Calibrated against actual project costs from 2026-04-02 → 2026-05-05 (91 real projects with ≥50 scored candidates), with CrustData realtime discount (−70%) applied and CrustData costs imputed onto LinkedIn-sourced projects so the reference is provider-agnostic.
0-2 agents: $0.07 × scored_candidates + $0.15 × companies
3+ agents: $0.20 × scored_candidates + $0.15 × companies
Calibrated to mean per-scored-candidate rates (not medians) so estimates lean conservative — better to slightly overquote than under.
| Companies | Scored candidates | Agents | Estimated cost | Reality check |
|---|---|---|---|---|
| 100 | 500 | 0 | $50 | <500 scored bucket: median $27, mean $52 |
| 100 | 2,000 | 1 | $155 | 500-2k bucket: median $82, mean $105 |
| 200 | 5,000 | 2 | $380 | similar to Treatwell ($164), Whatnot AE ($298) |
| 500 | 10,000 | 2 | $775 | similar to Molyon ($172), Cenotian Quants ($323) |
| 1,000 | 15,000 | 2 | $1,200 | Miro CTO actual = $579-700 — formula trends slightly higher |
| 1,000 | 20,000 | 3 | $4,150 | Helsing actual = $1,772 (16k scored, 3 agents); #1547 mapping = $6,389 |
| 1,500 | 25,000 | 4 | $5,225 | range Endeit ($1,277) → #1547 ($6,389) — top of bracket |
- "Scored candidates" = candidates with
score_value IS NOT NULL(i.e., AI scoring actually ran on them, not just sourced). - "Agents" = configured intelligent AI agents (
agents.type = 'intelligent'). Each agent runs against every candidate, so 3+ agents drive a step-change in cost per scored candidate (~3× the baseline). - Estimates exclude the very long tail of mapping-style projects with heavy realtime CrustData usage — those can land at $6,000+ regardless of formula.