Amazon Bedrock FAQs
General
Open allWhat is Amazon Bedrock?
Which FMs are available in Amazon Bedrock?
Amazon Bedrock customers can choose from some of the most cutting-edge FMs available today. This includes models from:
- AI21 Labs
- Amazon
- Anthropic
- Cohere
- DeepSeek
- Luma AI
- Meta
- Mistral AI
- OpenAI
- poolside (coming soon)
- Stability AI
- TwelveLabs
- Writer
See here for supported foundation models from each provider:
https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html
Why should I use Amazon Bedrock?
There are five reasons to use Amazon Bedrock for building generative AI applications.
Choice of leading FMs: Amazon Bedrock offers an easy-to-use developer experience to work with a broad range of high-performing FMs from leading AI companies. You can quickly experiment with a variety of FMs in the playground, and use a single API for inference regardless of the models you choose, giving you the flexibility to use FMs from different providers and keep up to date with the latest model versions with minimal code changes.
Easy model customization with your data: Privately customize FMs with your own data through a visual interface without writing any code. Simply select the training and validation data sets stored in Amazon Simple Storage Service (Amazon S3) and, if required, adjust the hyperparameters to achieve the best possible model performance.
Fully managed agents that can invoke APIs dynamically to execute tasks: Build agents that execute complex business tasks—from booking travel and processing insurance claims to creating ad campaigns, preparing tax filings, and managing your inventory—by dynamically calling your company systems and APIs. Fully managed agents for Amazon Bedrock extend the reasoning capabilities of FMs to break down tasks, create an orchestration plan, and execute it.
Native support for RAG to extend the power of FMs with proprietary data: With Amazon Bedrock Knowledge Bases, you can securely connect FMs to your data sources for retrieval augmentation—from within the managed service—extending the FM’s already powerful capabilities and making it more knowledgeable about your specific domain and organization.
Data security and compliance certifications: Amazon Bedrock offers several capabilities to support security and privacy requirements. Amazon Bedrock is in scope for common compliance standards such as Service and Organization Control (SOC), International Organization for Standardization (ISO), is Health Insurance Portability and Accountability Act (HIPAA) eligible, and customers can use Amazon Bedrock in compliance with the General Data Protection Regulation (GDPR). Amazon Bedrock is CSA Security Trust Assurance and Risk (STAR) Level 2 certified, which validates the use of best practices and the security posture of AWS cloud offerings. With Amazon Bedrock, your content is not used to improve the base models and is not shared with any model providers. Your data in Amazon Bedrock is always encrypted in transit and at rest, and you can optionally encrypt the data using your own keys. You can use AWS PrivateLink with Amazon Bedrock to establish private connectivity between your FMs and your Amazon Virtual Private Cloud (Amazon VPC) without exposing your traffic to the Internet.
How can I get started with Amazon Bedrock?
Link to Amazon Bedrock getting started course
Link to Amazon Bedrock user guide
What are the most common use cases for Amazon Bedrock?
You can quickly get started with use cases:
Create new pieces of original content, such as short stories, essays, social media posts, and web page copy.
Search, find, and synthesize information to answer questions from a large corpus of data.
Create realistic and artistic images of various subjects, environments, and scenes from language prompts.
Help customers find what they’re looking for with more relevant and contextual product recommendations than word matching.
Get a summary of textual content such as articles, blog posts, books, and documents to get the gist without having to read the full content.
Suggest products that match shopper preferences and past purchases
Explore more generative AI use cases.
What is Amazon Bedrock Playground?
In which AWS Regions is Amazon Bedrock available?
How do I customize a model on Amazon Bedrock?
Can I train a model and deploy it on Amazon Bedrock?
What is latency-optimized inference in Amazon Bedrock?
