Amazon Bedrock Knowledge Bases: Unlocking the Power of Customized AI Insights
Amazon Bedrock Knowledge Bases make it easy to connect your private data with AI models, improving accuracy and relevance without complex setup. This guide breaks down how they work, their key benefits, and how to get started.
Recently, we published a post highlighting what you can achieve with Amazon Bedrock and how it provides the easiest way to get started with generative AI on AWS. Bedrock offers a wide range of features designed to simplify these steps, with one key advantage being its seamless integration with other AWS services. Bedrock Knowledge Bases add even more value by indexing your private data, making it available for use with AI models or Bedrock Agents.
As of AWS re:Invent 2025, Bedrock Knowledge Bases have evolved significantly with new multimodal capabilities, cost-optimized vector storage, structured data retrieval, and enterprise governance features making them even more powerful for production deployments.
If you're also wondering how Amazon Bedrock compares to more commonly used tools like ChatGPT, take a look at our breakdown: Amazon Bedrock vs. ChatGPT. It highlights the key differences and helps you understand which solution fits your needs.
Understanding Amazon Bedrock Knowledge Bases
Knowledge bases in Amazon Bedrock are a capability that enables you to implement Retrieval Augmented Generation (RAG) for your generative AI applications. Bedrock's other features are designed to be simple and accessible for those who don't have coding experience.
Let's define a couple of relevant words like Retrieval Augmented Generation (RAG) and Vector Database.
Retrieval Augmented Generation (RAG)
RAG allows AI models to access and utilize up-to-date, domain-specific information that may not be part of their original training data. In Bedrock, this process is automatized:
- Retrieval: The system searches for relevant information from a designated data source.
- Augmentation: The retrieved information is added to the input prompt or query.
- Generation: The LLM uses the augmented input to generate a more informed and accurate response.
Vector Database
When establishing a connection between a Bedrock Knowledge Base and a data source, it is essential to configure a vector database. A vector database is a specialized type of database with embeddings of mathematical representations of data designed to store, update, and manage them.
As of December 2025, AWS introduced Amazon S3 Vectors as a cost-optimized vector storage option for Bedrock Knowledge Bases, offering up to 90% savings compared to traditional vector databases while supporting trillions of vectors at sub-second latency. This makes vector storage more accessible and economical for enterprises of any scale.
What are Bedrock Knowledge Bases
Here's an overview of what knowledge bases are and how they work:
- Knowledge bases allow you to integrate your information (documents, texts, images, audio, video) into AI applications, enhancing the relevance and accuracy of generated responses.
- They simplify the setup and execution of the RAG workflow, which involves retrieving relevant information from a data store to augment responses generated by Large Language Models (LLMs).
- Your application can query the knowledge base to retrieve information.
- Knowledge bases can be added to Amazon Bedrock agents, allowing the agent to identify and retrieve relevant information based on user input.
Bedrock Knowledge Bases help you to handle tasks like:
- Fetching documents from your specified data source,
- Chunking text into manageable pieces (with advanced semantic and hierarchical options),
- Converting and storing text as vector embeddings,
- Processing multimodal content including images, audio, and video.
Practical Use Cases for Amazon Bedrock Knowledge Bases
Knowledge bases are particularly useful for building applications that require context from proprietary private data, such as customer support systems, internal knowledge management tools, or domain-specific chatbots.
- Customer support systems: For example, a support agent using a knowledge base can quickly find solutions to common customer issues or access product-specific information to address inquiries. With multimodal retrieval, agents can now search across support videos, call recordings, and documentation simultaneously.
- Internal knowledge with provisioned feedback:
Knowledge bases are useful when a company already has data containing its internal knowledge. A dedicated system (or worker) can analyze this historical data and create informed decisions for future projects. For instance, based on doctors' past decisions and diagnoses in a clinic, the knowledge base can build a repository to guide future decision-making processes. Knowledge Bases can now extract insights from medical video recordings, diagnostic images, and patient records.
