Terraform is widely recognized for its declarative approach to managing infrastructure as code. Rather than scripting the specific steps needed to build resources, developers write configuration files that define what the final infrastructure should look like. Terraform then determines how to get from the current state to the desired state. This process is driven by an internal dependency graph that determines the optimal order of operations for creating, updating, or destroying infrastructure components.
At the heart of this system is the notion of automatic dependency resolution. Terraform is capable of analyzing relationships between resources based on references, which allows it to create a directed acyclic graph for execution. This graph outlines which resources depend on others and ensures they are provisioned in the correct sequence. However, not all dependencies are explicit in the configuration, and this is where the depends_on meta-argument becomes indispensable.
Implicit Dependency Resolution in Terraform
Terraform detects dependencies by analyzing the interpolations and references within your configuration files. For example, if an AWS instance references a subnet ID, Terraform knows the subnet must be created before the instance. This form of automatic analysis works well for straightforward scenarios where one resource clearly depends on another through input attributes.
This implicit behavior is one of Terraform’s core strengths. It allows parallel execution of independent resources and significantly accelerates infrastructure provisioning. When there are no hidden relationships, Terraform’s graph algorithm works efficiently and reliably.
However, this automatic detection mechanism is limited to direct and explicit references. In more complex configurations, especially those involving custom workflows, indirect logic, or modular design, the dependency graph may become incomplete. In such scenarios, Terraform may attempt to create or destroy resources in the wrong order, resulting in errors or unpredictable behavior.
Why You Need Explicit Dependencies
Infrastructure, especially in modern cloud environments, often includes nuanced relationships that aren’t captured by direct attribute references. Consider the following real-world cases:
- A configuration script must only run after a database is fully initialized, even though the script does not reference the database ID.
- A security audit scan must begin after all network resources and virtual machines are deployed, but there’s no direct link between them in the configuration.
- A monitoring agent should not start before the underlying application is deployed, even if both are managed by separate modules.
- An external system’s API must be notified only after certain infrastructure is fully live and stable.
In these scenarios, relying on Terraform’s implicit behavior can lead to fragile deployments. If Terraform executes resources out of order, it can trigger cascading failures, delays, or incomplete environments. To guard against such risks, the depends_on meta-argument allows users to manually enforce the desired order of operations.
What is the depends_on Meta-Argument?
The depends_on argument is a special meta-argument that can be added to resource and module blocks. It instructs Terraform to treat one or more listed resources or modules as prerequisites. In practice, this means Terraform will defer creating, updating, or destroying the resource until the declared dependencies have been successfully completed.
Importantly, depends_on does not influence the logic of the resource itself—it does not change its attributes or outputs. Instead, it influences the execution plan, ensuring a specific sequence of actions. This is particularly valuable for coordinating operations that involve external effects, side conditions, or timing constraints not captured by Terraform’s native dependency tracking.
Appropriate Use Cases for depends_on
The value of depends_on becomes evident in several distinct infrastructure patterns. Each highlights a situation where implicit references are insufficient or unavailable, making explicit dependency declaration a necessity.
Controlling Order in Provisioning Scripts
Provisioners are often used to execute commands on a local machine or remote instance. These may include installation scripts, configuration tools, or custom logic. Because provisioners typically operate outside the scope of Terraform-managed attributes, they often lack direct references to other resources.
For instance, a script that configures DNS settings must not run until the DNS zones are created. If the resource does not reference the DNS zone directly, Terraform may schedule the script prematurely. By attaching a depends_on reference to the DNS zone resource, you ensure that the script runs only after it is safely created.
Managing Dependencies Between Modules
Terraform modules allow you to break configurations into reusable units. This modular approach enhances maintainability and reusability but can obscure the relationship between resources. One module may rely on outputs from another, but if the output is not explicitly used as an input, Terraform won’t establish a dependency.
Imagine a scenario where Module A deploys a virtual network and Module B deploys application resources that rely on that network. If Module B does not explicitly consume the network ID or similar attribute, Terraform won’t understand that Module A must run first. Using depends_on to specify Module A as a prerequisite for Module B ensures the correct provisioning order.
Ensuring Readiness of External Systems
There are cases where infrastructure components interface with systems outside of Terraform’s direct management. These systems could include third-party APIs, monitoring platforms, or security scanning tools. Terraform cannot evaluate the state or readiness of these systems unless you build it into the configuration.
