Amazon Bedrock FAQs

Amazon Bedrock Agents are fully managed capabilities that make 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 break down tasks and create an orchestration plan–without any manual coding. The agent securely connects 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. For example, a manufacturing company might want to develop a generative AI application that automates tracking inventory levels, sales data, supply chain information and that can recommend optimal reorder points and quantities to maximize efficiency. As fully managed capabilities, Amazon Bedrock Agents remove the undifferentiated lifting of managing system integration and infrastructure provisioning, allowing developers to use generative AI to its full extent throughout their organization.

You can securely connect FMs to your company data sources using Amazon Bedrock Agents. With a knowledge base, you can use agents to give FMs in Amazon Bedrock access to additional data that helps the model generate more relevant, context-specific, and accurate responses without continually retraining the FM. Based on user input, agents identify the appropriate knowledge base, retrieve the relevant information, and add the information to the input prompt, giving the model more context information to generate a completion.

Amazon Bedrock Agents can help you increase productivity, improve your customer service experience, and automate workflows (such as processing insurance claims).

With agents, developers have seamless support for monitoring, encryption, user permissions, versioning, and API invocation management without writing custom code. Amazon Bedrock Agents automate the prompt engineering and orchestration of user-requested tasks. Developers can use the agent-created prompt template as a baseline to further refine it for an enhanced user experience. They can update the user input, orchestration plan, and the FM response. With access to the prompt template developers have better control over the Agent orchestration.

With fully managed agents, you don’t have to worry about provisioning or managing infrastructure and can take applications to production faster.

Any customer content processed by Amazon Bedrock is encrypted and stored at rest in the AWS Region where you are using Amazon Bedrock.

No. Users' inputs and model outputs are not shared with any model providers.

Amazon Bedrock offers several capabilities to support security and privacy requirements. Amazon Bedrock is in scope for common compliance standards such as Fedramp Moderate, Service and Organization Control (SOC), International Organization for Standardization (ISO), Health Insurance Portability and Accountability Act (HIPAA) eligibility, and customers can use Bedrock in compliance with the General Data Protection Regulation (GDPR). Amazon Bedrock is included in the scope of the SOC 1, 2, 3 reports, allowing customers to gain insights into our security controls. We demonstrate compliance through extensive third-party audits of our AWS controls. Amazon Bedrock is one of the AWS services under ISO Compliance for the ISO 9001, ISO 27001, ISO 27017, ISO 27018, ISO 27701, ISO 22301, and ISO 20000 standards. 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. You can use AWS PrivateLink to establish private connectivity from Amazon VPC to Amazon Bedrock, without having to expose your data to internet traffic.

 

No, AWS and the third-party model providers will not use any inputs to or outputs from Amazon Bedrock to train Amazon Titan or any third-party models.

Amazon Bedrock supports SDKs for runtime services. iOS and Android SDKs, as well as Java, JS, Python, CLI, .Net, Ruby, PHP, Go, and C++, support both text and speech input.

Streaming is supported on all the SDKs.

Please see the Amazon Bedrock pricing page for current pricing information.

Depending on your AWS Support contract, Amazon Bedrock is supported under Developer Support, Business Support and Enterprise Support plans.

You can use CloudWatch metrics to track the inputs and output token.

With Amazon Bedrock, you can privately customize FMs, retaining control over how your data is used and encrypted. Amazon Bedrock makes a separate copy of the base FM and trains this private copy of the model. Your data including prompts, information used to supplement a prompt, and FM responses. Customized FMs remain in the Region where the API call is processed.

When you’re fine-tuning a model, your data is never exposed to the public internet, never leaves the AWS network, is securely transferred through your VPC, and is encrypted in transit and at rest. Amazon Bedrock also enforces the same AWS access controls that you have with any of our other services.

We launched continued pretraining for Amazon Titan Text Express and Amazon Titan models on Amazon Bedrock. Continued pretraining allows you to continue the pretraining on an Amazon Titan base model using large amounts of unlabeled data. This type of training will adapt the model from a general domain corpus to a more specific domain corpus such as medical, law, finance, and so on, while still preserving most of the capabilities of the Amazon Titan base model. 

