Terraform No-Code, Part 1: The Module Design Problem

Here’s the pitch: Terraform No-Code lets non-technical users deploy infrastructure through a web form. No HCL, no terminal, no state file management. Your platform team publishes modules to a catalog, users fill out forms, infrastructure appears.

It sounds straightforward. It isn’t.

The challenge isn’t the feature itself—HCP Terraform’s No-Code provisioning works as advertised. The challenge is designing modules that work well when users can’t see the code, can’t debug failures, and can’t extend functionality beyond what you’ve anticipated. You’re not just writing infrastructure code anymore. You’re designing a product interface that happens to provision cloud resources.

This series is about that design problem: how to think about modules when your users never touch HCL, what breaks in production, and how No-Code fits (or doesn’t) into a broader platform strategy.

The Core Problem: Abstraction Without Visibility

Traditional Terraform modules are consumed by engineers who can read the source. When something breaks, they can dive into main.tf, trace the resource graph, and understand what went wrong. They can fork the module, add a missing parameter, or work around limitations.

No-Code users have none of this. They see a form, they see outputs, and they see error messages that reference resources they didn’t create and code they didn’t write. When a deployment fails with Error: creating RDS Cluster: DBClusterAlreadyExistsFault, they don’t know if that’s a naming collision, a permission issue, or a problem with the module itself.

This changes everything about how you design modules.

You’re no longer optimizing for flexibility and composability—the hallmarks of good traditional modules. You’re optimizing for constrained success: making it hard for users to create configurations that fail, and making failures interpretable when they occur.

This is a fundamentally different design philosophy, and it’s where most No-Code implementations struggle.

The Input Abstraction Problem

Every No-Code module faces a tension: expose too many inputs and users get overwhelmed; expose too few and the module can’t cover legitimate use cases.

Consider a “Team Aurora Cluster” module. An aws_rds_cluster configuration has dozens of parameters: instance class, instance count, engine version, cluster parameter groups, DB parameter groups, serverless scaling configuration, backup retention, deletion protection, VPC placement, security groups, IAM authentication, KMS encryption, Performance Insights…

Which of these should users control?

The naive approach: expose everything with good defaults. This produces forms with 30+ fields where users scroll past configurations they don’t understand to find the one setting they care about. Worse, it creates a combinatorial explosion of configurations you need to test and support.

The opposite extreme: hardcode everything except the cluster name. This works until someone needs more reader instances or a different engine version, and now you’re maintaining multiple near-identical modules.

The solution is tiered abstraction. Group inputs by user expertise and use case:

# Primary inputs - every user needs these
variable "cluster_name" {
  type        = string
  description = "Name for your Aurora cluster (lowercase letters, numbers, hyphens only)"
  validation {
    condition     = can(regex("^[a-z][a-z0-9]*(-[a-z0-9]+)*$", var.cluster_name)) && length(var.cluster_name) >= 3 && length(var.cluster_name) <= 40
    error_message = "Cluster name must start with a letter, end with a letter or number, and contain only lowercase letters, numbers, and single hyphens. 3-40 characters — the limit ensures the final identifier (team-env-name) stays within the 63-character RDS maximum."
  }
}

variable "size" {
  type        = string
  description = "Instance size: small (db.r6g.large, 2 vCPU), medium (db.r6g.xlarge, 4 vCPU), or large (db.r6g.2xlarge, 8 vCPU)"
  default     = "small"
  validation {
    condition     = contains(["small", "medium", "large"], var.size)
    error_message = "Size must be small, medium, or large."
  }
}

variable "environment" {
  type        = string
  description = "Target environment"
  validation {
    condition     = contains(["dev", "staging", "prod"], var.environment)
    error_message = "Environment must be dev, staging, or prod."
  }
}

# Secondary inputs - power users who know what they need
variable "engine_version" {
  type        = string
  description = "Aurora PostgreSQL version"
  default     = "15.4"
  validation {
    condition     = contains(["14.9", "15.4", "16.1"], var.engine_version)
    error_message = "Org-approved Aurora PostgreSQL versions: 14.9, 15.4, 16.1"
  }
}

variable "instance_count" {
  type        = number
  description = "Number of instances in the cluster (1 for dev, 2+ for production failover)"
  default     = 2
  validation {
    condition     = var.instance_count >= 1 && var.instance_count <= 5
    error_message = "Instance count must be between 1 and 5."
  }
}

The key insight: your size variable abstracts away instance classes. Users don’t need to know that “medium” maps to db.r6g.xlarge. You’ve reduced cognitive load while preserving the flexibility that matters.

But this abstraction has costs. When AWS releases a new Graviton generation with better price/performance, you need to update the module and decide whether to migrate existing clusters. When users hit performance issues, they’ll ask “can I go bigger than large?” and you’ll need to either add an “xlarge” tier or explain why not.

Constraint Design: Less is More (Until It Isn’t)

The most successful No-Code modules I’ve seen are aggressively constrained. They solve one use case well rather than attempting to handle every scenario.

Compare these two approaches for an Aurora cluster module:

Approach B is less flexible but dramatically more reliable. There’s one configuration to test, one configuration to document, and one configuration to support. Users can’t accidentally deploy a single-instance production cluster because a precondition on the resource rejects it at plan time.

The trade-off: when someone needs Aurora MySQL for a legacy application, they can’t use this module. You need a strategy for handling these cases:

1. Create specialized modules — A separate “Team Aurora MySQL” module with its own parameter group defaults
2. Escape hatch to traditional Terraform — Document how users can provision custom infrastructure through your standard IaC workflow
3. Accept the limitation — Some use cases just aren’t served by No-Code, and that’s okay

The flexibility trap is real. Every optional feature you add is a feature you need to test in combination with every other optional feature, document clearly, and support when users misconfigure it. Flexibility creates support burden exponentially, not linearly.

