The demand for large language models (LLMs) has skyrocketed and the big cloud platforms including AWS have taken note. These models power everything from chatbots to content generation tools, requiring robust and scalable infrastructure. Amazon Web Services (AWS) has stepped up to meet this demand with Amazon Bedrock, a fully managed service that makes it easy for AWS customers to build, deploy, and scale generative AI applications that use LLMs.

What is Amazon Bedrock and what LLMs are available on AWS?

Amazon Bedrock offers a seamless way to access a variety of high-performing foundation models (FMs) from leading AI companies, including AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon itself. This service provides single API access to these models, enabling developers to experiment, evaluate, and integrate them into their applications without worrying about the underlying infrastructure.

One of the advantages of AWS and Amazon Bedrock is its flexibility and wide selection of additional services. With a serverless architecture, Bedrock allows you to customize models with your data using advanced techniques like fine-tuning and Retrieval Augmented Generation (RAG). Whether you need to build sophisticated AI-driven agents or simply want to add intelligent features to your existing applications, Bedrock provides the tools and security needed to do so effectively.

Security and Privacy with Amazon Bedrock

Security is a top priority when dealing with sensitive data in AI applications. Amazon Bedrock has been built to keep your data encrypted both in transit and at rest, offering full control over encryption keys via AWS Key Management Service (KMS). Additionally, identity-based policies allow you to manage user access and actions, ensuring that your data remains secure throughout the AI development lifecycle.

Building Responsible AI Applications

Amazon Bedrock also comes equipped with guardrails to help you build responsible AI applications. These include safeguards that filter harmful content and reduce hallucinated responses, particularly in RAG and summarization workloads. This helps you build AI applications that are powerful but also ethical and safe for end-users.

Integrating Nebuly with Amazon Bedrock

For developers looking to maximize the potential of their LLMs, integrating Nebuly with Amazon Bedrock offers an additional layer of insights and analytics. Nebuly's SDK allows you to monitor all API requests made to various models within Bedrock, including those from AI21, Anthropic, and Cohere.

Getting started with Nebuly is straightforward:

1. Install the Nebuly SDK: Begin by installing the SDK using: pip install nebuly
2. API Key Setup: Obtain your Nebuly API key by creating a project in the Nebuly dashboard.
3. Integration: Initialize the SDK with your API key and include the user_id in your AWS Bedrock method calls.

Learn more from Nebuly Docs.

With this integration with Nebuly, you can analyze user interactions, track LLM performance, and gain valuable insights to refine your AI models. Whether you're making single API calls to Bedrock models or managing a chain of calls across different providers, Nebuly ensures you have the data you need to optimize your AI solutions.

Conclusion

AWS Bedrock is an excellent option for developers working with large language models on AWS, offering an easy, scalable, and secure way to build and deploy generative AI applications. By integrating Nebuly with Bedrock, you can further enhance your LLM capabilities, ensuring that your applications are optimized for a great user experience.

Combining AWS Bedrock's robust infrastructure with Nebuly's insightful LLM user analytics is a powerful combination for building user-centric and effective LLM-powered products.
If you’d like to a Nebuly expert, please book a meeting with us HERE.

‍

‍