for building Gen AI applications with AWS bedrock.

Amazon Bedrock UI

Amazon Bedrock Studio (also known as Bedrock IDE) is now available in Amazon SageMaker Unified Studio. This is a web application that lets users easily experiment and build with Amazon Bedrock models, both serverless and via the Bedrock marketplace from industry-leading providers and emerging leaders, without having to set up and use a developer environment. The studio comprises two workspaces: the Explore mode to experiment with Amazon Bedrock models and the Build mode for building Amazon Bedrock-enabled apps.

Amazon SageMaker Unified Studio (Preview), Author

Amazon Bedrock integrates with leading providers, as of the time of writing this article. Let’s look at its model catalog, which contains serverless models (which will be the main focus) as well as marketplace models.


Model Providers	Models	Use Cases
A121labs	Jamba 1.5 Large, Jamba 1.5 Mini, Jamba-Instruct, Jurassic-2 Mid (Legacy), Jurassic-2 Ultra (Legacy)	Text generation, Conversation, Document summarization, Question answering, Test Classification, Instruction following
Amazon	Nova Models & Titan Models	Text generation, Image generation, Image-to-image, Video-to-text, Text-to-video, Embeddings, Code Generation, Instruction following, Fine-tuning, Rich text formatting, Background removal, Image conditioning
Anthropic	Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku, Claude 3 Sonnet, Claude 2.1, Claude, Claude Instant	Agents, Chat optimized, Code generation, Complex reasoning analysis, Conversation, Image-to-text, Math, Multilingual support, Natural language processing, Question answering, RAG, Text generation, Text summarization, Text-to-text, Translation
Cohere	Command R, Command R+, Command Light, Embed English, Embed Multilingual, Command	Natural language processing, Text generation, Text summarization, Instruction following, Embeddings
Meta	Llama 3.3 70B Instruct, Llama 3.2 11B Vision Instruct, Llama 3.2 1B Instruct, Llama 3.2 3B Instruct, Llama 3.2 90B Vision Instruct, Llama 3.1 70B Instruct, Llama 3.1 8B Instruct, Llama 3 70B Instruct, Llama 3 8B Instruct	Agents, Chat optimized, Code generation, Complex reasoning analysis, Instruction following, multilingual translation, Image Analysis, Text summarization, Text classification
Mistral AI	Mistral Large, Mistral 7B Instruct, Mixtral 8x7B Instruct, Mistral Small	Complex reasoning analysis, Text generation, Code generation, Text Classification, RAG, Agents
Stability AI	SDXL 1.0, Stable Diffusion 3.5 Large	Text-to-Image generation
Others (Marketplace Deployment)	Models from: Deepseek, Arcee AI, Camb AI, EvolutionaryScale, Gretel, HuggingFace, IBM Data & AI, John Snows Labs, KaraKuri Inc, LG CNS, Liquid AI, NCSOFT, NVIDIA, Preferred Networks, Stockmark Inc, Upstage, Widn.AI, Writer	Text Generation, Text Summarization, Text-to-Audio, Multimodal Generation, Generative AI, Natural Language Processing, Translation

Overview of Amazon Sagemaker AI

Amazon Sagemaker AI (formerly Amazon SageMaker) is a suite of tools designed to help build, train, and deploy ML models at scale. It is a fully managed service that supports the entire ML lifecycle.

Amazon SageMaker AI Overview, Author

The Sagemaker environments (applications and IDEs) include:

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). These notebook instances can be spun up quickly without the need to manage infrastructure.
RStudio: This is a fully managed RStudio Workbench IDE in the cloud for practitioners who prefer using R for data science and ML workflows. This environment allows data analysis and wrangling, training, and deploying models in R while utilizing Amazon scalable compute resources.
Canvas: This visual interface allows practitioners without a programming background to build ML models with no code and utilize a point-and-click interface, making ML more accessible.
Studio: SageMaker AI studio provides a centralized code-based IDE with all the tools and AWS offerings required for model building, training, tuning, and deployment. This enables teams to manage ML workflows with greater efficiency and collaboration. Studio is great for large-scale projects as it gives users a unified interface for the entire ML lifecycle. It is worth noting that the Amazon SageMaker Unified Studio (as of the time of writing this article, which is in preview release) is a bigger studio interface for data, analytics, and AI operations.

