OpenAI vs Bedrock: Optimizing Generative AI on AWS

The ever-increasing pace of technological innovation is pushing businesses to seek ways to stay ahead of the curve and leverage the latest technologies. One area that has gained significant traction is artificial intelligence (AI) and machine learning (ML). With the advent of Generative AI (GenAI), many business leaders have been challenging their teams on how GenAI could enable new business cases, improve efficiency and productivity, and also reduce costs.

Many organizations initially turned to OpenAI to explore these capabilities due to its first-mover advantage. However, the past 12 months have shown the world that putting all your eggs in one basket with one model provider is not a safe bet, as Anthropic briefly launched into the lead with Claude 3, and then Meta released Llama 3 as open source, which quickly proved it has the best price-performance, followed by GPT-4o, only to be supplanted by Claude 3.5 Sonnet's release. As the landscape evolves, these same organizations are realizing the need to continuously look for better alternatives to simplify and scale model deployment with more flexibility, faster integration with corporate systems, native integration with AI tools, protection of intellectual property, enhanced compliance, security and privacy capabilities, and focusing on the business core instead of managing complex infrastructure components. 

At Caylent we're building flexible architectures that anticipate evolution and seeing an increasing demand from customers eager to move from OpenAI to this type of approach. For these customers, Amazon Bedrock becomes a compelling option to consider. It is a serverless managed service that allows you to choose from a range of foundation models developed by a growing list of different providers, such as AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon themselves, giving the option to have different models for different tasks. 

This marketplace of models makes it quick and easy for you to get started with AI as a developer. Bedrock itself is growing into a suite of services beyond serving foundation models, including Agents for Amazon Bedrock, Guardrails for Amazon Bedrock, Bedrock Studio and more. Additionally, Amazon Bedrock benefits from native integration with other AWS services, like Amazon SageMaker and Amazon Kendra; Lambda and container services, S3, Athena, Redshift, RDS, and other data services; and IAM, CloudWatch, CloudTrail, SecurityHub, and other security and operations services. Your data is securely kept in your own AWS account, ensuring that you have full control and comply with your organization's and industry's security standards.

In this blog post, we will share a few customer stories and review some of the architectural best practices and features of Amazon Bedrock that are attracting product teams away from OpenAI. We will also look at some of its key advantages and how you can leverage them to kickstart your AI journey.

Why Move to Bedrock: Case Studies

Before we get into the technical details, we wanted to share a few real-world examples. With Amazon Bedrock we can apply Caylent’s frameworks, reference architectures and Catalyst solutions that will help organizations at any size achieve their key results from the first use case giving them full visibility and control needed to scale solutions rapidly and reduce business risks.

Let’s take a look at a few success stories:

• IdenX faced challenges with traditional research methods and sought to streamline processes using AI. After unsuccessful attempts with OpenAI, they partnered with Caylent to develop a solution using Amazon Bedrock and Anthropic Claude. The solution involved metadata extraction across a wide variety of file formats, data processing pipelines, and cost optimization strategies. As a result, the company could query thousands of files instantly without preprocessing, significantly increasing efficiency by reducing time and resources while improving customer experience.

• A third-party risk management company needed to automate the process of correlating compliance documents in a variety of formats with their 120-question compliance questionnaire. After exploring OpenAI without success, they turned to Amazon Bedrock. Caylent helped them design and implement a pipeline to scan clients' compliance documents using Bedrock, Kendra, and other AWS services while optimizing for cost efficiency. The pipeline processed documents and correlated information to answer the questionnaire. Bedrock's integration with AWS services streamlined workflow orchestration and data management, surpassing the limitations they encountered with OpenAI. This solution improved operational efficiency by automating the document correlation process and allowed the company to deliver a more streamlined compliance evaluation experience to their clients.

• Another organization initially used OpenAI and was concerned about Bedrock models' ability to match their existing solution's performance. Caylent created a comprehensive test harness that allowed us to compare legacy inputs and outputs with Bedrock outputs, deterministically guiding the development process and demonstrating that Bedrock models could achieve 100% parity, alleviating their concerns and enabling a successful migration.

Reasons to use Bedrock vs. OpenAI

OpenAI recently experienced a notable outage. Instead of putting all your eggs in one basket, we recommend designing a highly resilient architecture and having an ecosystem of partners. AWS' global presence consists of dozens of globally distributed regional options, each having 3 or more fault-isolated availability zones, enabling geographically distributed solutions that can be built for stringent SLAs through redundant and self-healing implementations.

Amazon Bedrock enables you to access many different best in class GenAI models from multiple providers, such as Anthropic's Claude family, Cohere Command and Embed, A121 Labs Jurassic, Meta Llama 3, Mistral AI models, Stability AI Stable Diffusion and Amazon Titan (of course) in a single API, across multiple regions worldwide. These providers have launched more than 3 dozen major releases since Bedrock debuted.