Enterprises may want to build models for tasks in a specific domain. The base models may not be trained on the technical jargon used in that specific domain. Thus, directly fine-tuning the base model requires large amounts of labeled training records and a long training duration to get accurate results. To ease this burden, the customer can instead provide large amounts of unlabeled data for a continued pretraining job. This job will adapt the Amazon Titan base model to the new domain. Then the customer may fine-tune the newly pretrained custom model to downstream tasks, using significantly fewer labeled training records and with a shorter training duration. 

Amazon Bedrock continued pretraining and fine-tuning have very similar requirements. For this reason, we are choosing to create unified APIs that support both continued pretraining and fine-tuning. Unification of the APIs reduces the learning curve and will help customers use standard features such as Amazon EventBridge to track long running jobs, Amazon S3 integration for fetching training data, resource tags, and model encryption. 

Continued pretraining helps you adapt the Amazon Titan models to your domain specific data while still preserving the base functionality of the Amazon Titan models. To create a continued pretraining job, navigate to the Amazon Bedrock console and click on "Custom Models." You will navigate to the custom model page that has two tabs: Models and Training jobs. Both tabs provide a “Customize Model” drop-down menu on the right. Select “Continued Pretraining” from the drop-down menu to navigate to “Create Continued Pretraining Job." You will provide the source model, name, model encryption, input data, hyper-parameters and output data. Additionally, you can provide tags, along with details about AWS Identity and Access Management (IAM) roles and resource policies for the job.

Exclusive to Amazon Bedrock, the Amazon Titan family of models incorporates 25 years of Amazon experience innovating with AI and machine learning across the business. Amazon Titan FMs provide customers with a breadth of high-performing image, multimodal, and text model choices through a fully managed API. Amazon Titan models are created by AWS and pretrained on large datasets, making them powerful, general-purpose models built to support a variety of use cases, while also supporting the responsible use of AI. Use them as is or privately customize them with your own data. Learn more about Amazon Titan.

To learn more about data processed to develop and train Amazon Titan FMs, visit Amazon Titan Model Training and Privacy page.

You can ingest content from various sources, including the web, Amazon Simple Storage Service (Amazon S3), Confluence (preview), Salesforce (preview), and SharePoint (preview). You can also programmatically ingest streaming data or data from unsupported sources. You can also connect to your structured data sources such as Redshift datawarehouse and AWS Glue data catalog.

Amazon Bedrock Knowledge Bases provides a managed Natural Language to SQL to convert natural language into actionable SQL queries and retrieve data, allowing you to build application using data from these sources.

Yes, session context management is built-in, allowing your applications to maintain context across multiple interactions, which is essential for supporting multi-turn conversations.

Yes, all information retrieved includes citations, improving transparency and minimizing the risk of hallucinations in the generated responses.

Amazon Bedrock Knowledge Bases supports multi-modal data processing, allowing developers to build generative AI applications that analyze both text and visual data, including images, charts, diagrams, and tables. Model responses can leverage insights from visual elements in addition to text, providing. more accurate and contextually relevant answers. Additionally, source attribution for responses includes visual elements, enhancing transparency and trust in the responses.

Amazon Bedrock Knowledge Bases can process visually rich documents in PDF format, which may contain images, tables, charts, and diagrams. For image-only data, Bedrock Knowledge Bases supports standard image formats like JPEG and PNG, enabling search capabilities where users can retrieve relevant images based on text-based queries.

Customers have three parsing options for Bedrock Knowledge Bases. For text-only processing, the built-in default Bedrock parser is available at no additional cost, ideal for cases where multimodal data processing is not required. Amazon Bedrock Data Automation (BDA) or foundation models can be used to parse multimodal data. For more information, refer to the product documentation. 

Amazon Bedrock Knowledge Base handles various workflow complexities such as content comparison, failure handling, throughput control, and encryption, ensuring that your data is securely processed and managed according to AWS’s stringent security standards.

Model Evaluation on Amazon Bedrock allows you to evaluate, compare, and select the best FM for your use case in just a few short steps. Amazon Bedrock offers a choice of automatic evaluation and human evaluation. You can use automatic evaluation with predefined metrics such as accuracy, robustness, and toxicity. You can use human evaluation workflows for subjective or custom metrics such as friendliness, style, and alignment to brand voice. For human evaluation, you can use your in-house employees or an AWS-managed team as reviewers. Model Evaluation on Amazon Bedrock provides built-in curated datasets or you can bring your own datasets.

