Inside AWS Bedrock: Capabilities | Analysis | Impact

Inside AWS Bedrock: Capabilities | Analysis | Impact

Explore the heart of GenAI, uncover the core features, in-depth analysis, and the profound impact of Amazon Bedrock guiding through the intricacies of cloud excellence!!

Published Dec 5, 2023
AWS Bedrock is a machine learning platform used to build generative AI applications on AWS. Bedrock uses foundation models to simplify the creation of these apps and make the process more efficient.


They are adaptable AI models trained on large data sets to perform many kinds of tasks. They’re versatile, reusable and don’t require retraining for each new task. Bedrock replaces the physical infrastructure typically used to build generative AI apps with foundation models, simplifying the app-building process.
Bedrock is a competitor to OpenAI’s ChatGPT and Dall-E. It is also compared to Amazon Sage Maker, which is used to build and train complex machine learning models; Bedrock is more focused on building generative AI apps.
With the comprehensive capabilities of Amazon Bedrock, you can easily experiment with a variety of top FMs, privately customize them with your data using techniques such as fine-tuning and retrieval augmented generation (RAG), and create managed agents that execute complex business tasks — from booking travel and processing insurance claims to creating ad campaigns and managing inventory — all without writing any code. Since Amazon Bedrock is serverless, you don’t have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with.


Amazon’s vision for AWS Bedrock is to democratize access to generative AI, catering to customers across various industries and making it easy for businesses to leverage the power of AI in their operations. By offering a wide selection of foundation models, AWS Bedrock ensures that customers have the flexibility and choice to use the best models tailored to their specific needs.



AWS released Agents for Amazon Bedrock to designate and automate complex tasks for a model without requiring a developer to manually write the code needed to do so. Specifically, developers can use agents to connect foundation models to their proprietary data sources so the apps they build will produce up-to-date answers based on their data. When a user employs a generative AI app built with Bedrock, an agent makes API calls that retrieve the data needed from proprietary sources to answer the user’s requests or queries.
Agents for Amazon Bedrock make it easy to create and deploy fully managed agents that execute complex business tasks by dynamically invoking APIs. With just a few clicks, agents for Amazon Bedrock automatically break down tasks and create an orchestration plan — without any manual coding. The agent securely connects to company data through a simple API, automatically converting data into a machine-readable format, and augmenting the request with relevant information to generate a more accurate response.
Agents can also automatically call APIs to fulfil a user’s request. As a fully managed capability, agents for Amazon Bedrock remove the undifferentiated lifting of managing system integration and infrastructure provisioning, allowing customers to leverage generative AI to its full extent throughout their business.
Automatic prompt creation: Amazon Bedrock creates a prompt from the developer-provided instructions (such as “you are an insurance agent designed to process open claims”), API schemas needed to complete the tasks, and company data source details from knowledge bases such as vector engine for Amazon OpenSearch Serverless, Pinecone, and Redis Enterprise Cloud. The automatic prompt creation saves weeks of experimenting with prompts for different FMs.
Orchestration plan: Agents for Amazon Bedrock orchestrate the user-requested task, such as “send a reminder to all policyholders with pending documents,” by breaking it into smaller subtasks like getting claims for a certain period, identifying paperwork required, and sending reminders. The agent determines the right sequence of tasks and handles any error scenarios along the way.
Retrieval augmented generation: Agents for Amazon Bedrock securely connect to your company’s data sources, automatically convert your data into numerical representations, and augment the user request with relevant information to generate a more accurate and relevant response. For example, if the user enquires about documents required for claims, the agent will look up information from an appropriate knowledge base that you choose (such as the vector engine for Amazon OpenSearch Serverless, Pinecone, or Redis Enterprise Cloud) and provide the correct answer: “You need a driver’s license, pictures of the car, and an accident report.”


