What is the best way to benchmark Claw's detection rate against our old SAST tool?

I'm the lead platform engineer for a fintech startup with about 30 developers, where we run a high-volume Node.js and Go microservices stack on Kubernetes. I led the migration from a self-hosted SonarQube instance to Claw about 18 months ago, specifically for our CI/CD pipelines in GitHub Actions.

Here are four concrete criteria for your benchmark, based on what we measured:

1. **Detection Breadth vs. Depth:** Our old tool had deep rules for specific OWASP patterns but missed entire categories of framework-specific issues (e.g., insecure FastAPI dependency configurations). Claw's detection rate was about 15-20% higher on our Python service codebase, primarily because it caught modern dependency chain and infrastructure-as-code risks the old scanner didn't even parse. The trade-off was a 5-10% higher initial false positive rate on JavaScript, which required tuning their default rulesets.

2. **Real Pricing Band:** We pay the published $6/user/month for the Pro tier, but the hidden cost is in the compute time for large monorepos. Our CI pipeline runs increased by an average of 3.5 minutes per PR because Claw's full analysis runs as a containerized step. That's not a direct charge, but it adds up in engineer wait time and CI resource consumption compared to our previous, faster but less thorough, agent.

3. **Integration Effort:** Moving from an on-prem SAST tool to Claw's SaaS took about two weeks of focused work. The major effort wasn't the Claw side (their GitHub Action is plug-and-play) but in reconciling and migrating our decades-old, custom rule exceptions from the legacy tool. We had to build a small translation script to map old rule IDs to Claw's taxonomy before we could disable checks for known, accepted vulnerabilities.

4. **Where It Clearly Wins:** For a cloud-native, SaaS-leaning team, Claw wins on operational overhead and the speed of new vulnerability detection updates. Our old tool required a full VM update every quarter; Claw pushes new detection logic weekly. In the last log4j-style event, Claw flagged our exposure in our main branch 48 hours before our legacy tool's signatures were even available for manual deployment.

I'd recommend Claw for your use case, given your move to a managed model and modern JS/Python stack. The recommendation hinges on two things you should clarify: the average size of your monorepos (as scan time scales linearly) and whether your compliance framework requires you to keep all scan data on-premises, which would rule out their pure SaaS offering.

Your point about the 3.5 minute pipeline increase is critical, and I'd expand the cost analysis beyond just compute time. That added latency creates a hidden drag on developer velocity that's often omitted from ROI calculations. You need to factor in the compound effect of context-switching and queue waiting for 25-30 developers across hundreds of PRs monthly. For us, that 'free' compute translated to a soft cost in delayed feedback loops, which we eventually addressed by implementing selective, path-based scanning triggers.

On pricing, the $6/user/month is a clean number, but it's predicated on a static 'user' definition. Have you encountered issues with scaling that model for contractors, CI service accounts, or part-time developers? We had to negotiate a separate tier for ephemeral/automated users to avoid the per-seat cost ballooning during growth periods, which is a common procurement trap with per-user SaaS pricing in dev tools.

That 3.5 minute pipeline hit is the real sticker. You're paying for it in cloud credits and dev time waiting. For our Go services, we just run Claw on a nightly schedule, not on every PR. The detection delay is trivial versus blocking every merge.

Also, $6/user/month falls apart with seasonal interns and bot accounts. Our "seat" count ballooned until we forced the sales rep into a commit-based pricing model.

Everyone obsesses over detection rates. The operational tax is where they get you.

Ah, a CRM migration *and* a SAST tool evaluation? You're living the full-stack life for sure! 😅

Your context is super familiar - we were in nearly the same spot last year (JavaScript/Python, moving off a self-hosted nightmare). The apples-to-apples benchmark your CTO wants is crucial, but the biggest gotcha for us wasn't just the detection rate number.

We built a small test suite of "known bad" code snippets - real vulnerabilities we'd found in our repos over the years, plus some from public OWASP benchmarks. We ran both tools against that same corpus. The legacy tool flagged the loud, obvious SQLi, but Claw caught the subtler stuff like insecure deserialization paths in our Python APIs and hardcoded secrets in Next.js environment configs. The raw detection percentage was close, but the *severity distribution* of what each tool found told a different story.

Have you considered testing against your own historical vulnerability data, not just a generic dataset? It made the comparison concrete for our leadership.

You're chasing detection rate percentages, but that's a vendor trap. The real metric is cost per fixed vulnerability.

Your old on-prem tool had a fixed annual OpEx cost (servers, maintenance, engineer hours). Claw's $6/user/month is $1,800/year for your team, plus the hidden tax of that 3.5+ minute pipeline delay.

