Semgrep Review 2026: Developer-First Static Analysis That Security Teams Actually Trust

Most security tools are built for security teams and tolerated by developers. Semgrep is built for developers and trusted by security teams. That distinction matters more than you might think.

The traditional SAST (Static Application Security Testing) workflow goes like this: security team buys a scanner, runs it against the codebase, generates a report with hundreds of findings, hands it to engineering, engineering complains that 80% of it is noise, security insists it's all valid, nothing gets fixed, everyone resents each other. Rinse and repeat every quarter.

Semgrep breaks that cycle because it operates at the level of actual code patterns rather than opaque rule engines, its findings are legible to the developers who need to fix them, and — critically — it lets those same developers write the rules. Custom rules for your specific codebase, your specific patterns, your specific risks. Not generic findings from a vendor's threat database that may or may not apply to how you've built your software.

This is a serious security tool for engineering-led organisations. Here's what it can actually do.

What Semgrep Actually Does

Semgrep is a static analysis engine. It works by parsing your code into an abstract syntax tree and then matching patterns against that tree. The patterns — called rules — can range from trivially simple ("flag any use of `eval()`") to nuanced multi-file dataflow analyses ("find user input that reaches a SQL query without going through a parameterisation function").

The core engine is open source. The platform around it — Semgrep Code, Supply Chain, and Secrets — is where the commercial product lives.

Semgrep Code is the SAST product. It ships with a large library of community and professionally maintained rules covering common vulnerability classes: injection flaws, authentication issues, insecure cryptography, dangerous API usage, and much more. But the differentiation is the rule language. Semgrep rules are written in YAML with a pattern syntax that closely mirrors the code they're matching. A developer who understands Python can write a Semgrep rule to detect a Python vulnerability pattern in under ten minutes. This is genuinely unusual in the SAST market.

The AI-assisted scanning, added more recently, uses language model assistance to reduce false positives and provide more contextual guidance on findings. Rather than just flagging a pattern match, Semgrep can provide reasoning about whether a specific instance is actually exploitable given its context. This is meaningful progress on the noise problem that plagues static analysis.

Semgrep Supply Chain is the SCA (Software Composition Analysis) component. It analyses your dependencies for known vulnerabilities — similar in concept to Snyk Open Source or Dependabot — but with a key difference in how it assesses reachability. Rather than simply flagging every vulnerable dependency, Semgrep Supply Chain attempts to determine whether your code actually calls the vulnerable function in the affected library. A vulnerable function you never call is a dramatically lower priority than one you invoke on every request. This reachability analysis cuts alert volume significantly and improves the signal-to-noise ratio that's the bane of SCA tooling.

Semgrep Secrets handles secrets detection — scanning for API keys, tokens, credentials, and other sensitive values committed to code. This market is increasingly crowded (GitGuardian, Trufflehog, GitHub's own secret scanning all compete here), but Semgrep's secrets detection benefits from being integrated into the same platform and CI/CD workflow as the rest of its findings. One tool, one interface, one set of policies.

Custom Rules deserve their own emphasis because they're a genuine differentiator. Security teams can write rules that enforce organisation-specific patterns — your internal authentication library, your approved cryptography functions, your required input validation wrappers. When a developer introduces code that bypasses your internal security controls, Semgrep can catch it before it ever reaches code review. This turns security policy from a document that gets ignored into an enforced code standard.

IDE Integration means findings appear in real time as developers write code, not as a post-commit report that interrupts a different context. VS Code, IntelliJ, and other major editors are supported. This is the "shift left" principle in practice: finding issues when the developer is already thinking about the code, not days later when they've moved on.

Pricing

Semgrep's pricing is structured around team size and which products you need.

The free tier is legitimately useful for individual developers and small teams. The open source engine with community rules runs in CI/CD and catches a meaningful number of issues. The step up to Team adds the managed platform, which is where the collaboration and findings management features that security teams need come in.

Enterprise pricing is negotiable and volume-dependent. Expect conversations rather than a pricing page.

Comparison: Semgrep vs. SonarQube, Snyk Code, and CodeQL

The comparison with CodeQL is worth dwelling on. CodeQL (now owned by GitHub) is arguably more powerful for deep, interprocedural analysis. But the QL query language is genuinely difficult. Writing a custom CodeQL query requires specialist knowledge; writing a custom Semgrep rule does not. For most organisations, the practical coverage you get from Semgrep's more accessible custom rules outweighs CodeQL's theoretical depth.

Against SonarQube: Sonar has a large installed base, particularly in enterprise Java and .NET shops, and its quality gates are well-established. But it's historically been noisy, the developer experience is not great, and the licensing model for the enterprise features is expensive. Semgrep is meaningfully better on developer experience and false positive rate.

