Streamlining Application Deployment: Your Guide to Virtual Machines, Domains, and Docker with AI Assistance

Introduction

Deploying an application can often feel like a daunting task, especially when setting up the underlying infrastructure. While powerful tools like Kamal simplify the actual deployment process—allowing you to run multiple applications on a modest, cost-effective Virtual Machine (VM) using Docker—the initial setup can still pose challenges for newcomers. This article aims to demystify these foundational steps, offering a clear roadmap for aspiring developers.

Many find themselves stuck on what seem like basic tasks, such as acquiring a VM or a domain. That’s why this guide focuses on three crucial prerequisites for modern application deployment, applicable whether you’re using Kamal or another platform:

1. Procuring a Virtual Machine (VM): How to select and configure your cloud server.
2. Registering a Domain Name: Linking a memorable address to your application.
3. Crafting a Dockerfile: Preparing your application for containerization.

These preparatory steps, often considered less exciting than coding, are nonetheless essential. We’ll explore how contemporary AI tools can significantly streamline these processes, transforming potential frustrations into an engaging experience.

Acquiring Your Virtual Machine (VM)

The first step in deploying your application is securing a VM. Cloud providers offer various options, each with its own branding, but all providing essentially the same core service: a virtualized server. Prominent providers include:

• Amazon AWS EC2
• Google GCP Compute Engine
• Microsoft Azure Virtual Machine
• DigitalOcean Droplet
• Akamai Cloud Compute

If you’re unsure which to choose, an AI assistant can suggest options tailored to your region or specific needs (e.g., “What VPS options are available in my area?” or “Recommend cloud providers for a VM”).

For this guide, we’ll demonstrate the process using DigitalOcean Droplet, chosen for its user-friendly interface and local data centers (for this example, Toronto). The principles, however, are broadly applicable across all major cloud platforms.

Setting Up Your New VM

Begin by navigating to DigitalOcean. After signing in or creating an account, proceed to the “Droplets” section and select “Create Droplet.” Here, you’ll configure your VM’s specifications.

For the operating system, Ubuntu or Debian are excellent choices for beginners due to their widespread support and community resources. When it comes to machine specifications, a minimal configuration is perfect for learning and initial deployments. As of September 2025, a typical entry-level VM might offer:

• 1 CPU
• 1 GB RAM
• 25 GB SSD Storage
• 1 TB Network Transfer

Such a configuration can be surprisingly affordable, often costing less than a daily coffee.

During creation, DigitalOcean offers the convenience of generating an SSH key for login, enhancing security and simplifying future access. Alternatively, you can set a root password and configure SSH key authentication later.

Once your Droplet is successfully created, it will appear in your list of VMs, displaying its unique IP address. Copy this address. You can now establish an SSH connection to your new VM:

ssh root@YOUR_VM_IP_ADDRESS
# If using a specific SSH key:
ssh -i ~/.ssh/your_ssh_private_key root@YOUR_VM_IP_ADDRESS

A successful connection confirms your VM is ready for the next steps.

Enhancing Security with SSH Keys

(Skip this if you configured SSH keys during VM creation.)

Adding an SSH key for login is a critical security measure and simplifies future deployment workflows. If you’re unfamiliar with this process, an AI can provide detailed instructions with a query like, “How to generate an SSH key pair and use it to log into my VM?”

The general procedure involves:

1. Generating a key pair on your local machine, typically in ~/.ssh. This creates a private key (id_ed25519) and a public key (id_ed25519.pub).
   shell
ssh-keygen -t ed25519 -C "[email protected]"
2. Displaying and copying your public key’s content:
   shell
cat ~/.ssh/id_ed25519.pub
3. Logging into your VM using your password (if not already set up with a key).
   shell
ssh root@YOUR_VM_IP_ADDRESS
4. Editing ~/.ssh/authorized_keys on the VM and pasting your public key’s content into this file.

After these steps, you should be able to log into your VM directly without a password:

ssh root@YOUR_VM_IP_ADDRESS

Securing Your Online Presence: Buying a Domain

To make your application accessible via a memorable web address (e.g., `https://your-domain.com` instead of a cryptic IP address), you need to purchase a domain name. Various domain registrars offer competitive pricing:

• GoDaddy
• Porkbun
• Cloudflare

Again, AI can assist in finding providers, especially if you’re looking for specific Top-Level Domains (TLDs) like .org, .net, or niche options.

The process is akin to online shopping: search for your desired domain name, and the registrar will display its availability and cost. Once purchased, the crucial step is to configure its DNS (Domain Name System) records to point to your VM’s IP address. (Note: As of this writing, Kamal primarily supports IPv4, so ensure your A record uses an IPv4 address.)

Locate the DNS management section for your newly acquired domain. You will need to:

1. Add or modify an A record: This record maps your domain (e.g., your-domain.com) to your VM’s IPv4 address. If an existing A record is present, you can typically remove or update it.
2. Create a CNAME record for www: This typically points www.your-domain.com to your root domain (e.g., your-domain.com), ensuring both variations lead to your application.

DNS changes can take up to 24 hours to propagate across the internet. You can verify the settings by attempting to SSH into your VM using your domain name:

ssh [email protected]

If the connection is successful, your domain is correctly configured and pointing to your VM.

Containerizing Your Application: Setting Up the Dockerfile

This stage truly highlights the power of modern AI. Historically, writing a Dockerfile from scratch required a deep understanding of Docker, Linux, and your application’s specific dependencies. It often felt like manually setting up a new computer for each deployment.

Today, AI-powered code assistants excel at generating well-structured, optimized Dockerfiles. Given their adherence to a strict, reproducible format, LLMs (Large Language Models) can leverage your project’s context to produce effective configurations.

Let’s illustrate with an example using Gemini CLI and a Nuxt.js web application. You can use any AI tool like Claude Code, Copilot, or Cursor; the principle remains the same.

Leveraging AI for Dockerfile Generation (Example with Gemini CLI)

1. Install Gemini CLI: Follow the instructions on the official GitHub repository (https://github.com/google-gemini/gemini-cli). If you’re using VS Code, consider installing the Gemini CLI Companion extension for seamless integration.
2. Initialize Project Context: Open your project in your editor and launch Gemini CLI in the terminal. Running the /init command allows Gemini to analyze your entire project, creating a Gemini.md file that provides essential context for future queries.
3. Generate the Dockerfile: Now, simply prompt the AI. You can be as general or specific as needed:
   • Generate a Dockerfile for this project.
   • Generate a Dockerfile for this Nuxt project.
   • Generate a Dockerfile for this Nuxt project. Use Node 22 Alpine as the base image.

While this explanation simplifies the process, especially for complex projects with multiple services (databases, caches, etc.), AI will provide an excellent starting template. It significantly reduces the initial boilerplate, allowing you to focus on fine-tuning rather than building from scratch.

Conclusion

Mastering Linux servers, DNS management, and Dockerfile creation are invaluable skills in software development. While AI tools accelerate these initial setup phases, they don’t negate the need for understanding the underlying principles. AI acts as a powerful assistant, streamlining the process and providing a robust foundation, but critical evaluation and knowledge are still essential to catch potential errors and optimize solutions.

Ultimately, these foundational steps are means to an end: deploying your applications effectively. By embracing AI assistance, we can overcome the “boring” prerequisites more quickly and dedicate more energy to the core goal of bringing our applications to life.