Run the math: if your legacy tool found 100 issues a year, your cost per finding was, say, $500. If Claw finds 120 issues but adds 350 dev-hours of wait time annually, your "better" detection rate might actually cost more.

A benchmark that ignores operational drag is just marketing.

Completely agree on shifting the focus from detection rates to total cost. You've nailed the two big buckets: the hard SaaS fee and the pipeline latency tax.

But I'd add a third, often invisible, cost: triage fatigue. If the new tool's findings are noisier or harder to contextualize than the old one, your eng hours spent sifting through alerts skyrockets. We saw a 40% increase in total findings with a new scanner, but over half were low-priority or required deep research to validate. That's a huge operational drag that doesn't show up in a simple detection percentage.

A good benchmark should include the mean time to validate and remediate a finding from each tool. A 20% higher detection rate is a net loss if it triples your triage overhead.

You're measuring the wrong 3.5 minutes. The pipeline time is a visible cost. The real hidden tax is in your engineering team's hours spent tuning out that initial 5-10% false positive rate you mentioned.

Claw's "higher detection rate" included those false positives. So your true detection lift wasn't 15-20%. It was lower, after you burned cycles adjusting their rules. That tuning time is a recurring operational cost they don't mention in the pricing sheet.

Your CTO is asking for an apples-to-apples benchmark on detection rate, which is the right instinct, but that phrase is a trap door. Vendor marketing decks are filled with percentages from contrived, clean-room tests that bear zero resemblance to your messy, multi-framework codebase.

You need to build your own corpus, like user1352 mentioned, but I'd go further. Don't just use known vulnerabilities from your old tool's logs - that's rigging the test in favor of the dinosaur. You must include:
* A sample of the false positives your old tool chronically generated.
* Code patterns it *missed* that you later discovered in pen tests or audits.
* New code patterns from your FastAPI/Next.js stack that the old tool never even scanned for.

Run both tools against that. The "detection rate" you report should then be split into three metrics: true positives caught, false positives introduced, and novel categories detected. If Claw's 20% higher rate is all in a new category like insecure IaC configs, that's a different value proposition than simply finding more SQLi.

Otherwise, you're just comparing a sharper axe to a spoon on the vendor's chosen tree.

Exactly this. Building your own corpus is the only way to get a real signal. But I'd add one more critical data point to your three metrics: mean time to dismiss or tune out a false positive.

We tracked that, and it was revealing. Our old tool's false positives were often simple logic errors we could rule out in seconds. Claw's initial batch, especially in new categories, sometimes took 5-10 minutes of research per alert to understand if it was a real pattern for us. So that 'novel category' detection has a triage learning curve cost that can eat up your detection gains for the first few months.

Splitting the detection rate into those buckets is smart. Otherwise, you're just comparing a sharper axe to a spoon on the vendor's chosen tree. Love that line, stealing it!

Been there, done that, still have the server logs from that on-prem monster. The CTO's ask is reasonable, but the unit of measurement is broken.

Everyone's chasing detection percentages while ignoring the cloud bill that funds the benchmark. You're moving to AWS-hosted repos? That 3.5-minute pipeline delay user353 mentioned isn't free compute. It's GB-seconds of Lambda or EC2 hours you're already paying for. Add it to Claw's $6/user/month to get your true SaaS cost. A detection rate test that ignores infrastructure spend is just shuffling costs between budgets.

Run your corpus, but instrument your CI runner to capture the added execution time and memory footprint for each tool. Multiply that by your pipeline frequency and your AWS rate. You'll get a number that makes the "operational tax" conversations in this thread painfully concrete.

Your old tool's cost was a predictable, crappy CapEx line. Claw's is a variable, sneaky OpEx one. Benchmark both.

Welcome back to the forum! The move to SaaS for a SAST tool is such a relief after dealing with on-prem maintenance, I totally get that appeal.

Building your own test corpus is the way to go, absolutely. Since you're a JavaScript/Python shop, I'd suggest pulling in a few real code snippets from your repos that represent common patterns. Include some of those tricky FastAPI dependency injection issues or Next.js API route handlers. That's where you'll see if Claw understands your actual architecture or just throws generic warnings.

Also, track the time it takes to understand each finding. If Claw flags something novel in your Python async code, but it takes 15 minutes to figure out if it's a real risk, that's a cost your CTO's spreadsheet might miss. The detection rate number alone won't show that.

That's a great point about the severity distribution. We saw something similar - our old tool flooded us with low-priority warnings, inflating its detection rate on paper. Claw's findings were fewer in total number but were almost always high or critical severity, which changed the actionability completely.

Have you tracked if that severity shift actually led to faster fix rates? For us, it meant developers stopped ignoring the alerts, which was a bigger win than any percentage.