Against Snyk Code: Snyk's strength is in the SCA/open source dependency space, and Snyk Code benefits from being part of a platform many teams already have. But Semgrep's custom rules capability gives it an edge for organisations who need to enforce internal patterns beyond generic vulnerability classes.

Who It's For

Engineering-led organisations with strong DevOps practices. If your security posture is built around developers owning quality — and your CI/CD pipeline is where real controls live — Semgrep fits naturally. It's the tool developers actually use rather than work around.

Security teams who need to scale. One security engineer using Semgrep custom rules can enforce secure coding patterns across a 50-person engineering team. That leverage is significant. You write the rule once; every developer benefits (or gets caught).

Teams building internal security standards. If you have approved cryptography libraries, required authentication patterns, or banned API calls, Semgrep is the right tool to enforce them automatically. Policy-as-code, at the source.

Organisations with polyglot codebases. Semgrep supports a genuinely wide range of languages — Python, JavaScript/TypeScript, Go, Java, Ruby, PHP, Rust, C/C++, and more. If your stack is diverse, one tool covering the lot is operationally simpler than multiple specialised scanners.

Startups that want to build security in from early on. The free tier is real, the documentation is good, and the learning curve is manageable. There is no reason a seed-stage startup cannot have CI/CD-integrated SAST running within a day.

Who It's Not For

Teams expecting binary "pass/fail" with no tuning required. Semgrep, like all static analysis, produces findings that require triage. The AI features help, but you will need someone who can evaluate results and tune rules to your context. If you want to buy a box and declare yourself secure, this is not that box.

Organisations where developers have zero security engagement. Semgrep's model depends on developers paying attention to findings. If your development culture is entirely "security is someone else's problem," the IDE integration and readable rules won't help much. You need the culture before you need the tool.

Teams whose primary concern is open source dependency risk. If your biggest security concern is vulnerable npm packages or PyPI libraries, Snyk's SCA offering is more mature and more comprehensive than Semgrep Supply Chain. Use the right tool for the primary concern.

Enterprise organisations looking for a compliance checkbox. If you need a SAST tool primarily to satisfy an auditor and your appetite for actual developer integration is low, SonarQube's established enterprise track record may be easier to justify in that conversation. Semgrep's value is realised through use, not purchase.

How to Get Started

Day 1: Run it against your main repository. Install the CLI (`pip install semgrep` or via Homebrew), point it at your codebase with `semgrep --config auto`, and review the output. The `auto` configuration uses the community ruleset appropriate for your languages. This takes minutes and gives you an immediate baseline view of what's there.

Week 1: Integrate into CI/CD. Add a Semgrep scan step to your pull request pipeline. Findings on new code, not the entire historical backlog, keeps the signal relevant and the volume manageable. The GitHub Actions integration is straightforward; so are GitLab CI and CircleCI. Block merges on high-severity findings if your team culture can absorb that.

Week 2: Write your first custom rule. Pick one internal security pattern that matters to your organisation — an authentication bypass pattern you've seen before, a dangerous function call, a required validation wrapper — and write a Semgrep rule for it. The documentation is good and the playground (play.semgrep.dev) lets you test rules interactively. This is the moment where Semgrep starts differentiating from generic scanners.

Month 2: Onboard Semgrep Supply Chain. Once SAST is working, add supply chain scanning to get reachability-aware dependency analysis. Compare the findings against what your existing dependency scanner (Dependabot or similar) produces. The reachability filtering is where you'll see the difference — fewer total findings, higher average relevance.

Month 3+: Establish a rule review process. Treat your custom Semgrep rules like code. Review them, version them, test them. When your team discovers a new internal anti-pattern, add a rule. When a rule produces too many false positives, tune it. This is the compound return on the initial investment — the rule library grows with your knowledge of your own risks.

The Honest Assessment

Semgrep is the best developer-experience SAST tool on the market right now. The custom rules capability is genuinely differentiating. The AI-assisted analysis is making meaningful progress on the false positive problem. The open source core is well maintained and widely trusted.

The supply chain and secrets components are solid but not class-leading. If SCA or secrets detection is your primary concern, there are more mature dedicated tools. Semgrep's value is in the integration — doing SAST, SCA, and secrets in one place with one workflow — rather than being best-in-class at each individually.

The investment required to get real value is also real. Semgrep on default rules is useful. Semgrep with custom rules tuned to your codebase is transformative. That second version requires security engineering work. Teams who can make that investment will see disproportionate returns. Teams who cannot will get a decent generic scanner and wonder what the fuss is about.

For organisations who want security baked into the development workflow rather than bolted on afterwards, Semgrep is the clearest recommendation in the SAST space. The question is not whether to use it — it is whether your team is ready to use it properly.

Digital by Default helps businesses evaluate and implement developer security tooling that actually gets used. If you're trying to mature your application security programme, reduce CVE exposure in code, or build a security-aware engineering culture, [get in touch](/contact).