AI code review tools can look affordable when you're comparing subscription prices. But what happens when your team starts reviewing hundreds of pull requests every month?
The answer depends on how each tool measures usage, what happens when you reach its limits, and whether the feedback is actually useful.
We examined the pricing models of several AI code review tools and analyzed 203 Greptile findings across 20 pull requests. Here's what the research revealed.
1. Every tool measures usage differently
AI code review tools don't all use the same billing model.
- Codzee: Uses credits based on pull request size. Its Team plan adds 100 credits per developer to a shared team pool.
- Greptile: Provides 50 credits per seat. Base reviews cost 1 credit, Plus reviews cost 3, and Apex reviews cost 10. Additional credits cost $1 each.
- CodeRabbit: Publishes hourly review limits of 5, 8, or 10 reviews depending on the paid plan. Independent analyses also report adaptive throttling and per-file overage charges, but these details require confirmation from CodeRabbit.
- Bito: Research identified a limit of 5,000 lines per seat per month, followed by a charge of $5 per additional 1,000 lines.
The important point is that a subscription price doesn't tell you the full cost. You need to understand what counts as usage and what happens when you exceed the allowance.
2. What happens with 250 pull requests a month?
Consider a modeled team of 10 developers reviewing 250 pull requests each month.
The supplied research produced these estimates:
| Scenario | Estimated monthly cost |
|---|---|
| CodeRabbit Essentials, 5 files per PR | $553 |
| CodeRabbit Essentials, 8 files per PR | $740 |
| CodeRabbit Essentials, 15 files per PR | $1,178 |
| Codzee Team, 250 medium-sized PRs | $240 |
These figures are based on the research's pricing assumptions, not actual customer invoices. The CodeRabbit estimates include reported overage charges that could not be confirmed through official rate-limit documentation.
For Codzee, the scenario assumes that 250 medium-sized pull requests consume 500 of the team's 1,000 pooled credits.
Why does the shared pool matter?
Imagine one developer handles most of the team's pull requests while others contribute less frequently. With pooled credits, available capacity can be used across the team instead of remaining tied to individual allowances.
That doesn't mean credits are unlimited. It means teams should evaluate how the allowance matches their actual workload.
3. We examined 203 Greptile findings
Pricing is only one part of evaluating an AI code reviewer. The quality of its findings matters just as much.
For this part of the research, we retrieved Greptile's inline comments through the GitHub API across 20 pull requests in the Johns223/John_Test_1 benchmark repository.
The 203 findings were distributed as follows:
| Priority | Findings | Examples from the sample |
|---|---|---|
| P0 | 2 | Authentication weaknesses involving forgeable session tokens and unauthorized privileged access |
| P1 | 154 | SQL injection, unsafe deserialization, path traversal, and monetary calculation errors |
| P2 | 47 | Test-quality issues |
| P3–P5 | 0 observed | Not present in the examined sample |
Twenty-six findings also carried a separate Security badge.
One detail is worth noting: the P-labels represented priority or severity, not confidence. Greptile expressed confidence separately in its finding text.
These results describe one repository and 20 pull requests. They do not establish Greptile's overall detection accuracy, and they are not a direct comparison with Codzee. We cannot use this sample to claim that one tool catches more bugs than another.
4. What should developers evaluate?
Before choosing an AI code review tool, consider three practical questions:
- How is usage measured? Pull request size, review depth, lines of code, and review frequency can produce very different costs.
- What happens at the limit? Check whether reviews stop, become throttled, or incur additional charges.
- Are the findings useful? A large number of comments doesn't automatically mean better code review. Engineers still need to verify issues and decide which ones matter.
Codzee's shared-credit approach may suit teams with uneven workloads. Whether it is the right choice depends on pull request size, review volume, pricing, and performance on your own repositories.
Final thoughts
The best AI code review tool isn't necessarily the one advertising unlimited reviews or the lowest starting price.
It's the one that fits your workflow, keeps usage understandable, and helps your team identify issues worth fixing.
If you're exploring AI-powered code review for GitHub, you can learn more about Codzee.
Top comments (0)