Best AI for Platform Engineers

Platform engineering sits at an interesting intersection: you're writing a lot of code, but the code you're writing is infrastructure configuration rather than application logic. Terraform modules, Pulumi stacks, Kubernetes manifests, Helm charts, CI/CD pipeline definitions. The syntax is different for each, the providers update constantly, and mistakes have a different risk profile than application bugs because they affect infrastructure that everything else depends on.

On top of the code work, platform engineers carry a documentation burden that's larger than most teams realize. Runbooks, on-call guides, architecture decision records, system documentation for services that other engineers need to understand and integrate with. This stuff is critical for organizational resilience and perpetually under-resourced.

AI tools have become useful in both areas, but they're useful in different ways. This guide covers which tools actually help and how to use them effectively.

How I evaluated these tools

Platform engineering has specific quality bars that generic coding tools don't always meet.

IaC accuracy: Does it generate syntactically correct Terraform and Pulumi code that reflects current provider versions? Does it understand resource dependencies and configuration patterns?

Security awareness: Does it write IaC with reasonable security defaults, or does it generate permissive configurations that need significant remediation?

System reasoning: Can it understand infrastructure architecture at a system level, not just individual resource syntax?

Documentation quality: Can it write runbooks and architecture docs that are actually useful, with clear steps and appropriate context?

1. Claude Code

Claude Code is the AI agent that platform engineers get the most consistent value from because it works in your actual codebase with filesystem access. It can read your existing Terraform modules, understand the patterns you've established, and generate new configurations that fit your conventions rather than producing generic boilerplate.

The Terraform work is where Claude Code's value is most immediate. Give it a task like "create a Terraform module for an EKS node group that inherits the tagging and security group patterns from our existing VPC module," and it reads your existing module, understands the patterns, and generates something consistent with your codebase. That's a different outcome than asking a context-free AI tool for an EKS node group configuration. The output is a first draft that requires review, but it's a first draft that fits your infrastructure rather than a generic example from the AWS documentation.

IAM policy generation is one of the highest-value tasks for platform engineers using Claude Code. IAM policies are tedious to write correctly, require specific JSON syntax, and it's easy to write policies that are either too permissive or too restrictive. Claude Code handles the structure well and understands least-privilege principles. The output still needs review, particularly for policies with broad resource scope, but it's a meaningful starting point.

For Pulumi, Claude Code handles both TypeScript and Python SDK code. It understands Pulumi's resource model and output/input handling, which is not trivial, and it generates code that follows Pulumi's patterns correctly. If your team is migrating from Terraform to Pulumi or evaluating the change, Claude Code can help translate existing Terraform configurations and explain the structural differences.

Multi-file reasoning is where Claude Code separates itself from simpler AI tools. When a platform change touches your main Terraform module, your CI/CD pipeline definition, your documentation, and your team's runbook, Claude Code can reason about all of those files together and help you make consistent changes across them. That's the kind of task that's awkward with a browser-based AI tool but natural for an agent with codebase access.

Best for: Platform engineers who write Terraform, Pulumi, or Kubernetes configuration regularly and want an AI agent that understands their existing infrastructure patterns. Pricing: Claude Pro at $20/month; API usage billed by token.

2. Cursor

Cursor is the IDE-first alternative for platform engineers who prefer visual editor environments. If you do your infrastructure work in VS Code, switching to Cursor is low-friction and the AI integration starts paying off immediately.

The autocomplete is particularly useful for IaC work because Terraform and Kubernetes YAML have significant repetition. After you've written one resource block, Cursor picks up the pattern and autocompletes similar blocks. Setting up ten S3 buckets with consistent configurations? Setting up multiple similar IAM roles? Cursor reduces the typing significantly once it's seen the first few instances of a pattern.

Cursor's workspace-aware chat is useful when you're debugging. Ask it why a specific resource is failing to create, reference the resource configuration and the error message, and get a focused response. The IDE integration means you don't have to copy-paste between your editor and a browser tab.