In such circumstances, you may deploy a resource whose sole purpose is to trigger a webhook or notify an external process. The timing of this trigger becomes critical. With depends_on, you can ensure that all relevant resources are deployed and healthy before the trigger resource activates.
Coordinating State Across Distributed Infrastructure
Distributed architectures often span multiple regions, availability zones, or even cloud providers. Coordinating state between such environments requires careful sequencing. For example, you might want to deploy a global DNS configuration only after all regional application instances are running.
Even though these instances may not share attributes directly with the DNS configuration, they represent a logical prerequisite. By declaring them in a depends_on clause, you can control the timing and avoid premature or invalid updates to shared resources.
How depends_on Impacts Terraform Execution
When Terraform encounters a resource with a depends_on clause, it incorporates the declared dependencies into its execution graph. This graph determines the order in which resources are created or destroyed, and Terraform uses it to plan and apply changes.
Resources with unmet dependencies are deferred until all their prerequisites are satisfied. This affects not only creation but also updates and deletions. In destroy plans, Terraform ensures that the dependent resource is removed before tearing down the items it relies on.
This behavior is deterministic and consistent across executions. However, it introduces a degree of manual control that must be used judiciously. Overuse of depends_on can lead to rigid configurations that resist optimization. It can also cause delays if Terraform defers actions unnecessarily due to overly broad dependency declarations.
Best Practices for Using depends_on
To get the most out of depends_on, consider the following best practices:
- Use it only when necessary. Terraform’s implicit dependency tracking is powerful and efficient. Overriding it without good reason can lead to complexity.
- Keep the list of dependencies as narrow as possible. Avoid declaring entire modules or broad resource sets unless absolutely required.
- Document your reasons for using depends_on. Explain the rationale so future maintainers understand its purpose.
- Combine with other best practices, such as modularization and input/output variables, to make dependencies more explicit and manageable.
- Regularly review and refactor. As your configuration evolves, what once required a depends_on directive may become implicitly referenced and thus no longer needed.
Potential Pitfalls and Misunderstandings
Despite its utility, depends_on is sometimes misunderstood or misapplied. One common error is expecting it to enforce runtime readiness rather than configuration sequencing. For example, a resource might complete its creation in Terraform’s eyes but still require time to become fully operational. In such cases, depends_on alone is insufficient, and additional health checks or provisioners may be needed.
Another risk is circular dependencies. Declaring resources that depend on each other can lead Terraform into a logical deadlock. While Terraform typically detects and reports such issues, resolving them can require significant rework or a change in architecture.
Also, depends_on cannot resolve issues caused by resources that are slow or flaky. If a dependency fails or times out, Terraform will fail regardless of the declared sequencing. Therefore, depends_on should be used in combination with robust resource definitions and, where necessary, retry logic or external validation mechanisms.
Evaluating the Role of depends_on in Infrastructure Design
Using depends_on is not simply a technical detail; it reflects broader decisions about how infrastructure is structured and managed. When you introduce manual sequencing into your Terraform configurations, you are asserting control over execution that may reveal deeper architectural concerns.
Ask yourself why a dependency is not already apparent in the configuration. Are modules too loosely coupled? Are outputs not being passed clearly between components? Could better modular design eliminate the need for explicit ordering?
Answering these questions can lead to better, more maintainable Terraform configurations. It can also inform organizational practices, encouraging collaboration between teams that manage interdependent infrastructure components.
Terraform’s depends_on meta-argument is a powerful tool for managing resource dependencies that fall outside the scope of automatic detection. It allows developers and operators to enforce the correct order of operations in complex, modular, or side-effect-laden configurations. While it should be used with care, its proper application can bring much-needed control and reliability to infrastructure provisioning.
Understanding when and how to use depends_on is part of mastering Terraform at scale. It’s a reflection of the broader challenge in infrastructure management: balancing automation with precision. When used thoughtfully, depends_on becomes not just a technical necessity but a strategic asset in delivering robust, dependable infrastructure across environments and teams.
Revisiting the Need for Controlled Dependency Management
While Terraform’s implicit dependency graph provides a reliable backbone for most standard infrastructure deployments, modern infrastructure challenges have added layers of complexity. Cloud-native architectures often include modular designs, cross-cloud dependencies, external integrations, and service initialization workflows. In such environments, relying exclusively on implicit dependencies may lead to fragile configurations and unpredictable outcomes.