You can evaluate variety of predefined metrics such as accuracy, robustness, and toxicity using automatic evaluations. You can also use human evaluation workflows for subjective or custom metrics, such as friendliness, relevance, style, and alignment to brand voice.

Automatic evaluations allow you to quickly narrow down the list of available FMs against standard criteria (such as accuracy, toxicity and robustness). Human-based evaluations are often used to evaluate more nuanced or subjective criteria that require human judgment and where automatic evaluations might not exist (such as brand voice, creative intent, friendliness).

You can quickly evaluate Amazon Bedrock models for metrics such as accuracy, robustness, and toxicity by using curated built-in data sets or by bringing your own prompt datasets. After your prompt datasets are sent to Amazon Bedrock models for inference, the model responses are scored with evaluation algorithms for each dimension. The backend engine aggregates individual prompt response scores into summary scores and presents them through easy-to-understand visual reports.

Amazon Bedrock allows you to set up human review workflows in a few short steps and bring your in-house employees, or use an expert team managed by AWS, to evaluate models. Through Amazon Bedrock’s intuitive interface, humans can review and give feedback on model responses by clicking thumbs up or down, rating on a scale of 1-5, choosing the best of multiple responses, or ranking prompts. For example, a team member can be shown how two models respond to the same prompt, and then be asked to select the model that shows more accurate, relevant, or stylistic outputs. You can specify the evaluation criteria that matter to you by customizing the instructions and buttons to appear on the evaluation UI for your team. You can also provide detailed instructions with examples and the overall goal of model evaluation, so users can align their work accordingly. This method is useful to evaluate subjective criteria that require human judgement or more nuanced subject matter expertise and that cannot be easily judged by automatic evaluations.

Amazon Bedrock Guardrails help you implement safeguards for your generative AI applications based on your use cases and responsible AI policies. Guardrails helps control the interaction between users and FMs by filtering undesirable and harmful content and will soon redact personally identifiable information (PII), enhancing content safety and privacy in generative AI applications. You can create multiple guardrails with different configurations tailored to specific use cases. Additionally, with the guardrails you can continually monitor and analyze user inputs and FM responses that might violate customer-defined policies.

Guardrails help you to define a set of policies to help safeguard your generative AI applications. You can configure the following policies in a guardrail.

• 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.
• Content filters: help you configure thresholds to detect and filter harmful text content across categories such as hate, insults, sexual, violence, misconduct, and prompt attacks. Additionally, content filters can detect and filter harmful image content across these categories thereby helping build safe multimodal applications.
• Denied topics: help you define a set of topics that are undesirable in the context of your application. For example, an online banking assistant can be designed to refrain from providing investment advice.
• Word filters: help you define a set of words to block in user inputs and FM–generated responses.
• Sensitive information filter: helps you react sensitive information like a set of PII that can be redacted in FM–generated responses. Based on the use case, Guardrails can also help you block a user input if it contains PII.

Amazon Bedrock Guardrails works with a wide range of models including FMs supported in Amazon Bedrock, fine-tuned models, as well as, self-hosted models outside Amazon Bedrock. User inputs and model outputs can be evaluated independently for third-party and self-hosted models using the ApplyGuardrail API. Amazon Bedrock Guardrails can also be integrated with Amazon Bedrock Agents and Amazon Bedrock Knowledge Bases to build safe and secure generative AI applications aligned with responsible AI policies

AWS offers an uncapped intellectual property (IP) indemnity for copyright claims arising from generative output of the following generally available Amazon generative AI services: Amazon Titan models, and other services listed in Section 50.10 of the Service Terms (the “Indemnified Generative AI Services”). This means that customers are protected from third-party claims alleging copyright infringement by the output generated by the Indemnified Generative AI Services in response to inputs or other data provided by the customer. Customers must also use the services responsibly, such as not inputting infringing data or disabling a service’s filtering features.

There are five 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 customer’s 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.

Foundations model have native safeguards and they are the default protections associated with each model. These native safeguards are NOT part of Amazon Bedrock Guardrails. Amazon Bedrock Guardrails is an added layer of customized safeguards that can be optionally applied by the customer based on their application requirements and responsible AI policies.

As part of Amazon Bedrock Guardrails, SSN and phone number detection are part of the 30+ off the shelf PIIs. Full list here.

There is a separate cost for using Amazon Bedrock Guardrails. It can be applied for both input and output. Pricing at the bottom of the page here. The pricing for image support with content filters (currently in public preview) will be announced during general availability (GA).

