Deploy Django on Arm-based Google Cloud C4A
Introduction
Get started with Django on Google Axion C4A
Configure firewall rules for Django on Google Cloud
Create a Google Axion C4A Arm virtual machine on GCP
Install Django on your Arm-based VM
Verify Django installation and run the development server
Deploy Django on GKE Axion with managed data services
Build a Django REST API with PostgreSQL and Redis
Containerize and deploy Django on Axion GKE
Benchmark Django application performance on Arm
Next Steps

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About this Learning Path

Skill level:  Introductory
Reading time:  1 hr
Last updated:  31 Jul 2026
Skill level:
  Introductory
Reading time:
  1 hr
Last updated:
  31 Jul 2026
Author:Pareena Verma, Arm
Arm IP:Neoverse
Tags: Web Google Cloud Linux Django Docker Kubernetes Google Artifact Registry Cloud SQL Memorystore
Author:
  • Pareena Verma, Arm
Arm IP:
Neoverse
Tags:
Web Google Cloud Linux Django Docker Kubernetes Google Artifact Registry Cloud SQL Memorystore

Who is this for?

This is an introductory topic for DevOps engineers and software developers who want to deploy, operate, and benchmark a production-grade Django REST API on Google Kubernetes Engine (GKE) running on Arm64 Axion processors, integrated with managed Google Cloud data services

What will you learn?

Upon completion of this Learning Path, you will be able to:

  • Provision Arm-based Axion compute on Google Cloud using virtual machines (VMs) and GKE node pools
  • Package a Django REST API into an Arm-native Docker container
  • Push container images to Google Artifact Registry
  • Deploy Django on GKE using Kubernetes manifests (Deployment, Service, ConfigMap, Secrets)
  • Integrate Django with Cloud SQL (PostgreSQL) over private IP
  • Integrate Django with Memorystore (Redis) for caching and sessions
  • Expose Django using a Kubernetes LoadBalancer
  • Validate application connectivity to PostgreSQL and Redis
  • Measure throughput and p95 latency using ApacheBench against Gunicorn on Arm

Prerequisites

Before starting, you will need the following:

Summary

AI-assisted

This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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You’ll deploy a Django REST API on a Google Cloud Axion C4A VM. You’ll create a SUSE Linux Enterprise Server VM, install Python and Django, and verify the development server. You’ll then containerize the application for Google Kubernetes Engine, connect Cloud SQL PostgreSQL over private IP and Memorystore Redis, expose the service with a LoadBalancer, and measure throughput and p95 latency with ApacheBench against Gunicorn on Arm.

Frequently asked questions

AI-assisted

These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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Which Axion C4A VM configuration should I use for the initial setup?
Use c4a-standard-4, which provides 4 vCPUs and 16 GB of memory. Create the instance from Compute Engine in the Google Cloud Console.
How do I open and verify access to the Django development server?
Create a firewall rule that allows inbound TCP traffic on port 8000 to your VM. After starting the server, browse to the VM’s external IP on port 8000 to confirm it responds.
What should I see after creating the Django project?
You should see a manage.py file and a project module directory containing settings.py, urls.py, asgi.py, and wsgi.py. Running the development server should serve a Django page from the VM on port 8000.
Which Linux distribution and Python version do I use?
Use SUSE Linux Enterprise Server (SLES) and install Python 3.11 with zypper. Verify the installation with python3.11 --version before creating the Django project.
How is state handled when deploying to GKE later in the path?
The deployment integrates Cloud SQL (PostgreSQL) over private IP and Memorystore (Redis) for caching and sessions. Kubernetes manifests use objects such as ConfigMap and Secrets to configure the application.
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