Temporal Worker Deployments

A core feature of Temporal is that you are able to deploy your Workers to any infrastructure where your Workflow and Activity code will actually run. This way, you have total control over your runtime environment, and can be responsive to any security or scaling needs that may arise over time, whether you are using Temporal Cloud or self-hosting a Temporal Service.

If you are just getting started, you want more guidance, or a refresher on Temporal concepts, our Tutorials and Courses help by using only one or two Temporal Workers to demonstrate core functionality. Once you have an understanding of the core concepts, the content in this section will provide clarity on real-world deployments that grow far beyond those examples.

Our Worker Deployments guide provides documentation of Temporal product features that make it easier to scale and revise your Workflows.

Worker Versioning allows you to pin Workflows to individual versions of your workers, which are called Worker Deployment Versions.

You can optionally use the Temporal Worker Controller to programmatically manage and scale your Worker deployments in Kubernetes pods.

This section also covers specific Worker Deployment examples:

• Deploy Workers to Amazon EKS Containerize your Worker, publish it to Amazon Elastic Container Registry (ECR), and deploy it to Amazon Elastic Kubernetes Service (EKS) using the Temporal Python SDK. This guide covers the full deployment lifecycle and shows how to configure your Worker to connect to Temporal Cloud using Kubernetes-native tools like ConfigMaps and Secrets. Running Workers on EKS gives you fine-grained control over scaling, resource allocation, and availability—ideal for production systems that need reliability and flexibility in the cloud.