Terraform has dominated infrastructure as code for years. Pulumi offers an alternative: write infrastructure in real programming languages instead of HCL. Both work. The question is which fits your team better.
The Core Difference
Terraform uses HCL (HashiCorp Configuration Language), a declarative DSL designed specifically for infrastructure. You describe what you want—a VPC with these subnets, an EC2 instance with these specs, a database with this configuration—and Terraform figures out how to get from the current state to the desired state. The language is intentionally limited; it’s not a general-purpose programming language, and that’s by design.
Pulumi takes a fundamentally different approach: write infrastructure in TypeScript, Python, Go, C#, or Java. You use the same languages, the same IDEs, the same testing frameworks, and the same patterns as your application code. Infrastructure definitions look like regular code because they are regular code.
This difference sounds simple—configuration language versus programming language—but it has significant implications for how teams work, what kinds of infrastructure they can easily express, and who can effectively contribute to infrastructure code.
Where Terraform Wins
Lower barrier to entry. HCL is limited by design, and that limitation is actually valuable for accessibility. Someone new to infrastructure can read and modify Terraform configurations without knowing a full programming language. The syntax is declarative and relatively flat—resources are defined, properties are set, dependencies are inferred. The constraints reduce the ways things can go wrong; you can’t write arbitrarily complex logic that becomes difficult to understand or debug. For teams where infrastructure is managed by people who aren’t primarily software developers, this matters.
Ecosystem maturity. Terraform has been the dominant infrastructure as code tool for years, and that head start translates into ecosystem advantages that are hard to replicate. More providers covering more services, more pre-built modules solving common patterns, more Stack Overflow answers addressing specific problems. When you hit an issue with Terraform and a particular cloud service, someone else has almost certainly encountered it before and documented the solution. This ecosystem depth accelerates development in ways that aren’t obvious until you need something niche.
Separation of concerns. Infrastructure code looks different from application code when written in HCL, and this visual distinction can be an advantage. It’s immediately clear what’s infrastructure configuration and what’s application logic. Different people with different skill sets can own different parts without needing overlapping capabilities. Operations engineers can work on Terraform while developers work on application code, with the boundary clearly marked by the different languages and file types.
State management is battle-tested. Terraform’s state handling has rough edges that anyone who’s used it extensively has encountered. But the problems are well-documented, the solutions are established, and the failure modes are understood. Remote state backends, locking mechanisms, workspaces for environment separation—these patterns have been refined over years of production use across thousands of organizations. The approach isn’t perfect, but it’s mature.
Organizational adoption. Many organizations have already standardized on Terraform. Their teams know it, their CI/CD pipelines are built around it, their processes assume it. Fighting that standardization to use Pulumi creates friction—political and practical—even if Pulumi would be technically better for a specific project. The cost of being different often exceeds the benefit of being “right.”
Where Pulumi Wins
Real programming language capabilities. Loops, conditionals, functions, classes, inheritance, composition—you get the full power of whatever language you choose. What takes awkward HCL workarounds or repetitive copy-paste becomes natural code in Python or TypeScript. Need to create 50 similar resources with slight variations? Write a loop. Need to abstract a common pattern into a reusable component? Write a function or class. Need complex conditional logic to handle different environments? Write the conditional the same way you would in any other code.
Testing. You can write real unit tests for infrastructure code using the same testing frameworks you use for application code: pytest for Python, Jest for TypeScript, go test for Go. Mock the cloud provider, verify that your infrastructure code produces the expected resources with the expected configurations, catch bugs before they reach deployment. Terraform testing options exist, but they feel bolted on rather than native to the development experience.
IDE support. Autocompletion that knows what properties a resource accepts. Type checking that catches errors before you run anything. Refactoring tools that let you rename variables or extract functions with confidence. Jump-to-definition to understand what a function does. Your normal development environment—VSCode, IntelliJ, whatever you use—works fully because you’re writing real code in a real language. HCL support in IDEs is limited by comparison, with fewer features and less sophisticated tooling.
Sharing code with applications. If your infrastructure and application share concepts—the same enum defining regions, the same type defining configuration structures, the same constants for resource naming conventions—Pulumi lets you share actual code instead of duplicating concepts across languages and hoping they stay in sync. When the infrastructure definition and the application code are in the same language, integration becomes seamless.
Complex dynamic infrastructure. When infrastructure needs to respond to runtime information, conditionally create resources based on complex business logic, or generate configurations from external data sources, real programming languages handle this naturally. Building infrastructure that varies significantly across environments, that provisions different resources based on feature flags, or that dynamically scales based on configuration files becomes straightforward code rather than HCL gymnastics.
The Learning Curve Question
Terraform advocates often claim HCL is easier to learn, and this is partially true. HCL has less surface area than a general-purpose programming language—there’s less syntax to learn, fewer concepts to understand, fewer ways to structure code. Someone who has never programmed can learn enough HCL to read and modify Terraform configurations more quickly than they could learn Python.
But this framing cuts both ways. Your developers probably already know Python or TypeScript or Go. For them, learning Pulumi means applying existing knowledge to infrastructure rather than learning a new language from scratch. They already know how to write loops, functions, and tests. They already know how to navigate their IDE. They already know how to structure code for maintainability. Pulumi leverages all of that existing knowledge.
