A practical guide to Azure Kubernetes Service (AKS) deployment

Published on 30 Sep 2025 by Adam Lloyd-Jones

Building robust, scalable applications using a microservices architecture is a critical skill for modern developers. Taking a project from an initial concept all the way to a production environment requires mastering a complex toolkit, including containers, continuous integration/continuous deployment (CI/CD), and orchestration platforms. For cloud-native development, Azure Kubernetes Service (AKS) provides the perfect foundation for a microservices lab, combining Microsoft Azure’s managed cloud infrastructure with the industry-standard container orchestration platform, Kubernetes.

This article draws on practical guidance for bootstrapping a microservices application, emphasizing tools like Docker, Kubernetes, GitHub Actions, and Terraform.

Why choose Azure Kubernetes Service (AKS) for microservices?

Microservices are defined as small, independent software processes that run on their own deployment schedule and can be updated independently. A microservices application is a distributed program composed of many such services that must collaborate to achieve the overall features and functionality.

Hosting these collaborating services requires a robust platform, and Kubernetes is the universal computing platform for distributed applications, supported by all major cloud players. AKS, as a managed service, handles the complexities of running and maintaining the Kubernetes control plane, offering significant advantages:

Orchestration and Automation: Kubernetes is commonly known as a container orchestration platform. It manages and automates the deployment and scaling of containers (virtualized servers hosting the microservices). This is essential for managing the complexity that emerges from the interactions of many small services.
Scalability and Resilience: Microservices applications built on Kubernetes are designed to be more flexible, scalable, reliable, and fault tolerant than monolithic applications. Kubernetes allows for replication and redundancy, distributing load and ensuring continuous service even if individual instances fail.
Vendor Freedom: Learning Kubernetes skills is transferable across cloud platforms (Azure, AWS, GCP). Using Kubernetes avoids vendor lock-in, providing freedom of choice for your future infrastructure needs.

For development and learning, if you have Docker Desktop installed, you can even enable a local Kubernetes instance for no-cost experimentation before moving to the cloud.

Setting up your production environment on Azure

The ultimate goal for a microservices lab is to successfully deploy the application to a customer-facing production environment. The production backbone for this application is the Kubernetes cluster hosted in Azure.

1. Creating the managed Kubernetes cluster

AKS clusters can be created manually through the Azure portal UI, which is often cited as the easiest route for beginners and can be done with a handful of clicks.

When creating your managed Kubernetes cluster on Azure:

You must specify a resource group to organize your cloud resources.
For affordability during experimentation and learning, you should choose the cheapest VM size and potentially select a single node for the cluster count.
Once created, the Azure CLI tool is necessary to authenticate and connect the Kubernetes command-line tool (kubectl) to your new cloud cluster.

2. Infrastructure as Code (IaC) for reliability

While manual setup is simple for a lab, professional microservices deployment mandates the use of Infrastructure as Code (IaC), making the process reliable and repeatable.

Tools like Terraform are crucial for defining your infrastructure declaratively. Terraform is used to provision the entire infrastructure, including the necessary container registry and the Kubernetes cluster itself. This automation ensures that complex setups are consistent across environments. Other modern IaC platforms like Pulumi, which supports general-purpose languages like TypeScript, also offer powerful ways to automate cloud infrastructure provisioning and management on Azure, Google Cloud, and AWS.

Packaging and preparing for AKS deployment

Before deployment to AKS can begin, your microservices need to be packaged and made accessible to the cluster.

1. Containerization with Docker

Each microservice must be encapsulated into an immutable package called a Docker image. This image bundles the code, assets, and dependencies needed to run the microservice. The definition of how to build this image is contained in a Dockerfile.

2. Publishing to a container registry

Once the image is built (using docker build), it must be published to a secure location accessible by the cluster. For proprietary applications, a private container registry is used, such as Azure Container Registry (ACR).

The process to publish involves:

Creating the private container registry on Azure (which can be done manually or via IaC tools).
Authenticating with the registry using docker login.
Pushing the image (the published microservice) using docker push.
Finally, the container registry must be connected to the Kubernetes cluster so that the cluster has permission to pull the images.

Deploying to AKS: the Kubernetes workflow

Deploying a packaged microservice involves configuring Kubernetes objects using YAML configuration files.

Core Kubernetes objects for deployment

Pods: The basic unit of computation in Kubernetes, which holds the running container(s) for the microservice.
Deployments: An object that manages the desired state for a set of replica Pods. The Deployment ensures that if a Pod crashes, it is automatically restarted.
Services: An object that provides a stable network endpoint for accessing the Pods. For testing public access in a production scenario, the Service type is configured as LoadBalancer, which instructs Azure to create an external load balancer and allocate an IP address.

The deployment itself is executed using kubectl apply -f <configuration_file.yaml>.

For security during the initial learning phase, you can deploy to the local Kubernetes instance bundled with Docker Desktop, which simplifies the process as it avoids the immediate need for a remote container registry and complex authentication.

Automating the pipeline with CI/CD

While manually building, pushing, and deploying microservices (revisiting steps learned in earlier phases) can be tedious, especially for multiple services, Continuous Deployment (CD) streamlines this process.

Using tools like GitHub Actions, you can define automated workflows that trigger upon code changes:

Continuous Integration (CI): Workflows run automated tests (unit, integration, and end-to-end tests using tools like Jest and Playwright).
Continuous Deployment (CD): Workflows automatically build the Docker image, push it to the container registry, and deploy the new version to the Kubernetes cluster.

A strong CI/CD process is vital for achieving the agility that microservices promise. Early investment in automated deployment will pay dividends every working day.

Conclusion: pathways to scalability on a budget

Successfully deploying your first microservice to AKS is a major milestone. This foundational architecture is highly flexible and provides many pathways for future scalability.

During the initial phase or “lab” environment, it is practical to operate on a budget:

Single Node Cluster: Create your Kubernetes cluster with a single virtual machine (VM).
No Replicas: Start with only a single instance (no replicas) for each microservice, as redundancy is often unnecessary when initially learning or prototyping.
External Data Management: Keep data and files (like Azure Storage for videos or MongoDB/PostgreSQL databases) outside the cluster to maintain a stateless cluster, increasing safety and flexibility when destroying and rebuilding infrastructure.

By mastering these practical steps—from containerization and IaC using Terraform, to deployment on AKS using kubectl, and finally, automation with CI/CD—developers gain the essential skills to build complex, resilient, and scalable microservices applications.

Adam Lloyd-Jones

Adam is a privacy-first SaaS builder, technical educator, and automation strategist. He leads modular infrastructure projects across AWS, Azure, and GCP, blending deep cloud expertise with ethical marketing and content strategy.