One engine finds 3× more, in minutes instead of hours.
On a six-query set-finding benchmark, Subindex — our web row-discovery engine — recovers far more of the true answer set than every tier of Parallel.ai FindAll, while returning in real time and at a fraction of the cost. It wins on all three axes at once: recall, speed, and price.
Every FindAll tier, measured on the same queries
Recall is the share of approved ground-truth entities each system actually returned across the six queries — total entities found ÷ total answer set (268 entities). FindAll offers four generator tiers that trade cost for thoroughness; Subindex runs one configuration.
| Pipeline | Recall | Cost · 6 queries | Wall time |
|---|---|---|---|
SubindexR15 |
0.49 |
$1.74 | ~13 min |
FindAllpreview |
0.03 |
$0.60 | 18 min |
FindAllbase |
0.11 |
$6.51 | 64 min |
FindAllcore |
0.16 |
$29.25 | 2.4 hrs |
FindAllpro · projected |
~0.19 |
$240–660 | 3–9 hrs |
Pro tier projected from Parallel's published pricing ($10 fixed + $1/match) and the recall-vs-tier trend; not directly run.
More of the answer, for less money
Each dot is one pipeline. Right is better (more recall); lower is better (less cost). FindAll's tiers climb steeply in price while recall barely moves — Subindex sits alone in the bottom-right: the most complete answer at the lowest spend.
Interactive, not overnight batch
The gap that matters most in practice: Subindex answers in about two minutes per query — fast enough to sit inside a live product. FindAll's higher tiers run for hours, built for offline jobs you kick off and walk away from.
Six hard set-finding queries, scored the same way
Set-finding — "list every entity that matches these criteria" — is the row-discovery step behind a research table. We chose long-tail queries where the answer set is scattered across the open web, not sitting on one clean list page. The number after each query is how many of its ground-truth entities Subindex returned.
- Fortune 500 CEOs hired in 202520 / 96
- Michelin 1-star restaurants, Manhattan 202562 / 76
- OpenAI safety-team departures, 2024–202623 / 32
- EU seed-stage AI-safety startups6 / 28
- Upscale restaurants in Lawrence, NY11 / 21
- S&P 500 top-10 movers, Q1 202610 / 15
- Metric
- Recall = approved ground-truth entities returned ÷ total approved GT, pooled across all six queries (268 entities). Every hit is an entity the system actually returned — nothing is credited that wasn't retrieved.
- Systems
- Subindex at its shipped R15 configuration, one run per query. FindAll run through Parallel.ai's public API across all four generator tiers; matched-status candidates counted directly.
- Fairness
- Same queries, same ground truth, same scorer for every system. Cost is each system's own billed total; wall time is measured end to end. Pro-tier figures are projected from published pricing, not run.
- Date
- Measured June 8, 2026. Ground truth was human-approved and frozen before the runs.