Open source SAST tools are worth evaluating before you buy anything commercial, because a handful of them — Semgrep, CodeQL, and language-specific linters like Bandit or gosec — cover a meaningful share of what enterprise static analysis platforms charge for, especially for teams that can invest engineering time in rule tuning. They won't fully replace a commercial platform once you need centralized reporting, compliance mapping, or support SLAs across hundreds of repos, but for small and mid-size teams, or as a first layer bolted onto CI before a bigger purchase, they're genuinely useful.
This is a practical guide to the tools worth trying, not a marketing list — each entry includes where it's strong and where it falls short.
What is Semgrep good for?
Semgrep is the most widely adopted open source static analysis tool of the last few years, largely because its rule syntax looks like the code it's scanning rather than a proprietary DSL, which makes writing custom rules far more approachable than with older tools. It supports dozens of languages, ships with a large open registry of community and Semgrep-maintained rules for common vulnerability classes (injection, hardcoded secrets, insecure deserialization), and runs fast enough to sit directly in a pre-commit hook or PR check. The tradeoff is that Semgrep's free tier does pattern matching rather than full interprocedural data-flow analysis by default — deeper taint tracking across function boundaries requires either paid Semgrep Supply Chain/Code tiers or careful custom rule design, so out of the box it catches less than a full commercial SAST engine on complex vulnerability chains.
Where does CodeQL fit?
CodeQL, GitHub's semantic code analysis engine, is arguably the most technically capable free static source code analysis tool available, treating source code as a queryable database and letting you write genuinely powerful data-flow queries. It's free for public repositories and bundled into GitHub Advanced Security for private ones, and its default rule packs for C/C++, Java, JavaScript, Python, Go, and C# are well-maintained by GitHub's security research team. The learning curve is real, though — writing a custom CodeQL query requires learning a dedicated query language (QL) that has a much steeper ramp than Semgrep's pattern syntax, so most teams rely on the default query packs rather than building bespoke rules.
Are language-specific linters still worth running?
Yes, and it's a mistake to skip them in favor of a general-purpose scanner alone. Bandit for Python, gosec for Go, Brakeman for Ruby on Rails, and ESLint security plugins for JavaScript are narrower in scope but tend to have very low false-positive rates for the specific issue classes they target, because they're purpose-built for one language's idioms rather than trying to generalize across dozens. Running one of these alongside a broader tool like Semgrep or CodeQL is a common and effective layering strategy — the specialist catches framework-specific footguns the generalist rule packs miss.
What are the real limitations of going open source only?
The gap between free static source code analysis tools and commercial platforms is rarely about raw detection capability on a single repo — it's about what happens at scale. Open source tools don't ship built-in compliance mappings (SOC 2, PCI-DSS, OWASP ASVS), centralized dashboards across hundreds of repositories, ticketing system integrations, or vendor support contracts that procurement and compliance teams often require. They also generally lack the reachability and runtime-context analysis that reduces false positives on large codebases — you get raw findings and you own the triage workload. For a five-person startup, that's a fine tradeoff. For an enterprise with a thousand repos and a compliance audit on the calendar, it usually isn't, and that's typically where teams look at SAST paired with SCA in a single managed platform instead.
How should a team start evaluating these tools?
Start with Semgrep against a handful of your most active repositories — it's the fastest to stand up and gives a quick read on signal quality before committing more time. Layer in a language-specific linter for your primary stack, since the incremental setup cost is low and the false-positive rate is typically better than a general tool alone. If you're already on GitHub and have Advanced Security licensing, run CodeQL's default query packs in parallel and compare overlap; the tools rarely find the exact same issues, and the union of results is usually more valuable than either alone.
FAQ
Is Semgrep completely free?
Semgrep's CLI and core rule engine are open source and free to run locally or in CI; deeper cross-file taint analysis and hosted features live behind Semgrep's paid Supply Chain and Code tiers.
Does CodeQL work on private repositories?
Yes, through GitHub Advanced Security, which is a paid add-on for private repos, though CodeQL itself is free for public open source projects.
Can open source SAST tools replace a commercial platform entirely?
For small teams and early-stage products, often yes. For enterprises needing compliance mapping, cross-repo dashboards, and vendor support, open source tools are usually a strong complement rather than a full replacement.
Which open source SAST tool has the lowest false-positive rate?
Language-specific linters like Bandit and gosec tend to have the lowest false-positive rates because they're scoped narrowly to one language's known vulnerability patterns rather than generalizing across many.