Comparison
Kolega vs GitHub Advanced Security
GitHub Advanced Security (GHAS) has one massive advantage over every other security tool: it's already there. If your code lives on GitHub, GHAS plugs into the PR workflow with zero integration work. That's worth a lot — and it's the reason most teams try GHAS first.
The trade-off is detection depth. GHAS uses CodeQL, which is a query-based analysis tool. It's strong on known patterns. It struggles on the semantic vulnerabilities that ship most often today.
On the RealVuln benchmark of 676 real vulnerabilities, Kolega scores 92.4%. CodeQL (the engine behind GHAS) scores 27.5%. That's a 3.4x gap on the same code.
View the full RealVuln benchmarkThe headline numbers
| Metric | Kolega | GitHub Advanced Security |
|---|---|---|
| RealVuln F3 score | 92.4% | 27.5% (CodeQL) |
| Engine | Semantic data flow analysis | CodeQL query-based pattern matching |
| Integration with GitHub PRs | ✓ | Yes (native) |
| Works with non-GitHub repos | Yes (GitLab, Azure DevOps) | No (GitHub-only) |
| Starting price | From $39/mo | $30/committer/mo (Code Security only) |
| Includes secrets scanning | ✓ | Yes ($19/committer/mo separately) |
| Built for AI-generated code | ✓ | ✗ |
GHAS is now sold as two separate products: Code Security ($30/committer/mo) and Secret Protection ($19/committer/mo). For a team of 20 active committers, that's $600/mo for code scanning alone, or $980/mo for both. Kolega's starter tier is $39/mo and includes both.
What GHAS does well
We'll start here, because GHAS is genuinely good at what it does.
Native integration. GHAS is built into GitHub. There's no OAuth flow, no separate dashboard to learn, no third-party app to install. Findings appear in the same PR review interface engineers already use. That's a real productivity win for teams that don't want to leave GitHub's UI for security.
Copilot Autofix. GHAS's autofix is generated by Copilot and lives inside the PR. The integration is tight and the suggestions are often reasonable. For known-pattern vulnerabilities, this works well.
CodeQL's depth on what it catches. When CodeQL has a query for a specific vulnerability class, it catches it thoroughly. The queries are written by GitHub's security research team, are open source, and benefit from years of refinement. For classic SAST findings — known SQL injection patterns, deprecated APIs, common XSS — CodeQL is strong.
Free for public repositories. If you're working on open source, GHAS is free. That's a hard offer to beat.
Where GHAS struggles
Coverage depends on queries existing. CodeQL only catches what someone's written a query for. If a vulnerability is semantic — the auth check is present but the query doesn't filter by the authenticated user, or the input is validated but the validation is wrong — there's often no query for it, and CodeQL misses it.
Limited to common patterns. The 27.5% RealVuln score reflects this. CodeQL catches roughly 1 in 4 of the real vulnerabilities in the benchmark, because the benchmark includes a lot of semantic flaws that don't match existing queries.
GitHub-only. If your code lives on GitLab, Bitbucket, or Azure DevOps, GHAS isn't an option. That sounds obvious, but enough teams use multiple Git providers that it's worth saying.
Pricing scales hard with team size. $30/committer/month means a 50-committer team pays $1,500/mo for Code Security alone. At 200 committers, $6,000/mo. Code Security and Secret Protection are also sold as separate add-ons.
Built for the bugs of 2018. CodeQL was designed before AI-generated code existed. The queries are tuned for the kinds of bugs humans write at scale, not the semantic flaws AI tools produce.
Feature comparison
| Feature | Kolega | GitHub Advanced Security |
|---|---|---|
| SAST (pattern matching) | ✓ | Yes (CodeQL) |
| Deep semantic analysis | ✓ | Limited |
| BOLA detection | ✓ | Limited |
| Race condition detection | ✓ | Limited |
| Logic flaw detection | ✓ | ✗ |
| AI-generated code coverage | ✓ | Limited |
| Secret scanning | ✓ | Yes (separate product) |
| Autofix PRs with tests | ✓ | Yes (Copilot, no tests) |
| Dependency scanning (SCA) | Coming soon | Yes (Dependabot, separate) |
| GitHub PR integration | ✓ | Yes (native) |
| GitLab support | ✓ | ✗ |
| Azure DevOps support | ✓ | ✗ |
| Bitbucket support | ✓ | ✗ |
| Free for public repos | ✓ | ✓ |
| Starting price (private repos) | From $39/mo | $30/committer/mo + $19 for secrets |
| Price for 20-committer team | $39/mo | $600–980/mo |
| Free for OSS contributors | ✓ | ✓ |
When to pick GHAS
If everything you do is on GitHub and you want the lowest possible integration cost, GHAS is hard to beat. The “it's already there” advantage is real.
If you're a 1–5 person team that's mostly on public repos, GHAS is free for public repositories and that covers a lot of use cases.
If your security workflow is entirely PR-based and you don't need a separate security dashboard, GHAS slots in cleanly with no extra surface area.
If you care more about clean PR comments than maximum detection depth, GHAS has the tighter Copilot Autofix integration.
When to pick Kolega
If detection depth is your top priority, the 3.4x gap on RealVuln is what this looks like in practice. Kolega catches semantic vulnerabilities CodeQL doesn't reach.
If your codebase is increasingly AI-generated, semantic analysis catches the bugs CodeQL's pattern-matching approach was never designed for.
If your code lives on multiple Git providers (GitHub, GitLab, Azure DevOps, Bitbucket), Kolega works across all of them. GHAS doesn't.
If autofix that comes with regression tests proving the fix works matters to you, Kolega's PR generation is built around that. Copilot Autofix doesn't include tests.
When to run both
Many teams run GHAS for the convenience of native PR integration and CodeQL for the pattern coverage, with Kolega running alongside for the deeper semantic detection. The two don't conflict — different engines, different bug classes, both reporting to the same PR.
This is actually a reasonable stack. GHAS catches the well-known patterns fast and natively. Kolega catches what GHAS misses.
FAQ
Why does CodeQL score 27.5% on RealVuln when GitHub's security team is among the best in the industry?
CodeQL is excellent for the bugs it has queries for. RealVuln tests a broad spectrum of real-world vulnerabilities, many of which are semantic in nature. No query database covers every possible bug shape, especially the ones AI tools are now generating at scale. The score reflects what's missing from the query coverage, not the engineering quality.
Is Kolega trying to replace GitHub Advanced Security?
No. GHAS includes Dependabot (dependency scanning), secret scanning, and code scanning. Kolega is specifically the code scanning piece. If you're already paying for GHAS for the Dependabot and secret scanning, keeping it and adding Kolega for deeper code analysis is a sensible pattern.
Does Kolega integrate with GitHub the same way GHAS does?
We integrate via OAuth and post findings as PR comments. The integration is good but it's a third-party app, not built into GitHub natively. If your team finds context-switching to a separate dashboard a non-starter, that's a legitimate reason to lean toward GHAS for the workflow even if you're giving up detection depth.
Does Kolega store my code?
Code is cloned into ephemeral containers for the scan and deleted immediately after. We don't store source code at rest. GHAS runs entirely inside GitHub's infrastructure, which is its own trust story.
See the benchmark for yourself
If you're paying for GHAS or considering it, scan one of your repos with Kolega in parallel and compare the findings. The two tools surface very different things.
Scan a repo freeNo credit card required. 7-day Pro trial. Drops to a free tier afterwards.