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AWS Bedrock vs. OpenAI: Comparison for Generative AI on AWS

Generative AI & LLMOps

Compare AWS Bedrock and OpenAI for generative AI: model diversity, security, scalability, integration, and real-world migration success stories for enterprises.

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.

Related reading: Whitepaper: The 2025 Outlook on Generative AI | Caylent

Overview: OpenAI vs Amazon Bedrock

In this blog, we’ll lay out the reasons why we think Bedrock is the better option.


OpenAI Amazon Bedrock
Model Diversity Limited model diversity (primarily GPT models and related tools). Extensive diversity (Claude, Cohere, Jurassic, Llama, Mistral, Stable Diffusion, Titan).
Scalability Challenges scaling for complex or rapidly changing requirements. Built on AWS’s robust infrastructure; supports scalable growth and fluctuating demand.
Integration Flexibility Limited flexibility; less granular control over deployment and customization. Highly flexible; deep integration with AWS ecosystem, offering granular deployment control.
Resilience and Availability Risk of single-point-of-failure, recent notable outage incidents. Highly resilient architecture with geographically distributed AWS availability zones
Fine-tuned Model Support Limited support for importing and managing externally fine-tuned models. Seamlessly imports fine-tuned models from SageMaker AI and other providers.)
Security and Privacy Less control; data and model customization remain on OpenAI infrastructure. Comprehensive security and compliance via AWS; data stays securely in customer-controlled accounts.
Ease of Prototyping and Model Switching Easy-to-use APIs and pre-configured tools, but limited flexibility for rapid model switching. Bedrock Studio and Converse API enable rapid prototyping and easy model switching with minimal disruption.
Price-Performance Optimization Single-provider reliance, which may not offer best price-performance across diverse use cases. Optimizes price-performance ratio by selecting the best-suited models for specific use cases.


We’ll dive into these comparisons in greater detail.

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

Related resources

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

Related resources:

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 from OpenAI to Bedrock? 

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

Related resources:

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.
  • 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.

Conclusion: Bedrock is the cost-effective, flexible option 

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 Bedrock

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.

Amazon Bedrock Deep Dive

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.

Whitepaper: The 2025 Outlook on Generative AI

Read Here

FAQs for AWS Bedrock vs OpenAI

What is Amazon Bedrock and what models does it support?

Amazon Bedrock is a fully managed service from AWS that provides a comprehensive platform for generative AI development. It offers a unified API to access foundation models from leading AI companies, enabling seamless integration of AI capabilities into business applications while abstracting away complex infrastructure challenges. 

Bedrock supports an impressive and ever growing array of models including Anthropic's Claude family, Cohere's Command and Embed, A121 Labs' Jurassic models, Meta's Llama 3, Mistral AI models, Stability AI's Stable Diffusion, and Amazon's proprietary Titan models.

What are the main limitations of using OpenAI for enterprise users?

OpenAI presents several significant limitations for enterprise users despite its strengths in language generation and conversational capabilities. The platform's model diversity is comparatively restricted, potentially constraining organizations' ability to explore varied AI solutions. 

Other challenges include scalability issues for businesses with complex requirements, limited integration flexibility with less granular control over deployment and customization, and potential single-point-of-failure risks. These limitations become increasingly pronounced as businesses seek more sophisticated and adaptable AI capabilities.

What advantages does Amazon Bedrock offer compared to OpenAI?

Amazon Bedrock offers significant advantages including access to diverse models from multiple providers through a single API across multiple global regions, offering better resilience than OpenAI which has experienced notable outages. 

Bedrock enables selecting optimized models for specific use cases, achieving better price-performance ratios, and provides tools like Bedrock Studio that streamline prototyping with a web interface. 

Additionally, Bedrock offers enterprise-grade scalability with AWS's robust infrastructure, easy management of fine-tuned models, and the Converse API which simplifies switching between different models with minimal application changes.

How does Amazon Bedrock address security and privacy concerns?

Amazon Bedrock prioritizes security, privacy, and responsible AI by providing a fully-managed service with comprehensive built-in products and solutions for governance, privacy, observability, and compliance. Data and customizations are maintained securely in service-team escrowed accounts, keeping all data and code within your own AWS accounts for full control and compliance with organizational security policies and industry regulations. 

Bedrock also includes Guardrails, a safeguards solution that helps create AI policies, filter harmful content, disallow denied topics, and redact or block sensitive information such as personally identifiable information, reducing business risks related to AI misuse.

What real-world successes have companies had after migrating from OpenAI to Bedrock?

Several companies have successfully migrated from OpenAI to Amazon Bedrock with impressive results. IdenX, after unsuccessful attempts with OpenAI, developed a solution using Bedrock and Claude that allowed them to query thousands of files instantly without preprocessing, significantly increasing efficiency. 

A third-party risk management company automated the process of correlating compliance documents with their questionnaire using Bedrock, Kendra, and other AWS services, surpassing the limitations they encountered with OpenAI. Another organization concerned about performance parity created a comprehensive test harness that demonstrated Bedrock models could achieve 100% parity with their existing OpenAI solution, enabling a successful migration.

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 more. 

How does Bedrock handle 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. 

How does Bedrock support fine-tuned models?

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). 

Generative AI & LLMOps
Marco Antonio Peres Barbosa

Marco Antonio Peres Barbosa

Marco Barbosa is a Data Engineering Manager at Caylent with over 20 years of experience working in technology consultancy, helping many diverse industries such as telecomunications, logistics consumer goods, insurance, and agriculture. Marco is curious and passionate about learning about different businesses and applying technology to make things work better. He conducted data projects related to support decision-making, M&A, capacity and resource optimization, operational transformation and digitization, customer satisfaction, digital transformation, innovation, and creation of data products.

View Marco's articles
Mark Olson

Mark Olson

Mark Olson, Caylent's Portfolio CTO, is passionate about helping clients transform and leverage AWS services to accelerate their objectives. He applies curiosity and a systems thinking mindset to find the optimal balance among technical and business requirements and constraints. His 20+ years of experience spans team leadership, technical sales, consulting, product development, cloud adoption, cloud native development, and enterprise-wide as well as line of business solution architecture and software development from Fortune 500s to startups. He recharges outdoors - you might find him and his wife climbing a rock, backpacking, hiking, or riding a bike up a road or down a mountain.

View Mark's articles

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