Faker & Mockaroo vs synthetic document data

What Faker is

Faker is an MIT-licensed Python package that, in its own words, “generates fake data for you” — to bootstrap a database, fill in persistence for stress testing, or anonymize data taken from a production service. It exposes providers — name(), address(), ssn(), company() and hundreds more — each of which returns a fresh random value on every call. It supports ~90 locales and seeded generation for reproducible runs, and it is actively maintained on PyPI.

Mockaroo is the same idea as a hosted product: a “random data generator and API mocking tool” with a schema designer, exports to CSV, JSON, SQL, and Excel, mock REST APIs, and a formula language (Ruby expressions) that can reference other fields in the same row. Its pricing is public and friendly: a free tier (up to 1,000 rows per file), paid plans from $60/year.

What Faker and Mockaroo are good at

• Database seeding and fixtures. Populating a dev database with a million plausible-looking rows is exactly what both tools were built for, and they are fast, free (Faker) or cheap (Mockaroo), and battle-tested at it.
• Mock APIs and test harnesses. Mockaroo’s hosted mock APIs — where you control URLs, responses, and error conditions — are a genuinely convenient way to unblock a frontend team before the real backend exists.
• Locale coverage. Faker ships ~90 locales, so a French address or a Japanese name is one constructor argument away.
• Zero learning curve. pip install faker and you have data in thirty seconds. There is real value in that.

If your task is “put plausible values in a table,” stop reading and use them. The rest of this page is about a different task.

Where random field generators stop for document AI

Training a document extraction model — LayoutLM, Donut, a Document AI custom processor, or your own — needs documents: rendered pages paired with ground-truth labels. Reaching for a value generator leaves three concrete gaps:

• Values, not documents. Faker returns strings and numbers; Mockaroo exports CSV, JSON, SQL, and Excel. Neither renders a filled W-2 page, and neither tool’s documentation describes any document-image or PDF-form output. The rendering pipeline — fonts, handwriting, scan-style degradation — becomes your project.
• No ground-truth labels. Even with a homegrown template renderer, you still need per-word bounding boxes, field IDs, and entity types for every page — the part that makes data training data. Neither tool produces annotations, because neither produces pages to annotate.
• Independence where reality has structure. Faker draws every field independently — a random name, a random employer, a random wage, none related. Mockaroo’s formulas can encode dependencies inside one row, but the logic is hand-authored per field and does not extend across records or across documents. Real documents are the opposite: a W-2’s boxes constrain each other, and the same person’s 1040 must agree with their W-2. Train on contradictions and the model learns that any combination of values is valid — our deep-dive post walks through the failure mode in detail.

Dimension by dimension

Faker and Mockaroo capabilities verified against their own documentation and pricing pages, June 2026. Sources: Faker docs, Mockaroo pricing, Mockaroo formulas.

The same task, side by side

Here is what “give me 500 W-2s with matching 1040s for training” looks like in each world. The Faker version produces values you still have to render, label, and reconcile; the SymageDocs version produces the finished dataset.

from faker import Faker

fake = Faker()

record = {
    "name": fake.name(),
    "address": fake.address(),
    "ssn": fake.ssn(),
    "employer": fake.company(),
    "wages": fake.pyint(18_000, 900_000),
}

# Every call is an independent draw. The wages have no
# relationship to the employer, the state on the address,
# or anything generated for the previous record. And the
# output is a dict of values — not a filled W-2, and not
# a labeled training sample.

When to use each

The two worlds also compose: plenty of teams keep Faker in their unit tests and use SymageDocs for the training corpus. If your pipeline starts at a W-2, a 1040, or a CMS-1500, those form pages show exactly what the generated output looks like. For the broader training-data picture, see the synthetic training data for document AI pillar.

Frequently asked questions

Is Faker bad? Should I stop using it?

No. Faker is an excellent, actively maintained, MIT-licensed library for what it was designed for: seeding databases, populating fixtures, and stress-testing persistence with fake values. If you are writing unit tests or filling a dev database, keep using Faker. The mismatch only appears when the thing you are building needs documents — rendered pages with labels — rather than values.

Can't I just render Faker output into a PDF template myself?

You can, and many teams start there. You then own three new problems: a rendering pipeline that produces realistic typed and handwritten pages, a labeling pipeline that emits per-word bounding boxes and entity types for every render, and a coherence problem — Faker draws each field independently, so your model trains on documents whose values contradict each other. Those three problems are the product surface SymageDocs sells.

Mockaroo has formulas — doesn't that solve coherence?

Partially, within a single row. Mockaroo formulas let you write Ruby expressions that reference other fields in the same record, which is genuinely useful for rule-based dependencies. But the logic is yours to author field-by-field, it does not extend across records or across documents, and the output is still structured data (CSV, JSON, SQL, Excel) — not rendered, labeled document images.

What does 'coherent across records' mean in practice?

A synthetic person's W-2, 1040, and pay records should all describe the same life: same name and address, wages that reconcile across forms, withholdings that are plausible for the income, a filing status consistent with the household. SymageDocs fills multiple forms from one simulated identity (form_ids=[...] in the SDK), so cross-document consistency checks in your pipeline see realistic agreement instead of random contradiction.

What do I actually get in a SymageDocs dataset that Faker can't give me?

Rendered filled forms (typed and handwritten PDFs plus page images), per-word bounding boxes with field IDs and entity types, structured per-instance JSON ground truth, and FUNSD-format annotations included in every bundle. Faker's output is values; its documentation describes no document rendering and no annotation capability — by design, because that was never its job.