The "best self-hosted AI code review tool" question gets searched constantly, and almost everything that ranks is a vendor listicle with no method. One source actually ran a test and published costs. Augment Code put ten open-source review tools against a 450K-file Python, TypeScript, Java and Go monorepo and documented what happened, published 2026-01-16 and updated 2026-08-17.
The number worth stealing from it is the cost of self-hosting. The license is free. Everything around it is not. Augment's estimate puts a self-hosted stack at $4,100 to $9,100 a month at any team size, from published GPU rates combined with 0.25 to 0.5 FTE of maintenance at the US Bureau of Labor Statistics mean developer wage. Compare that to $24 to $30 per developer per month for a commercial per-seat reviewer.
That range matters because it reframes the decision. Self-hosting stops being the cheap option. It is the privacy and residency option. You pay more per month, in a different shape: a capital and staffing cost instead of a subscription line item. A team that self-hosts for cost reasons is making the choice on the wrong number.
The rest of the test is worth a read for what it rules out. None of the ten tools detected cross-service breaking changes across the four languages. Every one of them operates at file level. SonarQube Community Build was the strongest, with near-zero false positives over 21 languages, and Semgrep came second on custom rules. But neither is an architecture reviewer. That ceiling is where commercial platforms claim to pick up.
Two concrete findings on the enterprise controls buyers care about. Augment reports that most free builds gate the controls that matter behind a commercial key: audit logging on SonarQube Community is gated at Enterprise Edition, and a commercial license is required for single sign-on, RBAC and audit logs in the tools tested, Kodus included. And there is a sharp detail on local inference: PR-Agent's configuration issue #2098 caused silent fallback to hosted models during testing, which defeats the point of a local stack if you do not notice. Tabby self-hosted as documented.
One caution on the numbers. The $4,100 bottom of Augment's range assumes modest GPU use. A team running large models on every PR, or needing high throughput on many repos, should expect the high end. The method behind the range is published, and the hourly components are itemized, but the assembly is an estimate, not a bill from a real deployment.
The decision to self-host is a model-placement decision first, and a price check second. Anyone comparing self-hosted and hosted review tools should run their own numbers with their own GPU sizing and their own maintenance time, because the published per-seat figures for hosted tools are stable and the monthly cost of self-hosting is the one teams most often forget to include.
Source: Augment Code, 10 Open Source AI Code Review Tools Tested on a 450K-File Monorepo, https://www.augmentcode.com/tools/open-source-ai-code-review-tools-worth-trying
Top comments (1)
Framing self-hosting as a privacy purchase rather than a discount clears up the balance sheet confusion immediately. Paying four to nine thousand dollars a month in dedicated hardware and staffing to replace a thirty-dollar seat license makes no financial sense until data sovereignty carries a compliance penalty larger than that spread.
The structural hidden cost that usually breaks these setups is capacity utilization against spiky demand. A commercial SaaS absorbs burst concurrency across its entire tenant base, so a development team only pays for the tokens consumed during active review cycles. With dedicated local GPUs, sizing the instance cluster for peak afternoon pull request volume means carrying expensive idle compute overnight and on weekends. Sizing for the average volume avoids that idle waste, but pushes review queues out past thirty minutes during code freeze windows, directly degrading engineering velocity.
The maintenance estimate of a quarter to half an FTE also assumes purely operational upkeep like host patching and container restarts. In practice, the real drag is model drift and prompt calibration. When a new framework version breaks syntax parsing or a model update introduces a subtle hallucination into the review output, debugging the harness pulls staff engineers away from production features. You end up converting variable software subscriptions into an unhedged internal systems project.