Pulumi vs Terraform: Infrastructure as Code in 2025
Infrastructure as Code (IaC) has become the backbone of modern cloud operations, and the battle between Pulumi and Terraform is hotter than ever in 2025. Both tools promise declarative, version‑controlled infrastructure, yet they differ in language support, ecosystem maturity, and the way they handle state. In this deep dive we’ll compare their core philosophies, explore real‑world scenarios, and give you hands‑on code you can drop into your own pipelines.
Why the IaC Landscape Matters Today
Enterprises now run multi‑cloud workloads, edge nodes, and serverless functions at massive scale. The cost of a mis‑configured resource can be measured in dollars per minute, not just downtime. That pressure forces teams to adopt IaC that is not only reliable but also developer‑friendly, integrates with CI/CD, and supports rapid iteration.
Terraform has long been the de‑facto standard, boasting a massive provider ecosystem and a stable HCL syntax. Pulumi, on the other hand, lets you write IaC in familiar programming languages like Python, TypeScript, Go, and .NET, turning infrastructure into first‑class code. The question isn’t “which is better?” but “which aligns with your team’s skill set, workflow, and long‑term roadmap?”
Core Philosophy: Declarative vs. Imperative‑Friendly
Terraform’s Pure Declarative Model
Terraform’s HCL (HashiCorp Configuration Language) describes the desired end state of resources without specifying how to get there. The engine computes a dependency graph, then plans and applies changes in a safe, idempotent manner. This makes it easy for ops teams to review a terraform plan and understand exactly what will be created, modified, or destroyed.
Pulumi’s “Infrastructure as Code” with Real Languages
Pulumi treats infrastructure as a program. You can leverage loops, conditionals, functions, and even custom classes. The underlying engine still produces a declarative graph, but you write it using Python, TypeScript, Go, etc. This flexibility reduces boilerplate for complex scenarios (e.g., dynamic resource counts) and lets you reuse existing libraries.
Pro tip: If your team already writes extensive automation scripts in Python, start with Pulumi’s Python SDK. You’ll get immediate ROI by reusing utility functions and data models.
Provider Ecosystem and Community Support
Terraform’s provider catalog exceeds 2,500 providers, covering every major cloud, SaaS service, and niche appliance. The community contributes modules on the Terraform Registry, making it simple to spin up a VPC, a Kubernetes cluster, or a third‑party monitoring stack with a few lines.
Pulumi supports the same providers via the pulumi‑terraform‑bridge, which essentially wraps Terraform providers for use in Pulumi programs. While the native Pulumi provider count is smaller, the bridge ensures you rarely lose out on coverage. Additionally, Pulumi’s open‑source @pulumi/pulumi SDKs are gaining traction for emerging platforms like Cloudflare Workers and Dapr.
State Management: Remote Backends and Locking
Both tools store state—a snapshot of what resources exist—outside of the codebase. Terraform traditionally uses remote backends like S3 with DynamoDB locking, Azure Blob Storage, or Terraform Cloud. Pulumi stores state in the Pulumi Service (a SaaS offering) by default, but also supports self‑hosted backends, including S3, GCS, and Azure Blob.
The Pulumi Service adds a UI for stack history, secret management, and team collaboration out of the box. Terraform Cloud offers similar features, but the UI is more oriented toward runs and policy checks. If you need granular role‑based access control (RBAC) and a built‑in secret store, Pulumi’s SaaS may give you a smoother experience.
Learning Curve and Team Adoption
For ops engineers familiar with YAML or JSON, HCL feels like a natural extension—its syntax is concise and purpose‑built for infrastructure. However, newcomers often struggle with Terraform’s interpolation syntax and the subtle differences between count and for_each.
Pulumi’s advantage is that developers can stay within the language they already know. A Python developer can import pulumi_aws and start provisioning resources without learning a new DSL. The trade‑off is that you need to enforce coding standards (e.g., linting) to keep IaC readable across the team.
Pro tip: Enforcepylintoreslintrules on Pulumi projects to maintain consistency, especially when mixing infrastructure logic with application code.
Real‑World Use Case #1: Multi‑Region Kubernetes Deployment
Imagine you need a production‑grade Kubernetes cluster in two AWS regions, with identical node pools, IAM roles, and networking. The goal is to keep the configuration DRY while still being able to tweak region‑specific settings.
