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What is Pulumi? A Beginner’s Guide to IaC in 2026

Pulumi is an Infrastructure as Code (IaC) platform that allows you to define, deploy, and manage cloud resources using standard programming languages like Python, TypeScript, or Go. By using Pulumi, you can automate the creation of servers, databases, and AI endpoints in under 10 minutes using the same code you use to build your apps. This approach eliminates the need to manually click buttons in cloud consoles or learn complex, proprietary configuration languages.

Why is Pulumi different from other tools?

Most traditional cloud tools require you to learn a specific, limited language that only works for that one tool. These are often called "Domain Specific Languages" (DSLs). Pulumi changes this by letting you use the programming languages you already know and love.

Using a real language means you get access to modern features like loops (repeating an action), functions (reusable blocks of code), and package managers (tools to install pre-made code libraries). You can also use your favorite code editor to catch errors before you ever try to deploy your infrastructure.

In our experience, this reduces the "context switching" tax because you don't have to shift your brain between your application code and your infrastructure settings. Everything lives together in the same ecosystem.

How does Pulumi keep track of your cloud?

Pulumi uses a concept called "State" to remember exactly what you have built in the cloud. Think of State as a digital map that records every resource, such as a virtual machine or a storage bucket, and its current settings.

When you run a command to update your setup, Pulumi compares your code to this map. It identifies only the specific parts that need to change. This ensures that you don't accidentally delete something important or recreate resources that already exist.

This map is typically stored in the Pulumi Cloud (a secure online dashboard), but you can also choose to store it yourself. This tracking system is what makes the tool "declarative," meaning you describe the end result you want, and Pulumi figures out the steps to get there.

What do you need to get started?

Before you run your first command, you need to set up your environment with a few modern tools. By early 2026, these are the standard versions used for building AI-integrated applications.

  • Node.js (Version 24 LTS or higher): This is the runtime (the engine that runs your code) required for TypeScript or JavaScript projects.
  • Python (Version 3.14 or higher): The current standard for AI and data science automation.
  • Pulumi CLI (Command Line Interface): A small program you install on your computer to talk to the Pulumi service.
  • A Cloud Provider Account: You will need an account with a provider like AWS (Amazon Web Services), Google Cloud, or Azure.
  • An Editor: We recommend VS Code (Visual Studio Code) because it has excellent plugins for Pulumi.

Step 1: Install the Pulumi CLI

The CLI is the primary tool you will use to interact with your cloud resources. It translates your code into instructions the cloud provider understands.

Open your terminal (the command prompt on your computer) and run the installation command. On macOS, you can use Homebrew:

brew install pulumi

On Windows, you can use the package manager "winget":

winget install pulumi

What you should see: After the installation finishes, type pulumi version. You should see a version number like v3.150.0 or higher appear in your terminal.

Step 2: Create a new project

Pulumi projects are organized into folders. Each project contains the code that defines one specific part of your infrastructure.

Create a new directory (folder) for your project and move into it:

mkdir my-first-ai-infra
cd my-first-ai-infra

Now, initialize a new project using a template. We will use Python for this example since it is the go-to for AI developers:

pulumi new python

What you should see: The CLI will ask you for a project name and a "stack" name. It is normal to just hit "Enter" to accept the default values. A stack is just an instance of your project, like "development" or "production."

Step 3: Define a resource in code

Open the file named __main__.py in your code editor. You will see some starter code already there. Let's add a simple storage bucket (a place to store files like AI training data).

Replace the contents of the file with this code:

import pulumi
import pulumi_aws as aws # This imports the AWS helper library

# Create an AWS S3 Bucket (a digital storage container)
# 'my-ai-data' is the name we give this resource in our code
bucket = aws.s3.BucketV2('my-ai-data',
    tags={
        "Environment": "Dev",
        "Project": "AI-Inference"
    })

# Export the name of the bucket so we can see it later
pulumi.export('bucket_name', bucket.id)

What this code does: The first line brings in the Pulumi tools. The aws.s3.BucketV2 line tells AWS to create a storage container. The tags help you organize your bill later.

Step 4: Preview and deploy

One of the safest features of Pulumi is the "Preview" mode. This shows you exactly what will happen before any changes are made to your actual cloud account.

Run this command in your terminal:

pulumi up

What you should see: Pulumi will show a list of "Actions." It should say "Create" next to your bucket. It will ask, "Do you want to perform this update?" Use your arrow keys to select "yes" and hit Enter.

Once it finishes, Pulumi will print the "bucket_name" at the bottom. You have officially deployed cloud infrastructure using code!

How do you fix common mistakes?

It is normal to run into errors when you are first starting out. Most issues come from "Permissions" or "Configuration."

  • "Credentials not found": This means Pulumi doesn't have permission to talk to your cloud account. Ensure you have run aws configure (for AWS) or the equivalent command for your provider.
  • "Resource already exists": This happens if you try to name a bucket something that someone else in the world is already using. S3 bucket names must be unique globally, so try adding a random string of numbers to your bucket name.
  • "Python version mismatch": If you get an error about Python, ensure your virtual environment (a private space for your project's tools) is active. Pulumi usually handles this for you, but you can restart it by typing source venv/bin/activate.

Next Steps

Now that you've built a basic storage bucket, you can move on to more complex AI infrastructure. You might try deploying a serverless function to run a Claude Sonnet 4 model or setting up a database to store your AI chat history.

The best way to learn is to look at pre-built templates. You can find "blueprints" for common tasks like launching an inference endpoint (a URL that runs an AI model) or setting up a secure website.

For more detailed guides on specific cloud providers and advanced language features, check out the official Pulumi documentation.

Read the Pulumi Documentation