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.
On April 13, 2023, Amazon announced Amazon Bedrock as part of a suite of new tools for building generative AI solutions on AWS. It simplifies the development process while maintaining privacy and security.
So, what is Amazon Bedrock? In a nutshell, it’s a fully managed service that offers a selection of high-performance foundation models (FMs) from leading AI companies, including AI21 Labs, Anthropic, Cohere, Stability AI, Meta AI, and Amazon, as well as a wide range of generative AI capabilities. Together, they enable Amazon Web Services clients to build generative AI applications through the Amazon cloud platform. In particular, with their help, you can create your own chatbot, as well as solutions for generating text and images based on specific requests.
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.
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.
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.
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.
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.
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.
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.
Amazon Bedrock can be widely used when creating services and applications in the following areas:
Now, let’s find out how companies can use the generative AI tools based on Amazon Bedrock for the benefit of their activities.
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.
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.
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.
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.
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.
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.
Amazon Bedrock currently has four pricing models. Let’s look at them in detail:
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.
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:
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.
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.
Max is our senior practice leader and evangelist for the “big triad” of machine learning, data analytics and data engineering, with a vast background in AI, BI services, and product management.
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