Verified May 2026
Best synthetic data tools, 2026 edition
Six tools, honest tradeoffs, verified facts. We make one of the products on this list, so we put ourselves first and tell you when one of the others is the better call.
How we picked these
We restricted the list to tools that are: (a) actively maintained as of May 2026; (b) used in production by real teams (not hobby projects); (c) cover at least one of the four major synthetic-data workflows (greenfield generation, library-level fake values, de-identification of real data, or model-backed privacy-preserving synthesis).
Excluded for this round: tools we could not verify against primary sources in May 2026, tools with major maintenance gaps, and tools whose category overlap with the six listed is small.
At a glance
| # | Tool | Best for | Free tier |
|---|---|---|---|
| 1 | SynthForge | Teams that need related tables, realistic distributions, and ready-to-load DDL across PostgreSQL, MySQL, SQL Server, SQLite, MariaDB, DuckDB, or CockroachDB. | Free for everyone. Quota-throttled. 10M-row hard cap per generation request. No credit card. |
| 2 | Tonic.ai (Structural / Textual / Fabricate) | Enterprise teams with a real production database that needs to become safe to share. Structural is the most mature de-identification platform on this list. | Tonic Fabricate Free ($0/mo, $10 credits). Textual: free trial via self-serve. Structural: no free tier; demo only. |
| 3 | NVIDIA NeMo (formerly Gretel.ai) | Teams that need privacy-preserving synthetic copies of a real sensitive dataset with differential-privacy guarantees, and that are already on NVIDIA AI Enterprise. | Legacy Gretel free tier no longer applies. NeMo microservices ship with NVIDIA AI Enterprise (sales-gated). |
| 4 | Mockaroo | Single-table workflows. Designers, analysts, and developers who want a fast grid editor and broad field-type catalog. | $0. 1,000 rows per file, 200 API requests/day, 5,000 rows per API call without background processing. |
| 5 | Faker (Python and Faker.js) | Developers writing inline test fixtures inside unit tests. Highest-locale-coverage option on this list. | Free, open source. pip install faker / npm install @faker-js/faker. |
| 6 | SDV (Synthetic Data Vault) | Researchers and ML practitioners who want a programmatic, model-backed approach (CTGAN, copulas, HMA1) and are comfortable writing Python. | Free, open source. Commercial offerings available via DataCebo for production support. |
SynthForge
Web-based, multi-table, foreign-key-respecting greenfield test data with seven SQL dialects and AI schema design.
Best for: Teams that need related tables, realistic distributions, and ready-to-load DDL across PostgreSQL, MySQL, SQL Server, SQLite, MariaDB, DuckDB, or CockroachDB.
Free tier: Free for everyone. Quota-throttled. 10M-row hard cap per generation request. No credit card.
Key strength: Multi-table FK integrity by construction; seven SQL dialects with loader scripts; AI schema design via Claude/OpenAI; pre-built ML domain templates with baseline evaluation.
Watch out for: No differential privacy, no de-identification of real source data, 45 field types (vs Mockaroo's 140+), no Excel export, cloud-only.
Tonic.ai (Structural / Textual / Fabricate)
Three products: Structural de-identifies real prod databases; Textual redacts unstructured docs; Fabricate generates greenfield data via an AI agent.
Best for: Enterprise teams with a real production database that needs to become safe to share. Structural is the most mature de-identification platform on this list.
Free tier: Tonic Fabricate Free ($0/mo, $10 credits). Textual: free trial via self-serve. Structural: no free tier; demo only.
Key strength: Structural is purpose-built for de-identifying real production data. NER-based PII detection, masking, format-preserving encryption, self-hosted available.
Watch out for: Structural pricing is contract-sales and reported as 'rather steep' on G2. Tonic Ephemeral was sunset in December 2025.
NVIDIA NeMo (formerly Gretel.ai)
Gretel was acquired by NVIDIA in March 2025. Capabilities now live as NeMo Data Designer (schema-driven) and NeMo Safe Synthesizer (DP-SGD on real data).
