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Let's TalkSoftfix is an Infrastructure as Code company in India. We take the servers, networks and databases your team clicked together over the years and turn them into Terraform code that lives in Git, goes through review and rebuilds the same way every time.
terraform {
backend "s3" {
bucket = "acme-tf-state"
key = "prod/network.tfstate"
region = "ap-south-1"
encrypt = true
use_lockfile = true # state locking
}
}
module "network" {
source = "git::ssh://git@github.com/acme/tf-modules.git//vpc?ref=v2.3.0"
env = "prod"
cidr = "10.20.0.0/16"
tags = {
owner = "platform-team"
cost_centre = "CC-104"
}
}A snowflake server is one nobody dares to touch, because nobody remembers exactly how it was set up. Most cloud accounts have a few. Here's what changes once everything is code.
Whether you're writing your first module or untangling five years of hand-built cloud, our Infrastructure as Code consulting covers it.
We write reusable modules for the things you build again and again, such as networks, clusters and databases. Each one is versioned, documented and tested, so teams can use them without reading the source.
We move Terraform code and state to OpenTofu safely, and bring resources that were created by hand under code management without recreating them.
If your team would rather write infrastructure in TypeScript, Python or Go, we build with AWS CDK or Pulumi and set up the same review and testing habits.
For AWS-only shops that prefer native tooling, we write and tidy up CloudFormation templates and StackSets, and split large stacks into manageable pieces.
On HCP Terraform or Terraform Enterprise, we write Sentinel policies that check every plan, for example blocking public buckets or oversized instances.
Scheduled plans spot changes made outside of code and alert your team. We then either bring the code in line or roll the change back, depending on which one was right.
State gets stored remotely with locking and encryption. Plans run on every pull request, and applies only happen from the pipeline after approval.
Tools like Checkov and Trivy scan your code for risky settings before anything is created. Findings show up as comments on the pull request.
A multi-account AWS setup with guardrails, central logging and account vending, built with Control Tower and Account Factory for Terraform.
Shared module patterns across AWS, Azure and Google Cloud, so your team learns one way of working, whichever provider a project ends up on.
All three can manage serious production setups. The choice mostly comes down to which clouds you use and what your team is comfortable writing.
| Point of comparison | Terraform / OpenTofu | AWS CloudFormation | Pulumi |
|---|---|---|---|
| Language | HCL, a simple declarative language | YAML or JSON (or AWS CDK on top) | TypeScript, Python, Go, C# or Java |
| Clouds | AWS, Azure, Google Cloud and thousands of other providers | AWS only | AWS, Azure, Google Cloud and many others |
| State | A state file you store remotely | Managed by AWS, no file to look after | Pulumi Cloud or your own backend |
| Hiring and community | Largest pool of people and modules | Common in AWS-focused teams | Smaller, but popular with developers |
| Best for | Most teams, especially multi-cloud | AWS-only teams that want native support | Developer-led teams who want loops, tests and types |
Verdict: Where we land for most clients: Terraform or OpenTofu, because it covers every cloud and it's easy to find people who know it. If you're fully on AWS and like staying native, CloudFormation with CDK is perfectly sound.
In 2023 HashiCorp moved Terraform to the Business Source License. OpenTofu started as an open-source fork under the Linux Foundation. The two still look very alike, though they're slowly growing apart.
You use HCP Terraform or Terraform Enterprise, depend on Sentinel, or your legal team is fine with the licence. There's no urgent reason to move if things are working.
Watch out for: licence terms if you build a product that competes with HashiCorp's, now part of IBM.
You want an open-source licence with community governance, or features like built-in state encryption. For most codebases the switch is small: swap the binary, test a plan, carry on.
Watch out for: newer Terraform-only features and provider versions, which we check before any move.
Configuration drift is the gap between what your code says and what's actually running. It usually starts with a quick fix in the console. Try it below.
What the code says
What's running
$ terraform plan
No changes. Your infrastructure matches the configuration.
