Infrastructure as code
Infrastructure as code turns cloud setup into reviewed, versioned, repeatable work. We use Terraform for provisioning, Ansible for configuration, and the cloud-native or code-first alternatives where they fit better.
These pages explain how we structure state, modules and environments, how we keep drift under control, and how we choose between tools so you do not end up locked into a pattern that does not suit your team.
Technologies in this category
Terraform
Terraform is a declarative infrastructure as code tool that provisions cloud resources from version-controlled configuration. We design its state layout, modules, pipelines and access controls so that every change to AWS or Azure is reviewed, planned and applied safely, and drift is detected rather than discovered during an outage.
Ansible
Ansible is an agentless automation tool that configures servers, applies patches and orchestrates deployments over SSH or WinRM using YAML playbooks. We build tested roles, manage inventory and secrets, and run them from CI or AWX so that server configuration is repeatable, auditable and safe to run again.
AWS CloudFormation
AWS CloudFormation is Amazon's native infrastructure as code service: you describe resources in JSON or YAML templates and AWS creates, updates and rolls back stacks for you. We design stack boundaries, StackSets, change-set review and pipelines so AWS-only teams deploy safely, and we advise honestly when another tool fits better.
Pulumi
Pulumi is an infrastructure as code platform that lets you define cloud resources in general-purpose languages such as TypeScript, Python, Go and C#. We help teams structure projects and stacks, manage state and secrets, test their code and run previews and updates from CI, and we say plainly when Terraform or CloudFormation is a better fit.
Terragrunt
Terragrunt is a thin wrapper around Terraform and OpenTofu that reduces repetition across environments by generating backend and provider configuration, managing dependencies between stacks and running commands across many of them. We introduce it only where repetition hurts, and design the layout, pipeline and exit path.