Best for: Teams that need privacy-preserving synthetic copies of a real sensitive dataset with differential-privacy guarantees, and that are already on NVIDIA AI Enterprise.
Free tier: Legacy Gretel free tier no longer applies. NeMo microservices ship with NVIDIA AI Enterprise (sales-gated).
Key strength: NeMo Safe Synthesizer's DP-SGD pipeline is the strongest privacy-grade synthetic-data product on this list.
Watch out for: Standalone Gretel SaaS is shut down (gretel.ai redirects to NVIDIA; gretelai GitHub org archived 2026-02-18). Enterprise procurement required. Safe Synthesizer needs a real seed dataset; greenfield use-cases do not fit.
Mockaroo
Long-running web tool for generating fake data, field-by-field, with 140+ types and a battle-tested API endpoint.
Best for: Single-table workflows. Designers, analysts, and developers who want a fast grid editor and broad field-type catalog.
Free tier: $0. 1,000 rows per file, 200 API requests/day, 5,000 rows per API call without background processing.
Key strength: 140+ built-in types, including AI-generated custom lists. Excel (.xlsx) output, which most competitors lack.
Watch out for: No native multi-table FK. Workflow for related tables: generate parent, download CSV, re-upload as Dataset, reference. Free-tier 1,000-row cap is easy to hit.
Faker (Python and Faker.js)
MIT-licensed library for generating one fake value at a time. Two implementations: Python (joke2k/faker) and Faker.js (the @faker-js community fork).
Best for: Developers writing inline test fixtures inside unit tests. Highest-locale-coverage option on this list.
Free tier: Free, open source. pip install faker / npm install @faker-js/faker.
Key strength: 134 locales (Python) or 70+ locales (JS). Deterministic seeding. Fully offline, embeddable into a unit test.
Watch out for: No multi-table FK, no statistical distributions for numeric fields, no native CSV/SQL/Parquet export. Library only, no UI. Faker.js had a 2022 sabotage incident; the @faker-js fork is the canonical maintained version.
SDV (Synthetic Data Vault)
MIT-licensed Python framework from MIT DAI Lab for generating synthetic tabular data, including multi-table relational synthesis.
Best for: Researchers and ML practitioners who want a programmatic, model-backed approach (CTGAN, copulas, HMA1) and are comfortable writing Python.
Free tier: Free, open source. Commercial offerings available via DataCebo for production support.
Key strength: Strong academic lineage. Native multi-table relational synthesis via HMA1. Privacy metrics (DCR, NNDR) and quality reports. Active research community.
Watch out for: Library only, no UI. Models train on real data, so privacy of output depends on careful configuration. Steeper learning curve than UI-based tools. Not currently covered by a SynthForge dedicated comparison page.
Pick by workflow, not by brand
Greenfield: you do not have data yet
You are pre-launch, designing a schema, or generating test data for a new feature. SynthForge, Tonic Fabricate, and Mockaroo all fit here. SDV fits if you are willing to write Python and start from a sample.
Inline fake values inside test code
You are writing a unit test and need one fake email per assertion. Faker. No competition.
De-identify a real production database
You have prod data with PII and you need a safe synthetic copy. Tonic Structural is the mature choice. NVIDIA NeMo Safe Synthesizer if differential privacy is a hard requirement.
Privacy-grade synthetic from a real seed dataset
You have a sensitive dataset and need a model-trained synthetic copy with formal privacy guarantees. NVIDIA NeMo Safe Synthesizer (DP-SGD). SDV with privacy-preserving plugins for the open-source path.
Frequently asked questions
Why does SynthForge list itself first on its own page?
What about MostlyAI, Synthesized.io, YData, Statice, Datomize?
Is there a single best tool?
Try SynthForge for free
Multi-table foreign-key integrity, AI schema design, seven SQL dialects, no credit card.