AWS Bedrock seamlessly integrates with other AWS tools and capabilities, making it convenient for businesses to incorporate FMs into their existing workflows. One such integration is with Amazon SageMaker, a popular machine-learning service. Amazon SageMaker offers various ML features like Experiments and Pipelines, which facilitate the testing and efficient management of FMs at scale. Users can leverage these integrations to optimize their AI models and improve overall performance.*


Amazon Bedrock makes it easy for developers to work with a broad range of high-performing foundation models (FMs).
Privately customize FMs: Use the Amazon Bedrock console to fine-tune models with your data for your company-specific tasks without writing code. Simply select the training and validation data sets stored in Amazon Simple Storage Service (Amazon S3) and, if required, adjust hyperparameters to achieve the best possible model performance.
Single API: Use a single API to perform inference, regardless of the model you choose. Having a single API provides the flexibility to use different models from different model providers and keep up to date with the latest model versions with minimal code changes.


With knowledge bases for Amazon Bedrock, from within the managed service, you can connect FMs to your data sources for retrieval augmented generation (RAG), extending the FM’s already powerful capabilities and making it more knowledgeable about your specific domain and organization.
Enable automatic data source detection: Using knowledge bases, Amazon Bedrock agents identify the appropriate data sources, retrieve the relevant information based on user input, incorporate the retrieved information context into the user query, and provide a more accurate response.
All the information retrieved from Amazon Bedrock knowledge bases comes with source attribution to improve transparency and minimize hallucinations.


The serverless nature of AWS Bedrock enables it to automatically scale according to the user’s requirements, eliminating the need to manage infrastructure manually. This ensures optimal performance, even as the demand for AI applications increases. Furthermore, the easy integration with other AWS services, such as Amazon SageMaker, allows users to experiment with different models and manage FMs at scale, ultimately optimizing their AI solutions for better outcomes.


Amazon Bedrock helps you build generative AI applications that support data security and compliance standards, including GDPR and HIPAA.
Secure your generative AI applications: Amazon Bedrock supports encryption. Your data is always encrypted in transit and at rest, and you can also use your keys to encrypt the data. Using AWS Key Management Service (AWS KMS) keys, developers can create, own, and manage encryption keys, so they have full control over how they encrypt the data used for FM customization.
Implement governance and auditability: Amazon Bedrock offers comprehensive monitoring and logging capabilities that can support your governance and audit requirements. You can use Amazon CloudWatch to track usage metrics and build customized dashboards with metrics that are required for your audit purposes. Additionally, you can use AWS CloudTrail to monitor API activity and troubleshoot issues as you integrate other systems into your generative AI applications. You can also choose to store the metadata, requests, and responses in your Amazon Simple Storage Service (Amazon S3) bucket. Lastly, to prevent potential misuse, Amazon Bedrock implements automated abuse detection mechanisms.


Text generation: Create new pieces of original content, such as blog posts, social media posts, and webpage copy.
Virtual Assistants: Build assistants that understand user requests, automatically break down tasks, engage in dialogue to collect information, and take actions to fulfil the request.
Search: Search, find, and synthesize relevant information to answer questions from a large corpus of data.
Text summarization: Get summaries of long documents such as articles, reports, and even books to quickly get the gist.
Image generation: Quickly create realistic and visually appealing images and animations for ad campaigns, websites, presentations, and more.



Amazon Titan foundation models (FMs) are a family of FMs pre trained by AWS on large datasets, making them powerful, general-purpose models built to support a variety of use cases. Use them as they are or privately customize them with your data.


Broad Range Of Applications: Generate natural language text for a broad range of tasks such as summarization, content creation, and question answering.
Delivers relevant search results: Enhance search accuracy and improve personalized recommendations.
Built-in support for responsible AI: Support responsible use of AI by reducing inappropriate or harmful content.
Handy Customization: Fine-tune Amazon Titan models with your data to customize the model and perform organization-specific tasks.


Text generation: Use Titan Text for creating copy for blog posts and web pages, classifying articles, open-ended Q&A, and information extraction.
Summarization: Get the gist of lengthy documents of text, such as reports and even books, with summaries based on natural language prompts.
Semantic search: Use Titan Embeddings for applications like personalization and search. By comparing embeddings, the model can produce more relevant and contextual responses than word matching.


