Beyond the basics: A comprehensive foundation model selection framework for generative AI

Most organizations evaluating foundation models limit their analysis to three primary dimensions: accuracy, latency, and cost. While these metrics provide a useful starting point, they represent an oversimplification of the complex interplay of factors that determine real-world model performance.

Foundation models have revolutionized how enterprises develop generative AI applications, offering unprecedented capabilities in understanding and generating human-like content. However, as the model landscape expands, organizations face complex scenarios when selecting the right foundation model for their applications. In this blog post we present a systematic evaluation methodology for Amazon Bedrock users, combining theoretical frameworks with practical implementation strategies that empower data scientists and machine learning (ML) engineers to make optimal model selections.

The challenge of foundation model selection

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models from leading AI companies such as AI21 Labs, Anthropic, Cohere, DeepSeek, Luma, Meta, Mistral AI, poolside (coming ...