PredictLeads vs People Data Labs
A general company database against a provider built specifically around funding and other company signals. The question is whether specialising shows up in the answer.
What both are asked for. Given a company domain, return that company's latest funding stage: the most recent round it raised, named correctly. Both receive the same domains and the same output schema, and every answer is judged against a human labelled dataset.
Two boards. Freshness measures rounds announced in the last 30 days: how fast a provider indexes news. Enrichment measures rounds older than that: how completely it has backfilled history. These reward opposite things, so they are ranked separately. Each freshness cohort ages into enrichment once its window closes.
What was called. PredictLeads through GET /api/v3/companies/{domain}/financing_events. People Data Labs through GET /v5/company/enrich.
How PredictLeads and People Data Labs scored on recent rounds
Rounds announced inside the window, with the cohort rebuilt each cycle. This is the rolling freshness board, pooled across every dated snapshot, so each row carries the number of companies that provider was measured on. The company funding benchmark has other vendors that were benchmarked on the same input data. The methodology covers cohort design, Ground Truth and the judging rules.
| Provider | Endpoint & configuration | Measured on | Latest stage correct | Correct when returned | Latest stage returned | Funding fields returned | Fields returned | Median latency | Est. cost | Official docs | Company identified |
|---|---|---|---|---|---|---|---|---|---|---|---|
| GTM data providers | |||||||||||
| People Data Labs | GET /v5/company/enrich | 181company measurements · 3 snapshots | 13.3% | 19.5% | 65.2% | 51.4% | 2.6 / 5 | 325 ms | $17.38$0.10 / successful match | People Data Labs official docs | 92.3%167/181 domains |
| PredictLeads | GET /api/v3/companies/{domain}/financing_events | 181company measurements · 3 snapshots | 68.0% | 82.4% | 81.8% | 64.3% | 3.2 / 5 | 561 ms | $4.83$0.04 / request · 100 free | PredictLeads official docs | 84.0%152/181 domains |
How PredictLeads and People Data Labs scored on settled rounds
The same vendors on rounds that were already more than 30 days old when each one ran. Read the company identified column before the rates: arms joined the benchmark at different points, so they do not all sit on the same number of companies. This is the funding enrichment board, scored on rounds that were already more than 30 days old when each provider ran. The company funding benchmark also carries the rolling freshness board. The methodology covers cohort design, Ground Truth and the judging rules.
| Provider | Endpoint & configuration | Latest stage correct | Correct when returned | Latest stage returned | Funding fields returned | Fields returned | Median latency | Est. cost | Official docs | Company identified |
|---|---|---|---|---|---|---|---|---|---|---|
| GTM data providers | ||||||||||
| People Data Labs | GET /v5/company/enrich | 67.6% | 80.9% | 83.6% | 64.7% | 3.2 / 5 | 276 ms | $21.02$0.10 / successful match | People Data Labs official docs | 95.9%210/219 domains |
| PredictLeads | GET /api/v3/companies/{domain}/financing_events | 37.9% | 70.1% | 53.4% | 39.0% | 1.9 / 5 | 562 ms | $5.84$0.04 / request · 100 free | PredictLeads official docs | 57.5%126/219 domains |
Contact data, intent, and integrations are not measured here. This is the company-funding slice only. Full leaderboard, methodology, and per-company evidence →
Where each one fits
- Enriching everything at once. A generalist returns funding alongside headcount, industry and location from one call. If you need those fields anyway, one request that fills the whole record beats two that each fill part of it.
- Tracking funding specifically. A specialist models funding as events rather than as a field on a company record. That shape suits anything that needs to know a round happened, rather than only what the latest one was.
- Paying per match. The two bill on different units, so cost depends on your match rate rather than on the rate card alone. Check the billing basis in the table above before modelling volume.
PredictLeads vs People Data Labs: common questions
Does a funding specialist beat a general company database?
On this benchmark the answer is only about the latest stage, which is a narrow slice of what either sells. A specialist has more reason to model funding carefully. A generalist has more reason to have the company in its index at all, which has to happen first.
Which one fills more of the record?
The benchmark counts that separately from correctness: how often the date, amount, total raised and round count come back at all. A provider can name the right stage and return an otherwise empty record, which matters if you are writing to a CRM.
Do both resolve the same companies?
Not always, and the board reports company resolution as its own column. A provider that never resolves the domain cannot answer the funding question, so a low resolution rate caps everything downstream of it.
Which one is easier to keep current?
Both are re-callable per domain, so keeping a record current is a question of what a repeat call costs and how often the underlying data moves. The provider that tracks funding as a tracked event tends to change between calls more often than the one exposing funding as a stored company attribute.