Fake Data Generator

Use Cases

Fake data is essential at every stage of software development. Seed a dev database without polluting it with real user records. Mock an API response for frontend development. Populate a spreadsheet for QA testing or a UI demo that needs to look realistic.

All generation happens client-side with no dependencies — names, addresses, and other values are drawn from curated word lists and combined randomly. UUID generation uses the browser’s crypto.randomUUID() API for cryptographic quality.

Field Formats

Every value comes from a fixed English word list or a random number. The formats below match the generator code:

Limits

• Fields are independent. In one record, Email is not built from Full Name, and City, State, Zip, and Country do not form a real address. A record can have "state": "NJ" and "country": "United Kingdom".
• US and English only. The data is the same on every language version of this page: English names, US state codes, US phone numbers.
• Values repeat. In 2,000 test runs of 100 records, 98.9% of the runs had at least one repeated Full Name and 1% had a repeated Email. Use UUID when you need a unique key.
• Some values can be real. Email domains include real providers such as gmail.com, and phone numbers and IPv4 addresses are random, so a value can belong to a real person or host. Do not send mail to them or call them. For values that are reserved for documentation, use example.com (RFC 2606) and 192.0.2.0/24 (RFC 5737).
• 100 records per run. Larger counts are reduced to 100. There is no SQL export and no custom fields.

When to Use a Library Instead

Use @faker-js/faker in code when you need related fields, other locales, thousands of rows, or the same data on every run:

import { faker } from '@faker-js/faker';

faker.seed(42); // same output on every run
const firstName = faker.person.firstName();
const lastName = faker.person.lastName();
const user = {
  name: `${firstName} ${lastName}`,
  email: faker.internet.email({ firstName, lastName }),
};