FakerForge

FakerForge is a synthetic-data generator that also seeds databases and mocks APIs. It is best for developers and QA teams who need connected test records alongside controllable endpoint behavior. The combination is useful, but the plan quotas and request caps will shape how far it can serve a busy project.

Overview

Upload a SQL schema or migration, set row counts and field rules, and FakerForge generates records that follow schema relationships. That makes it a better fit for populating structured test databases than for producing isolated sample records. It also combines database seeding and API mocking, so teams can prepare data and exercise response scenarios in one workflow; generation and request limits make it less suited to sustained high-volume work.

Key features

Schema-based generation: Related records can reflect an existing database structure, useful when tests depend on relationships between tables. Row and table ceilings determine how much each plan can create.

Database support and exports: Supported destinations include MySQL, PostgreSQL, SQLite, MariaDB, MongoDB, and BigQuery. SQL, JSON, CSV, and XML output gives teams several ways to move records into other workflows.

API scenarios: Configurable fake JSON payloads can cover success states, errors, edge cases, and timeouts. This is more useful for QA than a single static response, although mock and API request caps can constrain repeated or broader testing.

CLI and integrations: The open-source CLI introspects local or private PostgreSQL and MySQL databases and can push generated data into their tables. Install options cover Linux, macOS, and Windows on amd64 and arm64. CLI credentials stay 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 records and automation: FakerForge describes its generated records as fully synthetic, without real names, email addresses, or phone numbers, and supports automated refresh. Generation, API, and mocking actions may be capped by plan limits; high request volume may be throttled or rejected.

Pricing

The Free plan costs nothing, billed per month. It includes 1 database, 50,000 AI tokens, up to 100 rows per table and 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 practical starting point for small datasets or a limited proof of concept, but the single database and modest request allowances leave little room for parallel projects or frequent testing.

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 more workable choice for a small team or recurring QA cycle, though its row ceiling is still far below Pro's.

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. The much higher per-table and request ceilings make it the strongest fit for larger or busier test workloads; teams needing more capacity should account for the stated possibility of throttling or rejected high-volume requests.

Platforms

FakerForge supports API, Linux, macOS, web, and Windows. The CLI specifically offers Linux, macOS, and Windows installs for both amd64 and arm64.

Who it's for

Developers, QA engineers, data analysts, and product teams can use FakerForge to create synthetic datasets, seed mock databases, and exercise mocked endpoints. It is particularly well matched to teams that need schema-linked test records and multiple API response scenarios together. A project requiring large datasets or sustained request volume should compare its expected usage with the plan caps before committing.

Pros and cons

Pro: Schema and migration inputs preserve relationships, making generated data more relevant to structured database tests.

Pro: Database seeding, several export formats, and API scenario mocking cover adjacent test-data tasks in one product.

Pro: The CLI can write to local or private PostgreSQL and MySQL databases while keeping credentials in the operating system keychain.

Con: The free tier is limited to one database, 100 rows per table, and 500 API and mock requests, so it is a constrained option for repeated or broader test runs.

Con: Even though Pro expands row and request allowances substantially, high request volume may still be throttled or rejected.

Alternatives

For a narrower synthetic-data task, Synth Studio is free, runs across API, Linux, macOS, self-hosted, web, and Windows, and allows up to 1M rows per generation under an MIT license. Choose it when that generation allowance and license matter more than FakerForge's combined seeding and API-mocking workflow.

REaLTabFormer is free, MIT-licensed software for Linux, macOS, self-hosted, and Windows, and requires Python 3.7 or later. It is a fit when those platform and licensing terms suit the project better.

SimpleTest offers a free Starter plan with 50 parallel test runs per month, 500 execution minutes per month, and AI-generated datasets limited to one per test. Choose it when test execution allowances are the priority rather than FakerForge's database seeding and API-mocking combination.

Synthehol Dataset has a free plan with 1,000 credits per month, 50K rows per month, 5 projects, 5 datasets, 1 user, 1 GB of storage, 30 days of retention, and CSV and JSON formats. It is worth considering when those project, storage, and retention limits fit the need.

IRI DarkShield is a paid alternative with a free trial and API, Linux, macOS, self-hosted, and Windows platforms.

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

Anyverse is a paid web-based alternative with a free trial.

MOSTLY AI has a free tier and supports API, Linux, self-hosted, and web platforms; choose it if those platform options better suit the deployment.

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

Verdict

FakerForge is a strong choice for developers and QA teams who want schema-aware synthetic records, database seeding, and configurable API mocks in one workflow. Its main advantage is that breadth, backed by a CLI for local or private PostgreSQL and MySQL databases. Look elsewhere if the expected workload will regularly exceed the plan quotas or request limits.