What is Amazon Bedrock and How Can You Use it for Generative AI?

Would you like to make AI solutions available to your company without resorting to expensive custom development and time-consuming model training? That’s where Amazon Bedrock comes in: a fully managed service that provides businesses with customizable AI models from leading companies specialized in AI development services.

Below, we will describe this service in more detail and consider specific cases of its use. Also, you can resort to our AWS consulting services to get a more comprehensive vision of AI introduction into your business processes.

Overall, the capabilities of Amazon Bedrock go far beyond similar solutions that provide their models without proper customization options. In particular, in Bedrock, customization can be carried out through fine tuning of labeled data, continued pre-training for unlabeled data, and searching for augmented generation (RAG) for big sets of models. Moreover, this service allows no-code development of agents to perform complex scenarios, from creating marketing campaigns to booking.

Despite its youth, this service has already been successfully implemented by startups such as Coda, Hurone AI, and Nexxiot, as well as world-famous companies such as Adidas, GoDaddy, Clariant, Broadridge, Accenture, BCG, Salesforce, Leidos, and many others.

What Makes Amazon Bedrock Different

Unlike its analogs, Amazon Bedrock provides several models depending on the task at hand. That’s why this suite is better than the well-known OpenAI, which is based only on the company’s proprietary models. However, this is not the only difference making Bedrock a more useful solution than its counterparts. Let’s check the other differences below.

Scalability

AWS Bedrock has serverless architecture, which means that you can scale generative AI applications dynamically as the computing power requirements and the data storage size change (you can also find out more about serverless architecture provided by AWS Lambda benefits). Moreover, the scaling process is fully automated, which means you don’t have to burden your IT department with a bunch of new responsibilities associated with managing an expanded digital infrastructure. Finally, AWS allows companies to use multiple AI models simultaneously to get the most out of generative AI by optimizing their workflows.

Flexible Infrastructure

Amazon Bedrock provides developers with fully customizable, high-performance FMs that can be tailored to meet the most challenging business needs. These models are configured through the no-code console, which offers a choice of several data sets for training and testing, located in Amazon Simple Storage Service (Amazon S3). Also, here, developers can set hyperparameters that determine the level of performance of the selected model. The integration of models with third-party solutions (in particular, services and applications that are already part of your company’s IT infrastructure) is carried out through a single API, which allows developers to combine several models from different vendors in the same software solution.

End-to-end Tools

Amazon Bedrock allows no-code development of agents for the execution and automatization of complex scripts. In particular, these agents can be used to connect fundamental models to your enterprise data sources and run dynamically through APIs to tailor them to your specific business needs and goals. Moreover, they automatically break down scenarios into sub-tasks and manage them through orchestration tools, reducing the number of responsibilities placed on the shoulders of your IT department. If any of the tasks involves entering user data, Bedrock can generate hints to achieve the most accurate result independently. Thus, you get the opportunity to delegate a number of multi-step processes to highly intelligent and high-performance digital solutions that eliminate human error.

Integration with AWS Marketplace

AWS Bedrock is fully compatible with other AWS solutions, allowing companies to get the most out of what the cloud platform has to offer. In particular, developers can also use proprietary pipeline solutions to make it easier and faster to test new applications and services, orchestration and automated deployment solutions, and more.

High-end Security and Compliance

Finally, whatever solution you deploy with Amazon Bedrock AI, you have the confidence that it will meet generally accepted data security standards, including GDPR and HIPAA. This service also supports data encryption with AWS Key Management Service, data management and auditing with Amazon CloudWatch Logs, API activity monitoring and automated troubleshooting with AWS CloudTrail, storage of metadata, requests, and responses with Amazon Simple Storage Service, and a mechanism to prevent your own data abuse.

Types of Applications Developers Can Make with Amazon Bedrock

Amazon Bedrock can be widely used when creating services and applications in the following areas:

• Text generation. Amazon Bedrock allows you to integrate models for generating text in various formats, from short answers to user queries to marketing-oriented and detailed ones that imply a deep understanding of the subject.
• Conversational AI. This service can be used to create chatbots and virtual assistants that combine fundamental models with your internal corporate data. This approach allows you to deploy highly specialized automated solutions, for example, for helping specialists working in your company.
• Text summarizing. Amazon Bedrock AI can become a part of solutions aimed at analyzing text and summarizing its essence. Thanks to such software, you will no longer need to personally read dozens of documents to get the insights you need.
• Image generation. Finally, you can implement a smart solution to generate images based on user-entered queries with Bedrock.

6 Ways Amazon Bedrock Can Help Businesses Use Generative AI Tools

Now, let’s find out how companies can use the generative AI tools based on Amazon Bedrock for the benefit of their activities. 

