Model Evaluation in Amazon Bedrock is a capability that helps you access, compare, and select large language models (LLMs) and foundation models (FMs) for your generative AI applications.

Because model performance can vary significantly by task, domain, and data modality, choosing the right model is a critical first step. Model Evaluation is designed to simplify this step so you can:

• Quickly test multiple models side by side
• Understand how different models behave on your specific use case
• Make more informed decisions before committing to a model in production

In short, it helps you rethink how you select models by turning what used to be a manual, trial-and-error process into a more guided, data-informed workflow.

Selecting the right FM or LLM matters because model performance can vary drastically depending on:

• Task type (e.g., summarization, Q&A, content generation)
• Domain (e.g., finance, healthcare, customer support)
• Data modalities (text, potentially other formats)

A model that works well for one use case may underperform in another. Without a structured way to compare models, teams can spend significant time and cost on trial and error.

Model Evaluation in Amazon Bedrock helps you reimagine this step by giving you a more systematic way to:

• Evaluate multiple models against your own criteria
• Identify which model best fits your application needs
• Reduce the risk of deploying a poorly matched model

Model Evaluation is part of the broader Amazon Bedrock developer experience, which focuses on making it easier to build and scale generative AI applications on AWS.

AWS is a comprehensive cloud platform with over 200 fully featured services available from data centers around the world. Millions of customers—including fast-growing startups, large enterprises, and government agencies—use AWS to:

• Lower infrastructure and operational costs
• Increase agility in how they build and deploy applications
• Innovate faster with managed services and integrated tooling

Within this ecosystem, Amazon Bedrock and its Model Evaluation capability help you:

• Integrate model selection into your existing AWS workflows
• Leverage AWS tools, events, and community resources (such as AWS re:Post) as you build
• Reshape how your teams experiment with and operationalize generative AI, using services they may already rely on