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Store state remotely, with locking and recovery
Terraform state records the mapping between your configuration and the real objects it manages, and Terraform reads that record when it builds a plan. A state file on one laptop works until a second person or a CI job needs to run Terraform against the same stack. HashiCorp’s State documentation addresses this directly: “Remote state is the recommended solution to this problem.” The problem in that sentence is everyone working with the same state, so the recommendation is about shared, controlled access, not merely about where the file lives.
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HCP Terraform is HashiCorp’s managed option for centralized collaboration. Self-managed remote backends, including S3, Azure Blob Storage, Google Cloud Storage, and Consul, are valid alternatives. They do not all offer the same controls, so compare them on the same criteria before you commit a stack to one.
Compare backends on the criteria that matter
| Criterion | What to verify | Why it matters |
|---|---|---|
| Locking behavior and compatibility | Whether the backend locks state during writes, and which Terraform versions support its locking method | Overlapping writes can corrupt state or silently drop a change |
| Encryption and key control | Encryption at rest, who controls the keys, and whether keys can be rotated | State can hold credentials and other sensitive attributes |
| Access controls and auditability | Who can read or write state, and whether those accesses are logged | Read access to state can expose more than read access to code |
| Recovery and versioning | Whether earlier state versions are retained and how one is restored | A bad write or an accidental deletion must be recoverable |
| Operational ownership | Who patches, backs up, and responds when the backend fails | Ownership determines how quickly an incident is handled |
| Integration with existing cloud and CI | Whether your identity provider and pipeline can authenticate to the backend without stored long-lived secrets | Fewer long-lived credentials means fewer to leak or rotate |
S3 as a worked example
The S3 backend reference shows how backend details change over time. It describes bucket versioning as highly recommended, and it offers S3 lockfiles through the use_lockfile argument. It also marks DynamoDB-based locking as deprecated. A minimal configuration looks like this:
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terraform {
backend "s3" {
bucket = "example-terraform-state"
key = "network/prod/terraform.tfstate"
region = "us-east-1"
use_lockfile = true
}
}- Enable bucket versioning so an earlier state version can be restored after a bad write.
- Set
use_lockfile = trueon Terraform versions that support it. Confirm the minimum version in the S3 backend reference before you rely on it. - If a stack still locks with a DynamoDB table, schedule the migration. The current documentation marks that locking method as deprecated.
Also learn what a failed remote write leaves behind. Local recovery behavior depends on the backend, so find out before an incident where a failed write stores state and how to recover from it.
Protect state, plan files, and credentials
State files and saved plan files can contain credentials and other sensitive attributes. Handle them as secrets, not as ordinary build output.
- Keep
terraform.tfstate, its backups, saved plan files, sensitive.tfvarsfiles, and the.terraformdirectory out of source control. - Commit the Terraform configuration,
.terraform.lock.hcl, a.gitignorethat excludes the files above, and module documentation covering inputs, outputs, and assumptions. - Store saved plan artifacts in CI with the same access controls as state, because a plan can expose the same values.
Marking a variable or output as sensitive hides the value from Terraform’s display output. It does not encrypt the value in the state file. Treat the state file as sensitive whether or not any value is marked.
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In CI, supply backend credentials through the platform’s secret store or through dynamic credentials. Do not place secrets in backend configuration values, because Terraform persists those values in local files.
Structure modules around responsibility and environment
A module boundary should follow one coherent responsibility, not a directory habit. A reusable module works when a consumer can see what it needs, what it produces, and which provider versions it supports.
Root modules are stacks; child modules are patterns
Keep each root module focused on one deployable stack or environment. Move reusable infrastructure patterns into child modules, and only when those patterns have a meaningful interface. A module that wraps a single resource adds indirection without a stable abstraction. Skip it unless the wrapper enforces a real organizational standard.
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Draw state boundaries around ownership, blast radius, and how often each part changes. One state file for the whole estate means every plan touches everything. Many small states mean more wiring between them, which is the trade-off the next two sections address.
Hard-code shared values, expose environment choices
Google Cloud’s guidance for cloud root modules recommends hard-coding common service-module inputs and requiring environment-specific inputs as variables. The same split works for most stacks:
| Value type | Where it belongs | Examples |
|---|---|---|
| Shared across all deployments | Hard-coded in the module, or set as an internal default | Naming conventions, standard tags, logging settings |
| Varies by environment | Required root-level input variable | Region, instance size, CIDR range, account identifier |
| Owned by another stack | Remote state output or a provider-specific data source | Network identifiers owned by a platform team |
Share data between stacks deliberately
Remote state can expose root module outputs to other stacks, but it creates a dependency. The consuming stack must be able to read the producing stack’s state, which is an access relationship between two state owners. Where a provider or service data source can look up the value, prefer it. That approach avoids granting read access to another stack’s entire state.
