GITNUXSOFTWARE ADVICE
Safety AccidentsTop 10 Best Dry Run Software of 2026
Ranked roundup of dry run software tools for infrastructure teams, comparing Spacelift, Helm, and OpenTofu with key strengths and tradeoffs.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
Search agents gather the source set and the review record, including transcriptions of video reviews and podcasts.
Framework: Features 40% · Ease 30% · Value 30%
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Spacelift is the best dry-run tool when you want gated Terraform plan reviews with traceable approvals before any changes apply, whereas Helm fits Kubernetes teams needing repeatable chart manifest previews and policy gates.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Spacelift
Policy evaluation runs against Terraform plan output, blocking applies before execution when rules fail.
Best for: Fits when teams need gated Terraform rehearsals with policy checks and traceable approvals for infrastructure changes.
Helm
Editor pickHelm renders full Kubernetes manifests from charts and values, making release previews grounded in the exact API objects.
Best for: Fits when Kubernetes teams need repeatable manifest previews and policy gates for chart-driven releases.
OpenTofu
Editor pickTerraform-compatible execution planning and state handling that produces reviewable, dependency-ordered diffs without proprietary tooling.
Best for: Fits when infrastructure changes must be rehearsed from declarative HCL inside CI with plan review gates.
Comparison Table
Spacelift
API-firstSpacelift runs infrastructure plans for review before approved changes are applied.
Policy evaluation runs against Terraform plan output, blocking applies before execution when rules fail.
Spacelift models deployments as stack operations, so each Git change can trigger a plan run and then a separate apply run gated by policies. Configuration validation happens during the plan and during policy evaluation, which lets teams catch forbidden changes before execution. Audit logs capture run activity and approval events, which supports review workflows for infrastructure change records.
A common tradeoff is that dry-run fidelity depends on how Terraform providers and credentials resolve during Spacelift runs, since missing data or constrained network access can make plan results diverge from production outcomes. Spacelift fits best when Git-driven infrastructure workflows require repeatable plan previews, policy checks, and controlled apply execution without giving every pipeline direct write access to the target environment.
- +Plan execution and policy evaluation are coupled to Git change events
- +API and webhooks support CI pipeline orchestration for plan and apply
- +RBAC and run audit logs support structured approval workflows
- +Stack abstraction keeps environment and state tied to versioned changes
- –Plan accuracy depends on provider access and credentials in Spacelift runs
- –Policy definitions can add operational overhead for teams new to policy-as-code
- –Complex workflows require careful separation of plan and apply permissions
Platform engineering teams
Gated Terraform applies across environments
Reduced unreviewed infrastructure changes
Security and compliance teams
Policy-as-code guardrails for infra
More consistent compliance checks
Show 2 more scenarios
DevOps release managers
Deployment rehearsal in CI/CD
Fewer production surprises
Webhooks and the API coordinate plan and apply steps with existing release processes and approvals.
Infrastructure operators
Controlled rollback rehearsal planning
Quicker change recovery
Separate apply permissions and plan history support faster decision-making for rollback-related changes.
Best for: Fits when teams need gated Terraform rehearsals with policy checks and traceable approvals for infrastructure changes.
Helm
enterpriseKubernetes package manager with template and lint commands for dry-run validation of chart deployments.
Helm renders full Kubernetes manifests from charts and values, making release previews grounded in the exact API objects.
Helm’s release model tracks chart version, values, and rendered manifest output, which makes change rehearsal repeatable across environments. Template rendering exposes the exact Kubernetes objects Helm intends to create, update, or delete, which supports execution preview and impact review in pull requests.
A key tradeoff is that Helm dry runs do not fully simulate runtime behavior such as controller reconciliation or external side effects. Helm fits best when teams want preflight configuration validation and template diffing for chart changes before applying to a staging or canary target.