Available in public preview, latency-optimized inference in Amazon Bedrock offers reduced latency without compromising accuracy. As verified by Anthropic, with latency-optimized inference on Amazon Bedrock, Claude 3.5 Haiku runs faster on AWS than anywhere else. Additionally, with latency-optimized inference in Bedrock, Llama 3.1 70B and 405B runs faster on AWS than any other major cloud provider. Using purpose-built AI chips like AWS Trainium2 and advanced software optimizations in Amazon Bedrock, customers can access more options to optimize their inference for a particular use case.
Key Features:
Reduces response times for foundation model interactions
Maintains accuracy while improving speed
Requires no additional setup or model fine-tuning
Supported Models: Anthropic's Claude 3.5 Haiku and Meta's Llama 3.1 models 405B and 70B
Availability: The US East (Ohio) Region via cross-region inference
To get started, visit the Amazon Bedrock console. For more information visit the Amazon Bedrock documentation.
How do we get started with latency-optimized inference in Amazon Bedrock?
Accessing the latency-optimized inference in Amazon Bedrock requires no additional setup or model fine-tuning, allowing for immediate enhancement of existing generative AI applications with faster response times. You can toggle on the “Latency optimized” parameter while invoking the Bedrock inference API.
To get started, visit the Amazon Bedrock console. For more information visit the Amazon Bedrock documentation.
Agents
Open allWhat is Amazon Bedrock Agents?
Amazon Bedrock Agents is a fully managed capability that makes it easier for developers to create generative-AI based applications that can complete complex tasks for a wide range of use cases and deliver up-to-date answers based on proprietary knowledge sources. In just a few short steps, Amazon Bedrock Agents automatically breaks down tasks and creates an orchestration plan–without any manual coding. Agents created in Bedrock can securely connect to company data through an API, automatically converting data into a machine-readable format, and augmenting the request with relevant information to generate the most accurate response. Agents can then automatically call APIs to fulfill a user’s request. As a fully managed capability, Amazon Bedrock Agents removes the undifferentiated lifting of managing system integration and infrastructure provisioning, allowing developers to use generative AI to its full extent throughout their organization.
What is Amazon Bedrock AgentCore?
AgentCore enables developers to accelerate AI agents into production with the scale, reliability, and security, critical to real-world deployment. AgentCore provides tools and capabilities to make agents more effective and capable, purpose-built infrastructure to securely scale agents, and controls to operate trustworthy agents. AgentCore capabilities are composable and work with popular open-source frameworks and any model, so you don’t have to choose between open-source flexibility and enterprise-grade security and reliability. Learn more visit Amazon Bedrock AgentCore.
Who is AgentCore designed for?
AgentCore is designed for organizations who want to move AI agents from proofs of concept built using open source or custom agent frameworks to production. It serves developers and enterprises who need robust infrastructure to support dynamic execution paths at runtime, controls to monitor behavior, powerful tools to enhance agents, and the flexibility to adapt as the landscape evolves.
What key capabilities does AgentCore provide?
AgentCore includes services and tools that offer unique capabilities. These include:
Runtime: A secure, serverless runtime purpose-built for deploying and scaling dynamic AI agents and tools.
Memory: Makes it easy for developers to build context-aware agents by eliminating complex memory infrastructure management while providing full control over what the AI agent remembers.
Gateway: Provides a secure way for agents to discover and use tools along with easy transformation of APIs, Lambda functions, and existing services into agent-compatible tools.
Browser tool: Provides a fast, secure, cloud-based browser runtime to enable AI agents to interact with websites at scale.
Code Interpreter: Enables AI agents to write and execute code securely in sandbox environments, enhancing their accuracy and expanding their ability to solve complex end-to-end tasks.
Identity: Enables AI agents to securely access AWS services and third-party tools on behalf of users or autonomously with pre-authorization.
Observability: Gives developers complete visibility into agent workflows to trace, debug, and monitor AI agents' performance in production environments. With support for OpenTelemetry compatible telemetry and detailed visualizations of each step of the agent workflow, AgentCore enables developers to easily gain visibility into agent behavior and maintain quality standards at scale.
