What is Amazon Bedrock? What it can do, how it works, pricing, and how to safely decide whether to adopt it.

Amazon Bedrock is a fully managed platform on AWS that allows you to incorporate multiple generative AI models into your business. Unlike ChatGPT, it is not a standalone service, but rather provides a foundation for companies to safely operate generative AI applications, including internal data integration (RAG) and business agent development.

In this article, we will explain everything from the basic definition of Amazon Bedrock to what it can do, how it works, how it differs from other services, and typical use cases, so that you can understand what it can do and how it should be positioned.

What is Amazon Bedrock?

Amazon Bedrock is a fully managed service on AWS that allows you to build generative AI applications using multiple underlying models via APIs.

Rather than developing and operating models in-house, we provide a foundation for selecting generative AI models suited to the application and incorporating text generation, summarization, dialogue functions, and more into business systems.

Generative AI service that lets you choose and use the underlying model 

Its unique feature is that it is not tied to a specific generative AI model, and multiple underlying models can be used, allowing you to choose the model that best suits your business objectives.

For example, you can call up a model suited to each purpose, such as a model that excels at text generation, summarization, and inference. Choosing a model can often be the first hurdle when introducing generative AI, but Bedrock is a system that allows you to centralize comparison and use of models on AWS.

A platform for incorporating generative AI into business systems 

Amazon Bedrock is not a standalone chat tool, but is designed to incorporate generative AI into enterprise applications and existing systems.

Its value lies in applications where AI is used within business processes, such as responding to internal inquiries, supporting document creation, and automating business processes. It is easier to understand if we think of generative AI as a foundation for continued use as a business system, rather than ending with a PoC.

Why Amazon Bedrock is so popular

The use of AI is rapidly expanding, but when it comes to introducing it to a company, issues arise not only in model performance but also in handling internal data, operational design, and governance.

Amazon Bedrock is attracting attention at the stage of "putting generative AI into business operations" because it allows you to build typical business implementation patterns such as RAGs and agents on AWS. It is not just a model API, but a service that is often at the center of consideration for implementation as a generative AI platform for corporate use.

What can you do with Amazon Bedrock?

By introducing Amazon Bedrock, you can use all the main functions for utilizing generative AI in your business on AWS. It is not only possible to simply call up models, but also to combine them with internal data and build mechanisms to execute business procedures.

Using the base model as an API (text, chat, images)

The basis of Amazon Bedrock is the ability to use multiple underlying models via APIs, allowing you to call text generation, summarization, chat-style responses, image generation, and more from your own applications.

When incorporating generative AI into business processes, instead of managing individual APIs for each model, it can be handled uniformly on AWS, which helps reduce the burden of development and operation. Another practical benefit is the ability to switch models depending on the application.

Generative AI (RAG) using in-house data

When using generative AI in a company, it is important to be able to refer to "information specific to your company" in addition to general knowledge.

Amazon Bedrock makes it easy to configure RAG (Search Augmentation and Generation), which searches internal documents and knowledge and generates answers based on the results. Answers can be based on internal regulations, product information, FAQs, etc., bringing it closer to business use, which is difficult with a general-purpose model alone.

Run your business with agents

With Amazon Bedrock, you can use agents not only to have generative AI answer questions, but also to execute business procedures.

For example, when responding to an inquiry, AI can handle part of the business flow, such as referencing internal systems or calling up necessary processes. Bedrock's unique feature is that it is not just a chatbot, but can be incorporated into business processes.

How Amazon Bedrock works

Amazon Bedrock is not a service that provides stand-alone generative AI models, but is designed as a platform for companies to use generative AI in their business. The framework for use is set up on AWS, assuming that models will not only be called but will also be used in conjunction with internal data and business systems.

Securely use models and data on AWS

For enterprise use, what data to reference and where to process it are more important than the performance of the generative AI model. With Amazon Bedrock, you can use the underlying model in the AWS environment and configure it to link with internal data as needed.

For example, RAG generates answers based on the results of searching internal documents, enabling it to handle business knowledge that cannot be generated by general-purpose models alone.The core of the system is the ability to manage the combination of "model + data" on AWS when using generative AI in business.

