AI Code Review Cost Estimator
AI review versus human hours: the monthly cost of reviewing your team's pull requests.
per month for AI first-pass review of 174 PRs on Claude Sonnet 4.5 — the same review time costs $7,821.00 in engineer hours, 857× more, returning ~87 hours/month to the team.
Monthly cost, AI vs human first-pass
- AI cost per PR
- $0.05
- Human time per PR
- 30 min · $45.00
- Tokens per PR (input)
- 10.0k
- Hours returned / mo
- 87h
Hybrid recommendation: let AI run the first pass on every PR (lint-level bugs, missed edge cases, style drift), and keep humans on architecture, intent and anything security-sensitive. The hours saved come from faster human passes — not from removing them.
How it works
Code review is the most expensive ritual in software because it bills at engineer rates. This calculator puts a number on the alternative: what does it cost to run an AI first-pass over every PR, and what is the equivalent human review time worth? Enter your PR volume, typical diff size and loaded reviewer rate, pick a model, and both monthly figures — plus the hours returned to the team — compute instantly in your browser.
The assumptions are visible and deliberately stated as floors. AI cost per PR is the token cost of one review call: 4,000 input tokens of overhead (guidelines, PR description, file context) plus ~15 tokens per diff line, and 1,500 output tokens of comments, priced at rates verified 2026-06-11. Human cost models a careful 30-minute pass per 400-line diff, scaled linearly with a 15-minute minimum — if your reviews are quicker than that, they are skims, not reviews, and the comparison flatters humans less than you think. Months are 4.345 weeks.
The result is usually a two-orders-of-magnitude gap, and the right conclusion is not "replace reviewers". The mechanical layer of review — edge cases, null handling, style drift, obvious injection risks — is breadth work that machines do tirelessly and humans do resentfully. The judgment layer — does this change solve the right problem, does the design fit, what breaks at scale — is where human review earns its rate. The hybrid payoff shown in the results comes from the human pass getting faster because the noise is already gone when they open the diff.
What this estimate cannot capture is variance: a monorepo with 2,000-line generated diffs prices differently from a microservice with 80-line changes, and review agents that pull extra files for context can triple the token figure. That is a measurement problem rather than an estimation problem. FORG tracks real per-repo, per-session spend from your actual review automation, so the per-PR number stops being an assumption — and a runaway review bot triggers a budget alert instead of an invoice surprise. Share the link to drop your scenario into the next platform-team discussion.
Frequently asked questions
What does AI code review actually catch — and miss?
It reliably catches the mechanical layer: null-handling gaps, off-by-one errors, missed edge cases, inconsistent naming, dead code, obvious security smells like string-built SQL. It misses what requires intent: whether the change solves the right problem, architectural fit, performance implications of a design, and domain-specific invariants nobody wrote down. That split is exactly why the hybrid model works — machines do breadth, humans do judgment.
How is the token cost per PR calculated?
Input tokens ≈ 4,000 overhead (review guidelines, PR description, surrounding file context) plus roughly 15 tokens per diff line, which accounts for context lines around each hunk. Output is ~1,500 tokens of review comments. A 400-line diff lands near 10k input tokens — pennies on any model. Real agents that fetch extra files for context run higher; treat the figure as a per-PR floor.
What does a good hybrid review workflow look like?
AI reviews every PR within seconds of opening: mechanical findings, test-coverage gaps, style drift. The human reviewer then reads a PR that is already clean of noise, focusing on design and intent — which cuts their pass from thirty minutes toward ten. Critically, AI review gates nothing on its own; it comments, humans decide. Teams that let AI block merges generate alert fatigue and rubber-stamping.
How do I track what AI review actually costs per repository?
Per-PR estimates drift from reality fast — agents fetch extra context, retry on rate limits, and review sizes vary wildly between repos. FORG attributes real token spend to every session and repository, so you can see exactly what review automation costs per repo per month, compare it against this calculator's estimate, and set a budget alert before an enthusiastic review bot becomes a surprise invoice line.
Budgeting AI spend for a team? FORG is $15/dev with hard budget caps and per-seat attribution.
See FORG pricing