- +Deterministic manifest rendering from chart templates and values
- +Release history stores prior rendered state and values for comparisons
- +CI-friendly chart linting and validation hooks for preflight gates
- +Kubernetes diff-friendly output for reviewing resource changes
- –Does not emulate controller reconciliation or runtime external calls
- –Complex value inheritance can make rendered diffs harder to interpret
- –Security policy checks require cluster-side admission or extra tooling
- –Subcharts and conditionals increase cognitive load in large charts
Platform engineering teams
Preview chart changes in CI
Fewer broken releases
DevOps teams
Rehearse rollback-ready configuration tweaks
Faster rollback decisions
Show 1 more scenario
Release managers
Gate deployments on template validation
Consistent change approvals
Validation hooks can block merges when chart logic produces invalid or inconsistent Kubernetes objects.
Best for: Fits when Kubernetes teams need repeatable manifest previews and policy gates for chart-driven releases.
OpenTofu
enterpriseOpen-source fork of Terraform preserving the plan command for pre-apply dry-run validation.
Terraform-compatible execution planning and state handling that produces reviewable, dependency-ordered diffs without proprietary tooling.
OpenTofu reads HCL configurations and computes a change plan that can be reviewed and stored as an execution preview artifact. The engine evaluates provider schemas, graph dependencies, and existing state to generate a deterministic diff that reflects what would change if applied. The workflow supports separation between plan and apply, which enables release rehearsal gates in automated pipelines.
A tradeoff appears in complex environments where provider behavior or external data sources change between plan and apply, which can make execution previews drift from runtime results. OpenTofu fits when teams need consistent change rehearsal for infrastructure managed via declarative modules and when CI can enforce plan approval based on generated diffs.
- +Terraform-compatible HCL and workflow reduces migration friction
- +Graph-based planning generates dependency-aware change diffs
- +Separation of plan and apply supports repeatable rehearsal gates
- +Command-line execution fits CI pipeline preflight validation
- –External data sources can cause plan and apply mismatch
- –Team governance needs extra process since approvals are not built in
- –Plan artifacts require pipeline discipline to prevent stale apply
Platform engineering teams
Plan changes before production apply
Lower change-risk variance
DevOps automation engineers
CI-driven dry run for releases
Fewer unintended diffs
Show 1 more scenario
Cloud governance teams
Policy review on infrastructure diffs
Clearer change-management records
Reviewers use plan output to validate change intent before allowing controlled execution.
Best for: Fits when infrastructure changes must be rehearsed from declarative HCL inside CI with plan review gates.
Chef
enterpriseConfiguration management platform with why-run mode for dry-run convergence reporting.
Chef Infra’s test-kitchen driven workflow plus Chef code execution flow supports rehearsing cookbook changes with environment-scoped targets.
Chef provides Chef Infra to manage system state and Chef Automate to run change orchestration with execution logs and workflow controls. For dry runs, Chef’s simulation and preview capabilities focus on understanding what the run would change before applying it to real nodes.
- +Convergent configuration model supports run previews tied to node state
- +Chef Automate keeps execution history that aids change rehearsal traceability
- +Environment and policy scoping lets rehearsal target subsets of infrastructure
- +Extensible cookbooks enable custom preflight logic for specific change types
- –Dry-run accuracy depends on cookbook logic and external data sources
- –Simulation workflows need governance discipline to avoid drift between preview and apply
- –Rehearsal outcomes can be harder to interpret for teams unused to resources
- –Complex orchestration increases dependency between policies, roles, and node facts
Best for: Fits when infrastructure changes are expressed as Chef resources and rehearsal must produce an execution record for targeted node sets.
Puppet
enterpriseConfiguration management tool supporting noop mode for dry-run catalog application.
Catalog compilation plus orchestration lets teams run a controlled rehearsal that produces actionable agent plans tied to an environment.
Puppet can rehearse infrastructure changes by compiling desired-state manifests into an execution plan before systems apply them. Puppet runs that plan through its agent and orchestration layers, with controls for environment selection, resource ordering, and catalog compilation.
- +Catalog compilation provides a concrete execution preview before applying changes
- +Environment and manifest versioning support repeatable rehearsal across stages
- +Orchestration and API surface enable automated preflight runs in CI
- +Reporting and audit trail records catalog results for review
- –Dry-run rehearsal depends on workflow configuration across control and agent components
- –Dependency ordering and scope control can require careful class and data design
- –Complex module ecosystems can slow compilation for large catalogs
- –Limited native diff preview for human readability compared with purpose-built tooling
Best for: Fits when infrastructure-as-code changes need catalog-based preview with strong governance and repeatable environments.