Which agent frameworks does AgentCore support?
AgentCore works with any open source agent framework including popular open-source frameworks like CrewAI, LangGraph, Strands Agents, and custom frameworks.
I am using Amazon Bedrock Agents today. Should I switch to AgentCore?
If you are using Amazon Bedrock Agents today, you can continue to use it. However, if you need additional functionalities such as being able to use any agent authoring framework (such as Strands Agents, Crew AI, LangGraph, LangChain, or LlamaIndex) and use any model along with fine-grained control on identity, memory, and observability, we recommend using AgentCore. AgentCore also provides upgraded tools and infrastructure for running agents at scale including identity, customizable long-term memory, an enhanced code interpreter tool, built-in browser tool, observability, native support for Model Context Protocol for connection to thousands of tools and a runtime with industry-leading execution time, payload size, and complete session isolation. To help customers take advantage of these improvements, we will have an option to easily export existing Bedrock Agents configurations as code that is compatible with Strands (for orchestration) and AgentCore (for production-grade deployment and more).
Security
Open allIs the content processed by Amazon Bedrock moved outside the AWS Region where I am using Amazon Bedrock?
Are user inputs and model outputs made available to third-party model providers?
What security and compliance standards does Amazon Bedrock support?
Will AWS and third-party model providers use customer inputs to or outputs from Amazon Bedrock to train Amazon Nova, Amazon Titan or any third-party models?
SDK
Open allWhat SDKs are supported for Amazon Bedrock?
What SDKs support streaming functionality?
Billing and support
Open allHow much does Amazon Bedrock cost?
What support is provided for Amazon Bedrock?
How can I track the input and output tokens?
Why do I see a billing entry for AWS Marketplace for my usage of AWS Bedrock?
Customization
Open allHow can I securely use my data to customize FMs available through Amazon Bedrock?
How does Amazon Bedrock ensure my data used in fine-tuning remains private and confidential?
Does Amazon Bedrock support continued pretraining?
Why should I use continued pretraining in Amazon Bedrock?
How does the continued pretraining feature relate to other AWS services?
How do I use continued pre-training?
Amazon Titan
Open allWhat are Amazon Titan models?
Where can I learn more about the data processed to develop and train Amazon Titan FMs?
Knowledge Bases / RAG
Open allWhich data sources can I connect to Amazon Bedrock Knowledge Bases?
How does Amazon Bedrock Knowledge Base retrieve data from structured data sources?
Does Amazon Bedrock Knowledge Bases support multi-turn conversations?
Does Amazon Bedrock Knowledge Bases provide source attribution for retrieved information?
What multi-modal capabilities does Amazon Bedrock Knowledge Bases offer?
What multi-modal data formats does Amazon Bedrock Knowledge Bases support?
What are the different parsing options available in Amazon Bedrock Knowledge Bases?
How does Amazon Bedrock Knowledge Bases ensure data security and manage workflow complexities?
Model evaluation
Open allWhat is Model Evaluation on Amazon Bedrock?
Against what metrics can I evaluate FMs?
What is the difference between human-based and automatic evaluations?
How does automatic evaluation work?
How does human evaluation work?
Guardrails
Open allWhat is Amazon Bedrock Guardrails?
What are the safeguards available in Amazon Bedrock Guardrails?
Guardrails help you to define a set of six policies to help safeguard your generative AI applications. You can configure the following policies in Amazon Bedrock Guardrails:
Multi modal content filters – Configure thresholds to detect and filter harmful text and/or image content across categories including hate, insults, sexual, violence, misconduct, and prompt attacks.
Denied topics – Define a set of topics that are undesirable in the context of your application. The filter will help block them if detected in user queries or model responses.
Word filters – Configure filters to help block undesirable words, phrases, and profanity (exact match). Such words can include offensive terms, competitor names, etc.
