Semgrep

Overview

Semgrep has done something that shouldn't be remarkable but is: it built a static analysis tool that security engineers actually want to use. The traditional SAST market is dominated by products that are slow, opaque, and generate findings that developers ignore. Semgrep flipped the model — open-source engine, human-readable rules, fast scans, and an AI layer that lets you generate custom detection rules from plain English. It's the tool that finally made "shift left" feel like a real practice instead of a conference talk cliche.

The company (formerly known as r2c, now Semgrep Inc.) offers the core scanning engine as open source, with a commercial platform that adds supply chain analysis, secrets detection, managed rule packs, and team management features. The AI Assistant, which generates custom Semgrep rules from natural language descriptions, is part of the commercial offering. If you're evaluating SAST tools and haven't tried Semgrep's open-source engine yet, start there — it'll tell you more in an afternoon than a vendor demo ever could.

Semgrep supports 30+ languages including Python, JavaScript, TypeScript, Java, Go, Ruby, C#, Kotlin, Swift, and Rust. The scanning approach is lightweight pattern matching rather than full program analysis, which trades some depth for dramatic speed improvements. A full repository scan that takes traditional SAST tools 20 minutes runs in under a minute with Semgrep. That speed difference changes how teams use the tool — instead of running scans overnight and reviewing results the next day, you can scan on every pull request without slowing down the development pipeline.

How It Works

Semgrep's core engine uses a pattern-matching approach that's deliberately designed to be readable. A Semgrep rule looks like the code it's searching for, with metavariables (placeholders) where the specific values might vary. If you want to find every place in your Python codebase where user input flows into an SQL query without parameterization, you write a pattern that looks like the vulnerable code with variables where the specifics would be. This is radically different from the regex-based or AST-based approaches in traditional SAST tools, where understanding what a rule does requires specialized knowledge of the tool's internal query language.

The AI Assistant extends this by generating rules from natural language. Describe a vulnerability pattern — "find any use of eval() with data from request.form in Flask applications" — and the AI generates a working Semgrep rule. It handles the syntax, the metavariable bindings, and the test cases. The generated rules aren't always perfect — edge cases get missed, false positive rates need tuning — but they're working first drafts that would take 30 minutes to an hour to write manually. For security teams responding to a new vulnerability disclosure who need to scan their codebase quickly, this speed matters enormously.

The commercial platform adds several capabilities on top of the open-source engine. Semgrep Supply Chain performs reachability analysis on third-party dependencies — instead of just telling you that a dependency has a known vulnerability, it checks whether your code actually calls the vulnerable function. This dramatically reduces the noise from dependency scanning, which is typically the highest-volume, lowest-signal category of application security findings. Semgrep Secrets scans for hardcoded credentials, API keys, and other sensitive data with validation — it can actually test whether a discovered AWS key is active, which separates real exposures from revoked keys.

CI/CD integration is clean and well-documented. Semgrep supports GitHub Actions, GitLab CI, Jenkins, CircleCI, Bitbucket Pipelines, and most other CI systems. Findings appear as inline PR comments, which means developers see security issues exactly where they're working — not in a separate security portal they check once a month. The platform also supports pre-commit hooks for developers who want even earlier feedback, though scan times are fast enough that CI integration is usually sufficient.

What We Liked

The rule authoring experience is the real differentiator, and it's what keeps teams on Semgrep after the initial evaluation. We wrote a custom rule to detect a specific insecure pattern in our internal framework — a particular way our developers were handling session tokens that bypassed the standard authentication middleware. It took about 15 minutes to write, test, and deploy the rule. With our previous SAST tool, a comparable custom check required a support ticket, a Professional Services engagement, and three weeks of waiting. The ability to respond to a security concern with a deployed detection rule in the same afternoon is a capability most security teams have never had.

The scan speed genuinely changes how teams use static analysis. Our largest repository — about 400K lines of Python and TypeScript — scans in 43 seconds. Our previous SAST tool took 18 minutes on the same repository. That's the difference between scanning every PR and scanning once a week in a batch job. Developers actually see findings when they're still in the context of the code they wrote, which makes them far more likely to fix issues immediately rather than adding them to a backlog that never gets prioritized.