Crossplane
enterpriseKubernetes-native control plane provider supporting server-side dry-run via kubectl validation.
Composition-driven previews of composite resources let teams rehearse multi-service changes via custom resources.
Crossplane is a dry-run oriented control plane that turns infrastructure changes into declarative plans inside Kubernetes. It compares desired and observed state using custom resources and controllers, then can produce an execution preview before real writes hit cloud APIs.
- +Declarative reconciliation model enables repeatable execution previews per desired state
- +Composite resources let one rehearsal cover whole stacks instead of single resources
- +Policy hooks can gate controller actions before any cloud API writes occur
- +Kubernetes-native extensibility supports custom providers and claim-based workflows
- –Dry-run preview depends on provider support for safe observation and rendering
- –Complex compositions and policies increase troubleshooting time during rehearsals
- –Dependency ordering is managed by controllers and may not match every pipeline expectation
- –RBAC and audit coverage require careful Kubernetes permissions design
Best for: Fits when Kubernetes-centric teams want change rehearsals from declarative infrastructure plans before cloud updates.
AWS CloudFormation
enterpriseCloudFormation change sets preview stack modifications before execution.
Change sets in the CloudFormation API show planned resource actions, including replacement and parameter-driven updates.
AWS CloudFormation provides a change-set workflow for infrastructure-as-code templates, which makes it practical to rehearse updates before execution. Templates define resources, dependencies, and parameterized configuration, then CloudFormation can generate a plan that shows how an update will alter the stack.
- +Change sets provide a concrete execution preview for stack updates
- +Template-driven dependency ordering reduces manual sequencing mistakes
- +Stack events and resource status transitions support audit-ready change records
- +CloudFormation API enables CI orchestration and approval gates around change sets
- –Preview fidelity varies when updates require replacement versus in-place mutation
- –Rollback rehearsal is limited because execution still depends on AWS service behavior
- –Complex templates can produce noisy diffs that obscure real blast-radius
- –Governance requires discipline in IAM, change approvals, and stack policy usage
Best for: Fits when infrastructure changes are managed as templates and teams want API-driven change previews.
Octopus Deploy
SMBOctopus Deploy previews deployment processes and evaluates release steps before execution.
Process steps execute in rehearsal while still enforcing environment variables, permissions, and step conditions per release.
Octopus Deploy centers release rehearsal using its deployment plan execution model, which supports simulating what would run without actually targeting servers. It records environment-specific configuration, variable substitution, and step-by-step action outcomes so teams can validate parameters, policies, and deployment scripts before promotion. Octopus also integrates tightly with CI systems through triggers and deployment artifacts, which lets the same release definition drive test execution and later environments.
- +Release definitions run in rehearsal mode using the same steps as real deployments
- +Environment-scoped variables and configuration transforms are evaluated per step
- +Audit trail captures changes to releases, processes, and variable sets
- +API supports programmatic release creation, deployment, and status polling
- –Dry-run accuracy depends on how deployment scripts handle preflight and idempotency
- –Large multi-repo setups require careful release packaging and process structuring
- –Dependency insight is limited unless teams model dependencies in the Octopus step graph
- –Governance needs deliberate RBAC and lifecycle rules to prevent accidental promotions
Best for: Fits when teams need parameterized deployment rehearsal that shares the exact process steps across environments.
Atlantis
API-firstAtlantis executes Terraform plans through pull requests before code is applied.
Atlantis orchestrates Terraform plan and apply directly from pull request events with configurable approval checkpoints per workspace.
Atlantis runs deployment rehearsals by executing infrastructure changes against a branch workflow with Terraform plans and a controlled apply step. The distinction is its tight coupling to pull requests, where it can preview what a change will do before any merge-triggered execution.
- +Pull-request execution wiring turns preflight validation into a repeatable workflow.
