The number one question we get asked from prospects is how Terramate compares to Terragrunt. This is our opinionated take.

While Terragrunt was and is a great tool and was the go-to tool for the better part of the last decade, the challenges of 2026 look different from those of five years ago. Infrastructure systems are larger, teams are more distributed, AI-generated code is on the rise, and platform engineering has become a discipline in its own right. The question is no longer just how to keep stacks DRY — it's how to maintain clarity, performance, auditability, and evolvability at scale. This is where the design philosophies of Terragrunt and Terramate begin to diverge, and why we believe Terramate is the more future-proof approach.

So if you use Terragrunt today or consider starting with it, please check out how we see the comparison here at Terramate and why we believe we often are a better choice. Also, please feel free to disagree or point out what we missed.

Quick Primer on Terramate

If you have never heard of Terramate, let us share a quick list of capabilities before going deeper into the actual user experience.

Terramate offers an Open Source CLI and a Cloud Platform, which, in combination, provide you with the best IaC experience you can get:

The Open Source CLI gives every engineer:

Environment management with built-in promotion across stages
Stack orchestration with intelligent change detection
Code generation to eliminate boilerplate and enforce consistency
Developer self-service so teams ship without bottlenecks
AI-readiness via agent skills and an MCP server

Terramate Cloud layers enterprise-grade capabilities on top — with close to zero additional setup:

Pull Request Change Previews and Collaboration
Deployment Management and Audibility of Changes
Drift Detection and Drift Reconciliation
Resource Browser and Resource Policy Checks
Notifications and alerting on failures, violations, and successes
Dashboards, Analytics, and Reporting
AI to support understanding the complexity of failures and changes

Now, let us take a look at the actual user experience here. If you like to dive deeper, feel free to schedule a call.

Onboarding experience: close to zero migration effort

Onboarding existing OpenTofu, Terraform, or Terragrunt configurations into Terramate takes a single command. Running terramate create --all-terraform (or --all-terragrunt for Terragrunt projects) scans every relevant directory and creates a stack.tm.hcl file with a stack {} block and a unique UUID-based ID.

Once stacks are initialized, most Terramate features are available immediately, including full orchestration with Git-based change detection, triggered via terramate run --changed -- {any-command} . No code changes required.

Terragrunt takes a similar approach: each directory needs a terragrunt.hcl file, which can be empty. Terramate's equivalent requires at minimum an empty stack {} block and, by default, assigns an ID that unlocks additional benefits covered later.

The practical difference is small. Terramate's auto-detection gives it a slight onboarding edge, but at scale, the gap narrows.

	Terramate	Terragrunt
Onboarding existing infrastructure	✅ Auto detect and initialize all Terraform, OpenTofu, or Terragrunt directories as Terramate Stacks	✔ manual drop a `terragrunt.hcl` in all directories to enable support for Terragrunt
Migration required?	✅ no migration of existing code required	✅ no migration of existing code required
Templating support	✅ Terramate components allow to keep configuration DRY	✅ Stack code can be converted to a reusable Terraform/OpenTofu module.

Keeping stacks DRY - configuration complexity and long-term maintainability

Keeping configuration DRY (don’t repeat yourself) is one of the goals in both tools. The design decisions are fundamentally different here:

Terragrunt keeps configuration DRY by leveraging Terraform Modules that can be invoked across multiple stacks, making root modules reusable. Those modules are then initialized in temporary directories, and commands are executed in the temporary directories, wrapping OpenTofu or Terraform, passing different input values defined in the terragrunt.hcl via TF_var_* environment variables. This prevents running native tofu or terraform commands in the stack and always requires running terragrunt as a wrapper.

Once a Terragrunt stack uses a reusable module, no further Terraform or OpenTofu configuration files can be dropped into the stack, but always need to be added to the shared module.