Sensitive information filters – Configure filters to help block or mask sensitive information, such as personally identifiable information (PII), or custom regex in user inputs and model responses. Blocking or masking is done based on probabilistic detection of sensitive information in standard formats in entities such as SSN number, Date of Birth, address, etc. This also allows configuring regular expression-based detection of patterns for identifiers.
Contextual grounding checks– Help detect and filter hallucinations if the responses are not grounded (e.g., factually inaccurate or new information) in the source information and irrelevant to user’s query or instruction.
Automated Reasoning checks – Help detect factual inaccuracies in generated content, suggest corrections, and explain why responses are accurate by checking against a structured, mathematical representation of knowledge called an Automated Reasoning Policy.
What modalities are supported with Bedrock Guardrails?
Can I use Guardrails with all available FMs and tools on Amazon Bedrock?
What are the safeguard tiers in Bedrock Guardrails?
Bedrock Guardrails provides safeguard tiers for content filters and denied topics with distinct performance characteristics and expanded language support for different application requirements and use cases. There are two tiers with Bedrock Guardrails: Standard tiers that provide robust performance with comprehensive language support for up to 60 languages. This tier requires opting into cross-region inference. Classic tier with established functionality and limited language support of 3 languages.
What languages are supported by Amazon Bedrock Guardrails?
With Standard Tier, Bedrock Guardrails supports 60 languages with varying support depending on the policy. The details of language support can be found here. With Classic Tier, Bedrock Guardrails supports English, French, and Spanish languages. Any unsupported language in either Classic or Standard Tier will result in ineffective results.
Do you have a list of off-the-shelf (built-in) guardrails, and what can be customized?
There are six guardrail policies, each with different off-the-shelf protections:
Content filters – This has 6 off the shelf categories (hate, insults, sexual, violence, misconduct (incl. criminal activity) and prompt attack (jailbreak and prompt injection. Each category can have further customized thresholds in terms of aggressiveness of filtering - low/medium/high for both text and image content.
Denied topic – These are customized topics that customers can define using simple natural language description
Sensitive information filter – These come with 30+ off the shelf PIIs. It can be further customized by adding customers’ proprietary information that are sensitive.
Word filters – It comes with off the shelf profanity filtering and can be further customized with custom words.
- Contextual grounding checks – It can help detect hallucinations for RAG, summarization, and conversational applications, where source information can be used as reference to validate the model response.
- Automated Reasoning checks - This safeguard validates against a completely custom domain knowledge policy that you can create and refine starting from a simple document. Using formal verification techniques, Automated Reasoning checks identifies correct model responses with up to 99% accuracy to minimize hallucinations.
How can I enforce Guardrails across my organization?
Does AWS offer an intellectual property indemnity covering copyright claims for its generative AI services?
Do default Guardrails automatically detect social security numbers or phone numbers?
What is the pricing model for using Amazon Bedrock Guardrails?
Are customers able to run automated tests on the effectiveness of the Guardrails they set? Is there a “test case builder” (the journalist’s terminology) for ongoing monitoring?
Yes, Amazon Bedrock Guardrail APIs help customers run automated tests. “Test case builder” maybe something you want to use prior to deploying guardrails in production. There is no native test case builder yet. For ongoing monitoring of production traffic, guardrails help provide detailed logs of all violations for each input and output, so that customers can granularly monitor every input coming and going out of their gen AI application. These logs can be stored in Amazon CloudWatch or S3 and can be used to create custom dashboards based on customers’ requirements.
How is validation using Automated Reasoning checks different from Contextual Grounding checks?
Using an Automated Reasoning Policy, Automated Reasoning checks can point out both accurate claims and factual inaccuracies in content. For both accurate and inaccurate statements, Automated Reasoning check provides verifiable, logical explanations for its output. Automated Reasoning check requires upfront involvement from a domain expert to create a Policy and only supports content that defines rules. On the other hand, Contextual grounding checks in Bedrock Guardrails uses machine learning techniques to ensure the generated content closely follows the documents that were provided as input from a knowledge base, without requiring any additional upfront work. Both Automated Reasoning Checks and Contextual Grounding provide their feedback in the Guardrail API output. You can use feedback to update the generated content.
