Category: Amazon Bedrock Guardrails

At this year’s re:Invent, we’ve lined up a fantastic set of sessions to get you up to speed on the latest AI cost management frameworks, solutions, and real-life success stories. They will help you wrap your head around concepts, such as tokenomics, and get ahead of the AI cost management game. Across tracks, more than 30 sessions fall under the theme of cloud financial management and AI cost management. In this blog post, I highlight nine of them and share where you can find AWS FinOps experts and peers throughout the week.

As we continue our five-part series on optimizing costs for generative AI workloads on AWS, our third blog shifts our focus to Amazon Bedrock. In our previous posts, we explored general Cloud Financial Management principles on generative AI adoption and strategies for custom model development using Amazon EC2 and Amazon SageMaker AI. Today, we’ll guide you through cost optimization techniques for Amazon Bedrock, AWS’s fully managed service that provides access to leading foundation models. We’ll explore making informed decisions about pricing options, model selection, knowledge base optimization, prompt caching, and automated reasoning. Whether you’re just starting with foundation models or looking to optimize your existing Amazon Bedrock implementation, these techniques will help you balance capability and cost while leveraging the convenience of managed AI models.