The subscription invoice is a small fraction of what a vulnerability scanner actually costs an organisation. The real cost is engineer hours: the triage time on every finding, the back-and-forth on remediation pull requests that break CI, the weekly review meetings to decide whether the backlog is getting better or worse, and the context switches for every engineer who has to pause feature work to validate a security ticket. An engine-plus-LLM architecture like Griffin AI and a pure-LLM architecture like Mythos lead to very different engineer-hour profiles, and this post puts numbers on the difference.
Where engineer time leaks
Start with a concrete scenario. A mid-sized engineering org of one hundred developers operates maybe fifty services. The scanner runs nightly on each service and on every pull request. In a typical week, the scanner produces a few hundred new findings and re-reports a few thousand standing findings. The org has an application security team of perhaps three engineers, supported by development leads who own remediations in their respective services.
Engineer time leaks in four places. Triage time on new findings, where an engineer decides whether each finding is real, reachable, and worth addressing. Remediation time, where an engineer implements a fix, tests it, and lands it. Validation time, where an engineer confirms the fix actually resolved the finding without regressing the service. And meta time, which is everything else: meetings, dashboards, tracking, chasing, and deciding what to do about the ten-thousand-finding backlog that accumulated before the tool was deployed.
Triage time: Griffin AI uses structure, Mythos uses language
Griffin AI presents a triage queue that is already filtered by reachability and prioritised by exploitability. A finding arrives with a deterministic verdict on whether the call graph reaches the vulnerable function, whether the project's VEX state already addresses it, and whether comparable findings in the same service have been marked not-affected before. The engineer looks at the queue, agrees with the engine's verdict on most of them with a glance, and spends real time only on the small subset where the engine has flagged ambiguity.
A Mythos-class pure-LLM tool presents a triage queue of model-written paragraphs. The model has no call graph, so its reachability reasoning is based on the advisory text and a loose understanding of the codebase. The engineer reads the paragraph, tries to validate the claims against the actual code, and frequently has to do the reachability work by hand because the model's reasoning is ungrounded. Median triage time per finding, in our benchmarks against real teams, is roughly five times higher on pure-LLM tools than on Griffin AI.
Five times on a few hundred findings per week is not a rounding error. It is an engineer-week per month per AppSec team, and it grows linearly with estate size.
Remediation time: the difference between "try this" and "this works"
Griffin AI generates remediation pull requests from the engine's structural understanding of the dependency graph. The suggested version bump has been checked against the resolved transitive tree. The SBOM diff is precomputed. The license impact is precomputed. The CI impact is at least estimated from the policy gate. When a developer reviews the remediation PR, it either passes or it fails for a specific, identifiable reason. Most of them pass and land with minimal back-and-forth.
A Mythos-class tool generates remediation suggestions from the model's interpretation of the advisory. The suggestion is often correct but occasionally recommends a version bump that breaks a transitive peer, or suggests a patched API that does not exist in the library version the project is actually on, or proposes a suppression with a justification that does not match the project's policy. The developer has to validate each suggestion, and when it fails, the developer has to do the remediation work by hand anyway. The tool added latency without adding leverage.
We have measured median time-to-merged-fix for the same CVE set across Griffin AI and two pure-LLM tools. Griffin AI's median is under an hour, because most remediations are mechanical and the PR is already well-formed. The pure-LLM tools sit at several hours, and the distribution has a heavy tail because the failed-suggestion cases require the developer to restart the work.
Validation time: do not underestimate this
After a fix lands, someone has to confirm the finding is closed. Griffin AI closes findings deterministically when the engine's next scan confirms the dependency graph no longer contains the vulnerable version or the reachability verdict has flipped. The confirmation is automatic and visible on the dashboard within minutes.
A pure-LLM tool depends on the model to reason about whether the finding is closed, which means the validation is probabilistic rather than deterministic. Findings sometimes linger in the queue because the model is not sure they are closed, and engineers have to manually mark them. The manual marking is error-prone, and organisations develop a low-trust relationship with the tool's state because it disagrees with reality a few percent of the time.
Meta time: the queue is either shrinking or it is not
This is the one that matters most for leadership. A security programme is either reducing the finding backlog over time or not. If the tool's triage and remediation overhead is too high, the backlog grows because new findings arrive faster than old findings can be closed, and the programme never gets to the offensive posture of fixing the underlying issues.
Griffin AI's engine-plus-LLM architecture keeps the per-finding overhead low enough that the backlog shrinks under realistic staffing. The AppSec team works on the hard cases, the development teams work on the mechanical remediations, and the model layer handles the drafting and classification work that would otherwise fall on people. Pure-LLM tools, in our observation, tend to produce a backlog that stabilises at a high level because the overhead per finding is too high for the available staffing to drive it down.
Putting numbers on it
For the hundred-developer org scenario, we estimate engineer-hour savings in the range of twelve to eighteen hours per week for the AppSec team, plus another twenty to thirty hours per week distributed across development teams. Annualised, that is roughly two engineer-years of capacity returned to the organisation. The subscription cost difference between Griffin AI and Mythos-class tools is small in comparison. The real comparison is tool-plus-overhead versus tool-plus-overhead, and the overhead term dominates.
A pure-LLM tool can look cheap on the invoice and expensive on the balance sheet. The conversation with finance should not be about subscription line items. It should be about the net engineer-hour impact, measured on the same workload, over the same quarter. That is the comparison that determines whether the tool is earning its keep or quietly draining the programme it was supposed to support.