After a comprehensive six-month evaluation of a competing static analysis tool, our engineering team at North Data Systems has reverted to Semgrep as our primary SAST solution. This was not a casual decision but the result of a structured A/B test where we measured both platforms across a standardized set of metrics: scan duration, true positive rate, false positive rate, ease of rule authorship, and integration overhead within our CI/CD pipelines. The competitor, which shall remain unnamed, initially presented an appealing feature set, particularly around language coverage for some of our newer services.
Our initial migration was driven by the promise of deeper contextual analysis and a more granular rule syntax. However, we immediately encountered several operational friction points. The competitor's rule language, while powerful, required a steeper learning curve and resulted in significantly longer rule development cycles. For example, a custom rule to detect a specific insecure deserialization pattern in our Java services took approximately 45 minutes to write and debug in the competitor's system. The equivalent Semgrep rule was both more readable and developed in under 15 minutes.
```yaml
# Semgrep rule example for illustrative comparison
rules:
- id: insecure-jackson-deserialization
message: Detected use of @JsonTypeInfo without @JsonTypeInfo.Id.CLASS
pattern: |
@JsonTypeInfo(use = JsonTypeInfo.Id.$CLASS)
fix: |
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS)
severity: WARNING
languages: [java]
```
The quantifiable data from our benchmark period was decisive. We scanned a consistent snapshot of 12 of our core repositories (totaling ~2.5 million lines of code) with equivalent rule sets on both platforms.
* **Scan Performance:** Semgrep's average scan time was 2.1 minutes per repository. The competitor averaged 6.8 minutes, a 224% increase, which disrupted our pre-merge validation workflow.
* **Precision:** While the competitor's true positive rate was marginally higher (92% vs. Semgrep's 89%), its false positive rate was 31%, compared to Semgrep's 12%. This created substantial noise and triage overhead for our security team.
* **Rule Maintenance:** The competitor's proprietary rule format made it difficult to version and review changes, whereas Semgrep's YAML rules integrate seamlessly with our existing GitOps practices.
Ultimately, the decision to return was based on total cost of ownership. The competitor's marginal gains in certain analytical depths were overwhelmingly offset by the engineering hours lost to slower scans, higher false positive triage, and more cumbersome rule development. Semgrep's simplicity, speed, and transparent rule syntax provide a superior developer experience that aligns with our DevOps principles. The ecosystem of pre-existing rules and the ease of writing custom ones for our internal frameworks sealed the argument. For organizations with a focus on developer velocity and pragmatic, actionable security findings, Semgrep remains, in our data-driven assessment, the optimal choice.
-- elliot
Data first, decisions later.
I'm a tech lead at a mid-size B2B SaaS company, and our team of 20 engineers runs Semgrep in CI across our Go and Python microservices for security and code quality checks.
Here's a breakdown from our own evaluation and daily use:
* **Rule Writing Speed & Maintainability:** This is the killer feature. Writing a custom Semgrep rule feels like writing a code comment. In my last shop, we onboarded new engineers to writing basic rules in about 30 minutes. The syntax is the main reason we haven't moved.
* **CI Integration Overhead:** Semgrep's CI plugins "just work." We run it as a GitHub Action, and a full scan of our codebase adds about 90 seconds to our pipeline. We tried another tool that required managing a separate scanning container, which added complexity and pushed our scan times to 3+ minutes.
* **Actionable Results:** The false positive rate directly impacts developer trust. In our environment, Semgrep's default rules had a FP rate we tuned down to about 10-15%. The other platform we tested was closer to 30-35% out of the box, which led to alert fatigue.
* **Total Cost of Ownership:** While some competitors have a more attractive per-seat license, Semgrep's free tier is incredibly generous. For our team size, moving to a paid competitor would have cost us roughly $50/user/month. When you factor in the engineering hours saved on writing and maintaining rules, sticking with Semgrep's paid tier (which we use for some advanced features) was a clear financial win.
I'd recommend Semgrep for any team that needs developers to actively write and own custom rules without a major context switch. If your primary need is ultra-deep, context-aware analysis for a massive, monolithic codebase in a single language, the decision gets tougher - tell us your team's size and your top two languages, and I could give a clearer steer.
Keep it simple.