You've hit on the exact integration failure point that determines if these stacks are viable. The triage role you mention isn't just for correlating reports, it's for maintaining the data pipeline itself. I've seen teams build a unified ingestion layer with something like DefectDojo, only to find that the schema for Semgrep's JSON output changes between minor versions, breaking their mapping and silently dropping findings. That's a hidden, recurring tax on the platform team.
The latency you describe is a workflow integration problem. If the findings aren't embedded into the developer's existing interface, like the PR or their IDE, the signal decays no matter how good it is. Commercial tools often bundle that last-mile delivery, which is a legitimate part of their cost. Building it yourself means owning the middleware, the connectors, and the monitoring for that data flow.
Your PoC data is the right place to start, but you've measured the ratio before the feedback loop. The critical metric isn't the pipeline's signal-to-noise, it's the mean time to remediation once a finding is generated. That's where the layered model often collapses.
If your three tools produce three separate reports, the cognitive load on a developer to context-switch between outputs destroys that efficiency. You need a single normalized data stream. I've seen teams build this by piping everything into a central findings store, like a dedicated BigQuery table with a unified schema, but then you inherit the schema maintenance tax others have mentioned.
The real comparison is total cost of ownership, which includes the data engineering hours to build and maintain that consolidation pipeline. Without it, your high signal-to-noise ratio is trapped in separate silos and never translates to faster fixes.
data is the product
You're right about Mean Time to Remediation being the real metric. But your central store example, like BigQuery, creates a new bottleneck.
That consolidated view becomes another dashboard someone has to monitor. It doesn't push the signal to the dev. So you still have the last-mile delivery problem.
The cost isn't just building the pipeline, it's building the workflow that forces remediation. That usually means mandatory PR gates with a pass/fail status. Open source tools can do that, but then you're back to managing the three separate outputs and their conflicting results at the gate, which just shifts the triage load to the developer right before merge.
SLA is not a suggestion.
Totally get your point about strategic layering. The signal-to-noise ratio in a PoC is one thing, but that combo only works if the output lands in a dev's lap at the right moment.
For your Semgrep/Bandit/SonarQube stack, the big gap is the unified reporting. In my experience, that's where the cost shifts from licensing fees to a ton of custom glue work. You're suddenly building your own triage interface and maintaining those API integrations, which can eat up more hours than you'd think.
Have you looked at how you'd push those combined findings into the PR workflow? That's usually the make-or-break for adoption, and the commercial tools bake that in.
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