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. 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, lower cost, higher performance, 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. 

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.

What is Amazon Bedrock?

Amazon Bedrock is a fully managed service from AWS that provides a comprehensive platform for generative AI development. It offers developers and organizations a unified API to access a wide range of foundation models from leading AI companies, enabling seamless integration of advanced AI capabilities into business applications. The service abstracts away the complex infrastructure challenges, allowing teams to focus on innovation and solving business problems.

Supported LLM Models: Bedrock supports an impressive array of cutting-edge language models, including:

• Anthropic's Claude family of models
• Cohere's Command and Embed models
• A121 Labs' Jurassic models
• Meta's Llama 3
• Mistral AI models
• Stability AI's Stable Diffusion
• Amazon's proprietary Titan models

What is OpenAI?

OpenAI is an AI research company that provides access to advanced AI technologies. OpenAI offers cutting-edge AI models like GPT-4 and generative tools such as DALL-E. These tools empower enterprises to build innovative applications, automate tasks, and so much more. Their solutions are suitable for a wide range of industries and use cases.

Supported LLM Models: OpenAI provides access to:

• GPT-3.5 and GPT-4 models
• DALL-E image generation
• Embedding models
• o1 models
• TTS models
• Whisper models
• Moderation models

Why Companies Opt for OpenAI

Organizations continue to choose OpenAI for a few reasons. OpenAI’s GPT models are widely recognized for their state-of-the-art language generation, natural language understanding, and conversational capabilities partly because they were the first to market.

Additionally, OpenAI also offers easy-to-use APIs, fine-tuning capabilities, and pre-configured tools like ChatGPT for conversational AI to leverage AI for various applications.

The drawbacks of OpenAI

Despite its strengths, OpenAI presents several significant limitations for enterprise users. The platform's model diversity is comparatively restricted, potentially constraining organizations' ability to explore varied AI solutions. Scalability challenges can emerge, particularly for businesses with complex or rapidly changing computational requirements.

Integration flexibility remains a notable concern, with OpenAI offering less granular control over deployment and customization. The potential for single-point-of-failure risks introduces additional complexity for organizations seeking highly resilient AI infrastructure. These limitations become increasingly pronounced as businesses seek more sophisticated and adaptable AI capabilities.

The remaining sections can remain as they were in the original document, as they are already well-formatted and aligned with the blog's overall tone and structure. This approach maintains the technical depth while presenting the information in a more narrative, paragraph-based format.

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 AI or any other third party provider (currently supports Flan T5, Llama and Mistral models) and benefit from Bedrock's solutions (some product restrictions apply).

Why Move to Bedrock? 

Ease, security, and privacy are three of the main reasons. Here is how we typically explain it to clients.

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 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.

Case Studies: OpenAI vs Bedrock

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.