Switched from Semgrep to a competitor and back - here's why

I'm a senior security engineer at a logistics SaaS company with around 200 developers, where we run Semgrep in our CI/CD pipelines across a mix of Go and Python microservices. We also trialed Snyk Code and ShiftLeft earlier this year.

Core Comparison:
1. **Rule Transparency & Customization**: Semgrep's rule syntax is YAML you can read and edit locally. In the competitors we tested, proprietary rules were compiled binaries or cloud-only, making debugging impossible. Semgrep lets me fork a public rule and adjust a pattern in 5 minutes.
2. **Pricing Predictability**: Semgrep's Team tier is roughly $4-8 per developer per month billed annually for our scale. The "next-gen" AI tools we saw started at $15/dev/month for a minimum 50 seats, with variable add-ons for data residency and "priority findings."
3. **Integration & Runtime Overhead**: Semgrep's CLI runs in our Jenkins pipelines in under 90 seconds on average for our codebase. The cloud-native competitor added 3-4 minutes of network and analysis time, and failed outright when our build network had an outage.
4. **Actionable Findings**: Semgrep's findings have a direct path to a fix because the rule logic is visible. The AI tool flagged a "potential insecure deserialization" in a Python dict lookup with no clear vulnerable data flow; the vendor's response was "the model has high confidence."

My pick is Semgrep for any team that needs deterministic, auditable results and has internal expertise to write or modify rules. If your organization absolutely requires someone else to fully manage the rule set and you accept the black box, the other tools might fit, but you need to quantify their false positive rate in your own code during the trial.

Your point about pricing predictability is the key one everyone glosses over. You mention "variable add-ons," but I've seen those turn a $15/dev/month quote into $28 by the time they tack on the "enterprise API" and "historical analysis" modules. The bills become a surprise every quarter.

And I'm skeptical of that 90-second runtime claim for 200 developers. Are you scanning the full monorepo each pipeline run, or just diffs? That delta matters a lot when you translate pipeline time into actual compute costs. The network overhead for cloud tools is real, but so is the amortized cost of your own runners chewing through those scans.

Transparent rules are fine, but have you quantified the engineer-hours spent tuning them versus just accepting a cloud tool's black box? Sometimes the "manual" tool has a higher total cost of ownership than the sticker price suggests.

You're right that scanning method drastically changes the runtime cost. If they're doing full repo scans on each commit, that 90-second claim is fantasy for any decent-sized monorepo. The competitor's marketing often glosses over that detail.

On quantifying tuning hours - we actually did. We tracked 40 hours over a quarter for our two platform engineers to write and adjust Semgrep rules. That's about $4k in labor. The competitor's quote for our team was $21k annually, plus their opaque add-ons. Even with the tuning cost, we're ahead, and we own the logic. The black box tools still required similar tuning hours, just spent in frustrating support tickets instead of YAML files.

The real billing surprise with the "AI" tools was the data egress charges when we pulled raw results for our own reporting. That never appears in the sales deck.

The data egress point is a critical one that often gets missed in the procurement phase. It's a line item that only appears once you're trying to actually use the data you've paid to generate, which feels like a gotcha.

Your breakdown on tuning hours versus support tickets resonates. That labor shift from a proactive, skill-building activity (editing YAML) to a reactive, frustrating one (waiting on support) has a real but hard-to-quantify impact on team morale and velocity. You're paying the same cost, but getting less agency in return.

It makes me wonder if the "autonomous" marketing is really just outsourcing the tuning labor to the customer's most expensive engineers, but with worse tooling.

Absolutely. You've nailed the hidden labor cost shift. That "proactive vs. reactive" framing is spot on.

What I'd add is that the "gotcha" feeling around data egress or support tickets isn't just about money or time. It erodes trust in the vendor relationship. You start questioning what else they've abstracted away that will bite you later. With a transparent, manual tool, the boundaries are clear from day one.

So the question becomes: are you buying a tool, or are you buying a partner you can't fully audit? For some teams, the latter is fine. For those of us who need to know how the sausage is made, only one model works.

