What does 100% AI evaluation actually cost?
Every trace you score with an LLM-as-judge gets taxed twice — once on judge tokens, again on missed-incident risk if you sample less to control cost.
The calculator below puts a number on both. Real May 2026 pricing. Nine cost categories. The math most evaluation tools hide behind their pricing page.
Based on publicly listed vendor pricing as of May 20, 2026. Defaults are industry medians. Override any assumption inline.
| Industry | Volume / day | Incident cost | Protect · / yr |
|---|---|---|---|
→SaaS / DevTools | 1.0M | $15.0K | $54.8K |
→Fintech | 500K | $5.56M | $27.4K |
→Healthcare | 100K | $7.42M | $5.5K |
→Legal | 50K | $4.88M | $2.7K |
→Government | 200K | $5.50M | $10.9K |
99% lower than running this on GPT-5 mini at 1.0M traces/day.
At your settings: 1.0M traces/day · 100% sampling · 5 evals/trace = 5.0M evaluations/day · 1.8B over 12 months
Same workload, same volume, same sampling rate. The only variable is who's doing the evaluation. Lower is better.
Protect classifier runs at infrastructure cost (~$0.00003/call). Frontier judges run at per-token pricing — every token billed every time.
Sampling less to save on judge tokens isn't free.
Every unsampled trace is a missed-incident lottery ticket. At classifier economics, you don't have to choose.
LLM-as-judge cost grows with traffic volume — every trace incurs new judge tokens. Fixed-infra evaluation stays flat until you cross a GPU capacity tier.
Specialist classifiers instead of frontier-model judges.
Five fine-tuned models — Content Moderation, Bias, Security, Data Privacy, Faithfulness. Enterprise teams fine-tune custom dimensions on their own labelled data. Per-call cost lands ~99% below a frontier LLM running the same eval.
Branded PDF · share with your CFO.
Eight pages with the full nine-category breakdown, methodology, and your exact configuration. You'll also get Mission Control — one engineering email a week, unsubscribe anytime.
Nine categories instead of three.
Most public eval calculators show three: LLM API spend, infra, and one incident-risk bucket. That misses where teams actually bleed.
LLM API cost
Per-token billing on the judge model. Includes batch discounts and prompt-cache effects.
Classifier / SLM cost
Per-call cost on Luna-2-class evaluators, or GPU-hour cost if you self-host.
Engineering build labor
Loaded annual salary × FTE-fraction × build months. Vendor evals minimize this; self-host carries it; build-your-own carries 1.5×.
Engineering maintenance labor
Ongoing FTE share for the eval pipeline. Vendor ~0.05×; self-host 0.25×; build 0.25×+.
Eval-drift maintenance
Monthly drift checks + quarterly re-baseline + annual ground-truth refresh. Industry standard ~40 hours/year.
Incident-risk exposure
(1 − effective coverage) × incident rate × cost per incident × traces/year. Industry-keyed via IBM 2025.
Retention storage
traces × 0.3 MB × retention years × $/TB-year. WORM rates if SEC 17a-4 or HIPAA-audited.
Compliance audit overhead
SOC 2 ($60K/yr amortized), HIPAA ($40K), SIEM ($75K if regulated).
Observability platform fee
Vendor span ingest billing (Langfuse, Braintrust, Arize, Future AGI, Fiddler).
What teams actually run.
Sources: IBM Cost of a Data Breach 2025, Verizon DBIR 2026, Grafana Observability Survey 2025.
| Vertical | Sampling | Evals | Failure mode | Incident cost |
|---|---|---|---|---|
| SaaS / DevTools | 10% | 3 | Hallucination in tool calls | $15K |
| Fintech | 25% | 5 | Compliance / fair-claims | $5.56M |
| Healthcare | 50% | 5 | PHI leakage, clinical hallucination | $7.42M |
| Legal / regulated | 75% | 6 | Citation hallucination | $4.88M |
| Retail / e-commerce | 5% | 2 | Refund-policy hallucination | $3.7M |
| Government / federal | 100% | 8 | Compliance breach | $5.5M+ |
The only Apache 2.0 + hosted-cloud combination in this space.