Configuration values can be loaded by searching additional configuration files up the hierarchy. Maintaining the list of files to read makes the configuration less DRY as a side effect. Introducing more dryness by including a file that holds the configuration of what configuration files to load will lead to decreasing the readability of each stack's configuration, as it gets harder to maintain a mental overview of where values are coming from, where to change values and what the impact of such a change will be.

This complexity has not only mental overhead but also performance impact, which also Terramate suffers from when running the Terragrunt Change Detection Integration.

Terramate keeps configuration DRY by allowing to define templates that deterministically generate actual code. This code generation can either be inherited in the hierarchy or packaged into Terramate Components.

Terramate always has generated artefacts living in the stacks, which are not meant to be manually edited. Any additional files inside of stacks are not touched by terramate , so adding native configuration and running native terraform or tofu commands is possible at any point in time. Each stack always contains readable, reviewable HCL configuration.

Configuration values are inherited in the hierarchy, and there is no need to maintain a list of files that need to be loaded to access shared configuration. In addition, changing values of global variables will lead to changes in generated files, which can easily be identified using normal git operations. It is easier to maintain the mental model of the overall configuration and overwrite specific values. Terramate also offers changing values in complex objects without the need to clone the full object, thanks to a feature called Labeled Globals: Setting globals "terraform" "provider" "aws" { version = "6.33.0" } will only change the version of the AWS provider while keeping other provider configurations untouched.

Differences Highlights	Terramate	Terragrunt
extend stacks with additional `.tf` files	✅ always possible	❌ not possible once reusable modules are used.
run native `tofu` or `terraform` commands after entering the stack	✅ always possible	❌ requires to always run `terragrunt` as a wrapper, once inputs and re-usable modules are used.
reviewable `.tf` files and code in each stack	✅ full visibility of resulting Terraform code ✅ enables easy debugging in error cases ✅ makes actual configuration values reviewable in code and plan previews	❌ code is hidden in temporary directories and not checked in ❌ debugging is more complex as error messages need to be related to temporary files ✔ actual configuration values are reviewable in `terragrunt.hcl` but not in `.tf` files as long as they are not read from other files.
Code Generation to extend stacks with additional code	✅ full HCL support ✅ generated code gets persisted ✅ Terramate Globals allow to inherit and overwrite configurations dynamically and are always available ✅ inherited configuration is visible and reviewable in generated code. ✅ overwrite each global variable independently within the hierarchy ✅ overwrite deeply nested values in complex global variables within the hierarchy (Terramate Labeled Globals)	❌ string templating ❌ generated files are temporary and hard to debug ❌ shared configuration needs to be loaded manually in each stack. ❌ loaded configurations (local variables) are not visible nor reviewable ❌ only full files (all local variables in a file) can be overwritten within the hierarchy ❌ no deeply nested manipulation of configurations supported

Performance

Terragrunt had some performance regressions for quite some version numbers, improved a bit in latest releases, but overall can also be used with bigger setups.

Terramate's Terragrunt integration uses Terragrunt as a library under the hood, so any Terragrunt performance issue flows through to Terramate, most notably in change detection and dependency resolution. The core problem: change detection must repeatedly scan every stack for chained includes, regardless of what actually changed. This is the equivalent of a full table scan on every run. The result is slower change detection compared to using Terraform or native Terramate alone.

Terramate's file-generation approach adds some build-time overhead, plus optional runtime overhead if safeguards are enabled to verify generated files are current. However, change detection in this mode is highly optimized. It relies only on changes surfaced by the Git integration, with no need to scan the full file tree.

One option for speeding up Terragrunt workflows is to generate the final Terragrunt files via Terramate. But at that point, removing Terragrunt entirely may require similar effort and yield a more maintainable result long-term.

Output vs Information sharing

Terragrunt pioneered output sharing to solve a core problem when splitting monolithic Terraform code: how do you access cloud resource IDs that are only known after an apply? In a monolith, Terraform resolves these dependencies at apply time. In a multi-stack setup, that option disappears. Terragrunt's solution is stack dependencies: one stack's outputs become another stack's inputs.