Claude is based on Anthropic’s research into creating reliable, interpretable, and steerable AI systems. Created using techniques like Constitutional AI and harmlessness training, Claude excels at thoughtful dialogue, content creation, complex reasoning, creativity, and coding.


Frontier AI safety features: Claude is based on Anthropic’s leading safety research, and built with techniques including Constitutional AI. Designed to reduce brand risk, Claude aims to be helpful, honest, and harmless.
Cutting-edge capabilities: Claude can be used for sophisticated dialogue, creative content generation, complex reasoning, coding, and detailed instruction. It can edit, rewrite, summarize, classify, extract structured data, do Q&A based on the content, and more.


Customer service: Claude can act as an always-on virtual sales representative, ensure speedy and friendly resolution to service requests, and increase customer satisfaction.
Operations: Claude can extract relevant information from business emails and documents, categorize and summarize survey responses, and wrangle reams of text with high speed and accuracy.
Coding: Claude's models are constantly improving in coding, math, and reasoning. The latest model Claude 2 scored 71.2% (up from 56.0%) on the Codex Human Eval, a Python coding test.


Command is a text generation model for business use cases.


Improved productivity: Integrate generative AI capabilities into essential apps and workflows that improve business outcomes.
Data Privacy: Data is kept private where customers have complete control over customization and model inputs and outputs.
Model integrity: Models are trained from known, purchased, or public data sources, and subjected to adversarial testing and bias mitigation.


Chat: Enables apps like knowledge assistants and customer support chatbots with seamless and dynamic user experiences that maintain conversational context.
Text generation: Builds articles, product descriptions, and more based on user prompts.
Text summarization: Summarizes key ideas and themes from diverse long-form text, such as news, investor reports, legal, or medical information.


Build generative AI-driven applications with AI21’s advanced Jurassic LLMs. Jurassic is AI21 Labs’ production-ready family of large language models (LLMs), powering natural language AI in thousands of live applications.


Designed to fit your organizational needs: Highly versatile and capable of generating nuanced text across a wide variety of industry sectors.
Zero-shot instruction following: Follows natural language instructions without requiring examples.
Various models for cost and performance optimization: Follows natural language instructions without requiring examples.


Financial services: Condense financial reports into bite-sized summaries, extract key data from documents, and generate financial and legal statements tailored to specific needs and requirements.
Retail: Generate product descriptions, summarize and analyze product reviews, and craft bespoke marketing content according to your desired tone, length, and style.
Knowledge management: Make it easy for employees to access organizational data and extract insights using natural language.


Stable Diffusion XL generates images of high quality in virtually any art style and is the best open model for photorealism.


Complex compositions: Fine-tuned to create complex compositions with basic natural language prompting.
Cinematic photorealism: Native 1024x1024 image generation with cinematic photorealism and fine detail.


Gaming and metaverse: Create new characters, scenes, and worlds.
Media and entertainment: Develop unlimited creative assets and ideate with images.
Advertising and marketing: Create personalized ad campaigns and unlimited marketing assets.


Amazon Bedrock provides you the flexibility to choose from a wide range of FMs built by leading AI start-up's and Amazon so you can find the model that is best suited for what you are trying to get done. With Bedrock’s serverless experience, you can get started quickly, privately customize FMs with your data, and easily integrate and deploy them into your applications using the AWS tools and capabilities you are familiar with (including integrations with Amazon SageMaker ML features like Experiments to test different models and Pipelines to manage your FMs at scale) without having to manage any infrastructure.
Choose FMs from AI21 Labs, Anthropic, Stability AI, and Amazon to find the right FM for your use case.


With the On-Demand mode, you only pay for what you use, with no time-based term commitments. For text generation models, you are charged for every input token processed and every output token generated. For embedding models, you are charged for every input token processed. A token is comprised of a few characters and refers to the basic unit that a model learns to understand user input and prompt to generate results. For image generation models, you are charged for every image generated.