- Domain-specific chatbots: In finance, a chatbot could leverage a knowledge base to answer customer questions about tax regulations, loan applications, or investment strategies. By integrating domain-specific knowledge, these chatbots can deliver personalized and accurate responses, improving the overall user experience.
- Visual search and similarity matching:
Product teams can leverage Knowledge Bases to enable image-based search across product catalogs, technical diagrams, and manufacturing documentation, useful for e-commerce platforms, technical support, and supply chain applications.
Benefits and Challenges of Using Bedrock Knowledge Bases
Benefits
These are the main benefits of using Bedrock Knowledge Bases:
- Faster time to market by providing an out-of-the-box RAG solution.
- Increased cost-effectiveness by eliminating the need for continuous model training on private data and by leveraging S3 Vectors for up to 90% reduction in vector storage costs.
- Improved response accuracy and relevance by leveraging your own data.
- Multimodal retrieval capabilities enable search across text, images, audio, and video without converting all content to text first.
- Enterprise governance through AgentCore Evaluations (13 built-in quality evaluators) and AgentCore Policy (natural language policy definition) for production agent deployments.
Challenges
- Data Security and Privacy: Integrating sensitive data into a knowledge base requires a careful approach to security and privacy. Organizations must ensure compliance with data protection regulations (e.g., GDPR, HIPAA) and implement robust encryption and access controls. With Amazon Bedrock, these concerns can be mitigated using AWS's enterprise-grade security features. It will be easier to get a secure environment if you already have your data in AWS. But it's also possible to connect it securely to an on-premises or any other cloud environment.
- Technical Complexity:
Setting up and managing a knowledge base for Amazon Bedrock requires expertise in AWS and integration. Organizations may need skilled teams or managed services to overcome these technical hurdles effectively. However, as mentioned earlier, it's significantly easier to get started with Bedrock than other methods.
Step-by-Step Setup of a Bedrock Knowledge Base
Here you can find the step-by-step manual on how to start with Bedrock Knowledge Bases:
- Log in to the AWS Console and go to Amazon Bedrock (Amazon Bedrock models are not available in all AWS regions).
- Select Knowledge Bases from the left panel.
- Click on "Create" and "Vector Store."
- You either leave everything on default or select options as you need. You can choose between S3 as a data source (with S3 Vectors as the backing store, recommended for cost optimization) or other data sources.
- Configure your data source.
- Select the embeddings model and vector store.
Updated pricing note (re:invent 2025): Amazon OpenSearch Serverless traditionally costs around $100 per month even without usage. However, AWS now recommends Amazon S3 Vectors as a cost-optimized alternative, which offers pay-as-you-go pricing with up to 90% cost savings, making it ideal for enterprise-scale deployments.
- Review and create the Knowledge Base and start using it with AWS services, likeAmazon Bedrock Agents. Currently, you can also integrate with AgentCore for production-grade governance, including policy-based access control and automated quality evaluations.
Conclusion
Amazon Bedrock Knowledge Bases simplify the process of integrating private, domain-specific data into AI applications, enabling businesses to leverage Retrieval Augmented Generation (RAG) workflows with minimal effort. As of AWS re:Invent 2025, these capabilities have expanded to include multimodal retrieval (text, images, audio, video), cost-optimized vector storage through S3 Vectors, structured data retrieval, and enterprise governance through AgentCore, making Bedrock Knowledge Bases an even more compelling solution for production AI applications. Although challenges like data security and technical complexity exist, Bedrock Knowledge Bases offer a practical and accessible solution for unlocking the full potential of customized AI insights.
An AWS Solutions Architect with over 5 years of experience in designing, assessing, and optimizing AWS cloud architectures. At Stormit, he supports customers across the full cloud lifecycle — from pre-sales consulting and solution design to AWS funding programs such as AWS Activate, Proof of Concept (PoC), and the Migration Acceleration Program (MAP).
