Caylent Catalysts™
Generative AI Strategy
Accelerate your generative AI initiatives with ideation sessions for use case prioritization, foundation model selection, and an assessment of your data landscape and organizational readiness.
Discover the differences between Amazon Bedrock vs. OpenAI for Generative AI. Explore the benefits of Bedrock and understand why you should choose Bedrock vs OpenAI.
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.
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:
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:
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.
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.
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.
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.
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.
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.
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).
Ease, security, and privacy are three of the main reasons. Here is how we typically explain it to clients.
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.
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.
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.:
Amazon Bedrock offers several advantages over other AI solutions, including:
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.
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.
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 articlesMark 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 articlesCaylent Catalysts™
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