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A Faker.js alternative that needs no code

Faker is a library you call from code. This is a page you open. Both produce realistic synthetic data, and the difference decides which one fits your problem.

Library or file: the difference that decides it

Faker generates at runtime, inside your program. You install it, import it, and call faker.person.fullName() where you need a value. That is exactly right when the data is consumed by the code that generates it — a unit test that wants a fresh user each run, a seed script that fills a local database, a demo server making up rows on request.

This generates a file, once. You describe columns, press download, and get CSV, JSON, SQL, XML or a spreadsheet you can commit, attach, email or paste. That is right when the data is consumed by something that is not your JavaScript — a database import, a spreadsheet, a QA colleague, a bug report, a tutorial, a language with no faker binding you want to depend on.

Neither is a substitute for the other, and plenty of projects want both: a file for the fixtures that are checked in, a library for the values a test invents as it runs.

The reproducibility difference is smaller than it looks

Faker supports seeding — faker.seed(123) — so "reproducible" is not a distinguishing claim. What differs is what the seed is attached to. With a library, the seed lives in code and the data is reconstructed on every run, so a faker version bump can change the values under a passing test. With a generated file, the bytes are the artefact: the file in your repository is the same file next year regardless of what any dependency did. The methodology page covers how the seeding here works.

Side by side

How a downloaded file and a runtime library differ
 Fun Data PlaygroundFaker.js
Where generation happensIn your browser, on this pageIn your program, at run time
What you end up withA file — CSV, TSV, JSON, NDJSON, SQL, XML or .xlsxValues returned by a function call
SetupOpen the pageAdd a package to a JavaScript project
What the seed pinsThe bytes of a file you commitOutput rebuilt on every run from code
Custom generation logicThe field types and options the builder offersAny code you can write
Projects that are not JavaScriptA file imports anywhereNeeds a JavaScript runtime
Generating inside CINo API to call — commit the file insteadRuns in the pipeline like any dependency

Structural differences only — how each tool is used, not what it currently offers. Check the other tool's own documentation before deciding.

Where Faker.js is the better answer

Being straightforward about this is more useful than a feature table. Reach for the library when you need:

  • Fresh data on every test run, generated in-process, without a file in the way.
  • Generation logic you write yourself — a value derived from three others, a conditional, a loop that stops when a total is reached.
  • Locale coverage past what a UI can present. A library can carry dozens of locales because nobody has to browse them in a dropdown.
  • A build step that produces data. If data generation belongs in CI, it belongs in code.
  • Anything running server-side at request time. This tool has no API to call; it is a page.

Tools change. This page sticks to structural differences — how each one is used rather than what it currently offers — but check the current documentation of whichever you are comparing rather than trusting any comparison page, including this one.

Where a generated file wins

  • You are not in a JavaScript project. A CSV imports into anything; a JS library does not.
  • The consumer is a human. Nobody attaches a faker call to a bug report.
  • The fixture should be reviewable. A committed file shows up in a diff; a generator call does not show what it produced.
  • You want it now. No install, no project, no dependency added to something you were not planning to change.
  • You need a spreadsheet. Excel and Sheets take a file, and a real .xlsx keeps its column types where a renamed CSV does not.

Using both: generate the fixture, type it from the same schema

The two combine better than they compete. Generate a fixture file here, commit it, and take the schema straight to a type declaration so the code consuming it is typed from the same source — a TypeScript interface, a Zod schema or a Pydantic model. Then keep faker for the values an individual test invents.

The builder can also emit the fixture as a JavaScript module with a link that reproduces it, which is usually the fastest way to move a shape you designed here into a project that already uses a library.

Common questions

Is this a drop-in replacement for Faker.js?

No, and it is not trying to be. Faker generates values inside your program at runtime; this produces a file you download. If your data is consumed by the same JavaScript that generates it, the library is the right tool.

Can I get reproducible data like faker.seed()?

Yes — enter any string as a seed and the same schema produces byte-identical output every time. The practical difference is that a generated file is fixed bytes in your repository, while seeded library output is reconstructed on each run and can shift when the library version does.

Does it support as many locales as Faker?

No. A library can carry dozens of locales because nobody has to browse them; here names come in eight locales and other fields are pattern-driven. If broad locale coverage is the requirement, that is a reason to use the library.

Can I call this from CI?

Not directly — there is no generation API, because everything runs in the browser. For CI, either commit a generated file as a fixture or use a library in the pipeline.

Which formats can I get that a library does not give me directly?

CSV, TSV, SQL INSERT statements with a CREATE TABLE, XML, NDJSON and a real .xlsx workbook. All of those are possible from code too, of course — they just are not one click.

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