The depends_on meta-argument steps in to offer precision. By explicitly defining the order of execution, it serves as a bridge between Terraform’s declarative model and the procedural realities of real-world deployments. However, understanding the strategic use of depends_on requires more than basic familiarity. In this article, we dive into advanced use cases, examine edge scenarios, and highlight the implications of enforcing execution order in multi-dimensional infrastructure stacks.
Multi-Module Coordination and Execution Boundaries
Modern Terraform configurations are rarely monolithic. Instead, they consist of reusable modules—each encapsulating a set of resources. These modules may be maintained by different teams, sourced from external repositories, or reused across multiple projects. Though this modularity enhances consistency and scalability, it also introduces complexities in dependency management.
Consider a configuration where one module is responsible for provisioning a virtual network, while another sets up compute resources like virtual machines or containers. The compute module may logically depend on the network module but may not reference any of its outputs. This lack of direct reference results in Terraform treating both modules as independent, potentially initiating them in parallel.
In such cases, using depends_on at the module level ensures that the network module completes its provisioning before the compute module begins. This approach is critical when indirect dependencies exist that cannot be captured through variables or outputs. It guarantees correctness without requiring brittle workarounds.
Managing Cross-Provider Dependencies
Infrastructure in enterprise environments often spans multiple cloud providers. You may provision resources in AWS, configure services in Google Cloud, and link DNS entries in Azure. These cross-provider setups often require precise coordination.
Imagine deploying an S3 bucket in AWS and configuring a CDN in another provider that serves files from the bucket. Though Terraform manages resources across both providers, it lacks innate awareness of the operational readiness of the S3 bucket for consumption. Without depends_on, it may attempt to configure the CDN before the bucket exists.
By defining a depends_on relationship between the CDN resource and the S3 bucket, you ensure that the infrastructure setup aligns with the required operational sequence. This manual intervention can be the difference between a failed configuration and a smooth deployment across platforms.
Orchestrating Application Layer Readiness
Provisioning cloud infrastructure is only part of the story. Applications often have startup requirements or configuration scripts that must run in a specific order. While Terraform does not natively manage application logic, it frequently invokes shell scripts, initialization procedures, or API calls using provisioners.
These tasks, though not infrastructure resources, have real dependencies. For instance, a configuration script that populates a database must not run before the database is initialized. Similarly, an external monitoring service must not receive alerts until the application stack is fully running.
Provisioners can be attached to null resources or other placeholders, and depends_on can ensure they are only executed after prerequisite infrastructure is in place. This technique is commonly used in orchestration pipelines, where Terraform coordinates provisioning with configuration management tools.
Sequencing Resource Destruction
Terraform does not only create infrastructure; it also handles updates and deletions. In some situations, the destruction order of resources is just as important as the creation order. Consider scenarios where sensitive data must be securely deleted or access to a system must be revoked before the system itself is destroyed.
Using depends_on, you can ensure that deletion occurs in a specific sequence. For example, access control policies can be removed before the associated virtual machines are destroyed. Similarly, monitoring and alerting rules can be disabled before tearing down production services to prevent false alarms.
This approach is especially important in compliance-sensitive environments, where data handling and system decommissioning must follow strict protocols.
Synchronizing With External Systems
Terraform often operates within larger systems. It may be triggered by CI/CD pipelines, configuration management platforms, or integrated with service discovery tools. These integrations sometimes involve external systems that have their own lifecycle and readiness criteria.
For instance, a resource may depend on a manual approval process completed in an external ticketing system. Or a load balancer may need to wait until external DNS entries are propagated. While Terraform cannot enforce such out-of-band dependencies directly, depends_on can be used creatively.
By creating placeholder resources (such as null resources with external checks) and tying them into the dependency graph, you can delay execution until certain external criteria are met. This is not a perfect solution, but it introduces a degree of control over timing and sequencing that can help align Terraform execution with external processes.
Avoiding Circular Dependencies
While depends_on is powerful, it must be used carefully to avoid introducing circular dependencies. Terraform requires the dependency graph to be acyclic, meaning that no resource can depend on itself—either directly or indirectly. If a cycle is introduced, Terraform will raise an error and halt the execution plan.
To avoid such pitfalls, carefully design modules and resources to separate concerns. Use outputs and variables to pass only necessary data, and apply depends_on only where sequencing is truly required. Avoid making resources mutually dependent unless they are split into distinct phases or managed independently.
