Author: Adam Richter

Introduction Unlocking FinOps capabilities for modern cloud teams just got simpler with the introduction of the AWS Billing and Cost Management Model Context Protocol (MCP) server, which makes advanced cost analysis and optimization features directly available to your favorite AI assistant or chatbot. By integrating natural language queries, secure local credentials, and real-time access to […]

Building on our previous discussions about AWS generative AI cost optimization, the fourth blog of the five-part blog series focuses on maximizing value from Amazon Q, AWS’s generative AI-powered assistant. While our earlier posts covered custom model development with Amazon EC2 and SageMaker AI and foundation models with Amazon Bedrock, today we’ll explore strategies to optimize costs when implementing Amazon Q. From selecting the right pricing tier and implementing strategic user management to optimizing content indexing and improving cost predictability, we’ll share practical approaches that help you balance functionality with cost efficiency. Whether you’re using Amazon Q Business for your generative AI–powered assistant or Amazon Q Developer to enhance developer productivity, these best practices will help you make informed decisions about your Q implementation.

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.

Amazon EC2 and SageMaker AI are two of the foundational AWS services for Generative AI. Amazon EC2 provides the scalable computing power needed for training and inference, while SageMaker AI offers built-in tools for model development, deployment, and optimization. Cost optimization is crucial since Generative AI workloads require high-performance accelerators (GPU, Trainium, or Inferentia) and extensive processing, which can become expensive without efficient resource management. By leveraging the below cost optimization strategies, you can reduce costs while maintaining performance and scalability.