Semgrep sign-up experience: any tips for first-time users?

While I generally agree with starting on the CLI, the SaaS sign-up does have one immediate advantage for a structured evaluation, even for a first-timer. You can use their cloud platform's onboarding workflow as a free, temporary template to systematically understand what to look for.

It walks you through connecting a test repo, running default rulesets, and reviewing findings in a curated interface. This gives you a concrete framework for your audit, which you can then replicate offline. Just remember to treat it as a disposable learning session and not a production commitment.

The key is setting a hard cutoff before you need private rules or long-term storage, which aligns with your point about the meter starting to run.

That's a fair point about using the onboarding as a structured template. I've seen teams get real value from that guided path to understand the scope of findings, which can be overwhelming when you first run the CLI against a large codebase.

The trick is being absolutely rigid about treating it as a one-time tutorial. I'd suggest creating a throwaway GitHub account and a dummy repository specifically for this, so there's no accidental link to your real projects or any temptation to "just leave it connected."

What's your take on whether the curated findings in that onboarding give a representative sample of what the CLI would flag in a real, messy project?

I like the idea of using the guided onboarding as a temporary template, that's clever. It can be tough to know where to start when you're just handed a CLI.

But I'd add one caveat: the curated findings in the onboarding might be a bit too clean. Real projects with legacy code can produce a firehose of results that the tutorial doesn't prepare you for. That initial shock is part of the learning curve, maybe.

How do you gauge if you're ready to move from their tidy demo to your own messy reality?

Totally agree about the curated findings being too clean. It's a gentle intro, but real projects are a mess.

What helped me gauge readiness was to run the CLI on a small, stable module of our legacy code first - not the whole firehose. The jump from tutorial to "real" feels smaller that way, and you see what the unfiltered output actually looks like.

If you can triage the results from that one module without feeling completely lost, you're probably ready to scale up. The shock is real though!

Totally agree on starting with the CLI. I think a lot of us in the cloud automation space are used to "free tier" meaning "indefinitely usable for small stuff", so that 30-day limit on findings storage is the real tripwire. It's easy to set it and forget it.

Your point about calculating scan volume first is key. I'd add that if you're in a CI/CD pipeline, even a moderate commit frequency can push you past the free tier faster than you'd think, especially with monorepos. Doing a quick back-of-the-envelope estimate with `semgrep --metrics=off` on your main branch first gives you a data point before any sign-up.

The CLI really is powerful enough to build a whole security gate in your pipeline, which might be all you need.

The 30-day storage trap is real, but for pipelines, the scan volume itself is the quieter killer. You can estimate commits, but don't forget about the PR scans. That's where the free tier evaporates, especially if you're scanning on push to every feature branch.

Running with `--metrics=off` is the right first step, but if you're serious about the CLI-as-a-gate, you need to bake in suppression files from day one. Otherwise, the noise will make your team hate the tool before you even get value from it.

You've absolutely nailed the hidden cost with PR scans. The `--metrics=off` estimate on the main branch is just a baseline; the combinatorial explosion from feature branches is what breaks the model.

Your point about suppression files is critical, but I'd add they're also the first step in governance. Treating a `.semgrep.yml` suppression list as a living document, with each entry requiring a brief justification ticket ID, turns noise reduction into an audit trail. Without that, you're right - the team will revolt. But with it, you're building the prioritized backlog for actual fixes.

Do you have a strategy for managing that suppression file as the codebase evolves, to prevent it from becoming a permanent graveyard for ignored issues?

Your warning about the exit strategy is the most overlooked part. Teams treat the cloud portal as a trial, but the data gravity you create with findings and custom rules makes moving off it later a genuine migration project. You can't just download and go.

The CLI isn't just for a first audit. You can run the entire pipeline with it indefinitely if your need is just gating, not historical tracking. The cloud becomes a tax on your commit history.

You're right about the suppression file being critical for noise, but its format is also a common blocker. The inline `nosemgrep` comment in code is immediate and works, but a separate YAML suppression list becomes a maintenance headache across teams.

I suggest starting with inline comments for any high-noise rules, only moving to a central file once you have a clear process for pruning it. Otherwise, you end up with a `semgrep-ignore` file that no one owns.

I like your approach of starting with inline `nosemgrep` comments to avoid that orphaned YAML file problem. It's a pragmatic way to handle initial noise without creating a maintenance burden.

In our GitOps workflows, we've treated the central suppression file as a managed resource, similar to Kubernetes ConfigMaps. Every change requires a PR review, and we tie periodic audits to our release cycles. This keeps it from becoming a graveyard, but it does need team discipline.

How do you handle the trade-off when inline comments clutter critical parts of the code, like shared libraries or core modules?

Exactly. That "audit before you commit" step is often skipped in the rush to adopt a tool. I've seen teams push years of technical debt into a SaaS platform on day one, and then they're stuck either paying to store it or facing a massive cleanup effort to leave.

The CLI gives you that crucial evaluation period without the lock-in. Run it, see what it flags, and decide if those findings are something you'd even want preserved for 30 days. Many aren't.

Exactly. That first step is critical because you're not just evaluating the tool, you're evaluating your own code. The CLI lets you do a cost/benefit analysis on the findings themselves.

If 80% of the initial hits are low-priority or false positives you'd never preserve anyway, paying to store them in a portal is a waste. The 30-day trap only matters if the findings have a lifespan longer than your sprint cycle.

I've seen teams skip this and treat the cloud sign-up as step one. They end up with a bill for storing junk they'll never act on, and a migration tax to leave.

You're not wrong, but framing the CLI as just an 'audit' undersells it. It's a complete, production-grade tool. The cloud portal is for reporting and historical data, not the core scanning function.

The real trap is teams thinking they need that reporting before they've even decided which findings are worth tracking. I've run the CLI in CI for two years without touching their SaaS. The cloud isn't a progression, it's a different product.

It's not a different product, it's a vendor lock-in funnel. The CLI today is production-grade until they decide to cripple it to drive cloud adoption. I've seen this playbook before.

You can run the CLI in CI for years until they gate a critical new rule pack or engine update behind a login. Then your "complete" tool isn't so complete anymore.