The Aurora Module That Tried to Please Everyone

This is a common pattern. A platform team builds a No-Code Aurora module with a sensible initial design: users choose a cluster name, environment, and size. The module handles everything else—VPC placement, subnet groups, cluster parameter groups, backup configuration, encryption.

Then the requests start:

• “Can we add MySQL support? We only support Aurora PostgreSQL right now.”
• “Our team needs more reader instances for reporting queries.”
• “We’re running an analytics workload—can we get a larger instance class?”
• “Security wants us to use a customer-managed KMS key for encryption.”
• “Can we turn on Performance Insights for debugging slow queries?”

Each request was reasonable. The platform team, wanting to be helpful, added optional variables for each feature. Within six months, the module had 23 input variables, complex conditional logic for feature combinations, and a form that scrolled for three screens.

Then the problems emerged:

The module had become unmaintainable. Every new feature created new failure modes. Testing was a nightmare—there were thousands of possible variable combinations, and the team couldn’t test them all.

The redesign took a different approach: multiple opinionated modules instead of one flexible module.

• team-aurora-standard — Aurora PostgreSQL, fixed instance sizing tiers, 2-instance clusters in prod, AWS-managed encryption
• team-aurora-analytics — Larger instances, 3+ reader instances required, Performance Insights included
• team-aurora-regulated — Same as standard but with customer-managed KMS and pgaudit enabled in the parameter group, PostgreSQL logs exported to CloudWatch

Each module was tightly constrained. The “analytics” module required at least 3 instances—you couldn’t deploy without them. The “regulated” module required a KMS key ARN—no default. This eliminated the dangerous combinations entirely.

User adoption increased. Support tickets decreased. The platform team could actually maintain the code.

Naming and Tagging: Invisible Governance

No-Code modules are a governance mechanism disguised as a convenience feature. One of the most powerful governance controls is baking naming conventions and tagging strategies directly into the module:

locals {
  # Users never see this - it's derived from their inputs
  cluster_identifier = "${var.team}-${var.environment}-${var.cluster_name}"

  # Map user-friendly sizes to Aurora instance classes
  size_map = {
    small  = "db.r6g.large"
    medium = "db.r6g.xlarge"
    large  = "db.r6g.2xlarge"
  }

  instance_count = var.instance_count

  standard_tags = {
    Environment   = var.environment
    Team          = var.team
    Application   = var.cluster_name
    ManagedBy     = "terraform-no-code"
    ModuleVersion = "2.1.0"
    CostCenter    = var.cost_center
  }
}

resource "aws_rds_cluster" "this" {
  cluster_identifier          = local.cluster_identifier
  engine                      = "aurora-postgresql"
  engine_version              = var.engine_version
  master_username             = "app_admin"
  manage_master_user_password = true
  deletion_protection         = var.environment == "prod"
  db_subnet_group_name        = aws_db_subnet_group.this.name
  vpc_security_group_ids      = [aws_security_group.aurora.id]
  tags                        = local.standard_tags
}

resource "aws_rds_cluster_instance" "this" {
  count              = local.instance_count
  identifier         = "${local.cluster_identifier}-${count.index}"
  cluster_identifier = aws_rds_cluster.this.id
  instance_class     = local.size_map[var.size]
  engine             = aws_rds_cluster.this.engine
  engine_version     = aws_rds_cluster.this.engine_version

  performance_insights_enabled = true

  tags = local.standard_tags

  lifecycle {
    precondition {
      condition     = var.environment != "prod" || var.instance_count >= 2
      error_message = "Production clusters require at least 2 instances for automatic failover. Use 1 instance only in dev or staging."
    }
  }
}

Users never choose cluster identifiers directly—they provide a logical name, and the module constructs the physical identifier according to your conventions. This eliminates an entire class of support requests (“why is my cluster named asdf123?”) and ensures consistent naming for automation, cost allocation, and security scanning.

The ManagedBy tag is particularly useful. It lets you identify No-Code-provisioned resources in your environment, which matters for auditing, cost analysis, and understanding your infrastructure landscape.

One operational note: manage_master_user_password = true delegates credential management to AWS Secrets Manager. This is the right default for No-Code—users shouldn’t be setting database passwords through a form—but your application teams need to know credentials live in Secrets Manager, and you’ll want to configure rotation to match your security requirements.

Error Messages: Your Most Important Documentation

When No-Code deployments fail, users can’t debug them. The error message is your only chance to help.

Vague validation messages are a common pitfall:

Error: Invalid value for variable

  on main.tf line 8:
   8: variable "size" {

Expected one of: small, medium, large.

For No-Code users, write validation messages that explain why and what to do:

variable "size" {
  type        = string
  description = "Cluster instance size"
  default     = "small"
  validation {
    condition     = contains(["small", "medium", "large"], var.size)
    error_message = "Choose small (dev/testing, 2 vCPU), medium (standard production, 4 vCPU), or large (high-traffic production, 8 vCPU). Contact #platform-help if you need a size not listed here."
  }
}

For errors that can’t be caught by validation—cloud API errors, permission issues, quota limits—you need runbooks. We’ll cover this in Part 2.

What’s Next

In Part 2, we’ll cover the failure modes you’ll encounter in production: state drift, version migration nightmares, orphaned resources, and the debugging gap. We’ll also explore HCP Terraform-specific quirks and operational considerations like monitoring and runbook design.

Built No-Code modules and have thoughts on design trade-offs? I’d like to hear about them.