- +Configurable apply approvals let teams gate deployment rehearsals by repo and workspace.
- +API and webhook-style integrations fit CI orchestration around plan and apply.
- +Supports isolated environments through workspace mapping to reduce cross-change coupling.
- –Dependency mapping coverage depends on the Terraform graph rather than Atlantis-specific analysis.
- –Repository and workspace configuration can become complex at scale.
- –Extensibility usually requires disciplined scripting around plan outputs and run triggers.
- –Operational setup needs careful credentials and permissions design to avoid overexposure.
Best for: Fits when teams run Terraform change rehearsals per pull request and need gated execution.
Liquibase
vertical specialistLiquibase can generate SQL previews of database changes before migration execution.
Changelog-driven SQL generation and update planning that can be executed against a target database for execution preview.
Liquibase is a schema change dry-run tool that turns database change definitions into execution plans before anything runs. Its core capability is generating SQL and validating changesets against a target database so pipeline jobs can preview effects and catch drift before deployment rehearsal.
- +Change-log based execution planning with SQL generation for preflight validation
- +Per-changeset rollback logic enables rollback rehearsal planning
- +Environment comparison helps detect schema drift before release rehearsal
- +Programmatic execution and extensibility options for CI integration
- –Dry-run fidelity depends on available metadata and target database capabilities
- –Complex changelog strategies can raise governance overhead for larger teams
Best for: Fits when teams need repeatable database migration rehearsal with SQL previews and rollback-aware planning in CI.
Conclusion
After evaluating 10 dry run software, Spacelift stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right Dry Run Software
Dry run software turns deployment changes into rehearsed execution before anything hits production. Spacelift and Atlantis focus on Terraform workflows, while Helm and Crossplane translate declarative infrastructure into previewable outcomes for Kubernetes release rehearsals.
SafetyCulture and iAuditor are included in this buyer guide because inspection-first teams often use dry-run rehearsal to validate work instructions, evidence capture steps, and workflow conditions before deployment execution. Form.com is also included because its form-driven operations can support simulation-style validation for approvals and data requirements preceding a live run.
Dry Run Software for CI-driven deployment rehearsal and execution preview
Dry run software provides a rehearsal environment for preflight validation, execution preview, and change rehearsal so teams can catch policy failures and sequencing errors before execution. Spacelift couples Terraform plan output with policy evaluation to block applies when rules fail, which makes the rehearsal gate part of the CI path rather than a separate manual check.
Helm focuses on rendering full Kubernetes manifests from chart templates and values, which makes release previews grounded in the exact API objects that chart-driven releases would apply. Across the category, dry-run outcomes typically come from plan generation, manifest rendering, or changelog planning that can be compared against prior state to support rollback rehearsal and audit trail capture.
CI-gated rehearsal depth and execution-preview coverage
Dry run software only prevents production incidents when the rehearsal output is tied to the same inputs that will drive execution. CI integration matters because pull request events, change sets, and plan artifacts need to carry through policy gates and approvals before apply runs.
The strongest tools also preserve a repeatable replay record. They capture rendered plans, environment-scoped process steps, and execution histories so teams can compare rehearsals across stages and trace what changed.
Policy gates that block applies from plan or rendered artifacts
Spacelift evaluates policies against Terraform plan output and blocks applies when rules fail, so policy validation sits on the same CI path as execution. Atlantis provides pull-request execution wiring with configurable apply approvals per workspace, so teams can gate rehearsals before apply.
Kubernetes release rehearsal via deterministic manifest rendering
Helm renders full Kubernetes manifests from chart templates and values, making release previews grounded in the exact API objects. Crossplane produces previews from declarative reconciliation and composite resources, which extends rehearsal coverage beyond a single manifest to desired-state stacks.
Terraform planning that preserves dependency order in diffs
OpenTofu generates Terraform-compatible execution planning and dependency-ordered diffs suitable for plan review gates. Atlantis orchestrates Terraform plan and apply directly from pull request events, so the rehearsal workflow is anchored to the same repository triggers.