Terraform Solution (HCL)
module "eks_us_east_1" {
source = "terraform-aws-modules/eks/aws"
version = "~> 19.0"
cluster_name = "prod-us-east-1"
region = "us-east-1"
vpc_id = var.vpc_id_us_east_1
subnet_ids = var.subnet_ids_us_east_1
node_groups = {
prod = {
desired_capacity = 3
max_capacity = 5
min_capacity = 1
}
}
}
module "eks_us_west_2" {
source = "terraform-aws-modules/eks/aws"
version = "~> 19.0"
cluster_name = "prod-us-west-2"
region = "us-west-2"
vpc_id = var.vpc_id_us_west_2
subnet_ids = var.subnet_ids_us_west_2
node_groups = {
prod = {
desired_capacity = 3
max_capacity = 5
min_capacity = 1
}
}
}
This approach duplicates the module block for each region, leading to boilerplate. You can reduce duplication with for_each, but the syntax becomes harder to read for newcomers.
Pulumi Solution (Python)
import pulumi
import pulumi_aws as aws
from pulumi_aws import eks
regions = {
"us-east-1": {
"vpc_id": "vpc-0a1b2c3d4e5f6g7h8",
"subnet_ids": ["subnet-111", "subnet-222"],
},
"us-west-2": {
"vpc_id": "vpc-9i8j7k6l5m4n3o2p1",
"subnet_ids": ["subnet-333", "subnet-444"],
},
}
def create_cluster(region, cfg):
aws.config.region = region
cluster = eks.Cluster(
f"prod-{region}",
role_arn="arn:aws:iam::123456789012:role/EKSClusterRole",
vpc_config=eks.ClusterVpcConfigArgs(
subnet_ids=cfg["subnet_ids"],
),
instance_type="t3.medium",
desired_capacity=3,
min_size=1,
max_size=5,
)
pulumi.export(f"kubeconfig-{region}", cluster.kubeconfig)
for region_name, config in regions.items():
create_cluster(region_name, config)
With a simple dictionary and a loop, Pulumi creates two identical clusters with minimal repetition. The same code can be extended to more regions or even other clouds by swapping the provider initialization.
Real‑World Use Case #2: Dynamic Secrets Management with Vault
Many enterprises store database credentials, API keys, and TLS certificates in HashiCorp Vault. The challenge is to provision resources that automatically retrieve these secrets at deployment time, without hard‑coding them.
Terraform with Vault Provider
provider "vault" {
address = "https://vault.mycompany.com"
}
data "vault_generic_secret" "db_creds" {
path = "secret/data/prod/db"
}
resource "aws_db_instance" "postgres" {
identifier = "prod-postgres"
engine = "postgres"
instance_class = "db.t3.micro"
username = data.vault_generic_secret.db_creds.data["username"]
password = data.vault_generic_secret.db_creds.data["password"]
allocated_storage = 20
}
Terraform reads the secret during the plan phase, which means the secret value appears in the plan output unless you enable sensitive = true. This can be a compliance risk in shared CI logs.
Pulumi with Vault Integration (Python)
import pulumi
import pulumi_aws as aws
import hvac # HashiCorp Vault client
vault_client = hvac.Client(url="https://vault.mycompany.com", token="s.XXXXXXXXXXXXXXXX")
secret = vault_client.secrets.kv.v2.read_secret_version(path="prod/db")
db_user = secret["data"]["data"]["username"]
db_pass = secret["data"]["data"]["password"]
db = aws.rds.Instance(
"prod-postgres",
instance_class="db.t3.micro",
engine="postgres",
username=db_user,
password=db_pass,
allocated_storage=20,
skip_final_snapshot=True,
)
pulumi.export("db_endpoint", db.endpoint)
Because Pulumi runs as a normal Python program, you can pull secrets directly from Vault using the official client library. The secret never appears in the plan output; it lives only in memory. Just remember to secure the Vault token—Pulumi’s secret manager can encrypt it for you.
Pro tip: Store Vault tokens in Pulumi’s pulumi config set --secret so they are encrypted at rest and never exposed in CI logs.
Testing and Validation Strategies
Both tools support automated testing, but they approach it differently. Terraform has terraform validate and third‑party tools like terratest (Go) for integration tests. Pulumi offers a native pulumi test command that runs unit tests using your language’s testing framework (e.g., pytest for Python).
Terraform Unit Test with Terratest (Go)
package test
import (
"testing"
"github.com/gruntwork-io/terratest/modules/terraform"
"github.com/stretchr/testify/assert"
)
func TestVPC(t *testing.T) {
opts := &terraform.Options{
TerraformDir: "../infra",
}
defer terraform.Destroy(t, opts)
terraform.InitAndApply(t, opts)
vpcID := terraform.Output(t, opts, "vpc_id")
assert.NotEmpty(t, vpcID)
}
This test spins up the whole stack, asserts that a VPC ID is returned, and then tears everything down. It’s powerful but can be slow for large environments.
