DIY vs managed, at increasing scan frequency
Same app count and test-case depth, only scans/year changes. Continuous (CI-integrated) scanning is where DIY triage labor usually overtakes a flat SaaS subscription.
| Scans/app/yr | DIY annual | Managed SaaS annual | Cheaper |
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Where the DIY cost actually comes from
Open-source adversarial scanners like NVIDIA Garak and Microsoft PyRIT are free to license, which makes "DIY red-teaming" look like a rounding error next to a managed subscription. It isn't, because the license fee was never the cost. Every scan sends hundreds or thousands of attack prompts through the target model — that's the compute line, usually the smallest of the three. Every one of those results then has to be reviewed by a human to separate a real jailbreak from a false positive, and that triage line scales with test-case count and scan frequency in a way the compute line doesn't. The setup line — building the harness, wiring it into CI, mapping attack categories to your app's actual risk surface — is mostly one-time per app, but it's real hours at an engineer's fully-loaded rate, not free.
Managed SaaS platforms fold all three into one line item: a maintained attack library, a triage-reducing dashboard, and no setup hours. That's worth a real premium at low scan volume, where DIY's fixed setup cost dominates. The crossover happens as scan frequency climbs toward continuous/CI-integrated testing — at that point DIY's triage labor, which the tool doesn't do for you, compounds every deployment, while a flat monthly SaaS fee doesn't move. Run your own app count and triage-minutes-per-case through the table above rather than assuming either model wins by default.
How this calculator works
The AI Red-Teaming Cost Calculator estimates the annual cost of adversarial-testing your AI applications under two approaches: a DIY pipeline built on open-source scanners (Garak/PyRIT-style) and a managed SaaS subscription. DIY cost has three parts — one-time setup hours per app, compute cost for running attack test cases through your target model, and human triage time reviewing flagged results. Managed SaaS is modeled as a flat per-app monthly fee. The two biggest levers are scan frequency (one-time vs periodic vs continuous/CI) and triage minutes per test case, since triage labor is what makes DIY scale worse than compute cost alone would suggest.
Setup hours are amortized as a one-time cost in year one; recompute the DIY total without the setup line to see steady-state cost in later years. Managed SaaS pricing above $ thresholds typically moves to custom enterprise contracts (often $50K-200K/yr for large-scale continuous scanning) — the flat per-app rate here is representative of mid-market self-serve tiers, not enterprise deals.
Frequently asked questions
What is AI red-teaming and why does it cost money beyond the model bill?
AI red-teaming is adversarial testing of an LLM application before (and periodically after) deployment — throwing prompt injection, jailbreak, data-exfiltration and harmful-content attack cases at it to find failures before a user or attacker does. Beyond the token cost of running attack prompts through the target model, the real cost is engineering time: building or configuring the test harness, and triaging every flagged result to separate real vulnerabilities from false positives. That triage line usually dwarfs the compute line.
Is a DIY open-source scanner (Garak, PyRIT) actually cheaper than managed red-teaming SaaS?
At low scan volume and few apps, yes — open-source scanners like NVIDIA Garak or Microsoft PyRIT are free to license, so DIY cost is just engineer setup time plus per-scan compute and triage. Managed SaaS (Lakera, HiddenLayer-style vendors) charges a recurring per-app or per-call fee but ships a maintained attack library, dashboards and less triage overhead. As app count and scan frequency scale up — especially continuous/CI-integrated scanning — the labor cost of DIY triage tends to cross above a managed subscription, which is the breakeven this calculator estimates.
How often should an AI application be red-teamed?
One-time pre-launch testing catches obvious failures but misses regressions introduced by prompt changes, model upgrades or new features. Periodic testing (monthly/quarterly) catches drift. Continuous, CI-integrated scanning — running a red-team suite on every deployment, similar to a security regression test — is the standard being pushed for production agentic systems in 2026, because a single prompt or tool-permission change can reopen a previously-fixed jailbreak. Scan frequency is the single biggest lever in this calculator's cost estimate.