Steph Gooch

As AI workloads scale, engineers face complex trade-offs between cost, latency, and response quality. This code talk explores advanced optimisation techniques for production AI applications on Amazon Bedrock: prompt engineering, model selection, caching, and context window management.

Every architectural decision carries a cost implication, yet cost is typically discovered in monthly bills rather than design reviews. In this code talk, we demonstrate how to shift FinOps left, optimising before you deploy rather than reacting after.

Are you looking to take your AWS cost management to the next level? In this live coding session, discover how to leverage AWS Cost and Usage Reports (CUR) alongside Amazon Q to streamline query development and automate code generation for detailed cost allocation.

As cloud applications grow in complexity, optimizing costs becomes crucial for developers. This session explores AWS-native tools and coding practices that reduce expenses without compromising performance or scalability.

In this code talk, dive deep into AWS Cost and Usage Reports (AWS CUR) and learn how to write queries, analyze usage, derive insights, and connect other datasets. Learn the different data export options, including Legacy CUR, CUR 2.0, and FinOps Open Cost and Usage Specification (FOCUS) 1.0.

Are you curious about the breakdown of your AWS Savings Plans discounts per account? Do you want a complete picture of your application’s service usage by including all the resources it’s built upon?

Learn to improve the accuracy of your AWS cost forecast by defining a realistic plan for your new and existing workloads. Consider variables, such as usage organic growth, business seasons, and migrations and modernizations (for example, workload optimization).