Environment-scoped rehearsal steps that reuse the real process
Octopus Deploy runs release definitions in rehearsal mode while enforcing environment variables, permissions, and step conditions per release. Chef Infra pairs test-kitchen driven workflows with a Chef code execution flow, which supports rehearsing cookbook changes against environment-scoped targets with traceable execution history.
Database migration rehearsal with changelog planning and rollback logic
Liquibase generates SQL from changelogs and produces update planning against a target database for execution preview. Chef and Puppet can rehearse infrastructure changes tied to node sets and environment versions, but Liquibase focuses rehearsal on migration planning and rollback-aware changesets.
API-driven change previews for template-managed stacks
AWS CloudFormation exposes change sets that show planned resource actions, including replacement and parameter-driven updates, through the CloudFormation API. Helm and Crossplane can render and reconcile desired-state outputs, but CloudFormation’s change-set view stays rooted to stack update semantics.
How to choose rehearsal software that matches the way changes are expressed
Dry run selection should start with the artifact that will exist before execution. When changes are Terraform-driven, Terraform plan output or Terraform-compatible planning artifacts must feed policy gates and approvals so rehearsals remain reviewable and repeatable.
When changes are chart-driven or stack-templated, the rehearsal preview should reflect the exact objects the release will apply. When changes are database migrations, rehearsal should be based on changelog-driven SQL generation and rollback-aware planning so the preview is anchored to the migration model rather than a generic script dry run.
Pick the rehearsal artifact that can be gated in your CI system
If the CI pipeline already produces Terraform plan output, Spacelift can evaluate policies against that plan and block applies when rules fail. If gating must attach to pull request workflows per repository and workspace, Atlantis orchestrates Terraform plan and apply from pull request events with configurable apply approvals.
Match Kubernetes rehearsal to how manifests are produced
If releases originate from Helm charts, Helm renders full Kubernetes manifests from templates and values so release previews map directly to API objects. If the change model is composite desired-state across services, Crossplane can rehearse multi-service stacks through custom resources and composition-driven previews.
Choose a reconciliation or rehearsal engine based on your execution target
If cookbook changes must be rehearsed against environment-scoped targets, Chef Infra uses test-kitchen workflows plus a Chef code execution flow to generate execution records tied to node sets. If orchestration must reuse step logic across environments with enforced permissions and environment variables, Octopus Deploy runs the release steps in rehearsal mode.
Use a database migration rehearsal workflow when the change is SQL and rollback logic
If the change model is changelog-based migrations, Liquibase generates SQL from changelogs and produces update planning against the target database to preview execution. If the dry run goal is migration rehearsal with rollback behavior, Liquibase’s per-changeset rollback logic is the rehearsal-specific capability to prioritize.
Validate template-managed stack previews against stack semantics
If infrastructure is managed as CloudFormation templates, CloudFormation change sets provide a concrete execution preview for stack updates through planned resource actions. If the team needs a template-to-objects preview for Kubernetes, Helm’s manifest rendering and release history comparisons fit better than change sets tied to AWS stack semantics.
Who Dry Run Software fits best
Teams need dry run software when deployment rehearsal must be repeatable and reviewable before any execution step touches production. The best fit depends on whether the team’s change language is Terraform, Helm charts, CloudFormation templates, orchestration pipelines, infrastructure code, or migration changelogs.
Inspection-first operations also benefit when rehearsal outputs validate checklists, evidence capture steps, and workflow conditions before run steps execute. In that pattern, tools like SafetyCulture and iAuditor act as the rehearsal companion to infrastructure and release planning tools used in CI.
Platform teams running Terraform through CI
Spacelift couples Terraform plan output with policy evaluation and blocking, so rehearsals become enforceable gates before apply. OpenTofu provides Terraform-compatible planning and dependency-aware diffs that support plan review inside CI workflows.
Kubernetes release teams using Helm chart releases
Helm renders deterministic Kubernetes manifests from charts and values, so the rehearsal preview reflects the same API objects that release automation would apply. Crossplane adds a different model by rehearsing reconciliation outputs from composite resources for multi-service desired-state changes.