AWS SageMaker Studio UI, Author

TensorBoard: TensorBoard is a visualization toolkit for ML experimentation covering monitoring and debugging ML model training. In SageMaker, TensorBoard can visualize model output tensors, analyze parameters, and plot several training runs for comparison, all for improving model convergence and performance.
Profiler: The SageMaker Profiler helps in profiling, analyzing, and optimizing compute resource utilization for deep learning models. It helps identify bottlenecks, enabling developers to fine-tune their models for better efficiency.
Partner AI Apps: AWS now provides a carefully curated AI application for building GenAI and ML solutions in SageMaker AI as well as SageMaker Unified Studio (preview). These partners have been integrated with AWS to provide seamless workflow integration and provide streamlined access while still in the SageMaker development environment. Deepchecks is proud to be amongst the first partners and provides access to its LLM evaluation capabilities.

AWS SageMaker Partner AI Apps, AWS

Overall, SageMaker AI is a comprehensive suite of services and tools that makes building, training, and deploying models easier.

Comparing Functionalities

Below is a side-by-side comparison of the distinctions between the two AWS AI offerings.


Category	Amazon Bedrock	Amazon SageMaker AI
Use Cases	Ideal for quickly integrating generative AI capabilities into your application and experimenting with different pre‑trained foundation models (for example, prompt engineering, content moderation, chatbots, or rapid prototyping) without heavy investments in custom model development.	Optimized for building, training, and deploying custom machine learning models. Best suited for specialized or complex tasks-such as healthcare predictions or fraud detection-where deep control over the ML process is required.
Target Users	Geared toward developers and businesses with limited ML expertise who need a turnkey, API‐driven solution for generative AI applications.	Designed for data scientists, ML engineers, and experienced developers who want end‑to‑end control over the model lifecycle, including fine‑tuning and custom workflows.
Customization	Primarily offers pre‑trained foundation models with options for fine‑tuning with custom data in efficient isolation environments and making provision for custom model import, emphasizing simplicity and speed.	Provides extensive customization options across the entire ML workflow, cutting across data preprocessing, feature engineering, model training, hyperparameter tuning, and deployment-allowing tailored solutions for unique business needs.
Pricing	Uses a straightforward, pay‑as‑you‑go model based on the number of API calls. This pricing structure makes it cost‑effective and predictable for applications with intermittent or predictable workloads.	Charges are based on usage of computing resources, storage, and additional AWS services. This flexible model can be more cost‑variable and requires careful management, particularly for resource‑intensive projects. However, it provides a free tier on the first two months of use, which changes to an on-demand model upon expiration. It also provides a Savings Plan and gets a reduced charge in exchange for a commitment to use the service consistently.
Integration	Offers a serverless, API‑driven approach that abstracts away infrastructure management. Integration is simplified through a unified API that lets you plug pre‑trained models directly into applications.	Requires setting up and managing endpoints and leveraging SDKs, API Gateways, Lambda functions, and other AWS services. Although this offers greater flexibility, it demands a more hands‑on approach to integration and infrastructure.
Ease of Use & Setup	Emphasizes rapid deployment and ease of use with minimal configuration and automation-ideal for quick time‑to‑market.	Involves a steeper learning curve and more configuration effort due to the extensive control offered over the ML lifecycle, which is beneficial for complex, custom projects.
Key Differentiator	Focuses on reducing complexity by enabling developers to leverage pre‑trained foundation models quickly and securely, with a single API for accessing multiple models.	Acts as an end‑to‑end ML platform that supports everything from model experimentation to deployment, making it the choice for organizations needing deep customization and a full ML workflow.

The decision between Amazon SageMaker AI and Amazon Bedrock is not always mutually exclusive. In some cases, you may benefit from using both services together. For example, you could use Amazon Bedrock to quickly prototype and deploy a foundation model and then use SageMaker AI to refine and optimize the model for better performance.

Final Notes

When choosing between Amazon Bedrock and Amazon SageMaker, consider your team’s experience, project timelines, and long-term plans. If you have little or no AI experience or small development teams, Amazon Bedrock offers a serverless, simplified experience with pre-trained foundation models that allow for rapid prototyping and deployment. Pay-as-you-go pricing is attractive for MVPs or short projects where low predictability costs and ease of use are paramount.

In contrast, if your organization has experienced data scientists and ML engineers capable of managing end‑to‑end machine learning workflows, Amazon SageMaker is a full-fledged platform for deep customizability-from data preprocessing and model training to deployment and ongoing MLOps. This flexibility is particularly valuable for projects that require tailored models and robust scalability over the long term, although it may involve a steeper learning curve and more complex cost management.

In addition, adopting best practices is essential regardless of what you select. 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.

Ultimately, your decision should align with immediate project needs and future growth plans, ensuring that your selected service effectively supports your strategic AI initiatives over time.