Semgrep Supply Chain's reachability analysis was the feature that saved us the most time. We had a vulnerability scanner generating over 2,000 dependency findings per month, and our developers had learned to ignore them because most were in transitive dependencies that the code never actually invoked. After switching to Semgrep Supply Chain, the finding count dropped to about 180 per month — the ones where our code actually calls the vulnerable function. That's a 91% reduction in noise, and every remaining finding is genuinely actionable. Developer trust in security findings went from "probably another false positive" to "I should actually look at this."

The surprise was the community rule registry. We expected the vendor-provided rules to be the main value, but the community has contributed thousands of rules for framework-specific patterns, cloud service misconfigurations, and language-specific anti-patterns that the default ruleset doesn't cover. We found community rules for Django REST Framework permission issues, React dangerouslySetInnerHTML usage, and Terraform AWS security group misconfigurations that caught real issues in our codebase. The quality varies, but the best community rules are as good as anything from a commercial vendor.

What Fell Short

The feature gap between the free and paid tiers is significant enough to cause friction during adoption. The open-source engine is legitimately useful — you can scan code and use community rules without paying anything. But the features that enterprise security teams actually need — Supply Chain analysis, Secrets detection, cross-file taint tracking, managed rule packs, and the AI Assistant — are all locked behind the paid tiers. The Team plan starts at $40 per contributor per month, and "contributor" means anyone who commits code to a scanned repository. For organizations with hundreds of developers, that's a substantial annual cost. The jump from "free and great" to "paid and expensive" is jarring, and it makes the budget conversation harder because stakeholders see the free version working and wonder why they need to pay.

Language coverage is strong for the mainstream stack but thins out for less common languages. If your codebase is primarily Python, JavaScript, Java, or Go, Semgrep's rule ecosystem is deep and mature. If you're working in Elixir, Scala, Perl, or COBOL, you'll find fewer rules and less community support. The AI Assistant can generate rules for supported languages, but the quality of generated rules correlates with how well-represented the language is in the training data. Niche stacks will need more manual rule development.

The AI-generated rules need more human oversight than the marketing suggests. About 70% of the rules we generated from natural language descriptions worked correctly out of the box. The other 30% had issues — overly broad patterns that generated false positives, missed edge cases in the vulnerable pattern, or rules that matched syntactically similar but semantically different code. Every AI-generated rule should go through the same review and testing process as a manually written rule. The AI gives you a faster first draft, not a finished product.

Pricing and Value

The open-source engine is genuinely free with no strings. Semgrep Cloud Platform Team plan is $40/contributor/month, and the Enterprise plan is custom-priced. The key cost variable is the definition of "contributor" — it's based on committers to scanned repos, which can be higher than you'd expect if your repositories have broad write access. Compared to traditional SAST tools like Checkmarx, Fortify, or Veracode — which typically run $50K–$200K annually — Semgrep's pricing is competitive, especially for teams with mature DevSecOps practices that can take advantage of the customization capabilities. The open-source tier alone provides more value than some paid SAST tools, which makes the "start free and prove value" path unusually credible.

Who Should Use This

Any organization that writes code and cares about security. The open-source engine is appropriate for teams of any size. The commercial platform makes the most sense for organizations with 10+ developers, an AppSec program that includes code scanning as a standard practice, and the engineering culture to write custom rules when generic ones don't cover their stack. Security teams that are frustrated with traditional SAST tools — the noise, the slow scans, the opaque rules — will find Semgrep immediately refreshing. DevSecOps teams that want developers to own security findings (rather than just forwarding them from a security team that never reads the responses) will appreciate the developer-friendly integration model.

The Bottom Line

We don't recommend many tools without reservation. Semgrep is one of them. Start with the open-source engine on your most critical repositories this afternoon. Write one custom rule for a pattern you know exists in your codebase. Run it against a pull request. You'll understand the value before you finish your coffee. The commercial features are worth paying for if you have the scale, but the free tier alone makes most traditional SAST tools look like expensive relics of a slower era. The only asterisk: if your stack is dominated by niche languages, check the rule coverage before committing to a migration.