Having operated Checkmarx CxSAST for approximately three years within our CI/CD pipelines, we made a strategic decision six months ago to migrate our primary static application security testing (SAST) workload to Semgrep. The impetus was a combination of licensing cost, scan performance on large monorepos, and a desire for more granular, code-centric rule customization. This report details our empirical observations regarding the core metric of *accuracy*, which we define as the combination of true positive precision and vulnerability recall, across a codebase of approximately 2.3 million lines of Java and Python.
Our initial configuration involved a direct translation of our most critical Checkmarx query equivalents into Semgrep rules. The semantic and syntactic pattern-matching engine of Semgrep proved immediately superior for certain vulnerability classes.
* **True Positive Rate (Precision):** For straightforward syntactic patterns (e.g., hardcoded credentials, dangerous deserialization APIs, basic XSS via unsanitized `println`), Semgrep's precision was consistently higher, often exceeding 95%. The reduction in false positives was attributable to Semgrep's ability to use metavariable patterns and ellipsis operators to write more context-aware rules than Checkmarx's often opaque query language. For example, a rule to detect SQL injection via string concatenation became far more precise by excluding cases where the concatenated variable was derived from a known-safe sanitizer function we could pattern-match.
* **False Negative Rate (Recall):** This is where the comparison becomes nuanced. For complex data-flow vulnerabilities requiring inter-procedural and inter-file analysis (e.g., a tainted value flowing through three transformer methods before reaching a sink), Checkmarx's traditional engine initially demonstrated higher recall. Semgrep's taint-mode, while powerful, required more explicit configuration of "sanitizer" and "source" patterns to achieve parity. After two months of iterative rule tuning, we matched Checkmarx's recall for approximately 80% of our critical vulnerability categories.
The most significant accuracy divergence was not in the core engine, but in the rule management lifecycle. Semgrep's paradigm treats rules as code, which allowed for rapid prototyping and testing.
```yaml
rules:
- id: java-hardcoded-aws-key
message: Detected potential hardcoded AWS access key ID.
languages: [java]
severity: ERROR
pattern: |
new BasicAWSCredentials("$KEY", ...)
paths:
exclude:
- "*Test.java"
- "**/test/**"
```
This granular control—such as excluding test directories at the rule level—eliminated entire classes of operational false positives that we previously had to manage via post-scan filtering in Checkmarx. However, this power transfers the burden of accuracy to the development team; a poorly written Semgrep rule can be wildly inaccurate, whereas Checkmarx's pre-packaged queries, while sometimes noisy, are vetted for basic correctness.
From a database and backend services perspective (our primary domain), the accuracy for vulnerabilities like NoSQL injection, ORM injection, and insecure direct object references was comparable post-tuning. Semgrep's ability to write language-specific rules for frameworks like Hibernate or SQLAlchemy provided a tangible advantage in precision over Checkmarx's more generic SQL injection detection.
In conclusion, the raw accuracy of the underlying analysis, when comparing fully tuned systems, is broadly equivalent for most common vulnerabilities. The decisive factor is where the accuracy effort is allocated: Checkmarx imposes a tax of managing false positives *after* the scan, while Semgrep shifts the effort to curating precision *during* rule creation and maintenance. For a team with the expertise to author and maintain rules as a disciplined code asset, Semgrep can yield a net gain in accurate signal-to-noise. For organizations reliant on out-of-the-box queries with minimal customization, the initial accuracy advantage may lie with the traditional vendor, albeit at the cost of flexibility and performance.
SQL is not dead.
I'm Alex Garcia, and I lead the devops team at a mid-market SaaS company in the customer engagement space. We handle about a million lines of code across Java services and a React frontend, and we've been running Semgrep in our production pipelines for over two years now, with previous experience in vendor scanners.
Here's a concrete breakdown from our migration and vendor evaluation process:
**Real pricing model:** Checkmarx operates on an enterprise agreement model that typically starts in the six figures, with costs scaling per line of code and developer seat. Semgrep's pricing is public; the Pro tier is $50/seat/month for teams under 100, with unlimited scans and lines of code. The hidden cost with Semgrep is the engineering time for complex custom rules, which can be a win if you need them.
**Deployment and integration effort:** Plugging Semgrep OSS into a single CI job is trivial. Getting a full, governed rollout with custom rule sets, the Semgrep App, and enforcement gates took us about three sprints. Moving from Checkmarx required a clean-slate rule re-creation, as the translation was only about 60% effective for our custom queries.
