Your spreadsheet is tracking features, which is exactly what the vendors want. Track cost per finding instead.
Every scanner generates a ticket in your backlog. Your real cost is engineer time to triage and fix. A free tool like `npm audit` spits out 50 CVE tickets. A paid SCA tool prettifies them into 55 tickets. You just paid for 5 extra tickets, most of which are in dev dependencies you can't fix.
>How did you decide what to tackle first?
Start with the problem that has the clearest fix action. For your stack, that's SCA via `npm audit` and `pip-audit` in CI. The fix is a version bump in a file you control. Secrets scanning sounds simple, but fixing a found secret requires rotation, which is an ops project, not a PR.
The most revealing vendor question I ask is, "What's your average time to remediate a critical finding for a team our size?" If they don't have that data, they're not measuring outcomes, just selling scans.
Right-size or die
Forget the spreadsheet features column. Add a "fix action" column instead.
Prioritize based on clarity of the next step.
- SCA finding? Update package version. Clear.
- SAST finding? Rewrite code logic. Unclear.
- Secret in history? Rotate keys, purge git. Ops project, not dev task.
That's why everyone says start with SCA in CI. The win is obvious and contained.
Your vendor question should be, "Walk me through your recommended triage for 100 new critical SCA findings in our repo. What do we ignore, what do we patch, and what requires a PR?" Their answer shows if they understand engineering time is your real cost.
Integrate or die
The point on parallelization is critical, especially for a polyglot repo. I've seen scanners that can't handle a monorepo with concurrent Python and Node.js builds because they rely on a single, shared dependency cache. The answer to "how does it scale" often reveals whether they treat scanning as an isolated job or as part of a distributed pipeline.
Your advice to skip the all-in-one platform is sound, but the trade-off is integration debt. You'll have three separate tools reporting to three different dashboards. That's where a platform can offer real value, but only if its individual scanners are competitive. Most aren't.
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Your spreadsheet approach highlights the common trap of feature comparison. It leads to a false sense of objectivity when you're really just organizing vendor claims. I agree with others that you must start by establishing a baseline with free, discrete tools for your specific stack.
Run `gosec` for Go and `bandit` for Python in your pipeline for two weeks. Capture their runtime and findings. Then compare any vendor's demo output against that baseline. The difference in findings is their actual value-add, which is often shockingly small for SCA and secret scanning. The performance delta is critical, too, as added minutes per pipeline build have a compounding engineering cost.
The key question for a vendor isn't about their features. It's this: "Show me a benchmark of your scanner's runtime and result set on a snapshot of our main repo, compared to the output from the standard open-source linter for that language." Their willingness, and the results, will tell you everything. Most platforms falter here because their generalized engines can't match the speed and precision of native tools.