FakerForge

FakerForge generates synthetic data from database schemas, seeds databases and serves mocked API endpoints. It is best suited to developers and QA teams that need related test records and configurable responses in one workflow. Its schema-aware generation and local-database CLI stand out, though plan quotas can constrain heavier use.

Overview

Upload a SQL schema or migration, set row counts and field rules, and FakerForge generates records that respect schema relationships. That makes it a practical choice when test data must fit an existing database structure, rather than a collection of unrelated sample values. Database seeding and API mocking extend the workflow, but generation and request actions are subject to plan limits, and high request volume may be throttled or rejected.

Key features

• Schema-based data generation: Relationship-aware records can better reflect the shape of a test database. The usefulness depends on the plan’s per-table row and table caps, so larger test environments may require an upgrade.
• Database support and exports: Supported databases or destinations include MySQL, PostgreSQL, SQLite, MariaDB, MongoDB and BigQuery. SQL, JSON, CSV and XML outputs provide several ways to move records into a workflow.
• API mocking: Set fake JSON payloads and scenarios for success, errors, edge cases and timeouts. This gives QA teams more control over response behavior; the number of mocks, endpoints, scenarios and requests varies by plan.
• CLI and integrations: The open-source CLI introspects local or private PostgreSQL and MySQL databases and can push generated data into their tables. It supports Linux, macOS and Windows on amd64 and arm64. Database credentials stay in the operating system keychain, while only schema shape is uploaded. The MCP server works with GitHub Copilot, Claude, Cursor, Windsurf and other compatible clients.
• Synthetic records: FakerForge describes its output as entirely synthetic, without real names, email addresses or phone numbers. That is useful for test-data workflows that should avoid using real personal details.

Pricing

The free plan costs nothing, billed monthly. It includes 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 reasonable starting point for a small project, but the single database and modest row and request ceilings make it a poor fit for sustained or larger-scale 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 practical step up for teams that have outgrown the free caps but do not need the much larger PRO limits.

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. Its higher row and request ceilings suit more demanding generation and mock workloads. The plans are differentiated by databases and usage quotas; no seat count is stated. Contact FakerForge for integration help, enterprise licensing or product questions.

Platforms

FakerForge is available on the web and through API access, with Linux, macOS and Windows support. The CLI’s stated support for both amd64 and arm64 gives teams on those systems a way to work with local or private PostgreSQL and MySQL databases.

Who it's for

Developers and QA engineers are the clearest fit when they need related database records alongside mock responses for different application conditions. Data analysts and product teams may also find it useful for synthetic datasets. It is less suited to workloads whose row, database or request needs exceed the plan caps, especially where high request volume risks throttling or rejection.

Pros and cons

• Pro: Schema and migration uploads preserve relationships in generated records, making the output more relevant to structured database testing.
• Pro: Database seeding and scenario-based API mocks combine two test-data tasks, including error and timeout responses.
• Pro: The CLI can write generated data to local or private PostgreSQL and MySQL tables, while keeping credentials in the operating system keychain.
• Con: The free plan is limited to 100 rows per table, one mock API and 500 API and mock requests, which can be restrictive beyond a small project.
• Con: Even paid plans have explicit generation and request caps, and high request volume may be throttled or rejected.
• Con: The CLI supports PostgreSQL and MySQL, not every database or destination the site lists for FakerForge.

Alternatives

Choose REaLTabFormer if you want free, MIT-licensed software for Linux, macOS, Windows or self-hosting, and prefer its Python-based offering over FakerForge’s database-seeding and API-mocking combination.

Synth Studio is a free, MIT-licensed option that allows up to 1M rows per generation, making it worth considering if that per-generation allowance matters more than FakerForge’s schema and mock workflow.

MOSTLY AI has a free plan and supports API, Linux, self-hosted and web use; consider it if those platforms fit your needs better.

NVIDIA ShadowPlay is a free Windows app requiring Windows 10 or 11, GeForce driver 551.52 or later and a supported GPU.

SimpleTest has a free Starter plan with 50 parallel test runs and 500 execution minutes per month, plus AI-generated datasets limited to one per test; it is an option when those test-run allowances suit the job.

Synthehol Dataset offers a free plan with 1,000 credits, 50K rows per month, 5 projects, 5 datasets, one user, 1 GB storage and 30 days’ retention, plus CSV and JSON; consider it if those quotas and formats match your needs.

SynthEngyne offers 1,000 free monthly credits, with text, tabular and time-series data costing one credit per row and images costing 10 credits per image; consider it if that credit model fits your usage.

Synthetic AI Data has a free plan and supports API, self-hosted and web use.

Browse AI Synthetic Data Generators or Test Data Management Software for more options.

Verdict

FakerForge is a strong choice for developers and QA teams who need schema-aware synthetic records, database seeding and configurable API mocks together. Its local-database CLI and broad export options add practical flexibility. Choose another tool if the free plan’s database, row or mock limits are too tight, or if your workload needs more volume than the paid quotas allow.