AI-Generated DevOps Scripts Cost: Bash, PowerShell, Terraform per 100 Lines

Why DevOps Scripts Cost More per Line

A hundred lines of Python is one thing. A hundred lines of bash that will run in production on a fleet of servers is another. DevOps scripts have properties that inflate LLM cost per line:

• Idempotency matters. Running the script twice must not double-create resources or double-charge you.
• Error handling is not optional. A silent failure in a Terraform apply can produce a $5,000 cloud bill overnight.
• Environment variance is huge. Bash on Ubuntu 22.04 is not the same as bash on Alpine, is not the same as macOS zsh.
• Blast radius is real. A destroy statement that hits the wrong resource is unrecoverable in seconds.

All four force at least one verification pass and often a targeted "is this idempotent" review pass on top. That drives the effective per-line cost 2-4x above the naive tokenizer estimate.

Raw Token Math per 100 Lines

Baseline: how many tokens does 100 lines of each language cost to generate?

The Real Multiplier: Review Passes

A production DevOps script that lands in a repo typically goes through:

1. Initial generation: ~1,500 tokens input (context, examples) + N output.
2. Idempotency review: another ~2,500 tokens input (full script) + ~300 output notes.
3. Environment-portability check: ~2,000 tokens input + ~200 output.
4. Blast-radius review (for Terraform): ~2,500 tokens input + ~300 output.

Combined, a 100-line Terraform module effectively costs 8,000-10,000 tokens end-to-end, not the 1,400 raw output suggests.

Cost per 100 Production Lines, by Model

Numbers assume all four review passes are enabled. Skip the reviews at your peril; the cost of one wrong terraform destroy dwarfs a year of Sonnet spend.

Where AI Wins and Where It Loses

LLMs are strong at:

• Boilerplate: Kubernetes manifests, standard Terraform modules from AWS provider docs.
• Regex-heavy transforms, sed/awk one-liners.
• Translating between formats: Compose → Kubernetes, Bash → PowerShell.
• Adding logging and error handling to an existing script.

LLMs are weak at:

• Novel Terraform patterns (custom providers, dynamic blocks with complex loops).
• Real environment-portability testing — the model does not run the script, it guesses.
• Version drift: LLM output may reference deprecated Terraform or Kubernetes API versions if the training snapshot is stale.
• Security-hardening decisions where regulatory frameworks apply.

Recommended Pattern

1. Use Sonnet 5 as the primary generator — best price/quality on this workload.
2. Enforce idempotency by asking the model to list side effects on a dry-run first.
3. Run terraform plan or shellcheck before every apply; feed errors back to the model.
4. For destructive operations, require human approval on the plan output.
5. Keep a diff-review pass in place indefinitely — it is not optional overhead.

Bottom Line

A dollar or two per 100 lines of production DevOps code is not the true cost — the true cost is what an unreviewed rm -rf or terraform destroy costs when it hits the wrong target. Budget the review passes into the arithmetic and treat AI DevOps generation as accelerated, not replaced, engineering.