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 violation for each input and output, so that customers can granularly monitor each and every input coming and going out of their gen AI application. These logs can be stored in CloudWatch or S3 and can be used to create custom dashboards based on customers’ requirements.

Using an Automated Reasoning Policy, Automated Reasoning checks can point 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 the feedback to update the generated content.

Amazon Bedrock Marketplace offers customers over 100 popular, emerging, or specialized models, in addition to the serverless FMs of Amazon Bedrock so customers can easily build and optimize their generative AI applications. Within the Amazon Bedrock console, customers will be able to discover a broad catalog of FMs offered by various providers. You can then deploy these models onto fully managed endpoints, where you can choose your desired number of instances and instance types. Once the models are deployed, the models can be accessed through Amazon Bedrock’s Invoke API. For chat-tuned, text-to-text models, customers can use our new Converse API, a unified API that abstracts FM differences and enables model switching with a single parameter change. Where applicable, the models can be used with Amazon Bedrock Playground, Agents, Knowledge Bases, Prompt Management, Prompt Flows, Guardrails, and Model Evaluation.

You should use Amazon Bedrock Marketplace to benefit from the powerful models which are emerging rapidly as the generative AI industry continues to innovate. You can quickly access and deploy popular, emerging, and specialized models tailored to you unique requirements, which can accelerate the time-to-market, improve the accuracy, or reduce the cost of your generative AI workflows. You can access the models through Bedrock’s unified APIs and, if they are compatible with Bedrock’s Converse API, use them natively with Bedrock tools such as Agents, Knowledge Bases, and Guardrails. You can easily connect Amazon Bedrock Marketplace to Amazon Bedrock’s serverless models, all from a single place.
 

Simply navigate to the Amazon Bedrock Model Catalgo page in the Bedrock console where you can search for Amazon Bedrock Marketplace model listings along with the serverless Amazon Bedrock models. After you have selected the Amazon Bedrock Marketplace model you want to use, you can subscribe to the model through the Model Detail page, accepting the EULA and price(s) set by the provider. Once the subscription is complete, which typically takes a few minutes, you can deploy the model to a fully managed SageMaker endpoint by clicking on Deploy in the Model Detail page or by using APIs. In the deployment step, you can select your desired number of instances and instance types to meet your workload. Once the endpoint is setup, which typically takes 10 – 15 minutes, you can start making inference calls to the endpoint and use the model in Bedrock’s advanced tools, provided the model is compatible with Bedrock’s Converse API.

Models with architectures supported by Custom Model Import (Mistral, Mixtral, Flan, and Llama2/3/3.1/3.2) can be fine-tuned in SageMaker and made available in Amazon Bedrock via Custom Model Import. Models which are not supported by Custom Model Import can still be fine-tuned in SageMaker. However, the fine-tuned version of these models can not be used in Amazon Bedrock.

What is Bedrock Data Automation? Amazon Bedrock Data Automation is a GenAI-powered capability of Bedrock that streamlines the development of generative AI applications and automates workflows involving documents, images, audio, and videos. By leveraging Bedrock Data Automation, developers can reduce development time and effort, making it easier to build intelligent document processing, media analysis, and other multimodal data-centric automation solutions. Bedrock Data Automation offers industry-leading accuracy at lower cost than alternative solutions, along with features such as visual grounding with confidence scores for explainability and built-in hallucination mitigation. This ensures trustworthy and accurate insights from unstructured, multi-modal data sources. Customers can easily customize Bedrock Data Automation output to generate specific insights in consistent formats required by their systems and applications. Developers get started with Bedrock Data Automation on the Amazon Bedrock console, where they can configure and customize output using their sample data. They can then integrate Bedrock Data Automation’s unified multi-modal inference API into their applications to process their unstructured content at production scale with high accuracy and consistency. Bedrock Data Automation is also integrated with Bedrock Knowledge Bases, making it easier for developers to generate meaningful information from their unstructured multi-modal content to provide more relevant responses for retrieval augmented generation (RAG).

Bedrock Data Automation makes it easy to transform unstructured enterprise data into application-specific output formats that can be utilized by gen AI applications and ETL workflows. Customers no longer need to spend time and effort managing and orchestrating multiple models, engineering prompts, implementing safety guardrails, or stitching together outputs to align to downstream system requirements. Bedrock Data Automation delivers highly accurate, consistent, and cost-effective processing of unstructured data. Bedrock Data Automation is built with responsible AI in mind, providing customers with key features such as visual grounding and confidence scores, that make it easy to integrate Bedrock Data Automation within enterprise workflows.

