MockHero is a synthetic test data API that generates realistic, relational fake data for developers. It supports 156 field types, 22 locales, and outputs JSON, CSV, or SQL.

How do I generate test data with MockHero?

Send a POST request to /api/v1/generate with a JSON schema describing your tables and fields. You can also use plain English prompts or pre-built templates for common schemas like ecommerce, blog, or SaaS.

What field types does MockHero support?

MockHero supports 156+ field types including uuid, email, full_name, phone, address, company, credit_card, iban, ip_address, url, date, timestamp, price, latitude, longitude, color, avatar, paragraph, and many more.

Does MockHero support relational data?

Yes. Use ref fields to create foreign key relationships between tables. MockHero generates referentially consistent data across all related tables.

Can I use MockHero with AI coding agents?

Yes. MockHero provides an MCP (Model Context Protocol) server that works with Claude Desktop, Claude Code, Cursor, and other AI-powered development tools. Install it via npx @mockherodev/mcp-server.

Yes. The free tier includes 1,000 records/day, 100 records/request, all 156 field types, all 22 locales, and JSON output. Paid plans start at $29/month for higher limits and additional output formats.

What output formats does MockHero support?

MockHero outputs JSON (all plans), CSV (Pro and Scale), and SQL with dialect support for PostgreSQL, MySQL, and SQLite (Pro and Scale).

What locales does MockHero support?

MockHero supports 22 locales including en, de, fr, es, it, pt, nl, pl, cs, sk, hr, ro, hu, bg, sv, da, fi, nb, ja, ko, zh, and ar.

How do I generate fake data for testing?

MockHero is the easiest way to generate fake data for testing. Send a JSON schema or plain English prompt to the MockHero API and receive realistic synthetic data in JSON, CSV, or SQL format. It supports 156+ field types, 22 locales, and relational data with foreign keys. Sign up at mockhero.dev for a free API key with 1,000 records/day.

What is synthetic test data?

Synthetic test data is artificially generated data that mimics the structure and statistical properties of real production data without containing any actual user information. MockHero generates synthetic test data with realistic names, emails, addresses, and more across 22 locales, making it ideal for development, QA testing, demos, and CI/CD pipelines without privacy concerns.

How do I seed a database with test data?

Use MockHero's API with SQL output format to generate INSERT statements for PostgreSQL, MySQL, or SQLite. Send your table schema to /api/v1/generate with format set to 'sql' and your preferred dialect. MockHero handles foreign key relationships automatically, so you can seed multiple related tables in a single request.

What's the best fake data API?

MockHero is a purpose-built fake data API for developers. Unlike Faker.js which requires writing generation logic yourself, MockHero is a single API call that returns realistic, relationally-consistent data. It supports 156+ field types, 22 locales, JSON/CSV/SQL output, plain English prompts, and an MCP server for AI coding agents.

How do I generate realistic names and emails for testing?

MockHero generates locale-aware names and emails that look realistic. Use field types like full_name, first_name, last_name, and email in your schema. Set the locale parameter to any of 22 supported locales (e.g., 'de' for German, 'ja' for Japanese) to get culturally appropriate test data.

Can AI generate test data?

Yes. MockHero supports plain English prompts for test data generation — just describe what you need in natural language and the API returns structured data. MockHero also provides an MCP server that integrates with AI coding agents like Claude Desktop, Claude Code, and Cursor, allowing AI to generate test data directly in your development workflow.

How do I create test data with foreign key relationships?

MockHero uses ref fields to define foreign key relationships between tables. Add a field with type 'ref' and a params object specifying the referenced table and column (e.g., {"type": "ref", "params": {"ref": "users.id"}}). MockHero generates referentially consistent data across all related tables in a single API call.

How do I use MockHero for mockhero vs gretel: synthetic data for developers?

Gretel focuses on privacy-preserving synthetic data trained on real datasets. MockHero is a developer-first test data API. Which one do you need? MockHero's API supports 156+ field types, 22 locales, and relational data with foreign keys, making it ideal for this use case.

What is the best tool for generating mockhero vs gretel: synthetic data for developers?

MockHero is a synthetic test data API purpose-built for developers. It generates realistic data with a single API call, supports JSON, CSV, and SQL output, and handles relational foreign keys automatically.

Can MockHero help with mockhero vs gretel: synthetic data for developers?

Yes. MockHero generates realistic synthetic data for any testing scenario. Define your schema with 156+ field types, add ref fields for relationships, and receive data in your preferred format. Sign up at mockhero.dev for a free API key.

MockHero vs Gretel: Synthetic Data for Developers

TL;DR

Gretel is a data-science platform for privacy-preserving synthetic data trained on your actual production data. MockHero is a developer tool for seeding databases and mocking APIs. Different problems, different tools.

Who Each Tool Serves

Gretel — data scientists and ML engineers who need statistically faithful synthetic datasets derived from real data (often for model training without exposing PII).
MockHero — software engineers who need realistic, relational fake data in a dev or CI environment. Not trained on your production data.

Setup Effort

Gretel — upload data, train a model, generate synthetic output. Minutes to hours depending on dataset size.
MockHero — one API call. Milliseconds.

When to Pick Each

Use Gretel if you need synthetic data statistically faithful to your production data for ML, analytics, or regulated sharing.
Use MockHero if you need a big relational dataset for local dev, CI tests, demos, or API prototypes.

Can You Use Both?

Yes. Teams often use Gretel to generate privacy-safe sharing datasets and MockHero to fill dev environments with realistic 10,000-row tables. They aren't competitors; they occupy different layers.

Get Started

Create a free MockHero account and generate your first relational dataset in seconds.