This creates a real orchestration burden. The stack providing outputs must be fully applied before dependent stacks can even be planned, forcing multiple pull requests to be merged in strict order.

Terramate supports this workflow but goes further: it can read shared data from multiple sources beyond Terraform or OpenTofu outputs. The one-time setup of a sharing backend adds minimal overhead while unlocking flexibility in how and where data is sourced.

Both Terramate and Terragrunt support mocked output values for data not yet available. Mocks appear in plan previews as placeholders; once real data exists, the plan must be regenerated.

An alternative and even better Terraform -native approach is to use remote state lookups leveraging the terraform_remote_state data source. This also requires the first stack to be applied to access the data but both stacks can actually be planned at the same time, when actually reading the remote state data source is postponed to the apply time using depends_on . Multiple depending stacks can now be managed in a single initial pull request but changing outputs over time can lead to the initial problems.

Terraform also supports data sources for specific resources, those again can be postponed to be read at apply time for initial deployments and do not cause issues when adding additional resources to the game. The problem here is how to know what filters to use in such data sources: Terramate information sharing to the rescue.

Terramate information sharing is mainly supported within bundles as of today. Bundles allow to control what stacks shall be generated, and what Components shall be used to generate code into such stacks. More on Bundles further down.

Every Terramate Bundle gets a UUID when instantiated in a repository. You can use this UUID to tag cloud resources, filter data sources, and directly target specific resources without guessing at filter logic. The UUID is also accessible from other bundles or outside a bundle entirely. Each Bundle additionally exposes a class and a human-friendly alias as alternative ways to reference the same UUID.

This shared context eliminates the need for complex tagging strategies and brittle filter configurations. See https://github.com/terramate-io/terramate-catalyst-examples for working examples.

	Terramate	Terragrunt
Output sharing	✅ yes, defining sharing backends is required ✅ any type of data source can be used ✅ allow to mock data to prevent pr chaining ❌ requires chaining of pull requests or running with mocked data	✅ easy to set up dependencies ✔ Terraform and Tofu support out of the box ✅ allow to mock data to prevent pr chaining ❌ requires chaining of pull requests or running with mocked data
Remote State Data Source	✅ native Terraform/OpenTofu feature ✔ easy setup of the remote state details of another stack is planned (coming in a next release)	✅ native Terraform/OpenTofu feature ❌ no easy way to set up the data source
Data Sources to target specific resources	✅ When using bundles, UUIDs and other fields can be used to share information of resources to read via data sources ✔ Using stack IDs to achieve the same is planned for an upcoming release	❌ no native support to set up filters for data source

End-to-end Experience, Observability and Auditability with Terramate Cloud

Terramate offers an end-to-end experience to automate the IaC experience and run workflows anywhere.

To run IaC on scale and to enable self-service, it is important to understand the state of your infrastructure at any point in time.

Terramate CLIs orchestration features and the available automation blueprints for the main CI/CD providers like GitLab Actions, GitLab CI, or Bitbucket Pipelines allow for an easy start into full end-to-end automation.

Once automation kicks in, all cloud resources are created and maintained by Service Accounts or assume IAM roles and cloud audit logs do reflect only changes but not the correct actor. Terramate Cloud keeps track of collaborators requesting or deploying changes and thus creates a full auditability of changes over time, and can assign the correct actor to each and every change.

Terramate Clouds’ advanced preview process allows for faster reviews and highlights failures and risky operations.

Since Terramate Cloud is our commercial offering let’s not get into too many details here as we want to concentrate on comparing the different approaches: There is not much difference here when comparing the CLIs, as we fully support Terragrunt also on Terramate Cloud in addition to Terraform and OpenTofu.

AI-Readiness of Terramate

Terramates MCP Server allows to combine local development with globals state in Terramate Cloud.

When Terramate Cloud detected a drift, the MCP server can help to accept the cloud state and adjust the code to reflect the new desired state. It can also trigger drifted stacks to auto-reconcile the drift by redeploying the desired state in code.