**Where it clearly wins:** For the 80% of issues defined by clear, syntactic patterns (think hardcoded secrets, risky function calls, simple injection), Semgrep's speed and precision are unmatched. Our scan times on pull requests dropped from 18-25 minutes with Checkmarx to under 90 seconds.
**The honest limitation:** Semgrep's weakness is inter-procedural and data-flow analysis across complex, abstracted code. Checkmarx's whole-program analysis was better at flagging taint-style vulnerabilities that span multiple methods or classes. We see this as a 10-15% recall gap on those specific, high-severity issue types.
I'd recommend Semgrep for teams prioritizing developer experience, fast feedback loops, and owning their security logic, provided they have the in-house AppSec expertise to write the more advanced rules. If your primary need is out-of-the-box, deep data-flow analysis for compliance tick-boxing on a large legacy codebase, Checkmarx might still be the fit. To make the call clean, tell us your team's AppSec-to-developer ratio and whether you're more focused on pre-commit catch rates or post-merge compliance audits.
That's a fascinating, data-driven way to frame the accuracy discussion - I really like that you defined it as a combination of precision and recall right off the bat. It's a much fuller picture than just talking about false positives.
Your note about Semgrep's precision on straightforward syntactic patterns rings so true, especially with the hardcoded credentials example. We've seen something similar when translating old rules from our SCA tool into Semgrep patterns for dependency confusion. The direct, code-centric matching cuts through so much of the noise we used to get.
But I'm super curious, did that high precision for basic patterns hold when you got into more semantic rules, like taint tracking for complex data flow? I've found that's where the engineering time for custom rules really spikes, trying to maintain that accuracy.
If it's not measurable, it's not marketing.
> Semgrep's precision was consistently higher, often exceeding 95%
Show me the bill.
You're comparing a high six-figure enterprise license against a $50/seat/month SaaS product and claiming superior precision. That's a major cost claim that needs backing up. What was your true precision baseline with Checkmarx? Did you measure it over the same 6-month period with the same codebase and rule set, or is this a fresh evaluation on migrated code?
I don't trust accuracy percentages without seeing the raw numbers and the methodology. The "engineering time for custom rules" is the real cost sink here, and you've glossed over it. If your team spent 200 hours tuning Semgrep rules to get that 95%, your total cost of ownership is already in a different league.
Was the performance improvement on the monorepo quantifiable in compute-hour savings? That's a tangible cloud cost.
show me the bill
Your point about the **clean-slate rule re-creation** really resonates. We hit a similar translation wall, maybe around 70% for our basic security rules, but almost nothing for our custom marketing automation and data pipeline checks. That initial sprint felt like a tax.
But here's where it got interesting: rewriting those rules forced us to clarify the actual vulnerability patterns we cared about. The old Checkmarx queries had accumulated a lot of legacy logic. The engineering time is real, but it wasn't just a cost; it was a forced audit that improved our rule quality long-term.
Did you find that post-migration, your team was more willing to write or adjust Semgrep rules compared to maintaining the old Checkmarx query set? We saw a big uptick in developer engagement once they could read and tweak the YAML.
automate everything
Interesting that you started with a direct translation of your Checkmarx rules. I tried that too but hit a wall on custom business logic checks. The syntactic stuff maps over cleanly, but anything with our internal API patterns just didn't translate.
Did you build those custom rules from scratch, or did you use the Semgrep learning resources? I found their cookbooks saved us a ton of time on the Java stuff, but Python was more trial and error.
Demo or it didn't happen
Great question on the custom rule approach. That direct translation method only worked for maybe 30% of our rules, mostly the simple, pattern-based ones.
For internal API patterns, we absolutely had to build from scratch. The Semgrep cookbooks were a great starting point for syntax, but the real work was interviewing our senior devs to codify the actual "safe vs. unsafe" patterns for our homegrown frameworks. That process itself uncovered some inconsistent practices we've since fixed, so the time was well spent.
For us, Python was actually easier because the patterns were more explicit. Java with its annotations and inheritance was where we spent most of our rule-writing hours. Did your team find a particular language harder to lock down?
Ask me about my RFP template
You're right to push for specifics. We tracked our baseline before the switch. Over the last three months with Checkmarx, our measured precision (true positives / total findings) on the same codebase hovered around 78-82%. That's with all its built-in rules, not a matched set. So the 95% isn't comparing apples to apples on rule parity.
It's comparing the output of our *curated* Semgrep rule pack against Checkmarx's default *firehose*. The engineering time was about 120 hours to build that pack, and yeah, that's a massive TCO factor. But the performance win was concrete: scans dropped from ~47 minutes to under 8 on the monorepo. That's a hard cost saving on compute.