For teams that have invested heavily in VS Code extensions for Terraform validation, HashiCorp Language Server, or Kubernetes YAML linting, Cursor inherits all of those extensions, so you keep the tooling you have and gain the AI layer on top.

The tradeoff versus Claude Code is that Cursor's value is more tightly tied to being in the IDE. Claude Code is more flexible for terminal-based workflows and multi-file operations that span the codebase.

Best for: Platform engineers who prefer IDE-based development and want AI autocomplete and chat integrated into their VS Code workflow. Pricing: Free tier available; Pro plan at $20/month.

3. Claude (claude.ai)

Claude's app handles the parts of platform engineering that live outside the codebase: documentation, architecture reasoning, runbook writing, and incident post-mortem drafts.

Runbook writing is the use case that saves the most time per hour of effort invested. A runbook needs to be clear enough that an engineer who has never touched the system can follow it at 3am under pressure. That's a specific writing challenge: precise steps, explicit commands, clear decision branches, useful context about why each step matters. Claude handles this well when you give it the system context. Describe the service, the failure modes you care about, and the remediation steps you'd take, and it structures a runbook you can actually use. The draft will need your review and editing, but it's a real starting point rather than an empty template.

Architecture decision records (ADRs) are similar. When you're documenting the choice to use one load balancer over another, or the decision to build an internal Kubernetes operator rather than use an existing one, the ADR needs to capture the context, the options considered, and the reasoning. Claude helps you structure that documentation clearly and captures the reasoning in a form that's useful for future engineers.

For on-call documentation more broadly, Claude handles incident severity definitions, alert runbook entries, escalation procedures, and the post-incident writeup that explains what happened, what the contributing factors were, and what's being done to prevent recurrence. These are documents that are important but time-consuming to write well under the time pressure of being in the middle of an incident response cycle.

The architecture reasoning use case is worth a specific callout. When you're evaluating whether to use Vault versus cloud-native secret management, or whether your current Kubernetes network policy approach is the right one for your threat model, Claude reasons through these questions well if you give it the relevant context. It's a useful sounding board for infrastructure decisions even when it doesn't have access to your specific codebase.

Best for: Platform engineers who need help with runbook writing, on-call documentation, architecture decision records, and system-level reasoning. Pricing: Free tier available; Claude Pro at $20/month.

How to choose

Platform engineers doing active IaC development should start with Claude Code or Cursor. Use Claude's app for the documentation and reasoning work that doesn't fit in a code editor.

For most platform engineers, Claude Code plus Claude's app is the most practical combination. Claude Code handles the code work, Claude handles the documentation. Both at $20/month, so $40 total, and the hours saved in a typical month make the math obvious.

Frequently asked questions

Can AI help with Terraform module refactoring?

Yes. Give Claude Code the existing module and describe what you want to change: extracting submodules, adding variable validation, converting to a different resource pattern. It handles the refactoring with awareness of variable dependencies and output references. Review the output carefully for any changes that affect resource addressing, since those can cause Terraform to want to destroy and recreate resources.

What about AI for writing CI/CD pipeline configurations?

Claude Code handles GitHub Actions, GitLab CI, and CircleCI YAML well. Give it your requirements for the pipeline, any existing pipeline files as context, and it generates a starting configuration. For complex multi-stage pipelines with custom runners or Docker-in-Docker requirements, review the output carefully. Pipeline YAML has a lot of subtle whitespace and indentation requirements, and Claude Code usually gets this right but not always.

Is AI useful for compliance and security IaC?

Useful for drafting, but you need expert review for security-sensitive configurations. Claude Code understands patterns like encryption at rest, logging to audit trails, and restrictive security group rules. It doesn't fully replace a security review by someone who knows your threat model and compliance requirements. Use it to generate a starting configuration that applies reasonable defaults, then have a security-aware engineer review it against your specific requirements.