Performance Considerations and Parallelism
Introducing unnecessary depends_on declarations can degrade performance. Terraform excels in parallelizing resource creation when dependencies are clear and minimal. Adding artificial sequencing where none is needed serializes execution and slows down the deployment process.
For large configurations with hundreds or thousands of resources, the cumulative effect can be significant. Each dependency reduces Terraform’s ability to operate concurrently. Therefore, only use depends_on when the natural dependency graph is insufficient, and avoid blanket application across unrelated resources.
Documenting Intent and Future-Proofing
When using depends_on, always document the reason behind the dependency. This ensures that future maintainers understand its purpose and do not remove it without considering the consequences. Over time, as infrastructure evolves, some dependencies may become obsolete or replaced by explicit references.
Clear documentation can help teams refactor confidently, knowing when and where to remove manual constraints. It also supports onboarding, audits, and reviews, providing clarity on how the configuration operates beyond what Terraform infers.
Strategic Use in DevOps Workflows
DevOps practices emphasize automation, reliability, and continuous delivery. In such environments, Terraform is often part of a larger pipeline that includes testing, staging, and promotion. The use of depends_on aligns well with these practices, as it allows fine-grained control over sequencing.
By enforcing order during infrastructure changes, teams can reduce the risk of deploying partial environments or misconfigured services. This control is especially important when different pipeline stages rely on artifacts created in earlier phases. For instance, an infrastructure test suite may require specific environments to be fully provisioned, which can be enforced with depends_on.
The depends_on meta-argument is more than a tool for patching edge cases. When used thoughtfully, it becomes a strategic mechanism for managing complexity, aligning infrastructure with business processes, and ensuring the reliability of deployments in dynamic, distributed environments.
Understanding its advanced use cases allows you to unlock new levels of control and confidence in your Terraform configurations. As infrastructure scales and organizations adopt multi-cloud, microservices, and hybrid architectures, the ability to orchestrate execution order precisely becomes a key differentiator in infrastructure maturity.
In the next segment, we will explore patterns for combining depends_on with data sources, provisioners, and dynamic blocks to build highly flexible and resilient configurations that adapt to evolving needs.
Embracing Declarative Precision
Terraform’s depends_on meta-argument is a critical feature that brings intentional precision to the otherwise inferred execution graph. In the earlier segments, we explored why and when it’s essential, and how it can bridge implicit logic gaps in real-world deployments. Now, we extend the conversation toward architectural strategies, composable patterns, maintainability, and forward-thinking design, ensuring that the use of depends_on not only solves immediate needs but aligns with scalable, long-term infrastructure goals.
Integrating depends_on with Data Sources
Terraform data sources retrieve information from existing infrastructure or remote services for use in configuration files. However, data sources themselves are not resources and are typically fetched during the plan phase. This creates a complexity: Terraform assumes that data sources are always ready to be queried, which might not be true when dependent resources haven’t yet been provisioned.
In such situations, using depends_on indirectly becomes essential. While you cannot attach depends_on directly to a data source, you can wrap the data source usage in a null resource or dependent module. This null resource or placeholder can then carry the depends_on declaration, enforcing correct execution order indirectly.
This technique is particularly useful when querying APIs or remote infrastructure that isn’t instantly available upon provisioning. Ensuring that Terraform queries are timed after critical resources are live avoids plan-time failures and adds stability to provisioning flows.
Building Dynamic Resource Graphs with depends_on
Terraform supports dynamic blocks that generate multiple nested blocks programmatically. While these are excellent for reducing boilerplate, they can complicate the dependency graph. Because the exact resources being generated may vary based on conditions or loops, Terraform may not fully understand the sequence in which to provision them.
Using depends_on at the parent level of these dynamic blocks ensures consistent behavior. For instance, if you are generating multiple access policies or firewall rules based on input variables, and they must follow the deployment of a core service like a database or network component, depends_on acts as a synchronization point.
This approach guards against partial or out-of-order execution and simplifies error tracing by aligning conditional logic with enforced sequencing.
Coordinating Provisioners with Resource Readiness
Provisioners are frequently criticized within the Terraform community for being difficult to manage and troubleshoot. However, when used sparingly and thoughtfully, they can complement infrastructure provisioning by performing post-deployment tasks such as:
- Copying configuration files
- Executing shell scripts
- Sending webhook notifications
- Registering systems in CMDBs
Because provisioners often interact with newly created infrastructure, they are prone to race conditions. A script may attempt to connect to a virtual machine before SSH is ready, or update a service that hasn’t completed installation. Here, depends_on becomes a guardrail.
