FakerForge

FakerForge generates synthetic datasets, fills mock databases, and publishes mock API endpoints for development workflows. Users can upload SQL schemas or migration files, choose row counts and field rules, and create records that respect schema relationships. Listed database support includes MySQL, PostgreSQL, SQLite, MariaDB, MongoDB, and BigQuery. Exports are available as SQL, JSON, CSV, and XML. Mock endpoints can return configurable fake JSON for success states, errors, edge cases, or timeouts.

The open-source CLI can inspect local or private PostgreSQL and MySQL databases and push generated records into their tables. It has Linux, macOS, and Windows install options for amd64 and arm64. FakerForge says the CLI keeps database credentials in the operating system keychain and uploads only schema shape; it describes generated records as containing no real names, email addresses, or phone numbers. A free plan is available, with paid plans from $10/mo. Usage actions may be capped by plan limits, and high request volume may be throttled or rejected.

FakerForge combines schema-based synthetic data generation with database seeding and configurable API mocks. Its free plan and CLI provide options for getting started, while plan limits are worth checking for higher-volume use.

FakerForge suits developers, QA engineers, data analysts, and product teams who need synthetic data, mock databases, or API endpoints. Its CLI may also suit teams working with local or private PostgreSQL and MySQL databases.

FakerForge combines synthetic data generation with database seeding and configurable API mocks. It suits developers and QA teams that need related test records alongside controllable responses. Its free CLI-backed entry point is useful, but quotas make plan choice important as usage grows.

Overview

Upload a SQL schema or migration file, set row counts and field rules, and FakerForge generates records that respect database relationships. That makes it a better fit for populating structured test environments than for producing isolated sample records alone. Database seeding and API mocking in the same product can simplify a testing workflow, though generation and request limits shape how far that convenience goes.

Key features

• Schema-based generation: Relationship-aware output is useful when tests depend on records matching the structure of an existing database. Row and table caps still matter, especially for larger environments.
• Database support and exports: Supported databases or destinations include MySQL, PostgreSQL, SQLite, MariaDB, MongoDB, and BigQuery. SQL, JSON, CSV, and XML exports give teams several ways to move generated data into other tools.
• Configurable API mocks: Fake JSON payloads can cover success states, errors, edge cases, and timeouts. Those scenarios help QA teams exercise varied responses, while endpoint and request quotas limit the scale of mock services.
• CLI and integrations: The open-source CLI introspects local or private PostgreSQL and MySQL databases and can push generated data into their tables. It installs on Linux, macOS, and Windows for amd64 and arm64. Database credentials remain in the operating system keychain, and only schema shape is uploaded. The MCP server works with GitHub Copilot, Claude, Cursor, Windsurf, and other compatible clients.
• Synthetic output and automation: FakerForge describes its records as fully synthetic, without real names, email addresses, or phone numbers, and supports automated refresh. That is useful for repeatable test datasets, but high request volumes may be throttled or rejected under plan limits.

Pricing

The free plan costs nothing, billed per month. Its caps are 1 database, 50,000 AI tokens, 100 rows per table, 10 tables per database, 500 API requests, 1 mock API, 5 endpoints per mock, 2 scenarios per endpoint, and 500 mock requests. It is a sensible starting point for small projects, but the narrow database and mock allowances make it easy to outgrow.

Basic costs $10 a month, billed per month. It raises the allowances to 5 databases, 1,000,000 AI tokens, 1,000 rows per table, 20 tables per database, 10,000 API requests, 10 mock APIs, 50 endpoints per mock, 10 scenarios per endpoint, and 10,000 mock requests. This is the practical step up for teams whose test data or API exercises exceed the free tier.

Pro costs $20 a month, billed per month. It includes 20 databases, 5,000,000 AI tokens, 100,000 rows per table, 50 tables per database, 50,000 API requests, 50 mock APIs, 100 endpoints per mock, 50 scenarios per endpoint, and 100,000 mock requests. Its larger quotas suit heavier recurring workloads, although request volumes can still be capped or throttled.

Enterprise licensing is available by contacting the company for custom pricing. The plans are priced monthly; readers should match their expected database, row, endpoint, and request volume to the caps rather than assume unlimited use.

Platforms

FakerForge is available through the web, API, and Linux, macOS, and Windows platforms. The CLI supports Linux, macOS, and Windows on both amd64 and arm64.

Who it's for

Developers, QA engineers, data analysts, and product teams can use FakerForge to prepare synthetic datasets, seed databases, or simulate API responses. Its schema-aware generation and CLI are particularly relevant where tests need connected records in local or private PostgreSQL and MySQL databases. It is less compelling for workloads that routinely exceed plan request limits or need broader CLI database support than the two databases named for local introspection.

Pros and cons

• Pro: Schema and migration input preserves relationships, making generated data more useful for database testing than disconnected sample rows.
• Pro: Database seeding, exports, and API scenarios sit alongside generation, so teams can cover several test-data tasks in one product.
• Pro: The CLI can push data into local or private PostgreSQL and MySQL databases, with credentials kept in the operating system keychain.
• Con: Free use is tightly capped at one database, 100 rows per table, one mock API, and 500 mock requests, limiting sustained or multi-project use.
• Con: Paid plans still impose API and mock-request ceilings, and high volume may be throttled or rejected.

Alternatives

For a broader comparison, see Test Data Management Software and AI Synthetic Data Generators.

• Windocks is worth considering when database images and simultaneous containers are central; its free plan allows a 1 GB image and one container, with no support.
• Data Maestro may suit a self-hosted Linux or macOS workflow; its free tier provides a 30-day full trial before becoming limited, with 100 rows per export or database push and two saved database connections.
• Metasyn is a free, MIT-licensed open-source Python package for readers who prefer a self-hosted package.
• Mockaroo may be a better fit for browser-based file generation: its free plan allows 1,000 rows per file, unlimited browser downloads, and 200 API requests per day.
• Synthetic Data Vault offers a free community option for users who want local files, CSV and Excel support, and a defined set of data types, constraints, and models.
• Tonic Fabricate offers a free plan with $5 monthly credits and basic data exports; its Plus plan costs $29 a month with $25 monthly credits and metered additional usage.
• Databucket is another free, open-source option.
• Faker.js is a free, MIT-licensed choice for commercial or non-commercial use.

Verdict

Choose FakerForge if your team needs schema-aware synthetic records, database seeding, and configurable API mocks in one workflow, especially when its CLI's PostgreSQL and MySQL support fits your environment. Its free plan gives a no-cost way to start, but modest quotas are the main reason to look elsewhere or move to a paid tier as usage expands.