🚀 Case Study: How Brendi Cut Review Time by 70% with Kodus. See How They Did It →

Kodus vs GitHub Copilot

If you’re trying to decide which AI should review your team’s PRs, this benchmark will help. We put Kodus and GitHub Copilot side by side, focusing on what actually impacts engineering work: review depth, customizable rules, external context, metrics, and the freedom to choose the model and how you run it.

TL;DR

Custom rules (Kodus) – Natural-language policies applied at the repo, folder, file, or even a specific PR level.
MCP and external context (Kodus) – Pull CI results, Jira/Linear issues, Notion specs, tests, logs, scripts… all unified in a single review comment.
In-IDE review (Copilot) – Native VS Code/JetBrains integration to review changes before you push.
Cockpit + Kody Issues (Kodus) – A panel that shows what shipped to production plus an automatic backlog of ignored suggestions, organized by severity and category.
Full model freedom (Kodus) – Use your own API key and run whichever model makes the most sense for your team (OpenAI, Gemini, Claude, etc.).

Quick comparison

Feature

Kodus

GitHub Copilot

Supported Platforms

Github / Gitlab / Bitbucket / AZdevops

Github

Support for any provider

AI-generated code summaries

Inline comments

PR bot chat

Rules by repository, folder, file, or PR

(limited)

External Context (MCP and Plugins)

Native IDE plugin

Metrics dashboard

Tech debt backlog

Noise filters (limit and severity)

Self-host option

Pricing

BYOK – $10 / monthly

$4 – $21 / monthly

Bug Detection Comparison

To evaluate real-world performance, we compared Kody and GitHub Copilot reviews across several PRs from projects like Sentry, Cal.com, Grafana, Discourse, and Keycloak.

Kodus was a perfect fit for me. I used to be buried in slow code reviews. Now, the feedback happens much faster and I can finally focus on the work that truly matters.

André Diogo

Product Engineer

Flexibility & Control

Use any model you want—and keep every review fully under your control.

Run with your own API keys—GPT-5, Claude, Gemini, or any model you choose.

Locked to a single provider—no model choice or cost control.

Rules and customization

Versioned guidelines you can apply from the entire repository down to a single file — or import ready-made ones with a single click.

Granular, versioned rules for any file, folder, or PR, plus a community library you can import with one click.

A single ruleset per repo or branch. You only control when Copilot reviews. No natural-language code rules, no library, and no file or folder-level granularity.

Recommendation

If your team values precision, visibility, and control, Kodus is the most complete choice. It allows you to:

Set clear standards: Turn your team’s way of working into explicit rules, applied from the repository level down to a single directory, file, or PR, with full version history in Git.
Bring real context into the review: Combine CI results, Jira stories, Notion specs, test results, and more into a single review comment, making sure every suggestion comes with evidence and relevance.
Measure the impact: Use the Cockpit to track what shipped to production, what improved, and where your technical debt is.
Choose how you want to run: Use any AI model you want and keep full transparency and control over cost.

GitHub Copilot works well for teams that want something simple and fully managed. You turn it on and that is it, it comments on PRs, generates summaries, and helps directly in the IDE. But it stops there. There are no structured rules by file or PR, no automatic backlog of suggestions, no deep quality metrics, and no option to run outside GitHub’s cloud.

For teams looking for convenient plug and play inside GitHub, Copilot does the job. But if you want real customization, deep context, and the freedom to run however you prefer, Kodus gives you more without locking you into anything.