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January 17, 2024

Amazon Bedrock in Gen AI Development: Key Features and Use Cases


Artur Haponik

CEO & Co-Founder

Reading time:

7 minutes

Amazon Bedrock has emerged as a powerful tool for unlocking the full potential of generative AI. Launched in 2023 by Amazon [1], Bedrock is a machine learning platform that can be used to build generative AI applications on the AWS cloud computing platform.

It uses foundational models built by other companies to make it as easy as possible for developers to build and deploy gen AI applications. This empowers organizations to explore creativity and innovation in fields such as product design and content creation.

Below is a comprehensive guide that will provide an in-depth review of the key features of Amazon Bedrock, shedding light on its transformative capabilities, use cases, and the role it plays in shaping the future of generative AI.


Essential attributes of Amazon Bedrock

Some of the most essential attributes of Amazon Bedrock include:

Flexibility and scalability

One of the best things about Amazon Bedrock is that it allows users to access a wide variety of high-performing Foundation Models (FMs) from top AI research companies such as Meta, Amazon, Cohere, AI21 Labs, Stability AI, and Anthropic. [2] As a result, developers can easily experiment with different foundation models before choosing one that meets their unique needs.

Some of the most popular FMs offered by Amazon Bedrock include Amazon Titan by Amazon, Claude by Anthropic, Jurassic-2 by AI21 Labs, and Stable Diffusion by Stability AI. These foundation models cater to different use cases.

This tool is also ideally designed to scale effortlessly and accommodate growing workloads. This enables businesses and developers to effortlessly adapt their applications to evolving requirements.

Read more about Amazon Bedrock: A User’s Guide to benefits and utilization

Seamless integration with Amazon Web Services (AWS)

The biggest difference between Amazon Bedrock and other platforms is its seamless integration with Amazon Web Services (AWS). By leveraging various AWS tools, capabilities, and resources, developers can easily deploy scalable, reliable, and secure generative AI applications. Some of the AWS cloud-based services that support gen AI applications include Amazon SageMaker, CloudWatch, QuickSight Q, OpenSearch, and Neptune ML.[3]

Integration with Amazon SageMaker, for instance, provides users with core Machine Learning (ML) capabilities for testing different AI models and launching foundation models through SageMaker Jumpstart. Additionally, Amazon Bedrock’s integration with Amazon CloudWatch allows developers to track usage metrics in real time and build customized dashboards to suit their needs.

Data security and compliance

Generally, AI applications pose enormous data breach and privacy risks. Without the proper cybersecurity measures, vulnerable AI applications can easily be exposed to cybercriminals who can access sensitive data and cause extensive damage. Fortunately, Amazon Bedrock recognizes the sensitivity of generative AI applications and incorporates robust security protocols of other AWS services to ensure the safety and security of your data. These security measures include secure data handling and encryption of data at rest and in transit.

Most importantly, Amazon Bedrock strictly adheres to the requirements of the Health Insurance Portability and Accountability Act (HIPAA) and the EU General Data Protection Regulation (GDPR) compliance. [4], and presents you with the opportunity to encrypt sensitive data using your own keys. This allows developers to build generative AI applications in a secure and compliant setting without exposing private data to the internet.

Native support for retrieval augmented generation (RAG)

Amazon Bedrock Knowledge Bases offer native support for Retrieval Augmented Generation (RAG). RAG is a technique that enhances the reliability and accuracy of generative AI models with data from external sources. By connecting your preferred foundation models to domain-and company-specific data sources, you can make these models more knowledgeable of your specific domain and organization.

Bedrock’s native support for RAG also allows for automatic prompt generation, which helps save developers’ efforts in prompt engineering. The platform can create a relevant prompt from the developer-provided instructions, company-specific data, and available API schemas.

It’s also worth noting that Bedrock tracks every data source used to generate every response, thus making it clear where the information comes from.

Using Bedrock in Generative AI Scenarios

Here is a step-by-step guide on how to utilize Amazon Bedrock in generative AI scenarios:

  1. Identify Your Goals: The first step when utilizing Bedrock in gen AI scenarios is to clearly define the goals you intend to achieve with your generative AI project. You also need to identify the specific tasks you wish to perform with your AI model, whether it is text generation, product design, sentiment analysis, text summarization, chatbots, or image generation.
  2. Access Bedrock: Before embarking on your gen AI project, you need to ensure you have access to Amazon Bedrock through the appropriate channels. For example, you may access the Bedrock platform by simply signing up for any of the relevant AWS accounts.
  3. Choose a Foundation Model: Once you have access to this tool, proceed to choose a pre-trained foundation model that meets your needs. When choosing a foundation model, you need to consider factors such as model size, cost, capabilities, and domain alignment. [5]
  4. Provide Data: If you have any company or domain-specific data, use it to fine-tune the foundation model. This will help improve the model’s performance and make it more knowledgeable of your specific domain.
  5. Utilize Retrieval Augmented Generation (RAG): Proceed to connect your foundation model to your company’s knowledge bases to improve its accuracy, relevance, and domain specificity.
  6. Integrate with Other AWS Services: Although it’s optional, integrating Amazon Bedrock with other AWS services like Amazon SageMaker, Amazon Lambda, Amazon Cognito, Amazon Inspector, and Amazon S3 may help improve your generative AI application.
  7. Deploy and Monitor: Once your gen AI application or model is ready, deploy it to a production environment. Ensure you regularly monitor its performance to identify areas of improvement and optimization.

CaseStudies: Bedrock in Generative AI

The following are some captivating case studies showcasing how Amazon Bedrock is being used to break new ground in generative AI:


Adidas, one of the largest sports brands in the world, recently added Amazon Bedrock to its generative AI toolkit. According to Adidas, this has allowed the company to primarily focus on the main aspects of its Large Language Model (LLM) projects as Bedrock manages the infrastructure. Thanks to Bedrock, Adidas has been able to offer its engineers the ability to find answers from the company’s vast knowledge base using a single conversational interface. [6]


According to the Executive VP and CTO/CIO of Nasdaq, the global financial services and technology corporation uses Amazon Bedrock to power its technology and innovations[7]. Thanks to Bedrock’s choice, availability, and autonomy, Nasdaq intends to leverage the platform in various business areas, such as anti-financial crime and surveillance capabilities.

Final thoughts

As a business owner seeking to stay ahead of the competition in the age of generative AI, Amazon Bedrock is your ideal partner. Thanks to the platform’s unparalleled capabilities in generative AI, you can easily unlock new levels of creativity, efficiency, and innovation.

And since the technology is still evolving, you need to keep your eyes peeled for new developments that will further help streamline your business operations.


[1] AWS Announces More Model Choice and Powerful New Capabilities in Amazon Bedrock to Securely Build and Scale Generative AI Applications. URL:, Accessed on January 15, 2024
[2] The Top AI Companies to Follow: The Companies to Watch in the World of AI. URL: Accessed on January 15, 2024
[3] Top AWS Services. URL:  Accessed on January 15, 2024
[4] Data Protection Laws. URL: Accessed on January 15, 2024
[5] Foundational Models Explained. URL:, Accessed on January 15, 2024
[6] Bedrock Testimonials. URL: Accessed on January 15, 2024
[7] Bedrock Testimonials. URL: Accessed on January 15, 2024



Generative AI