Democratized AI

Since developing solutions based on generative AI from scratch is often very expensive and time-consuming, the use of no-code tools from the Amazon Bedrock suite helps to significantly democratize the implementation of this technology in business processes—both for startups and small- and medium-sized businesses.

New Level of Cost Efficiency

Through automation enabled by generative AI, companies can get maximum performance in carrying out their daily business tasks like intelligent document processing, customer support, sentiment analysis, transcription, translation, and/or debugging, as well as producing unique marketing content and image generation. All of this becomes available without requiring new human resources and, thus, allows companies to achieve better cost efficiency than ever before.

Full Customization of Services and Apps

Amazon Bedrock allows end-to-end customization of models and integration of business data from the company to which it belongs. This way, businesses can tailor generative AI-driven solutions to their unique requirements and goals.

Reduced Time to Market

Amazon Bedrock provides no-code tools for creating custom software solutions. This means that developers will not have to spend a lot of time writing complex program code to implement a generative AI model.

Better Customer Experience

By introducing generative AI into software solutions that directly or indirectly interact with customers, companies are able to achieve a better customer experience and increased loyalty without the need to modernize other business processes and exposing themselves to the risks associated with the loss of confidentiality of customer data.

Risk Reduction

Thanks to the capabilities of Amazon Bedrock’s generative AI, companies get the opportunity to delegate some tasks to software solutions and, thereby, reduce the risks associated with the human factor.

How Much Does Amazon Bedrock Cost?

Amazon Bedrock currently has four pricing models. Let’s look at them in detail:

• On-demand. With this model, you only pay for what you use. Specifically, you are charged for each request processed and response generated (text or image).
• Batch. This mode involves providing a set of hints on your part. In this case, responses to requests are stored in Amazon S3. Pricing for requests and responses to them is similar to the previous model.
• Provisioned Throughput. This model is designed to provide maximum performance. Thus, the cost is formed according to the maximum allowed number of requests and responses within one minute. Please note that payment is calculated hourly and is available in both 1-month and 6-month contracts.
• Model Customization. If you are going to use your own business data to connect it to the models provided by Amazon Bedrock, this model will be the only right solution for you. In this case, you will pay for the number of hours that each model you use processes requests and generates responses. This mode also provides 1- and 6-month contracts.

You can find the cost of processing requests and generating responses here. If your hesitations are more global, and you have not yet decided which cloud provider to choose, you can read our other article devoted to the analysis of AWS vs GCP vs Azure pricing.

What’s the Difference Between Amazon Bedrock and SageMaker?

If you plan to build generative AI applications and consider AWS for this, you may hesitate between two options: Amazon SageMaker and Amazon Bedrock. Indeed, in many aspects, they are similar, but in fact, they are used for different purposes. Let’s figure out what their main differences are.

In particular, speaking about Sagemaker in the context of comparison with Bedrock, people usually mean its separate service, Sagemaker Jumpstart, which provides tools to build, train, and deploy ML models (fundamental, computer vision, natural language processing, as well as for experiments and research). At the same time, with Jumpstart, you can independently select infrastructure components for deploying solutions based on these models, and also modify them using the Python SDK. As for Amazon Bedrock, this suite is fully managed and provides access to ready-made, customizable models and pre-built training scenarios hosted on AWS.

This key difference, in fact, determines the inconsistencies in the following parameters for each of the two services:

• Development speed: in the case of JumpStart, the development process will take much more time since this service is designed for building models and training them from scratch.
• Scalability: For Amazon Bedrock, the scalability is limited to the capabilities of models hosted on AWS.
• Cost of use: SageMaker JumpStart is not tied to pricing plans for specific models, so you get better flexibility in this aspect. However, at the same time, it will be important for you to understand how cloud computing pricing is formed—the cost efficiency of using this service correctly.
• Features: Because SageMaker JumpStart allows users to choose from a wide range of machine learning models and frameworks, it’s more flexible than Amazon Bedrock.
• Maintenance of created models: in the case of JumpStart, maintenance and updates become completely the responsibility of the company that uses this service.

Based on this, we can conclude that Amazon Bedrock is better suited for startups and small- and medium-sized businesses that do not have enough time and/or financial resources to develop their own models and train them. However, this service can also bring significant benefits to large corporations that find ready-made generative AI models hosted on AWS useful. 

Final Thoughts

We hope that we have helped you understand the advantages and features of such a promising, highly intelligent solution as Amazon Bedrock, and now, you have at least a general idea of exactly what benefits it can bring to your business. If you want to find out specific cases potentially applicable in your company or hire a team to develop software solutions based on Amazon Bedrock, feel free to contact us. We will provide you with the best specialists with experience in your business niche to help you implement generative AI into your business processes and, thus, maximize their productivity, accuracy, and cost-efficiency.