LocalStack now provides enhanced support for running AWS services in Kubernetes environments. In this presentation from the LocalStack 4.0 community meetup by Simon Walker, we explore how to deploy and manage local AWS resources within Kubernetes clusters with LocalStack, to help developers maintain consistency between development and production environments.The session further covers LocalStack’s Kubernetes integration, including deployment via Helm charts, configuration of services like Lambda and RDS as Kubernetes pods, and networking between components. A demo illustrates provisioning a serverless application (Lambda functions interacting with a MySQL database) using Terraform, with all resources managed within a local Kubernetes cluster.You'll additionally learn the practical approaches for local testing and infrastructure emulation by moving from Docker to Kubernetes-native solutions as well as upcoming features, including broader service support and new container runtime options.## Resources- Documentation: https://docs.localstack.cloud/user-guide/localstack-enterprise/kubernetes-executor/- Get access: https://www.localstack.cloud/contact

Recreating the unique state of a cloud app environment is notoriously complex, making it difficult to diagnose bugs, collaborate with teammates, or even just pick up where you left off. Join us as we deep dive into LocalStack’s powerful state management and persistence features, exploring how to maintain, snapshot, and seamlessly share your local cloud environments using core features like persistence, Cloud Pods, and state files. Learn how to restore a previous state, share preconfigured environments with colleagues for collaboration or onboarding, or restore preset services and data for functional tests in CI. We'll also showcase LocalStack's new Model Context Protocol (MCP) integration, revealing how AI can manage, inspect, and automate your local cloud state.

An agent will write you a CDK stack, a Terraform module, or a stack of IAM policies in seconds.
Whether any of it works is a separate question, and the usual way to find out is to deploy to a real AWS account and watch what breaks.
In an agentic workflow, that means giving AI access to a public cloud account, racking up costs on the AWS bill, and waiting for provisioning to complete every time you push new code to the environment.

The rise of agentic AI in the software delivery lifecycle creates a dilemma with high-stakes implications.
As agents create new applications at an unprecedented rate, how do you integrate security without slowing down delivery?