Optimized models for each use case

You can select the best model for any specific use case, achieving a better price-performance ratio versus relying on a one stop shop solution. To accomplish this, we recommend establishing an LLM benchmarking capability to measure and optimize the LLMs used in your applications. GenAI test suites like the one we developed for the customer example above will pay extra dividends when exploring model alternatives. This approach holds even if your next model change is a planned upgrade from a version to another. 

Faster time to market

Bedrock Studio can streamline your prototyping process by leveraging a web interface that offers to your development team a rapid manner to create and test new GenAI based solutions, creating multiple workspaces integrated with your corporate authentication for enhanced security.

With the new Converse API, it's simple to switch between different models with little or no change in your application and a fast learning curve for the development team in a more complex environment. This can help accelerate your agent based solutions integrating corporate systems and data sources to extract maximum value from LLMs.

Scalability 

Amazon Bedrock is built to scale. Companies can start small and expand their usage as needed, leveraging AWS’s robust infrastructure to handle increased loads efficiently. This scalability is crucial for businesses expecting growth or fluctuating demand. If necessary, you can even count on provisioned throughput instead of suffering with rate limits without dedicated nodes.

Managing fine-tuned models made easy

Do you have your own fine-tuned model? No problem, you can easily import models from SageMaker or any other third party provider (currently supports Flan T5, Llama and Mistral models) and benefit from Bedrock's solutions (some product restrictions apply).

Bedrock is a fully-managed service

With Amazon Bedrock you can focus on what really matters for your business and let AWS take care of the technical details, focusing your effort on prototyping, evaluating models, applying fine tuning and RAG (Retrieval Augmented Generation) techniques, building agents and deploying tasks.

It is possible to enhance models performance monitoring by integrating Amazon Bedrock with CloudWatch, so you can analyze your running models in real time.

Security, Privacy and Responsible AI

At AWS you can benefit from a proven environment with comprehensive built-in products and solutions so you can guarantee governance, privacy, observability and compliance.

With Bedrock, data and customizations are maintained securely in service-team escrowed accounts. By keeping all of your data and code securely within your own AWS accounts, you have full control over your data and can comply with your organization's security policies and industry regulations.

Amazon DataZone can help catalog, discover, share, and govern data stored across AWS, on premises, and third-party sources so you can have a single data management tool for all your data spreaded over the organization and apply fine grained access controls to it.

Amazon Bedrock brings you Guardrails, a safeguards solution that help you creating your AI policies, filter harmful content, disallow denied topics, redact or block sensitive information such as PII (personally identifiable information) that can be shared with different applications and use cases, including Agents and Knowledge Bases for Amazon Bedrock, reducing business risks related to AI misuse.

Summary

Amazon Bedrock offers several advantages over other AI solutions, including:

• Access to a diverse range of models that can be continuously upgraded as new advancements emerge.
• The ability to develop comprehensive test suites to accelerate the evaluation and adoption of alternative models or even use automatic’s Bedrock evaluation jobs.
• Greater control and flexibility to tailor AI solutions to your unique requirements, allowing you to adapt quickly to changing market dynamics and customer needs.
• Opportunities to develop robust model switching capabilities, minimizing disruptions to your core applications and services as model upgrades become more frequent.
• Cost-effective on-demand pricing that optimizes your AI investments based on your specific use case requirements.
• Streamlined data management processes while maintaining compliance and control over your valuable data assets.

Migrating to Amazon Bedrock presents a compelling opportunity for organizations seeking innovation, cost-effectiveness, and control over their AI solutions. By leveraging Bedrock's powerful capabilities and seamless AWS integration, businesses can unlock new possibilities, drive operational efficiencies, and deliver exceptional customer experiences.

Next Steps

Is your company planning your journey with generative AI? Are you replacing first-generation solutions or prototypes built on OpenAI? Consider engaging a partner with proven experience to help you get there. At Caylent, we have a full suite of GenAI offerings spanning the entire AI adoption lifecycle.

Starting with our Generative AI Strategy Caylent Catalyst, we can start the ideation process and guide you through all the possibilities that can be impactful for your business. Using these new ideas, our Generative AI Proof of Value Caylent Catalyst can help you build your first production-ready AI solution aligned to business outcomes. To strategically adopt AI at scale, our Innovation Engine offers a continuous innovation framework that accelerates your journey to production across a portfolio of use cases.

As part of these Catalysts, our teams can help you create, implement, and operate your custom roadmap for GenAI. For companies ready to take their GenAI initiatives beyond the scope of Caylent's prebuilt offerings, we can tailor an engagement exactly to your requirements. Get in touch to discuss how we can help.