50+ built-in rubrics
Plus unlimited custom evaluators authored by an in-product agent.
Self-improving evaluators
Learn from production feedback — the drift category most calculators ignore.
Protect classifier family
5 fine-tuned dimensions at ~$0.00003/call — Luna-2-class economics.
Heuristic evals free, BYOK $0
The most competitive economics in the published comparison table.
OpenTelemetry-native via traceAI ↗
35+ framework integrations. No separate observability vendor needed.
SOC 2 + HIPAA + GDPR + CCPA ↗
All certified per the trust page; ISO 27001 in active audit.
Where we genuinely rank #2: Patronus has FinanceBench (the fintech-cited benchmark); Holistic AI is the NYC Local Law 144 AEDT-certified auditor. Layer the specialist on top of Future AGI for production scoring.
Frequently asked.
How is this different from Fiddler's calculator?
Fiddler compares two options across three cost categories. We compare five approaches across nine — adding engineering labor, drift maintenance, retention storage, audit overhead, and observability vendor fees. Our defaults also use industry-median tokens/trace (4,000, not 50,000) so the baseline isn't loaded.
Why default to 100% sampling instead of the 10% most teams run?
Because the math is the point. At classifier economics ($0.01/call or less), 100% is in budget for most teams; the historical 5-10% sampling was an LLM-as-judge anchor. The calculator shows the difference. Override to whatever you actually run.
Where does the "incident risk" number come from?
(1 − effective coverage) × incident rate × cost per incident × traces/year. We use IBM Cost of a Data Breach 2025 industry medians (Healthcare $7.42M, Financial $5.56M, SaaS $15K) and DBIR 2026 incident rate of 0.02% on unsampled traces. Every number has an override row.
Are the GPU rates current?
Yes — fetched May 2026. L40S at $1.10/hr Modal-equivalent, A100 at $1.19/hr RunPod, H100 at $2.99/hr Lambda Labs. The calculator's self-host default is L40S on Modal with 50% utilization headroom.
Does this account for prompt caching discounts?
Yes. Toggle "Cache discount" — the calculator applies the model-specific cache-read discount (e.g. Gemini $0.025/M for cached input vs $0.25/M base). Default is 25% cached, which is the upper limit Fiddler uses in their own methodology.
Why include the Opus 4.7 tokenizer adjustment?
Opus 4.7 ships a new tokenizer that emits up to 35% more tokens for identical text than 4.6. If we didn't multiply output tokens by 1.35, the Opus row would look about 25% cheaper than it actually bills.
How is "sampling sweet-spot" calculated?
For each method we evaluate riskAdjustedCost(s) for s ∈ [5%, 100%] in 5% steps and return the argmin. For classifier approaches the answer is always 100%; for LLM-as-judge on SaaS workloads it's typically 30–50%.
What about teams running a mix of approaches?
Standard pattern. Set the approach-mix sliders (e.g. 60% LLM-judge / 20% classifier / 20% heuristic) — the calculator computes effective coverage and combined TCO. The "Your mix" tile shows your specific number.
Can I share my configuration?
Yes — the calculator state lives in the URL. Click "Copy share link" to share the exact scenario.
How current is the pricing data?
Last refresh 2026-05-20. Refresh cadence: quarterly. See the methodology section above for primary sources.
How does Future AGI's ai-evaluation actually price?
$10 per 1,000 AI credits (~$0.01 per classifier call), heuristic evals free, BYOK $0. Boost tier $250/mo + $5 per 100K gateway events; Scale $750 + $2.50/100K. See futureagi.com/pricing.
Results are for illustrative comparison only. Pricing reflects publicly listed vendor rates as of 2026-05-20 — actual contracts may vary. Incident cost defaults are industry medians from IBM Cost of a Data Breach 2025 and Verizon DBIR 2026; your actual exposure depends on data sensitivity, attack surface, and remediation maturity. Recommendations are not investment, procurement, or compliance advice.