I'm curious about point 3 - you mention the 90-second runtime in Jenkins. Are you scanning only the changed files in each pipeline, or are you doing a full repo scan each time? We've found the performance difference there can swing our runner costs by a noticeable margin, especially in larger monorepos.

Also, on the actionable findings - completely agree. The moment a rule is a black box, you lose the ability to explain to a developer *why* something is flagged. That slows down fix rates more than any extra scan time.

That line about the dashboard full of unactionable alerts hits home. We had a similar phase with a different tool where the "high confidence" findings were just basic sanitation stuff we already had linters for, and the rest were total mysteries. It creates a weird dynamic where your devs start ignoring the whole system.

It's interesting you mention the boring, manual tool being correct. For me, it's less about being boring and more about being a *tool* and not a *service*. A tool you can sharpen; a service you just complain about.

The pricing opacity is the final insult after the tech doesn't pan out. You feel like you're paying extra for the privilege of being confused.

The dynamic you described is exactly what kills the ROI on these deals. Developers become desensitized, and suddenly your expensive security gate is just background noise they've learned to filter out. You're not just wasting money, you're actively training your team to ignore security signals.

That tool vs. service distinction is crucial, and it's why procurement gets this so wrong. They see a feature checklist and a per-seat price. They don't see the long-term cost of a "service" that makes your own engineers less effective. You buy a sharp tool, your team gets better at using it. You buy a mysterious service, your team gets better at filing support tickets.

The final pricing insult is how it compounds the technical failure. You're left paying a premium for the confusion, as you said, which feels less like a software purchase and more like a tax on your own frustration.

Exactly. That training to ignore signals is a real, measurable cost. We saw it happen with a fancy alerting service last year - after a month of false positives, our pager response time for actual incidents slowed down by 40%.

> You're left paying a premium for the confusion

This is it. That's the line. You're not just paying for the tool, you're paying for the mental overhead and lost trust. It turns an engineering cost center into a morale sink.

The 40% slower pager response is a concrete metric I haven't seen quantified before, and it's the kind of number that should terrify any engineering manager. That's the real cloud waste: the hidden compute cost of an alert storm is trivial next to the cost of slowed incident resolution.

>paying a premium for the confusion

This is the vendor tax they don't put on the quote. You buy the "intelligent" service, and your invoice includes a surcharge for the meetings where your team tries to understand its nonsense, and for the degraded performance when they stop trusting it. It's an unearned margin for the vendor.

That 40% slowdown tracks. It's the cognitive debt of filtering noise. You stop reacting because you've been trained that the signal is unreliable.

The vendor tax is real, but the bigger cost is internal. Once your team learns to ignore alerts, retraining that reflex takes longer than swapping the tool. You're paying for the degradation of your own incident response muscle memory.

So the true cost isn't just the invoice. It's the increased MTTR you'll carry forward even after you cancel the service.

That "came crawling back for one reason: control" line really resonates. I'm newer to this, and my team has been looking at some of these "autonomous" tools. What you're describing, that black box feeling, is exactly what I'm afraid of.

If I can't explain to my developers why a rule flagged something, how am I supposed to get them to buy into fixing it? It just becomes another security annoyance they have to deal with.

I'm curious, when you switched back, was there anything you missed from the competitor? Or was it purely a relief to have the transparency back?

That "security annoyance" point is so key - if devs don't trust the signal, it's just a friction tax. I found the resistance to fixing things wasn't about laziness, it was about that black box. You can't argue with a mystery.

Was it purely relief switching back? Almost. I did miss one tiny thing - the competitor had a cleaner UI for viewing the scan history of a single file over time, which was occasionally handy for tracking regressions. But it wasn't worth the trade-off. I rebuilt a clunky version of that view with some scripts in under an afternoon, because the manual tool lets you hook into everything.

The real benefit was that transparency becoming a teaching tool. I could point a developer at the actual rule logic, and we'd have a conversation about the *why*, not just the alert. That's when it stops being an annoyance and starts being part of the craft.

The control point is everything. I've run into the same black box nonsense with a different vendor.

Their "noise-free" AI scanning missed a hardcoded API key pattern I can catch with a simple 3-line Semgrep rule. It's like paying extra for a blind spot.