Running AI/ML workloads in the cloud can be expensive, opaque, and difficult to iterate on. LocalStack changes this by enabling engineers to develop and test AI-powered cloud applications entirely locally, emulating services like SageMaker, Bedrock, Redshift, and Snowflake. In this presentation, Waldemar Hummer, CTO of LocalStack, demonstrates how to prototype and validate AI & ML data pipelines safely and cost-effectively using LocalStack’s cloud emulators. You’ll see how to emulate complex AI workflows, test integrations, and use “vibe coding” techniques confidently in a fully sandboxed local environment.

Running your Spring Boot app on AWS for production is common, but testing there can be slow and costly. In this video, we’ll show you how to speed up development using LocalStack. By provisioning your infrastructure with Terraform, you can easily switch to local testing in just three steps: configure your dev environment variables, start LocalStack in Docker, and run your IaC files. Get faster feedback and reduce costs by testing locally with LocalStack!

In this live session, WireMock CTO Tom Akehurst will introduce hybrid API simulation (local + cloud) with WireMock Runner. Tom will explain why we built Runner, how developers are using it today, and how it fits into modern dev and test workflows - such as simulating APIs during testing, prototyping, and AI-native development.

LocalStack Chaos API enables you to simulate outages in any AWS region or service. Chaos API provides an easy way to implement chaos engineering experiments to test a wide variety of simulated outages and failures within your application safely, without impacting your production users. Common examples can include region-wide outages, DNS failovers, service failures, and network faults. All the testing scenarios described above can be executed within LocalStack, providing thorough coverage for critical situations in a matter of minutes rather than hours or days. In this presentation by Viren Nadkarni, we explore how Chaos API is leveraged to perform service failures in a local environment while using robust error handling to address and mitigate such issues.