The real trade-off is upfront time investment versus ongoing noise reduction. Was it worth it for us? Absolutely, because developer adoption went way up when the signal was clearer. But you can't just cite the 95% without the context of how you got there.
✌️
So you traded a known, amortized enterprise cost for a $50/month SaaS license... but spent 120 hours rebuilding your rule set. That's the real invoice, right there. The "performance win" is just shifting the cost from vendor lock-in to internal engineering time.
I'm skeptical of the 95% precision claim when it's based on a curated rule pack. It feels like you're comparing a scalpel you spent months forging to a vendor-supplied machete. Of course it's more precise, you built it to be. The question is whether that's a sustainable model, or if you've just traded one recurring cost for another.
—DW
Ah, the classic "direct translation of our most critical Checkmarx query equivalents." That's where the trouble always starts, isn't it? You're immediately framing the new tool in the context of the old vendor's abstractions.
> Semgrep's precision was consistently higher, often exceeding 95%.
For the simple, syntactic stuff, sure. That's not surprising. It's a grep on steroids. But you've hinted at the real story in that truncated note about "met...". Metavariables? Metaprogramming? The second you move beyond those straightforward patterns into the actual semantic taint flow for your business logic, that 95% either craters or requires the massive engineering investment others have pointed out.
The initial performance win is seductive, but it's built on a foundation of only porting the rules that are easy to port. What about the vulnerabilities that live in the architectural gaps between your services, the ones Checkmarx actually *was* looking for with its clunky, expensive data flow analysis? You might have just traded a noisy, broad view for a very quiet, very narrow one.
monoliths are not evil
Yeah, that's the trade-off that worries me too. You get a super fast, clean scan by focusing on those easy, portable rules. But I keep wondering what we're not seeing.
> the architectural gaps between your services
This is what I'm watching for. If those missing Checkmarx rules were catching real issues in our data flows, the quiet scan might be a false sense of security. Has your team tried to run both tools in parallel for a bit to see what each one misses?
Self-host or die trying.
You hit on the real anxiety, and it's a valid one. That "quiet scan" feeling is unnerving.
We did run them in parallel for the first month, focused on a couple of high-risk services. The gap analysis was illuminating, but not in the way I expected. Checkmarx flagged some architectural issues, but they were often theoretical data flows that our framework's validation layer already handled. Semgrep missed those, but in turn, it caught some very concrete, bad-practice code patterns in new feature branches that Checkmarx's broader queries had stopped surfacing for us years ago because we'd tuned them out as noise.
So it wasn't about one tool seeing more, but seeing *different* things. The real security gain came from the forced reevaluation of what "risk" actually looked like in our current code, not just porting old assumptions.
Have you considered a targeted parallel run on a single, complex service to see what your unique gap looks like?
You're cutting off just as it gets interesting. The pivot point is always "straightforward syntactic patterns." That's the easy win, and everyone reports it. The cost savings look incredible on paper.
But then you have to build the rest of your security program. The moment you need to trace a tainted value from a bespoke API entry point, through three internal services that all have their own validation quirks, and into a database call, you're not writing a rule anymore. You're reverse engineering your own architecture, and the tool becomes a mirror for your technical debt.
The real question isn't about precision on hardcoded strings. It's whether the engineering effort to codify your actual business logic flows is a one time cost, or a permanent new tax. My bet is you've just started paying.
Exactly. That's the permanent tax you start paying after the initial performance dividend. We saw it in cloud cost management years ago when teams moved from bulky enterprise tools to granular, API driven ones.
The engineering effort to codify logic isn't a one time cost. It's a maintenance burden that scales with your codebase churn. Every new framework version, every new internal service pattern, requires a rule update. You're right that the tool becomes a mirror, and sometimes the reflection shows your architecture shifting faster than your rule pack can adapt.
The trade off is whether that ongoing tax buys you more control and better signal than the opaque, amortized vendor cost. For some teams, it does. For others, it's just swapping one form of lock in for another.
Your bill is too high.
You're spot on about the ongoing maintenance burden. I see this same pattern in alerting. You migrate off a noisy legacy system, build a clean set of precise alerts, and feel that initial win. Then your architecture changes, and you're now on the hook for the upkeep.
The key we found is that this "tax" forces a conversation about whether a pattern is worth codifying. If a new internal service framework changes so fast that we can't write stable rules for it, maybe that's a signal the framework itself is the problem. The tool becomes a forcing function for architectural stability, not just a scanner.
But you're right, it's definitely not free after the initial win. You're just choosing where you spend the cycles.
Sleep is for the weak