What image formats are supported for multimodal content?
How are you able to deliver 99% correctness of model responses using Automated Reasoning checks in Bedrock Guardrails?
We use Automated Reasoning formal verification techniques alongside LLMs to identify up to 99% of the valid statements. Automated Reasoning checks' feedback on content points out ambiguity and suggests corrections for wrong or incomplete answers. The feedback makes it easy to rewrite answers until they are judged valid.
Marketplace
Open allWhat is Amazon Bedrock Marketplace?
Why should I use Amazon Bedrock Marketplace?
How do I get started with Amazon Bedrock Marketplace?
Can I fine-tune Amazon Bedrock Marketplace models?
Data Automation
Open allWhat is Bedrock Data Automation?
Why should I use Bedrock Data Automation?
What does Amazon Bedrock Data Automation manage on my behalf?
What is a blueprint?
What features and file formats are supported per modality by Amazon Bedrock Data Automation
Documents
Bedrock Data Automation supports both standard output and custom output for documents.
Standard output will provide extraction of text from documents and generative output such as document summary and captions for tables/figures/diagrams. Output is returned in reading order and can optionally be grouped by layout element, which will include headers/footers/titles/tables/figures/diagrams. Standard output will be used for BDA integration with Bedrock Knowledge Bases.
Custom Output leverages blueprints, which specify output requirements using natural language or a schema editor. Blueprints include a list of fields to extract and a data format for each field.
Bedrock Data Automation supports PDF, PNG, JPG, TIFF, a max of 1500 pages, and a max file size of 500MB per API request. By default, BDA will support 50 concurrent jobs and 10 transactions per second per customer.
Images
Bedrock Data Automation supports both standard output and custom output for images.
Standard output will provide summarization, detected explicit content, detected text, logo detection and Ad taxonomy: IAB for images. Standard output will be used for BDA integration with Bedrock Knowledge Bases.
Custom Output leverages blueprints, which specify output requirements using natural language or a schema editor. Blueprints include a list of fields to extract and a data format for each field.
Bedrock Data Automation supports JPG, PNG, a max resolution of 4K, and a max file size of 5 MB per API request. By default, BDA supports a max concurrency of 20 images at 10 transactions per second (TPS) per customer.
Videos
Bedrock Data Automation supports both standard output for videos.
Standard output will provide full video summary, chapter segmentation, chapter summary, full audio transcription, speaker identification, detected explicit content, detected text, logo detection and Interactive Advertising Bureau (IAB) taxonomy for videos. Full video summary is optimized for content with descriptive dialogue such as product overviews, trainings, news casts, and documentaries.
Bedrock Data Automation supports MOV and MKV with H.264, VP8, VP9, a max video duration of 4 hours, and a max file size of 2 GB per API request. By default, BDA supports a max concurrency of 20 videos at 10 transactions per second (TPS) per customer.
Audio
Bedrock Data Automation supports both standard output for audio.
Standard output will provide summarization including chapter summarization, full transcription, and detect explicit content moderation for audio files.
Bedrock Data Automation supports FLAC, M4A, MP3, MP4, Ogg, WebM, WAV, a max audio duration of 4 hours, and a max file size of 2 GB per API request.
In which AWS regions is Amazon Bedrock Data Automation available?
What languages does Amazon Bedrock Data Automation support?
Amazon Bedrock in SageMaker Unified Studio
Open allWhat is Amazon Bedrock in SageMaker Unified Studio?
How do I access Amazon Bedrock's capabilities in Amazon SageMaker Unified Studio?
To access Amazon Bedrock's capabilities within Amazon SageMaker Unified Studio, developers and their admins will need to follow these steps:
Create a new domain in Amazon SageMaker Unified Studio.
Enable the Gen AI application development project profile.
