From Zero to AI-Ready: Deploy Any Open Source Project on AWS with Pulumi

Deploy smarter: spin up full AI-ready stacks on AWS using Pulumi and open source tools.

A few weeks ago, I hit that familiar wall. I found a cool open-source AI tool on GitHub — self-hosted, well-documented, and actually useful (not just another chat wrapper). I wanted to deploy it in AWS, spin up the backend, give it an LLM to talk to, and maybe hook up some logging and monitoring.

So I did what anyone in DevOps would do: opened up my Terraform repo… and sighed.

Too many modules, too many moving parts. Every tweak meant waiting, reapplying, debugging weird errors, and wrestling with state files.

So I tried something different: I rebuilt the whole thing using Pulumi — with Python.

Here’s how I did it. It was fast, clean, and actually fun.

What We’re Building

Let’s say you’re deploying an open source project like Continue.dev or PrivateGPT — something with a web UI, an LLM backend, and maybe a vector DB like Qdrant or Weaviate. You want to host it in AWS.

Here’s what we’ll spin up:

• A VPC with public/private subnets
• An EC2 instance or ECS Fargate container to host the app
• An S3 bucket for file storage (or model artifacts)
• IAM roles and security groups
• (Optional) Bedrock or SageMaker for your LLM backend
• Pulumi handling it all in Python

Why Use Pulumi Instead of Terraform?

Because code is better than YAML. Let me show you:

import pulumi_aws as aws

bucket = aws.s3.Bucket("my-model-bucket")

instance = aws.ec2.Instance("app-server",
    instance_type="t3.medium",
    ami="ami-0abcdef1234567890",
    vpc_security_group_ids=[...],
    user_data=open('bootstrap.sh').read(),
)

No crazy HCL syntax. No waiting for terraform plan. You get real programming constructs like loops, conditions, modules, classes. It’s Infra-as-Code-as-Code.

And the best part: you can deploy this with a simple pulumi up. It gives you a preview, then runs.

🚀 Getting Started with Pulumi

If you’re starting from scratch:

curl -fsSL https://get.pulumi.com | sh
pulumi new aws-python

Pulumi will scaffold the project and walk you through creating a new stack (like dev or prod).

Make sure you’ve got your AWS credentials set (aws configure or via environment vars). Then install a few packages:

pip install pulumi pulumi-aws

🧱 Build the Foundation: VPC, Subnets, Security

Use Pulumi to define a simple VPC:

vpc = aws.ec2.Vpc("main-vpc", cidr_block="10.0.0.0/16")

public_subnet = aws.ec2.Subnet("public-subnet",
    vpc_id=vpc.id,
    cidr_block="10.0.1.0/24"
)

Create a security group to allow HTTP:

sg = aws.ec2.SecurityGroup("web-sg",
    vpc_id=vpc.id,
    ingress=[
        {"protocol": "tcp", "from_port": 80, "to_port": 80, "cidr_blocks": ["0.0.0.0/0"]},
    ]
)

📦 Spin Up Your Open Source Project

If your project is containerized, Fargate is your friend. You can define a task, pull an image from Docker Hub or ECR, and launch it:

import json

task = aws.ecs.TaskDefinition("app-task",
    family="open-source-ai",
    cpu="512",
    memory="1024",
    network_mode="awsvpc",
    requires_compatibilities=["FARGATE"],
    container_definitions=json.dumps([{
        "name": "app",
        "image": "your-dockerhub-user/open-source-app",
        "portMappings": [{"containerPort": 80, "protocol": "tcp"}],
    }])
)

Then deploy it to a service:

service = aws.ecs.Service("app-service",
    cluster=ecs_cluster.arn,
    task_definition=task.arn,
    desired_count=1,
    launch_type="FARGATE",
    network_configuration={
        "subnets": [public_subnet.id],
        "security_groups": [sg.id],
        "assign_public_ip": True,
    },
)

🧠 Hook It Up to an AI Backend

Want your app to talk to an LLM? You can use AWS Bedrock:

from boto3 import client
import json

bedrock = client("bedrock-runtime")

resp = bedrock.invoke_model(
    modelId="anthropic.claude-v2",
    contentType="application/json",
    accept="application/json",
    body=json.dumps({"prompt": "Hello!", "max_tokens": 100}),
)

Or run your own model with something like text-generation-webui in a GPU-enabled EC2 instance (Pulumi can provision that too — just swap the instance type and AMI).

🔒 Don’t Forget IAM

IAM in Pulumi is refreshingly straightforward. You can create roles and policies like this:

import json

role = aws.iam.Role("ecs-task-role", assume_role_policy=json.dumps({
    "Version": "2012-10-17",
    "Statement": [{
        "Action": "sts:AssumeRole",
        "Principal": {"Service": "ecs-tasks.amazonaws.com"},
        "Effect": "Allow",
    }]
}))

Attach permissions (e.g., to access S3 or Bedrock):

policy_attachment = aws.iam.RolePolicyAttachment("attach-s3",
    role=role.name,
    policy_arn="arn:aws:iam::aws:policy/AmazonS3FullAccess"
)

🧪 Bonus: Testing & Iteration

Here’s where Pulumi really shines. You can use Python’s standard testing tools (like pytest) to write infra tests. You can also create dynamic configurations and deploy multiple stacks for dev, staging, and prod — all from the same codebase.

🌎 Final Thoughts

Pulumi made deploying this open source AI project way more approachable.
No crazy syntax. No separate DSL. Just Python.

If you’re building anything in the cloud that needs real infrastructure — and especially if you’re tying it to LLMs or modern open source tools — Pulumi is worth trying.

You’ll go from prototype to production in way less time, with way more control.

👇 Try It Yourself

If you’ve got a favorite open source project you’ve been meaning to deploy, give Pulumi a spin.
Write your infra in a language you actually like. Let the cloud feel like home again.

And hey — if you do deploy something cool, send it my way. Always happy to swap notes.

Want more like this? Follow me for guides on self-hosted AI tools, DevOps tips, and real-world automation workflows.