Bedrock Data Automation capabilities are available via a fully managed API that customers can easily integrate into their applications. Customers do not need to worry about scaling underlying compute resources, selecting and orchestrating models, or managing prompts for FMs.

A blueprint is a feature that customers use to specify their output requirements using natural language or a schema editor. It includes a list of fields that they desire to extract, a data format for each field, and natural language instructions for each field. For example, developers can type, “Create a blueprint for invoices with the following fields: tax, dueDate, ReceiptDate” or “Confirm the invoice total matches the sum of line items.” They reference blueprints as part of the inference API calls so that the system returns information in the format described in the blueprint.

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 will support PDF, PNG, JPG, TIFF, a max of 100 pages, and a max file size of 500MB per API request. BDA will support a max concurrency of 5 document packages and throughput of 1 page 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, 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 will support JPG, PNG, a max resolution of 4K, and a max file size of 5 MB per API request. BDA will support a max concurrency of 100 images at 1 image per second per customer.

Videos

Bedrock Data Automation supports both standard output for videos.

• Standard output will provide full video summary, scene segmentation, scene summary, full audio transcription, speaker identification, detected explicit content, detected text, 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 will support MOV and MKV with H.264, a max video duration of 4 hours, and a max file size of 2 GB per API request. BDA will support a max concurrency of 25 videos at 20 video minutes per minute per customer.

Audio

Bedrock Data Automation supports both standard output for audio.

• Standard output will provide summarization, full transcription, and detected explicit content for audio files.

Bedrock Data Automation will support 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. Standard output will provide summarization, full transcription, and detected explicit content for audio files.

Amazon Bedrock Data Automation is currently available in the US West (Oregon) Region.

Amazon Bedrock IDE (preview) is a governed collaborative environment integrated within Amazon SageMaker Unified Studio (preview) that enables developers to quickly build and iterate on generative AI applications using high-performing foundation models (FMs). It provides an intuitive interface to experiment with these models, collaborate on projects, and streamline access to various Bedrock tools and resources in order to build generative AI applications quickly.

To access Amazon Bedrock IDE within Amazon SageMaker Unified Studio, developers and their admins will need to follow these steps:

1. Create a new domain in Amazon SageMaker Unified Studio.
2. Enable the Gen AI application development project profile.
3. Access Amazon Bedrock IDE using their company's single sign-on (SSO) credentials within Amazon SageMaker Unified Studio.

Amazon Bedrock IDE, now integrated into Amazon SageMaker Unified Studio, builds upon Amazon Bedrock Studio (preview) with several key improvements. It provides access to advanced AI models from leading companies, tools for creating and testing AI prompts, and seamless integration with Bedrock Knowledge Bases, Guardrails, Flows, and Agents. Teams can collaborate in a shared workspace to build custom AI applications tailored to their needs.

New features in Bedrock IDE 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. Bedrock IDE 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 enables collaboration among teams by providing a governed development environment within Amazon SageMaker Unified Studio. Teams can create projects, invite colleagues, and collaboratively build generative AI applications together. They can receive quick feedback on their prototypes and share the applications with anyone in Amazon SageMaker Unified Studio or with specific users in the domain. Robust access controls and governance features allow only authorized members to access project resources such as data or the generative AI applications, supporting data privacy and compliance, and thus fostering secure cross-functional collaboration and sharing. In addition, generative AI applications can be shared from a builder to specific users in the Amazon SageMaker Unified Studio domain, or with specific individuals, allowing for proper access rights, controls, and governance of such assets.

Amazon Bedrock IDE's integration into Amazon SageMaker Unified Studio represents AWS's move to simplify and streamline generative AI development. This integration creates a comprehensive environment that breaks down barriers between data, tools, and developers, enabling efficient building and deployment of generative AI applications.

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, model fine-tuning, 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 IDE 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.
 

