AI for relational test data generation

For what you’re describing, you want a relational synthetic data generator — ideally one that can create millions of fake users, their profiles, sessions, events, purchases, and other activity while keeping everything logically consistent.

The key distinction is that generating fake names and random rows isn't enough. You need a tool that understands relationships, preserves foreign keys, and creates believable behavior across time.

My top 3 recommendations

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1. Tonic Fabricate

(Best overall for AI-driven generation)

Best if you want to describe your application in natural language and generate a coherent dataset from scratch, or supply an existing database schema. It supports cross-table relationships, validation, and mock APIs backed by generated data.www.tonic.ai+1

Best for: realistic product simulations, application testing, and complex user-activity histories.

Explore Tonic Fabricate

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2. SDV (Synthetic Data Vault)

(Best open-source option)

A Python library that supports multi-table relational synthesis, sequential data, and configurable constraints. It is a strong choice if you want programmatic control over generation and want to integrate it into your own testing pipeline.docs.sdv.dev+1

Best for: developers who want reproducible, customizable datasets without relying entirely on a hosted AI service.

Explore SDV documentation

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3. MOSTLY AI

A strong choice when you have existing data and want synthetic users and activity that preserve statistical patterns and relationships across tables. Its multi-table support explicitly addresses referential integrity.mostly.ai

Best for: production-like datasets modeled on an existing database.

Explore MOSTLY AI

What a coherent dataset should look like

For example, suppose you're simulating a social or SaaS application:

Users — 100,000 records

User ID, signup date, plan, country, account status

Shared IDs and consistent relationships

Sessions

500,000 sessions linked to existing users

Events

5 million clicks, views, and actions linked to sessions

Subscriptions

Plans consistent with billing history

Messages

Conversations between valid users, ordered in time

The generator should also model behavioral patterns: new users explore more, some users become inactive, subscriptions change over time, and activity varies by day. It should validate these rules, not just the database's foreign keys.

Which should you choose?

• Want AI to build the dataset from a description? Start with Tonic Fabricate.
• Want code-level control and an open-source foundation? Start with SDV.
• Have real data and want realistic statistical distributions? Evaluate MOSTLY AI.
• Want to seed a local PostgreSQL or MySQL database directly from a schema? Consider FakerForge, which advertises schema-aware generation and direct database seeding.fakerforge.com+1

One important caveat: for very large datasets, benchmark generation speed, output volume, and validation on your actual schema before committing to a platform.

One question to narrow this down: Are you generating data from an existing database schema, or do you want the AI to invent the entire application world — users, relationships, and activity — from scratch?

For your use case, I'd start with Tonic Fabricate. You're looking for more than a fake-data generator: you need a tool that can build a large, realistic, interconnected synthetic world, where user profiles, sessions, events, and activity logs all reference the correct records.

The key capability to look for is relationally consistent synthetic data generation — including referential integrity, realistic behavioral patterns, and the ability to generate data at scale.

Best AI tools to evaluate

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1. Tonic Fabricate

(Best overall fit)

Generate data from natural-language prompts, database schemas, or existing database connections. Fabricate is designed to create connected relational datasets, structured and unstructured content, and mock APIs, with a validation agent to check generated results.www.tonic.ai+1

Why it fits: You can describe a fictional user population and its activity history, then refine the dataset conversationally.

Website:tonic.ai/products/fabricate

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2. Gretel

(Best for programmatic generation)

Gretel's synthetic-data tooling includes relational generation designed to preserve relationships across multiple database tables while modeling realistic data patterns.gretel.ai+1

Why it fits: Worth evaluating if you need repeatable, code-driven pipelines and large volumes of structured data.

Website:gretel.ai

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3. Tonic Structural

Best if you already have a real application database and need realistic test data derived from it. It transforms or synthesizes data while preserving table relationships and can support large database environments.docs.tonic.ai+1

Why it fits: Especially useful when your existing schema and data distributions are too complex to recreate from scratch.

Website:tonic.ai

What I'd make the tool generate

For a realistic application test dataset, I'd define a relational model along these lines:

Users

user_id, email, signup_date, region, account_status

Sessions

session_id, user_id, device, start_time

Activity events

event_id, user_id, session_id, event_type, timestamp

Transactions

transaction_id, user_id, amount, status

Support tickets

ticket_id, user_id, created_at, resolution

Illustrative schema — not generated data.

The generator should enforce these rules:

• Referential integrity: Every event's user_id points to an existing user, and every session_id points to the correct session.
• Temporal consistency: Events happen during their associated sessions; transactions and support tickets have plausible timelines.
• Behavioral realism: Users exhibit different patterns, such as daily engagement, occasional use, inactivity, churn, and reactivation.
• Scale: Generate millions of events without creating a bottleneck in memory or export.
• Reproducibility: Support fixed random seeds or repeatable generation configurations so the same test scenario can be recreated.
• Validation: Check foreign keys, uniqueness, timestamp ordering, distributions, and business rules after generation.

For this specific combination of AI-assisted creation and connected datasets, Tonic Fabricate is the strongest starting point based on its documented features.www.tonic.ai+1

One question that would narrow this down

How massive is the dataset you need to create?

1. Approximate volume

Up to 1 million records

10–100 million records

Hundreds of millions to billions

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2. Where should the data come from?

From scratch, using a description of my application

From my existing database schema, but with entirely fake data

From production data patterns, with synthetic replacements

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3. Preferred output

PostgreSQL / MySQL / SQL Server

CSV / JSON / Parquet files

Data warehouse or cloud storage

No preference — whatever scales best

[Find the best fit]