For teams with strong programming backgrounds—which increasingly includes infrastructure teams as the field matures—Pulumi’s learning curve may actually be gentler. The concepts are familiar; only the domain is new. For teams where infrastructure is managed by people who aren’t primarily developers, who came from operations backgrounds rather than software engineering, HCL’s constraints may be helpful precisely because they limit complexity.
State and Backend Differences
Both tools track state to understand what infrastructure currently exists and what changes need to be made to reach the desired configuration. This state is essential to how infrastructure as code works—without it, the tool wouldn’t know whether to create, update, or delete resources.
Terraform stores state in files, either locally on disk or remotely in backends like S3, Azure Blob Storage, or Terraform Cloud. You configure the backend, manage locking to prevent concurrent modifications, and handle state operations like imports and moves yourself. It works, and the patterns are well-established, but state file management is a common source of problems. Corrupted state, state drift, concurrent modification issues, and the need to manually manipulate state when resources are moved or imported are all pain points that experienced Terraform users know well.
Pulumi can use similar file-based backends for state storage, giving you the same level of control (and the same operational burden). But it also offers Pulumi Cloud, a managed service that handles state storage, locking, secrets management, and history automatically. The managed option simplifies operations significantly—no need to configure backends, manage locks, or worry about state file corruption—at the cost of introducing another service dependency and, depending on your usage, another cost.
Migration Realities
If you’re considering moving from Terraform to Pulumi—or evaluating whether that move makes sense—understand what the migration actually involves.
Pulumi can import existing Terraform state. You don’t have to recreate your infrastructure from scratch or risk a destructive re-provisioning. Pulumi provides tools to read Terraform state and import existing resources into Pulumi management. But this conversion isn’t magic—it handles the state, but you still need to rewrite your infrastructure definitions in your target language. The state import saves you from destroying and recreating resources; it doesn’t save you from rewriting code.
Coexistence is possible. Pulumi can reference Terraform state, letting you migrate incrementally. One project can move to Pulumi while others remain in Terraform, with Pulumi reading outputs from Terraform state when cross-project references are needed. Not everything needs to move at once, and for large organizations with substantial Terraform investments, gradual migration over time is more practical than a big-bang conversion.
The work is mostly rewriting configurations. If you have extensive Terraform modules—reusable components that encapsulate common patterns—rewriting them in Python or TypeScript takes time regardless of how good the tooling is. Each module needs to be understood, translated, and tested. Estimate this work honestly before committing to migration; the effort scales with your existing Terraform footprint.
When to Choose Terraform
Terraform is likely the better choice when:
- Your team isn’t primarily developers and benefits from HCL’s constraints. Operations engineers, system administrators, and infrastructure specialists who come from non-programming backgrounds often find HCL’s limited but approachable syntax easier to work with than a full programming language.
- You’re joining an organization that has standardized on Terraform. Existing Terraform investments—modules, pipelines, team expertise, documentation—have real value. Introducing a different tool creates friction and fragmentation.
- You need providers or modules that don’t have Pulumi equivalents. While Pulumi covers major cloud providers well, niche providers or specialized modules may only exist in the Terraform ecosystem.
- Your infrastructure is straightforward and doesn’t need programming language features. Standard cloud resources, basic networking, typical deployments—these are well-served by HCL without the overhead of a full programming language.
- You want the largest community and most established patterns. Terraform’s longer history means more solved problems, more examples, and more people who can help when you’re stuck.
When to Choose Pulumi
Pulumi is likely the better choice when:
- Your team has strong programming backgrounds and will be more productive in familiar languages. If everyone already knows TypeScript or Python, Pulumi lets them apply that knowledge immediately instead of learning HCL.
- You need complex logic, loops, or abstraction that HCL handles awkwardly. Dynamic infrastructure generation, complex conditional logic, and reusable abstractions are natural in programming languages and awkward in HCL.
- Testing infrastructure code with real unit tests matters to you. If infrastructure reliability is critical enough that you want comprehensive test coverage, Pulumi’s integration with standard testing frameworks makes this practical.
- You’re starting fresh without existing Terraform investment. With no migration burden and no existing patterns to preserve, you can choose purely based on which tool fits your team better.
- You want to share code between infrastructure and applications. When infrastructure definitions and application code benefit from shared types, constants, or logic, having both in the same language eliminates duplication and synchronization problems.
What We’ve Seen
In practice, most infrastructure doesn’t need Pulumi’s programming language power. Standard cloud resources—compute instances, databases, storage buckets—networking configurations, IAM policies, and typical deployment pipelines are all perfectly well-served by HCL. Terraform’s ecosystem makes these common patterns easier because someone has probably already written a module that does exactly what you need.
Pulumi shines in specific situations that genuinely benefit from programming language capabilities. Generating infrastructure dynamically from application configurations—where the infrastructure definition reads from the same config files the application uses—is natural in Pulumi and awkward in Terraform. Complex multi-environment setups with significant variation between environments, where the differences aren’t just variable values but actual structural differences, benefit from the expressiveness of a real programming language. Organizations where the same engineers write application code and infrastructure code, and where that code benefits from sharing types and logic, find Pulumi reduces duplication and integration friction.
Neither choice is wrong. Both tools can manage production infrastructure reliably. Both have organizations running critical workloads on them. The question isn’t which tool is objectively better—it’s which tool fits your team, your skills, and what you’re actually building. Pick based on those real factors, not on which tool is newer or more discussed in conference talks.