Pin versions and review dependency changes
Most surprise plans come from upgrades nobody reviewed. Pin what can change, commit what was selected, and upgrade on purpose.
Declare Terraform and provider constraints
- Declare the Terraform core versions each configuration supports, so the supported range is explicit.
- Reusable modules should state the minimum versions they need. Root modules can set bounded provider version constraints to control when upgrades arrive.
- Specify provider sources explicitly rather than relying on defaults.
Commit the provider lock file
Running terraform init generates .terraform.lock.hcl. Commit it with the configuration so later runs select the same provider builds. Review changes to the lock file in the same pull request as the configuration change that caused them.
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External modules are not in the lock file
The provider lock file tracks providers only. Terraform does not record remote module selections there, so an external module can change underneath you. Pin remote module versions exactly, or set a narrow range that your team manages on purpose.
Upgrade in isolation
Run provider and module upgrades as their own change, and read the full plan before merging. If an upgrade is mixed with an infrastructure change, it becomes hard to tell which one produced a given diff.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Build a reviewed plan pipeline
A pull-request pipeline makes review the default and keeps applies behind controls. The sequence below is a model. The exact commands, approval gates, and credential handling depend on your Terraform version, backend, and CI platform.
- Run formatting and validation checks on every change.
- Initialize using the committed lock file and the backend configuration.
- Produce a plan against the intended state or workspace, and save it as an artifact.
- Expose the plan to reviewers, and require human approval before anything is applied.
- Apply only the plan that was reviewed, under your team’s approval controls.
- Add policy checks where the organization needs hard limits, such as blocked resource types or disallowed regions.
Detect drift and decide what to do about it
Drift is any difference between what Terraform’s state and configuration describe and what exists in the cloud, usually because someone changed a resource in the console, through a script, or with another tool. Terraform refreshes resource attributes during ordinary plan and apply operations. The harder question is how to investigate drift without changing anything by accident.
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Investigate with a refresh-only plan
- Change into the directory for the stack in question, run
terraform init, and confirm you are using the intended state. If you use workspaces, check the active one withterraform workspace show. - Run
terraform plan -refresh-only. Terraform reads the live resources and shows the state updates it would record. - Review every proposed state change. Each one is an attribute whose live value differs from what Terraform last recorded.
- Decide which description should be authoritative, using the table below.
- Verify with a normal
terraform plan. The expected result is no changes, or only the changes you intend.
HashiCorp’s Terraform documentation for resource drift sets the boundary clearly: “A refresh-only operation does not attempt to modify your infrastructure to match your Terraform configuration — it only gives you the option to review and track the drift in your state file.” If you accept the refresh-only result with terraform apply -refresh-only, Terraform records the observed values in state. Infrastructure is still not changed.
Choose which description wins
| Situation | Action | How to verify |
|---|---|---|
| The external change was intended | Update the configuration so it captures the change | A normal plan shows no changes |
| The change was accidental or unauthorized | Run a normal plan that restores the declared configuration, and review what it modifies or replaces | The plan shows only the restoration you expect. Some corrections are disruptive or hard to reverse, so check them against the resource’s behavior before applying. |
| A real resource exists but Terraform does not manage it | Bring it under management with an import workflow | The plan does not propose creating a duplicate of that resource |
| The change must stay outside Terraform | Record a documented exception with an owner and a review date | The exception is listed and reviewed on schedule |
Choose a drift-check approach
| Approach | Changes state or infrastructure? | Requirements | Best suited to |
|---|---|---|---|
| On-demand refresh-only plan (Terraform CLI) | The plan changes neither. Accepting it with terraform apply -refresh-only updates state only. |
Backend access to the stack | Investigating a specific suspected change |
| HCP Terraform scheduled health assessment | Non-actionable. Runs refresh-only plans and does not change state or infrastructure, per HashiCorp’s drift-detection tutorial. | An HCP Terraform workspace. The tutorial lists drift detection as a Standard Edition feature, so confirm your edition includes it before building a process around it. | Recurring checks across managed workspaces |
| Third-party continuous discovery and remediation | Not stated in Terraform’s documentation | Depends on the product | Teams that want continuous monitoring beyond what the Terraform-native options provide |
Third-party tools fall outside the official guidance covered here, so judge any of them by the same questions. Can it run in a refresh-only mode that never applies changes? Does it remediate without review? Where do its findings and its credentials live, given that they may describe sensitive resources?
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