A pipeline job runs terraform plan -refresh-only every night against each environment. If anything has changed, your team gets a message in Slack or Teams.
Sometimes the console change was a real emergency fix, and the code should be updated to match. Other times it was a mistake that needs reverting.
Read-only console access for most people, plus pipeline-only applies, means drift becomes rare rather than routine.
You don't need to tear anything down. This is how we bring a live, hand-built environment under code, including how to import existing AWS and Azure resources into Terraform.
We list every resource across your accounts or subscriptions and group them by application and environment.
For AWS we use Terraform import blocks with generated config. For Azure, Microsoft's aztfexport tool does much of the first pass.
Generated code is messy. We tidy it into modules, remove hard-coded values and add variables per environment.
We keep adjusting until terraform plan shows nothing to change. That's how we know code and cloud match.
From here, changes go through pull requests and the pipeline. Console access is narrowed to stop new drift.
The state file is how Terraform knows what it manages. Keep it on a laptop and you'll eventually lose it or overwrite a colleague's changes. Remote state with locking stops both.
An encrypted, versioned S3 bucket. Recent Terraform and OpenTofu versions can lock state in S3 itself with use_lockfile, so a separate DynamoDB table is no longer required.
A storage account blob container. Locking uses blob leases automatically, and access can be limited with Entra ID roles.
A Cloud Storage bucket with versioning switched on. Locking is built in, with no extra setup.
State, locking, run history and policies in one hosted service, with workspaces for each environment.
Additional note: We split state by environment and by area, such as network, data and apps, so a mistake in one place can't affect everything. Bucket versioning means any state file can be rolled back.
Good modules make dev, staging and production look the same apart from size. These are the rules we follow, and they apply equally to multi-cloud Terraform modules for AWS, Azure and Google Cloud.
Each environment calls the same reusable modules with its own variables. No copy-pasted code.
Environments pin a tag, so a module update reaches staging first and production only when you're ready.
A module should do one job. Huge modules are hard to test and slow to plan.
Workspaces suit identical copies, like per-developer sandboxes. For real environments, separate folders and state are clearer.
We use terraform test or Terratest to check modules create what they promise.
Where it makes sense, AWS, Azure and GCP modules share variable names, so switching provider feels familiar.
Policy as code checks each plan against your rules before anything is created. It turns a security review from a meeting into an automatic step.
We write rules in Rego and run them with Conftest against the plan output in your pipeline. It works with Terraform, OpenTofu or anything else that produces JSON, and costs nothing to run.
Example rule: every resource must carry owner and cost centre tags.
Scanners like Checkov map findings to frameworks such as CIS, SOC 2 and HIPAA. Combined with tagging policies and pull request checks, you get audit evidence as a side effect of normal work.
Example rule: storage must be encrypted, and no database can be public.
Healthcare teams get encryption, private networking, logging and access controls written into every module, with scans that check HIPAA safeguards on each change.
Starting with code from day one costs very little and saves a painful clean-up later. We'll set up a small, tidy foundation you can grow on.
A shared module library, landing zones and policy checks give central teams control while product teams move at their own pace.
Common patterns and naming across AWS, Azure and Google Cloud, with one pipeline that plans and applies for all of them.
Cost depends mainly on how much infrastructure you have, how many clouds and accounts are involved, and whether we're starting fresh or importing what already exists. You'll get a fixed quote before we begin.
We go through your current code, state setup and cloud accounts, then send back a prioritised plan.
We write or import your infrastructure as code and set up the pipeline around it.
We look after your code and cloud each month, including drift checks and module updates.
Tell us which cloud you're on and how much of it is already in code. One of our engineers will look it over and tell you where to start.
sales@softfix.in
Phone
+91 81890 10044
Address
1st Floor, CP/140, Viraj Khand-4, Gomti Nagar, Lucknow, Uttar Pradesh 226010
to help our experts understand your business objectives and create your customized plan.