A Faker Alternative for Population-Realistic People
Faker (faker.js, Python Faker) is a well-built open-source code library: you install it, call it from your own program, and it returns independent fake values — names, emails, addresses. SimpleIDGen is a different shape of tool: a hosted, population-calibrated dataset and API that returns whole synthetic people as CSV or JSONL. No library to install, no generation code to write.
No signup for the sample. Generate your own — 5,000 rows/day free →
Faker generates each field independently. A row's age, income, BMI, and conditions bear no statistical relationship to one another — which is exactly right when you only need plausible-looking strings to fill a form or a unit test.
SimpleIDGen draws its 65 attributes together — conditioned on one another and calibrated to public US references: NHANES 2017–2020, ACS 2022, CDC NDSS, KFF, BLS, US Census. A 60-year-old's A1c, BMI, blood pressure, and insurance type follow the distributions those sources measured for that age and sex, not independent noise, and cross-field invariants are enforced: BMI = weight / (height/100)², insulin appears only for diagnosed diabetics, and ZIP matches state. Both tools are reproducible — Faker via its own seed, SimpleIDGen via a seed parameter where the same seed always yields the same people.
| Dimension | Faker (faker.js / Python) | SimpleIDGen |
|---|---|---|
| Form factor | Code library, per language | Hosted API + downloadable dataset |
| Setup | Install package, write generation code | None — download CSV or JSONL |
| Field relationships | Independent per field | Jointly distributed across the record |
| Population realism | Plausible values, not population-calibrated | Calibrated to NHANES, ACS, CDC, KFF |
| Health vitals & conditions | Not a built-in focus | A1c, BMI, blood pressure, diabetes, hypertension, CKD |
| Reproducibility | Seedable | Deterministic by seed |
| Stack | Many language ports | Language-agnostic files — any stack |
| Cost | Free, open source | Free — 5,000 rows/day; no-login sample |
Faker is a fine library and a fair point of comparison; the two tools solve different problems.
If you reach for Faker because you need test people but don't want to maintain a seed factory in every service, the no-code path is simpler: download a file, or call the API with a count and a seed and pull back CSV or JSONL. There is no language port to choose and no glue code to keep in sync as your schema grows.
The trade-off is realism. Independent values are enough for layout and load tests. When a downstream model, dashboard, or demo needs the joint structure of a real population — correlated demographics, vitals, and conditions — the calibrated Person Profile generator produces it directly, and the NHANES-calibrated reference documents which distributions each attribute is fitted to.
Not literally — they are different shapes. Faker runs in-process in your language and returns values you assemble yourself. SimpleIDGen delivers finished, calibrated people as a dataset or API. If you want population-correct records without writing generation code, it replaces that work; if you need a throwaway string inside a unit test, Faker is lighter.
Yes. Download the 1,000-row sample above with no account, or create a free account and generate up to 5,000 rows per day in CSV or JSONL. No library, no language port, no glue code.
Because attributes are drawn together and calibrated to public references. Independent values can put a 25-year-old on Medicare or a non-diabetic on insulin; calibrated, invariant-checked records keep age, income, vitals, and conditions consistent with one another and with the US population.
Free — see pricing for the tiers. And safe: every record is synthetic, built from public reference distributions rather than learned from real records, so no real PII ever enters the system. That makes it GDPR- and DPDP-safe.