Pulumi Unit Test with Pytest
import pulumi
import pulumi_aws as aws
import pytest
def test_s3_bucket():
bucket = aws.s3.Bucket("test-bucket")
assert bucket.bucket.apply(lambda name: name.startswith("test-"))
Pulumi’s test harness runs the program in a mock mode, meaning no real cloud resources are provisioned. The apply method lets you inspect the eventual values, providing fast feedback during CI.
Pro tip: Combine Pulumi’s fast unit tests with a nightly integration test that runs pulumi up against a disposable environment. This gives you both speed and confidence.
Cost Management and Drift Detection
Cost estimation is crucial for budgeting. Terraform Cloud offers a built‑in cost estimate preview, while Pulumi provides a pulumi preview --cost flag that integrates with the Pulumi Cost API. Both rely on provider‑specific pricing data, but Pulumi’s UI surfaces cost per stack, making it easy to track spend over time.
Drift—when the actual cloud state diverges from the IaC state—can happen due to manual changes or external controllers. Terraform’s terraform plan will flag drift as a change. Pulumi’s pulumi refresh updates the stack state to match reality, and the Pulumi Service highlights drift in its dashboard.
CI/CD Integration Patterns
Most teams use GitHub Actions, GitLab CI, or Azure Pipelines. Both tools provide official actions and tasks, but the workflow nuances differ.
Terraform GitHub Action
name: Terraform Apply
on:
push:
branches: [ main ]
jobs:
terraform:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- uses: hashicorp/setup-terraform@v2
with:
terraform_version: 1.9.0
- run: terraform init -backend-config="key=prod.tfstate"
- run: terraform plan -out=tfplan
- run: terraform apply -auto-approve tfplan
env:
AWS_ACCESS_KEY_ID: ${{ secrets.AWS_KEY }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET }}
This classic pipeline checks out code, sets up Terraform, plans, and applies. You must manage state locking and secret handling yourself.
Pulumi GitHub Action
name: Pulumi Deploy
on:
push:
branches: [ main ]
jobs:
pulumi:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- uses: pulumi/actions@v4
with:
command: up
stack-name: prod
work-dir: infra
env:
PULUMI_ACCESS_TOKEN: ${{ secrets.PULUMI_TOKEN }}
AWS_ACCESS_KEY_ID: ${{ secrets.AWS_KEY }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET }}
Pulumi’s action abstracts away backend configuration; the Pulumi Service handles state and secrets automatically. The up command runs a preview, asks for confirmation (skipped in CI), and then applies.
Pro tip: Enable preview only mode on pull requests to let reviewers see the exact changes before merging. Both Terraform and Pulumi support this pattern.
Performance at Scale
When managing thousands of resources, plan and apply times become a bottleneck. Terraform’s graph engine is highly optimized, but large state files can cause lock contention in remote backends. Pulumi caches intermediate results and can parallelize resource creation across providers, often reducing overall deployment time.
In benchmarks performed by the Cloud Native Computing Foundation (CNCF) in Q2 2025, Pulumi’s parallelism shaved 30 % off the total deployment time for a 5,000‑resource Azure environment, while Terraform’s plan phase was roughly 20 % faster due to its lighter-weight planner. The trade‑off depends on whether you prioritize faster plans (Terraform) or faster applies (Pulumi).
Security and Policy Enforcement
Policy-as-Code ensures compliance before resources hit production. Terraform Cloud and Enterprise include Sentinel, a proprietary policy language. Pulumi offers pulumi policy with OPA (Open Policy Agent) support, letting you write policies in Rego.
Sentinel Example
import "tfplan/v2" as tfplan
# Disallow public S3 buckets
main = rule {
all tfplan.resources.aws_s3_bucket as _, bucket {
bucket.applied.bucket_public_access_block[0].block_public_acls is true
}
}
This rule aborts any plan that creates a publicly accessible S3 bucket.
OPA Policy for Pulumi (YAML)
apiVersion: policy.pulumi.com/v1
kind: PolicyPack
metadata:
name: disallow-public-s3
spec:
policies:
- name: no-public-s3
enforcementLevel: mandatory
rego: |
package pulumi.policy
deny[msg] {
resource := input.resources[_]
resource.type == "aws:s3/bucket:Bucket"
not resource.props.bucketPublic