Attaching provisioners to resources with well-defined depends_on chains ensures they only run when all prerequisites are met. This not only increases the likelihood of success but also improves debuggability and testability.
Combining depends_on with Remote State
Remote state is a powerful Terraform feature that allows different configurations or workspaces to share data through outputs. This capability supports modularization, team collaboration, and infrastructure segmentation.
However, remote state is also asynchronous. Outputs from one state may not be immediately available to another unless the upstream configuration has been applied. If your configuration consumes remote outputs without guaranteeing they are ready, it may break unpredictably.
You can build synchronization bridges using depends_on and placeholder resources that represent the remote state consumer. Though Terraform does not let you declare a direct dependency on a remote state file, wrapping outputs in a module and attaching a depends_on clause to the consuming component ensures that all linked infrastructure has been provisioned upstream.
This is particularly useful in CI/CD workflows where multiple stages of the pipeline execute separate Terraform configurations that rely on shared context.
Encapsulating Dependency Logic Inside Modules
To keep root configurations clean and maintainable, it is often best to push depends_on logic down into modules. This encapsulation allows each module to manage its own sequencing, ensuring consistent behavior regardless of where or how it is instantiated.
For example, a Kubernetes module might require that a VPC and subnet are created before any worker nodes are deployed. Instead of requiring the parent configuration to manage this order, the module can include internal depends_on logic on the worker node resources.
Encapsulation promotes reuse and reduces the cognitive overhead for teams assembling infrastructure from building blocks. It also supports automated testing by ensuring each module is self-sufficient and aware of its own prerequisites.
Auditing and Validating depends_on Usage
As configurations grow, so too does the risk of misusing depends_on. It’s easy to accumulate dependencies that are no longer relevant, overly broad, or create performance bottlenecks. Periodic auditing ensures that your usage of depends_on remains justified, lean, and effective.
Begin by tagging resources that use depends_on with comments explaining their purpose. Over time, validate whether the dependency still exists or if it can be expressed more naturally through attribute references. Where possible, rewrite configurations to reduce manual sequencing in favor of Terraform’s default logic.
In highly regulated environments, dependency documentation may be required for compliance. Keeping a dedicated section in your documentation for intentional dependency chains ensures transparency and accountability.
Terraform Upgrades and Compatibility
Terraform is an evolving tool, and changes to its core behavior can affect how depends_on operates. Always test your configurations when upgrading versions. What worked in one release might introduce subtle regressions or require revised logic in another.
Modules that rely heavily on depends_on should include version compatibility declarations and test scenarios. This helps identify breakage early and supports continuous integration pipelines.
Training Teams to Use depends_on Effectively
In large teams, infrastructure management is a shared responsibility. Inconsistent or overzealous usage of depends_on can undermine the benefits it offers. To ensure consistent practices:
- Create internal guidelines for when and how to use depends_on
- Encourage encapsulation of logic in modules
- Establish review processes for dependency chains
- Offer training sessions on Terraform execution behavior and graph logic
By fostering a culture of thoughtful usage, teams can avoid pitfalls and maximize reliability.
depends_on and Future Infrastructure Trends
As infrastructure becomes more abstracted through Kubernetes, serverless architectures, and platform engineering, the nature of dependencies will evolve. Terraform must increasingly coordinate ephemeral, auto-scaling, and externally managed services that may not have stable resource identifiers.
In these contexts, depends_on continues to offer value by acting as an intent-based ordering mechanism. Rather than encoding low-level references, it can express higher-order logic like “this needs to happen after that,” regardless of the underlying abstraction.
This adaptability ensures that depends_on will remain relevant in an infrastructure landscape that prioritizes orchestration, observability, and service mesh integration.
Conclusion
The depends_on meta-argument in Terraform is more than a feature—it is a design principle. It gives infrastructure engineers the ability to express relationships, enforce order, and navigate complexity without sacrificing the declarative elegance of Terraform.
When applied with care, depends_on becomes a catalyst for creating predictable, resilient, and scalable infrastructure. By understanding its mechanics, integrating it into modules, aligning it with workflows, and auditing its usage, teams can wield it as a strategic tool rather than a last resort.
As the boundaries of infrastructure continue to expand—from physical machines to serverless platforms and everything in between—depends_on will serve as a fundamental building block for orchestrating the systems of tomorrow.