Terramate Agent Skills allow to maintain a Terraform, OpenTofu, and Terramate code base in modern AI environments.

We think AI with different audiences by design. Experts can leverage AI to create a contract layer on top of Terraform with reusable Bundles and Components that follow best practices, are compliant and enforce specific settings on scale. Non-expert users can leverage AI to configure existing, curated Terramate Bundles to deploy cloud services, like databases, Kubernetes workloads, etc, on scale fully safeguarded by the bundle interfaces the experts provided in the first place.

Full control for experts and full power for non-experts on a day-2-day use. Curated and configured new infrastructure can be set up to run a review-less workflow, leading to faster deployments of infrastructure and applications.

Environment Promotion

Terragrunt supports template reuse across environments via Stacks or Units, but the process is largely manual, copying files and configurations between environments.

Terramate Bundles enforce a strict separation between code and configuration. A Bundle's YAML configuration only needs three things: a reference to the Bundle source, a version, and the desired configuration values. All implementation details, such as directory structure, stack layout, Component wiring are encapsulated inside the Bundle itself.

Deploying the same Bundle to multiple environments is straightforward. All environment configurations live in a single file. New environments can be added by duplicating a configuration block or using the Terramate CLI's TUI, a form-based interface that eliminates manual YAML editing.

With Terramate Bundles, environment promotion is a solved problem.

We will publish a follow-up article going into details about how Terragrunt Stacks and Terramate Bundles differ.

Going beyond Terraform and OpenTofu

Terramate is not limited to Terraform or OpenTofu. Terragrunt, being a wrapper for either, can only work in such environments.

Terramate can orchestrate any commands, including but not limited to terraform , tofu , helm , kubectl , kustomize .

Terramate can generate files of any type: HCL , JSON , YAML , etc. Allowing to also template CloudFormation templates, values YAML files and others.

Ease of use (also for non-experts)

Terragrunt helped paper over Terraform's early shortcomings, but many of those fixes are now built into Terraform and OpenTofu natively. Terramate addresses what remains: the problems that still slow teams down every day, across both the Terramate CLI and Terramate Cloud.

Terraform state and backend configuration — State management is no longer a blocker. Both Terraform and OpenTofu support state migrations in code. Terramate lets you template backend configuration at scale and uses a Stack ID (not the stack's path) as the state storage key, so renaming or restructuring directories never breaks state. Large state files can be split into stacks to reduce blast radius and speed up deployments with Change Detection.
Provider configuration — Dynamic provider configuration is already supported via templates. Terramate Bundles and Components will extend this further in upcoming releases. Rolling out provider updates scoped to a single environment or subsystem is fully solved today.
Keeping code DRY — Code generation and Terramate Components eliminate duplication without sacrificing clarity.
Environment promotion — With Terramate Bundles, promoting across environments is a single configuration file change.
Separation of configuration and code — Terramate is built by platform engineers for platform engineers. Experts define and templatize the infrastructure; non-experts consume it safely. One tool, two audiences.
AI readiness — Build reusable building blocks now and let AI configure them, rather than generate unbounded infrastructure code. Maintainability and speed are not a trade-off.

Developer self-service & layer for agentic infrastructure

Terragrunt was the go-to solution for years and remains a great tool. But the landscape has shifted. Terramate addresses the gaps that Terragrunt leaves open. Most importantly, self-service capabilities to enable non-expert developers and a built-in observability layer that tracks infrastructure changes at scale, keeping you audit-ready and giving you the data to move KPIs in the right direction.

We're also building for what's next: AI-generated infrastructure code is already producing massive volumes of HCL. Our answer is to let experts leverage AI to generate reusable patterns once, package those as Bundles, and then let every engineer use AI to configure them — preventing slop at the source and turning AI-generated Terraform from a maintenance problem into a force multiplier.

Terramate vs Terragrunt: A 2026 Comparison