"Generative AI application platform" rather than model provision

Amazon Bedrock is positioned as a platform for building and operating generative AI applications, rather than a service that sells models themselves. It combines elements such as the use of multiple models, internal data integration, and agent-based task execution to run generative AI within business systems.

It may be easier to understand if you think of Bedrock as the foundation for moving from the stage of trying out generative AI to the stage of incorporating it into business operations and using it on an ongoing basis.

Differences between Amazon Bedrock and other services

There are services with similar names from AWS and other companies, so it's easy to get confused. Here, we'll clarify the differences between this and the representative services that are often compared.

Differences from SageMaker

Amazon SageMaker is an integrated platform for developing, training, and operating machine learning models. It is used when you want to train models in-house or build your own models.

Amazon Bedrock is a service that allows you to build generative AI applications using APIs based on pre-existing infrastructure models. The difference is that its purpose is not model development, but rather "incorporating generative AI into business processes."

Differences with Amazon Q

Amazon Q is a generative AI assistant for business provided by AWS. Users can instantly search for information or receive business assistance in chat format, and it is positioned as a product-like, fully-fledged assistant.

Amazon Bedrock is a platform for incorporating such business assistant and generative AI functions into your own systems. It may be easier to understand if you think of Amazon Q as the "service you use" and Bedrock as the "platform for creating."

Differences from ChatGPT API

The ChatGPT API is an API for directly calling OpenAI models to incorporate generative AI functions. While it is simple to implement, aspects such as integrated management within the AWS environment and multiple model options require separate design.

Amazon Bedrock is offered on AWS with multiple platform models to choose from, and is designed as a generative AI platform for integration and operation into business systems. For companies using AWS, the difference is that it can be designed in conjunction with the cloud platform.

Amazon Bedrock use cases

The focus of Amazon Bedrock is not on generating one-off sentences, but on how to incorporate it into your internal business processes. Here we will summarize some typical use cases that are likely to be adopted by companies.

Internal knowledge search and FAQ

Typical examples include knowledge search and FAQ support for internal documents.

Answers can be generated by referencing company regulations, manuals, product documents, etc., reducing the burden on employees in responding to inquiries and searching for information. By combining it with a RAG configuration, it becomes possible to provide answers based on company information, which differentiates it from general-purpose chatbots.

Document summarization and business support

Generative AI is well suited to tasks that involve long texts, and can be used to summarize and organize meeting materials, reports, inquiry histories, etc.

This is an area where it is likely to be introduced as support for quickly grasping information handled in work, rather than simply generating text. It can also be applied to improving the efficiency of back-office work, such as drafting and classifying standard phrases.

Enhanced response to inquiries

When responding to inquiries from customers or within the company, support is required not only to generate answers but also to search for related information and carry out procedures.

By combining it with the agent function, it is possible to design it so that it references the internal system and invokes the necessary processing depending on the content of the inquiry. This is attracting attention as an example of how it can be used to standardize response quality and reduce the burden on staff.

Amazon Bedrock FAQ

Can I try Amazon Bedrock for free?

Amazon Bedrock is a pay-as-you-go service, so you only pay for what you use. This means it's not completely free to use. However, AWS provides a framework and trial environment that makes it easy to start testing, so it's practical to start by testing on a small scale and get a sense of usage and costs.

What AWS settings are required before starting use?

To start using it, you need to enable Amazon Bedrock on your AWS account and set up permissions (IAM) for using the model. If you are incorporating it into a business system, it will go more smoothly if you organize the permissions design, including which application will call it and who can use it.

Are there fixed models available? Can I choose one based on my needs?

It is not fixed. Amazon Bedrock offers multiple base models, allowing you to choose the model that best suits your purpose, such as text generation, summarization, translation, or image generation.

Conclusion

Amazon Bedrock is a fully managed platform on AWS that uses multiple foundational models via APIs to incorporate generative AI into business systems. It is designed for enterprise AI use, allowing you to not only select and use models, but also internal data integration (RAG) and business execution by agents.

Bedrock is a strong option when moving from trying out generative AI as a standalone tool to continuously using it in your business. The first step in implementation is to first determine what the platform can do, and then consider how to incorporate it into your company's use case.