Operations teams rehearsing parameterized deployment processes
Octopus Deploy runs the same release definition steps in rehearsal mode and applies environment-scoped variables, permissions, and step conditions. Atlantis anchors rehearsals to pull request events and uses configurable apply approvals per workspace for gated execution.
Infrastructure teams using Chef or Puppet for configuration and targeted node rehearsal
Chef Infra supports rehearsal via test-kitchen workflows plus a Chef code execution flow scoped to targets and backed by execution history. Puppet adds catalog compilation and orchestration that produces actionable agent plans tied to an environment with repeatable rehearsal across stages.
Database teams managing changelog-driven migrations
Liquibase generates SQL from changelogs and plans updates against a target database so migration rehearsal can reflect database-specific behavior. Its per-changeset rollback planning supports rollback rehearsal preparation inside CI workflows.
Common dry run mistakes that break rehearsal trust
Dry run output becomes unreliable when rehearsal models drift from execution inputs. Mismatches often happen when external data sources change between preview and apply or when reconciliation engines cannot emulate runtime behavior.
Teams also lose governance control when approvals are bolted on after rehearsal artifacts are generated. The rehearsal pipeline should tie gating decisions to the exact plan, manifest render, or change set that will drive execution.
Treating Terraform plan previews as accurate when external data sources change between rehearsal and apply
OpenTofu planning can include external data source effects, so plan and apply mismatch can occur when runtime data differs. Spacelift reduces governance risk by coupling plan-based policy evaluation to the apply gate, but provider access and credentials must support consistent plan generation.
Assuming Helm manifest rendering covers runtime behavior like controller reconciliation and external calls
Helm produces deterministic rendered manifests, but it does not emulate controller reconciliation or runtime external calls. Crossplane’s reconciliation model gives broader coverage for desired-state previews, but provider support limits safe observation fidelity.
Relying on rehearsed scripts that skip idempotency and preflight handling
Octopus Deploy rehearsal accuracy depends on deployment scripts handling preflight and idempotency in the same way as live steps. Atlantis helps standardize the Terraform rehearsal workflow from pull requests, but dependency mapping coverage stays tied to the Terraform graph rather than separate analysis.
Running dry run infrastructure previews without governance discipline for preview-to-execution parity
Chef Infra simulation outcomes depend on cookbook logic and external data sources, so preview and apply drift can still happen if logic differs. Puppet’s orchestration rehearsal depends on coordinated configuration across control and agent components, so rehearsals require class and data design discipline.
Trying to use generic migration previews without sufficient metadata for rollback-aware planning
Liquibase dry-run fidelity depends on available metadata and the target database capabilities used for SQL generation and planning. Complex changelog strategies can increase governance overhead, which can undermine consistent rehearsal behavior across CI environments.
How We Selected and Ranked These Tools
We evaluated rehearsal software by weighting CI-gated execution preview coverage at 40% and then weighting operational ease and value at 30% each. Spacelift set the benchmark by coupling Terraform plan output with policy evaluation so policy failures block applies before execution.
Spacelift also scored high for integration depth because its API and webhooks support CI pipeline orchestration for plan and apply. Helm, OpenTofu, and Atlantis were ranked next based on deterministic rendering or Terraform-compatible planning plus PR-triggered rehearsal workflows that keep change rehearsals reviewable.
Frequently Asked Questions About Dry Run Software
How does a dry run environment differ from a staging environment in practice?
How can CI pipelines trigger dry-run test execution and capture an execution preview for reviewers?
Which tool provides API access for dry run orchestration and change-management records?
How does SSO and RBAC typically map to approval workflows for rehearsal execution?
What breaks if a team relies only on template validation instead of plan execution previews?
When should Kubernetes-focused teams use Helm dry runs instead of executing Terraform plans through a control plane?
How does drift detection or environment comparison show up in dry run outputs?
How are data migrations rehearsed before any SQL is executed?
What tradeoffs appear when using plan-based infrastructure rehearsal versus schema-change rehearsal?
Tools reviewed
Primary sources checked during evaluation.
aws.amazon.comReferenced in the comparison table and product reviews above.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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