FakerForge

FakerForge is a synthetic-data generator that also seeds databases and serves mocked APIs. It suits developers and QA teams who need connected test records alongside controllable API responses. Schema-aware generation is its strongest draw, while plan quotas make the right tier depend on data volume and request traffic.

Overview

Upload a SQL schema or migration file, set row counts and field rules, and FakerForge generates records that respect relationships in the schema. That makes it a practical option for filling structured test databases, rather than creating isolated sample records. Database seeding, exports, and API mocking extend the workflow, although generation and request limits constrain how much a project can do on each plan.

Key features

• Schema-based data generation: Relationship-aware records are useful when tests depend on connected tables. Row and table ceilings, however, set a real boundary on dataset size, particularly on the free tier.
• Database support and exports: Supported databases or destinations include MySQL, PostgreSQL, SQLite, MariaDB, MongoDB, and BigQuery. SQL, JSON, CSV, and XML outputs give teams several ways to move generated records into their workflows.
• API mocking: Fake JSON payloads can be paired with success, error, edge-case, and timeout scenarios. That variety helps QA teams exercise different response paths, but mock-request quotas matter for sustained or busy testing.
• Local database CLI: The open-source CLI can inspect local or private PostgreSQL and MySQL databases and push generated records into their tables. It supports Linux, macOS, and Windows on amd64 and arm64. Credentials remain in the operating system keychain, and only schema shape is uploaded, which can suit teams reluctant to send database credentials to a service.
• MCP integrations and synthetic records: The MCP server works with GitHub Copilot, Claude, Cursor, Windsurf, and other compatible clients. FakerForge describes generated records as fully synthetic, without real names, email addresses, or phone numbers.
• Automated refresh and usage controls: Refresh automation is supported. Generation, API, and mocking actions can be capped by plan, and high request volume may be throttled or rejected, so teams with bursty workloads should account for these constraints.

Pricing

The free plan costs nothing, billed monthly. It allows 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 useful starting point for a small project, but its row and request ceilings are restrictive for larger datasets or repeated API testing.

BASIC costs $10 a month, billed monthly. 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 sensible step up for a small team or project that has outgrown the free quotas.

PRO costs $20 a month, billed monthly. 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. It fits heavier data generation and broader mock coverage, though request caps still make it a poor match for workloads that cannot tolerate throttling or rejection.

Platforms

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

Who it's for

Developers and QA engineers can use the schema-aware generation and configurable mock scenarios to prepare structured test environments and exercise API behavior. Data analysts and product teams may also find the exports and automated refresh useful. It is less suited to high-volume projects unless the selected plan's quotas match the expected generation and request load.

Pros and cons

• Pros: Schema relationships carry through generated records, making the output more relevant to structured database tests.
• Pros: Database seeding, multiple export formats, and API mocking bring several test-data tasks together.
• Pros: The CLI can work with local or private PostgreSQL and MySQL databases while keeping credentials in the operating system keychain.
• Cons: The free plan's 100-row table limit and 500 API and mock requests can quickly constrain active projects.
• Cons: Paid plans still impose request limits, and high-volume traffic 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 includes a 1 GB database image and one simultaneous container, while the free plan has no support.
• Data Maestro may suit a self-hosted Linux or macOS workflow; its free offering includes a 30-day full trial, then remains limited forever, with 100 rows per export or database push and two saved database connections.
• Metasyn is an option for teams that want a no-cost, open-source Python package under the MIT license.
• MOSTLY AI is another freemium option for teams seeking synthetic-data tooling across API, Linux, self-hosted, and web platforms.
• Tonic Fabricate may appeal to teams that prefer monthly credits: its free plan includes $5 monthly credits, basic exports, Discord support, and Tonic Cloud.
• Mockaroo is a fit for browser-based sample-file generation: its free tier allows 1,000 rows per file, unlimited browser downloads, and 200 API requests per day.
• Faker.js is a no-cost alternative for teams that want an MIT-licensed option for commercial or non-commercial use.
• Synthetic Data Vault offers a freemium alternative with a no-cost community plan.

Verdict

Choose FakerForge when your team needs schema-aware synthetic records, database seeding, and configurable API mocks in one workflow, particularly if its local-database CLI fits your environment. Its free and BASIC tiers provide clear entry points, but look elsewhere if your workload requires request volumes beyond the plan ceilings or cannot accommodate possible throttling.