Currently, Amazon Bedrock Studio is available as a preview feature accessed through the AWS Management Console. Now, Amazon Bedrock Studio has been renamed Amazon Bedrock IDE and is in preview within Amazon SageMaker Unified Studio and provides a dedicated environment for building, evaluating, and sharing generative AI applications with advanced capabilities like Knowledge Bases, Guardrails, Agents, Flows, and prompt engineering tools. This integration into Amazon SageMaker Unified Studio offers a more feature-rich, governed, and collaborative development experience compared to the previous preview version in the AWS Management Console.

All of Amazon Bedrock Studio is a part of Amazon SageMaker Unified Studio under Amazon Bedrock IDE. The Generative AI Playground, available in the 'Discover' section of Amazon SageMaker Unified Studio, allows you to experiment with foundation models (FMs) and any generative AI applications shared by your colleagues through a conversational interface. Amazon Bedrock IDE, the full generative AI application environment, is located in the 'Build' section of Amazon SageMaker Unified Studio and can be accessed through projects.

Regarding when to use each offering:

• Existing Amazon Bedrock Studio in the AWS Management Console: You can continue using the existing Amazon Bedrock Studio in the AWS Console for ongoing projects until 02/28/2025 after which support will end. You will need to set up a new Amazon SageMaker domain that includes Amazon Bedrock IDE to access it within Amazon SageMaker's governed environment.
• Generative AI Playground in Amazon SageMaker Unified Studio (Discover section): Use the chat, image and video playgrounds for initial experimentation with FMs, testing different models and configurations before building applications in Amazon Bedrock IDE.
• Amazon Bedrock IDE in Amazon SageMaker Unified Studio (Build section): Utilize Amazon Bedrock IDE, available in the Build section, to take advantage of advanced capabilities for building production-ready generative AI applications. These include integrated governance, secure collaboration, Knowledge Bases, Agents, Flows, Guardrails, and prompts engineering tools.

Amazon Bedrock IDE is a governed collaborative environment focused on building generative AI applications using foundation models (FMs). Integrated within Amazon SageMaker Unified Studio, it provides an intuitive interface to access and experiment with Bedrock's high-performing FMs, as well as tools for customization like Knowledge Bases, Guardrails, Agents, and Flows.

Within Amazon SageMaker Unified Studio, Amazon Bedrock IDE seamlessly integrates with Amazon SageMaker’s analytics, machine learning (ML), and generative AI capabilities. Users can leverage analytics services to generate insights from their data, build ML models using Amazon SageMaker AI's training and deployment tools, and combine these components with generative AI applications created in Amazon Bedrock IDE. This unified environment enables end-to-end development of data-driven applications that combine analytics, ML, and generative AI capabilities. Users can build and deploy ML and generative AI models, create and share generative AI applications tailored with proprietary data and customizations, and streamline collaboration – all within the same governed Amazon SageMaker Unified Studio environment
 

Existing Amazon Bedrock Studio users who have been accessing the service through the AWS Management Console cannot directly migrate their projects to Amazon SageMaker Unified Studio. To access Amazon Bedrock IDE within Amazon SageMaker's governed environment, developers and their admins will need to create a new domain in Amazon SageMaker Unified Studio, enable the Gen AI application development project profile and access Amazon Bedrock IDE using their company's single sign-on (SSO) credentials within Amazon SageMaker Unified Studio.

However, existing users can continue to access Amazon Bedrock Studio (Preview) through the AWS Management Console until 02/28/2025. After this date, they will need to transition to the new Amazon Bedrock IDE experience within Amazon SageMaker Unified Studio.

Amazon Bedrock IDE within Amazon SageMaker Unified Studio is bound by the account limits and quotas defined for the platform and the underlying Amazon Bedrock resources, such as foundation models (FMs), Knowledge Bases, Agents, Flows, and Guardrails.

Amazon Bedrock IDE comes at no extra cost, and users only pay for the usage of the underlying resources that are required by the generative AI applications that they build. For example, customers will only pay for the associated model, Guardrail and Knowledge Base that they have used on their generative AI application. For more information, please visit the Amazon Bedrock pricing page.

Amazon Bedrock IDE within Amazon SageMaker Unified Studio is bound by the same SLAs as Amazon Bedrock. For more information, visit the Amazon Bedrock Service Level Agreement page.

To facilitate a smooth onboarding experience with Amazon Bedrock IDE in Amazon SageMaker Unified Studio, you can find detailed documentation on the Amazon Bedrock IDE User Guide. If you have any additional questions or need further assistance, please don't hesitate to reach out to your AWS account team.