Augment combines coding and review in a flat subscription with metered usage. CodeRabbit sells review plans with local and PR surfaces. Existing coding-agent usage can change the additional cost of review, so compare the entire required bundle and its consumption.
Code Review Guide · Reviewed October 14, 2026 · Published by Perch / Verglas LLC
CodeRabbit and Augment Code Review at a glance
Documented review inputs, requirements and licensing
Decision
CodeRabbit
Augment Code Review
Review scope
Pull requests, local changes, and agent-loop review.
GitHub code review within a broader coding-agent subscription.
Local review input
CLI and IDE review committed and uncommitted changes.
CLI access is included; GitHub AI review is a documented workflow.
Custom requirements
Guideline files and natural-language pre-merge checks; custom-check allowances depend on plan.
Project context guides review; precise custom-review rule limits were not verified.
Compare a coding-agent usage bundle with review plans
Record Augment API and compute usage and any top-ups, then compare CodeRabbit review allowances for the same workload. Confirm the custom-review behavior the team needs during the trial.
CodeRabbit billing
Essentials: $24/user/month billed annually, or $30 monthly. Public-repository reviews are free.
Essentials annual subscription floor; 1 repository and 5 included reviews/developer/hour. Eligible extra reviews cost 25¢ per reviewed file. Built-in checks are included; custom pre-merge checks start on Team.
Standard: $20/month flat, up to 50 seats and $20 usage. Business: $100/month with $100 usage.
Current pricing meters model API cost plus a 40% service fee and compute, with top-ups. The flat subscription is not a guaranteed cost for every review volume.
Subscription amounts are plan floors or quoted allowances. Repeated reviews, selected effort, provider charges and exclusions can change the bill. Equal review counts do not establish equal analysis scope.
A third option for the agent’s next edit
When Perch is the better choice
Perch is a stronger fit for a separate, named requirement with a direct check rate. Keep the coding agent and add a reusable check after its edits.
For an agent that repeatedly removes tenant authorization from a database update, save the requirement as a named Perch rule:
- name: tenant-scoped-update
where: "orders.py"
ensure: >-
The cancellation UPDATE must constrain tenant_id to actor.tenant_id
before changing the order. An id-only or owner-only update breaks
this requirement.
This requirement comes from the FastAPI tenant-authorization recipe, which includes the implementation, deliberate regression and captured Perch checks. The same named check can run against the working file after the next agent edit. Perch working-file checks support selecting rules directly; rule selectors cover project, file and method scope.
Use the setup command for your coding agent. Download the focused pack without replacing existing files:
curl -fL 'https://reporules.fyi/fastapi/tenant-cancellation/rule-pack.zip' -o tenant-rule.zip
unzip -n tenant-rule.zip
perch check orders.py --rules tenant-scoped-update --json
# Repair the reported finding after the agent edit.
perch check orders.py --rules tenant-scoped-update --json
Keep your existing root perch.yaml. The ZIP adds its focused rule under .perch/rules/; unzip -n preserves existing files. Review and adapt the selector and application path before running it in your repository. Run the saved check after an edit, repair any finding, and repeat the identical check alongside application regression tests.
Perch charges 10¢ per 1,000 fresh rule checks, with no developer seat fees. Cached unchanged checks are free. About 50¢ per mid-sized PR is a rounded estimate for approximately 150 methods read at the captured September–October 2026 density of 34 questions per method; the unrounded example is 51¢. A single named-rule check is a different scope. Team workload calculations show the assumptions.
The Perch CLI is MIT licensed; hosted analysis is paid and relevant source is sent to the configured model service. Fresh model judgments can vary. Unchanged requests can reuse cached results.
Test the same requirement before choosing
Use one authorized public change with a known regression and a clean control. Pin the base and reviewed code, and keep the documented requirement available to both tools.
Record each tool’s configuration, reviewed files, review effort, findings and false positives. Record cost and elapsed time from the actual run.
Repair the regression and repeat the relevant review. For Perch, preserve existing configuration, run the saved rule after the edit, and repeat the same check after repairing a finding.
This article compares documented product workflows and terms. The public evaluation materials include captured Perch requirement checks; authenticated CodeRabbit and Greptile runs remain unscored. They do not establish a measured accuracy ranking between these tools.
Sources and related comparisons
Reviewed October 14, 2026. Unverified features are marked as unverified rather than treated as absent. Current terms and actual trial results should determine a purchase.