Access Amazon Bedrock through the Generative AI Playground (Discover) and Generative AI App Development (Build) sections, using their company's single sign-on (SSO) credentials within Amazon SageMaker Unified Studio.
What are the key features and capabilities of Amazon Bedrock in Amazon SageMaker Unified Studio? How is it different from Amazon Bedrock Studio and Amazon Bedrock IDE?
New features include a model hub for side-by-side AI model comparison, an expanded playground supporting chat, image, and video interactions, and improved Knowledge Base creation with web crawling. It introduces Agent creation for more complex chat applications and simplifies sharing of AI apps and prompts within organizations. It also offers access to underlying application code and the ability to export chat apps as CloudFormation templates. By managing AWS infrastructure details, it enables users of various skill levels to create AI applications more efficiently, making it a more versatile and powerful tool than its predecessor.
Amazon Bedrock IDE was renamed to better represent the core capability of Amazon Bedrock being accessed through Amazon SageMaker Unified Studio's governed environment.
How does Amazon Bedrock in SageMaker Unified Studio enable collaboration among teams within an organization?
Why is Amazon Bedrock being integrated into Amazon SageMaker Unified Studio?
The unified environment allows seamless collaboration among developers of various skill levels throughout the development lifecycle - from data preparation to model development and generative AI application building. Teams can access integrated tools for knowledge base creation, guardrail configuration, and high-performing generative AI application development, all within a secure and governed framework.
Within Amazon SageMaker Unified Studio, developers can effortlessly switch between different tools based on their needs, combining analytics, machine learning, and generative AI capabilities in a single workspace. This consolidated approach reduces development complexity and accelerates time-to-value for generative AI projects. By bringing Amazon Bedrock into Amazon SageMaker Unified Studio, AWS lowers the barriers to entry for generative AI development while maintaining enterprise-grade security and governance, ultimately enabling organizations to innovate faster and more effectively with generative AI.
When should I use Amazon Bedrock's capabilities in Amazon SageMaker Unified Studio?
Amazon Bedrock's capabilities in Amazon SageMaker Unified Studio are ideal for enterprise teams who need a governed environment for collaboratively building and deploying generative AI applications. Through Amazon SageMaker Unified Studio, teams can access:
The Generative AI Playground in the Discover section enables teams to experiment with foundation models (FMs), test different models and configurations, compare model outputs, and collaborate on prompts and applications. This environment provides a seamless way for teams to evaluate and understand the capabilities of different models before implementing them in their applications.
The Generative AI App Development in the Build section provides teams with the tools needed to create production-ready generative AI applications. Teams can create and manage Knowledge Bases, implement Guardrails for responsible AI, develop Agents and Flows, and collaborate securely while maintaining governance and compliance controls. This environment is particularly valuable for organizations that require secure collaboration and seamless access to Amazon Bedrock's full range of capabilities while maintaining enterprise security and compliance standards.
How does Amazon Bedrock integrate with other AWS services within Amazon SageMaker Unified Studio to create generative AI applications?
Within Amazon SageMaker Unified Studio, Amazon Bedrock seamlessly integrates with Amazon SageMaker's analytics, machine learning (ML), and generative AI capabilities. Organizations can move from concept to production faster by prototyping and experimenting with foundation models in Amazon Bedrock, then easily transitioning to JupyterLab notebooks or code editors to integrate these resources into broader applications and workflows. This consolidated workspace streamlines complexity, enabling faster prototyping, iteration, and deployment of production-ready, responsible generative AI applications that align with specific business requirements.
Are there any limits or quotas on the usage of Amazon Bedrock in SageMaker Unified Studio?
What are the pricing and billing models for using Amazon Bedrock in SageMaker Unified Studio?
What are the Service Level Agreements (SLAs) for Amazon Bedrock in SageMaker Unified Studio?
What documentation and support resources are available for Amazon Bedrock in SageMaker Unified Studio?
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