Comparison · Verified May 2026
SynthForge vs Mockaroo
Mockaroo is the longest-running web tool for generating fake data. SynthForge is built around multi-table schemas with foreign-key integrity in a single generation pass. Here is what each one is best at, with verified facts and zero spin.
TL;DR
Mockaroo is excellent for one-off, single-table fake data and ships 140+ field types including obscure regional variants. SynthForge ships 111 field types across 19 categories and is built for relational test data: design a multi-table schema once and generate referentially-intact rows across every table in one pass, with seven SQL dialects and AI-assisted schema design. If you only need one table at a time, Mockaroo's UI is hard to beat. If you need related tables, SynthForge fits the workflow better.
Recent context
Mockaroo's creator, Mark Brocato, launched a separate AI-driven multi-table product called Fabricate in September 2024. Tonic.ai acquired Fabricate in April 2025 and relaunched it in November 2025 as the Tonic Fabricate Data Agent. Mockaroo itself continues to operate independently with the same field-by-field schema editor it has had for years; the relational gap that Fabricate addressed is still present in Mockaroo proper.
When Mockaroo is the right call
- • You only need a single flat table or CSV. Mockaroo's editor is a fast row-per-field grid that beats almost everything else for that workflow.
- • You need an obscure regional or international type (e.g. country-specific passport numbers, regional commerce codes). Mockaroo's 140+ catalog covers more long-tail cases than SynthForge's 111 types. SynthForge also exposes user-defined enums and regex patterns, but Mockaroo has the breadth advantage on niche built-ins.
- • You need Excel (.xlsx) output. Mockaroo emits Excel directly; SynthForge does not.
- • You are already on a paid Mockaroo plan and have schemas saved in their account. Migration cost is real.
- • You want a battle-tested API endpoint with an established pattern for downloading rows by schema name plus an API key.
When SynthForge is the right call
- • You need related tables in one shot. Define customers + orders + line_items with foreign keys, generate them all together, and child rows reference parent IDs by construction.
- • You want AI-assisted schema design across an entire database, not just AI-assisted lists for a single field.
- • You want to specify cardinality as ratios ('8 to 12 patients per doctor', '1 to 5 visits per patient') instead of hand-typing a row count for every child table. Mockaroo asks for a row count per Dataset; SynthForge derives child counts from parent counts in topological FK order.
- • You need realistic numeric distributions: Normal, LogNormal, Exponential, and Triangular are first-class, not 'use the random module yourself'.
- • You generate for multiple databases. SynthForge emits dialect-specific DDL plus loader scripts for PostgreSQL, MySQL, SQLite, SQL Server, MariaDB, DuckDB, and CockroachDB.
- • You want a free tier without a 1,000-row-per-file cap, and you do not want to chain a download / re-upload / cross-reference workflow to fake foreign keys.
- • You want last-generation row counts and ratios to come back next time, automatically. SynthForge persists them on the schema; the next time you open the generation form, the inputs are pre-filled.
- • You are building ML training data: SynthForge ships pre-built domain templates (healthcare, e-commerce, fraud, IoT, marketing, real estate) with class-balance controls and baseline model evaluation.
Feature comparison
Verified against primary sources in May 2026.
| Feature | SynthForge | Mockaroo |
|---|---|---|
| Free tier row cap | Generous quotas; up to 10,000,000 rows per request | 1,000 rows per file |
| Free API requests/day | Subject to per-account rate limits, no fixed daily request cap | 200 requests/day; 5,000 rows per call without background processing |
| Multi-table generation in one pass | Yes, with foreign-key sampling strategies (random, uniform, sequential, weighted) | No. Workflow: generate parent → download CSV → re-upload as Dataset → reference from child schema |
| Foreign key integrity | Single-column FK guaranteed by construction | Manual via the 'Dataset Column' chained-workflow pattern |
| Composite / self-referential FK | Composite FK not supported; self-referential possible but not first-class | Unverified as official feature; community workarounds only |
| Field types | 111 across 19 categories | 140+ advertised |
| Statistical distributions | Normal, LogNormal, Exponential, Triangular, Uniform | Uniform / range-based; no Normal/LogNormal first-class |
| Per-relationship cardinality ratios | Yes - 'N to M children per parent', resolved in topological FK order; multi-parent ratios sum | Per-Dataset row counts only; no concept of 'children per parent' |
| Auto-saved generation defaults | Last submitted row counts + ratios persist on the schema; next form open pre-fills | Schema definitions persist; generation row counts re-entered each time |
| AI schema design | Whole-schema generation from a natural-language description (Claude default, OpenAI optional) | Field-level: AI-generated custom lists and types, not whole-schema |
| SQL DDL import | LLM-based parser; handles standard CREATE TABLE syntax across dialects | Yes; user reports it parses Postgres syntax and rejects T-SQL/MSSQL |
| SQL output dialects | PostgreSQL, MySQL, SQLite, SQL Server, MariaDB, DuckDB, CockroachDB | SQL INSERT output; specific dialect coverage not publicly documented |
| Export formats | CSV, SQL, JSON, JSONL, Parquet (also JSON/JSONL importable into MongoDB) | CSV, JSON, SQL, Excel, XML, custom-delimited TXT |
| ML training datasets | Pre-built templates, class-balance control, train/test split, baseline model evaluation | Not a positioned use case |
| Public REST API | OpenAPI 3.1 spec, 15+ endpoints, Clerk auth | REST API with API key; defined in /api/docs |
| Self-hosted / on-prem | No | Yes, on Enterprise plan ($7,500/yr) via Docker |
Pricing comparison
SynthForge
All features. No credit card. Per-account rate limits and a 10M-row hard cap per generation request.
Mockaroo
1,000 rows per file. 200 API req/day. 5,000 rows per API call without background processing.
Up to 100,000 rows per file. 1M records/day API. 8x speed.
Up to 10M rows per file. 10M records/day API. 8x speed.
Unlimited data and users. Unlimited API. On-prem Docker.
What SynthForge does not do that Mockaroo does
Honest tradeoffs, in case they decide the comparison for you.
- • Mockaroo has 140+ field types vs SynthForge's 111. The gap is narrow for common types, but if you need an obscure regional or international type (e.g. country-specific passport numbers or regional commerce codes), check Mockaroo's catalog first.
- • SynthForge does not export Excel (.xlsx) files directly. Mockaroo does. Note: SynthForge's CSV export opens directly in Excel.
- • Mockaroo offers an Enterprise on-prem Docker option. SynthForge is cloud-only today.
Frequently asked questions
Is SynthForge free?
Can SynthForge generate 1,000 related tables at once like Mockaroo's chained Dataset workflow?
Does Mockaroo have AI schema generation now?
Why does Mockaroo's free tier cap at 1,000 rows?
Can I import a SQL CREATE TABLE script into either tool?
Does either product de-identify real production data?
Sources used to verify these claims
Other SynthForge comparisons
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Design a multi-table schema, generate referentially-intact data, and export to your database. No credit card.