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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11For a new AI-agent project, start with Amazon Bedrock if you want managed foundation-model access and agent capabilities without taking on as much infrastructure work. Choose Amazon SageMaker AI when your project depends on deeper model training or customization, or you need more direct control over deployment, cost, throughput, and latency. You can also combine them: AWS documents deploying a model trained in SageMaker AI to Bedrock for serverless inference.
One important update changes the agent decision: Amazon Bedrock Agents has been renamed Amazon Bedrock Agents Classic and is no longer open to new customers. AWS directs new projects toward Amazon Bedrock AgentCore. Evaluate AgentCore’s current feature fit rather than treating older Agents Classic tutorials as a new-build guide.
Bedrock and SageMaker AI solve different parts of an agent system
These services overlap in AI applications, but their centers of gravity differ. Bedrock emphasizes managed access to foundation models and application-building capabilities. SageMaker AI emphasizes the model lifecycle: building, training, customizing, and deploying models, including predictive and classical machine learning.
| Decision area | Amazon Bedrock | Amazon SageMaker AI |
|---|---|---|
| Primary role | Managed, serverless services for building, running, and operating AI applications and agents. | Tools to build, train, customize, and deploy AI, predictive ML, and classical ML models. |
| Agent role | AgentCore is AWS’s current named offering for building, deploying, and operating agents at scale. Bedrock also offers related capabilities such as Knowledge Bases and Guardrails. | Can provide the model development, customization, or inference layer within a broader agent system. |
| Control and operations | Pre-trained model access and a simpler API approach can reduce infrastructure management. | Training jobs, dedicated endpoints, and HyperPod provide more direct control over models and infrastructure. |
| Customization options | AWS lists fine-tuning, distillation, reinforcement fine-tuning, and custom model import with minimal infrastructure management. | Offers serverless customization and managed training, as well as more hands-on training and deployment through training jobs and HyperPod. |
| Pricing shape | Primarily per-token pricing, with service tiers described in AWS’s guide. Check current rates and eligibility. | Per-token charges for serverless customization; usage-based charges for compute resources, training, inference, and HyperPod. Check current rates and instance needs. |
These are broad service-level distinctions, not a claim that every workload or feature is available in every AWS Region. Confirm current model availability, AgentCore capabilities, and pricing for the Region and architecture you plan to use.
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When Bedrock is the better starting point
You want managed agent development
Choose Bedrock when reducing infrastructure work is a primary goal and you want to build around managed foundation-model access. AWS positions AgentCore as its current offering for building, deploying, and operating agents at scale. Bedrock’s adjacent services, including Knowledge Bases and Guardrails, may also suit applications that need managed information retrieval or safeguards.
Your priority is application behavior, not training a model
If the project mainly involves connecting a model to application tools, data, and user interactions, Bedrock is the more natural starting point. It keeps the focus on the AI application and its managed services rather than requiring you to first build a custom model-training and hosting workflow.
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When SageMaker AI is the better fit
Your agent depends on a customized model
Lean toward SageMaker AI when model development is central: for example, when you need to train or customize a model, or need the model lifecycle controls offered through training jobs and HyperPod. AWS also lists serverless customization options, so the choice is not simply “managed Bedrock” versus “fully manual SageMaker.” The relevant question is how much control your model work requires.
You need to manage deployment tradeoffs directly
SageMaker AI is a stronger fit when the team needs to manage cost, throughput, and latency tradeoffs more directly through deployment and compute choices. That flexibility comes with more infrastructure decisions than a managed, serverless path; weigh it against your team’s operational capacity and workload requirements.
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- What You Get: Anker USB-C Hub (7-in-1), welcome guide, 18-month warranty, and our friendly customer service.
Can you use SageMaker AI and Bedrock together?
Yes. AWS documents a combined pattern in which a model trained in SageMaker AI is deployed either to a SageMaker endpoint or HyperPod, or to Bedrock for serverless inference. This lets you separate model development from the way the model is served to an application or agent.
Use a combined design when SageMaker AI’s training or customization capabilities suit the model, while Bedrock’s managed inference or agent-adjacent services suit the application. Decide where the model will run and which service owns each responsibility before implementation; do not assume the same deployment and operational characteristics across the available targets.
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What changed for Bedrock agents
Older AWS material and tutorials may describe Amazon Bedrock Agents. AWS has renamed that offering Amazon Bedrock Agents Classic and says it is no longer open to new customers; existing customers can continue using it. AWS directs customers seeking similar capabilities to AgentCore. For a new project, investigate AgentCore and verify its current feature fit rather than assuming that Agents Classic documentation applies unchanged.
Agents Classic documentation describes an agent that can orchestrate foundation models, data sources, software applications, and conversations. Its configuration concepts include action groups for APIs and actions, Knowledge Bases for retrieval, natural-language conversational configuration, and inline invocation with capabilities specified at runtime. AWS Prescriptive Guidance also describes a configuration-led managed approach with knowledge-base integration, prompt customization, tracing, and agent versioning. These are useful architectural concepts, but they describe the legacy product and do not establish that AgentCore behaves identically.
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How to choose an agent framework within your AWS architecture
Choosing Bedrock or SageMaker AI does not by itself settle every framework decision. AWS advises evaluating an agent framework against the application and team, including:
- Integration with AWS infrastructure.
- Compatibility with the preferred model and API interfaces.
- Multimodal requirements.
- Workflow complexity and whether agents must collaborate.
- Production deployment and monitoring needs.
- The team’s learning curve.
AWS’s framework comparison describes Bedrock Agents as fully managed with a low learning curve, while frameworks such as LangGraph and Strands make different tradeoffs. That framework-specific comparison is not a head-to-head rating of Bedrock versus SageMaker AI.
Quick Recap
A practical decision sequence
- Start with the model work. If the main challenge is training or substantially customizing a model, assess SageMaker AI first. If a pre-trained model and managed application services are sufficient, assess Bedrock first.
- Set the infrastructure boundary. Decide how much responsibility your team wants for compute, training, deployment, and serving. A preference for less infrastructure management favors Bedrock; a need for more direct control favors SageMaker AI.
- Choose the current agent route. For new projects, evaluate AgentCore. Treat Agents Classic guides as legacy references, not as evidence that new customers can adopt that service.
- Check fit beyond the service name. Validate model and API compatibility, multimodal needs, workflow complexity, AWS integration, production monitoring, and team learning curve.
- Consider a split architecture. If model customization and managed serving are both priorities, assess the documented path from SageMaker AI training to Bedrock inference alongside endpoint or HyperPod deployment.
- Verify current specifics. Check the selected Region’s availability, the current feature set, pricing, and any eligibility conditions before committing to an architecture.
Sources and current availability
- AWS decision guide: Amazon Bedrock or Amazon SageMaker AI? The guide was last updated July 23, 2026.
- AWS documentation: Amazon Bedrock Agents (legacy Agents Classic documentation).
- AWS Prescriptive Guidance: Amazon Bedrock Agents (legacy product guidance).
- AWS Prescriptive Guidance: Choosing an agentic AI framework.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