Provisioned Throughput

With this mode, you can purchase model units for a specific base or custom model. The Provisioned Throughput mode is primarily designed for large consistent inference workloads that need guaranteed throughput. Custom models can only be accessed using Provisioned Throughput. A model unit provides a certain throughput, which is measured by the maximum number of input or output tokens processed per minute. With this Provisioned Throughput pricing, charged by the hour, you have the flexibility to choose between 1-month or 6-month commitment terms.


AWS Bedrock, powered by generative AI, has the potential to revolutionize the way B2B businesses operate. By leveraging foundation models and the ease of customization, companies can harness the power of artificial intelligence to streamline their operations, improve efficiency, and enhance customer experiences.

Personalized Marketing and Sales

B2B businesses can harness AWS Bedrock to create customized marketing materials based on their target audience’s preferences. By providing a few labelled examples, Bedrock can generate targeted ad copy, campaign materials, and social media content. This personalized approach can lead to higher conversion rates and increased customer satisfaction

Improved Customer Support and Engagement

Bedrock can be used to develop chatbots and virtual assistants that provide personalized, contextually relevant support for customers. These AI-powered tools can help businesses streamline their customer support processes, reduce response times, and improve overall customer satisfaction.

Enhanced Data Analysis and Decision Making

By utilizing generative AI, B2B businesses can analyse and synthesize large volumes of data more effectively. Bedrock’s text summarization, classification, and information extraction capabilities enable companies to gain insights from their data faster and more accurately, supporting informed decision-making.

Optimized Product Recommendations

With Bedrock’s embeddings LLM, businesses can offer more relevant and contextual product recommendations to their customers, going beyond simple keyword matching. This can lead to increased sales and a better overall customer experience.

Democratized Access to AI Technologies

Bedrock makes foundation models accessible to businesses of all sizes, helping them accelerate the adoption of machine learning and generative AI applications. This democratization of AI technologies can level the playing field for smaller businesses, allowing them to compete more effectively with larger enterprises. Additionally, integrating artificial intelligence brings numerous benefits, including enhanced automation, data-driven insights, and improved decision-making capabilities. Businesses can streamline processes, optimize operations, and unlock new growth opportunities by leveraging AI.


AWS BEDROCK WORKSHOPS: The goal of this workshop is to give you hands-on experience leveraging foundation models (FMs) through Amazon Bedrock.
AWS BEDROCK USER GUIDE: Helps to know more about Bedrock.
AWS BEDROCK API DOCUMENTATION: This document provides detailed information about the Bedrock API actions and their parameters.
AWS BEDROCK TESTIMONIALS: Bedrock has addressed diverse challenges and delivered tangible benefits to different organizations.



In wrapping up this exploration of AWS Bedrock, I can’t help but be impressed by the potential it offers. It’s a powerful tool that can transform the way businesses operate and leverage AI. As I delved deeper into its features, benefits, and real-world applications, a few key takeaways stood out to me.*
First and foremost, the versatility of Bedrock is remarkable. It’s not limited to a single industry or use case. From customer support to marketing, data analysis, and content generation, Bedrock can adapt to a wide range of challenges. This adaptability is a testament to the thoughtfulness behind its design.
Moreover, the seamless integration with other AWS services and the serverless architecture of Bedrock simplify the deployment of generative AI applications. It allows businesses to harness the power of AI without the complexity of managing infrastructure. This is a game-changer, particularly for smaller businesses looking to compete with larger enterprises.
The case studies and testimonials provided earlier in this blog reinforce the idea that AWS Bedrock is more than just a service - it’s a solution that’s making a tangible difference for organizations across various domains. Whether it’s improving customer support, optimizing marketing strategies, or enhancing data analysis, Bedrock is making operations more efficient and customers more satisfied.
In conclusion, AWS Bedrock is a remarkable addition to the AI landscape, and it’s fascinating to witness the impact it’s having on businesses of all sizes. Its ability to address diverse challenges is something worth celebrating. I look forward to seeing how the world continues to harness its potential and where AWS Bedrock takes us on the exciting journey of AI.