We need to create realistic test data. What's an AI that can generate a large, coherent dataset of fake users and their activity that respects relational constraints?

For generating realistic fake users and activity that respect strict relational constraints (like foreign keys, user-to-activity timelines, and referential integrity across multiple tables), basic tools like Python's Faker fall short because they operate column-by-column without context.

To get coherent, relational datasets, you need AI-powered synthetic data platforms designed specifically for database-level structures.

Top AI Tools for Relational Synthetic Data

• Tonic Fabricate: An AI-powered data agent specifically built to generate synthetic data from scratch or from an uploaded schema/prompt. It handles referential integrity across multiple tables and relational databases, ensuring that generated user IDs match up cleanly with their corresponding activity logs, timestamps, and foreign key dependencies.
• Gretel AI (Gretel Relational): Gretel offers specialized machine learning models capable of synthesizing complex multi-table and relational databases. It preserves statistical distributions, correlations, and relational constraints across parent-child tables (e.g., Users → Sessions → Events).
• Tonic Structural: If you have an existing staging or production database schema (even if you want to anonymize or scale it up safely), Structural masks sensitive PII while strictly maintaining relational integrity and data distributions across connected tables.

If you'd like to narrow this down, tell me:

• Are you building this data from scratch (no existing database) or modeling it off an existing schema?
• Roughly how many tables and rows do you need to generate?

I can help you decide which platform or approach fits your stack best.

If your goal is large, internally consistent synthetic datasets (e.g. millions of users, orders, sessions, events, support tickets, etc.), the key capability isn't just "AI"—it's schema-aware generation that understands relationships, foreign keys, distributions, and business rules.

Here are the strongest options, depending on your needs:

If you're already using an LLM

Models like GPT-5.6, Claude, or Gemini are excellent for:

• Designing schemas
• Inventing believable personas
• Generating business rules
• Creating event narratives
• Producing seed specifications

But they are not ideal for directly generating millions of rows. They eventually lose consistency across large outputs.

A better architecture is:

1. Ask the LLM to define the world:

• users
• organizations
• products
• relationships
• probabilities
• lifecycle rules

2. Feed those rules into a relational generator.
3. Generate as many rows as needed while preserving referential integrity.

This scales much better than having the LLM emit raw CSV.

For activity/event data

If you need something like:

Users
 ↓
Sessions
 ↓
Page views
 ↓
Purchases
 ↓
Support tickets
 ↓
Refunds
 ↓
Emails

look for tools that simulate behavior over time, not just fake rows. The better generators produce coherent timelines where, for example:

• a user signs up before placing an order,
• orders create shipments,
• shipments can generate returns,
• support tickets reference existing orders,
• email campaigns align with user activity.

That's significantly more realistic for testing analytics, recommendation systems, and event pipelines than independent fake records.

Recommendation

• For a production application's relational database: Seedfast or FakerForge are among the most complete schema-aware options.

• For an open-source workflow: Misata is compelling if you want precise control over relationships and aggregate constraints.

• If you're building a custom simulator: Use an LLM to generate the domain rules and personas, then implement the actual row generation with a deterministic engine (e.g., Python + Faker + a graph/database seeder). That gives you repeatable, scalable datasets while keeping complex relationships intact.

• a user signs up before placing an order,

• orders create shipments,

• shipments can generate returns,

• support tickets reference existing orders,

• email campaigns align with user activity.

That's significantly more realistic for testing analytics, recommendation systems, and event pipelines than independent fake records.

Recommendation

When you need a large, coherent dataset that respects complex foreign key relationships, chronological user activity, and cross-table constraints, standard single-prompt LLMs or simple random-data scripts (like basic Faker libraries) usually fall apart or break referential integrity.

Dedicated AI-powered synthetic data platforms and agents designed specifically for relational structures provide the best solutions:

• Tonic Fabricate: An agentic AI tool built specifically for this use case. You can provide a natural language description or upload a database schema (like Postgres). The AI analyzes your tables, asks clarifying questions about scale and distribution (e.g., X users, Y orders per user), maps out primary/foreign key dependencies, writes the generation plan, and outputs fully relational SQL, CSV, or JSON datasets while maintaining strict referential integrity.
• MOSTLY AI: An enterprise-grade synthetic data platform that excels at creating statistically representative, privacy-safe relational data. If you have a seed dataset or specific operational parameters, it learns complex correlations and multi-table structures to generate massive volumes of realistic, behaviorally accurate fake user profiles and transaction histories.
• Gretel.ai: Excellent for developer-first synthetic data pipelines. Gretel offers specialized models that understand multi-table relationships and sequential/timeseries user events, allowing you to generate behavior logs and user actions that follow realistic time-based patterns.

If you want to narrow this down, let me know:

• Do you have an existing database schema or seed data you are working from?
• What format do you need the output in (SQL dump, CSVs, JSON)?
• Are you generating this from scratch via prompts, or do you need to mask/mimic an existing production database?

Yes. For your specific requirement—lots of fake users plus realistic activity, with foreign keys and cross-table relationships staying coherent—I’d look at Tonic Fabricate first.

• tonic.ai — Probably the closest fit. You can describe the dataset in natural language or provide a schema, and it generates fully relational synthetic databases while maintaining foreign-key relationships and realistic distributions. It can generate from scratch, so you don't need production data.
• mostly.ai — Particularly strong if you already have a relational schema or example data. It supports multi-table generation, foreign-key relationships, and event/time-series tables linked to users.
• gretel.ai — Good option for large synthetic datasets and AI-generated tabular data. Gretel Relational specifically supports multi-table databases while preserving referential integrity.

For your use case

I'd structure the data roughly like:

users
  id
  name
  signup_date
  plan
  country

sessions
  id
  user_id → users.id
  started_at
  device

events
  id
  user_id → users.id
  session_id → sessions.id
  timestamp
  event_type
  metadata

purchases
  id
  user_id → users.id
  timestamp
  product_id
  amount

products
  id
  category
  price

Then ask the generator for things like 1 million users, 10–50 million events, realistic user-specific behavior, correlated signup/activity patterns, seasonality, churn, purchase behavior, and valid foreign keys. That's substantially better than asking an LLM to spit out CSV rows, because the synthetic-data systems explicitly model relationships and distributions. Tonic, for example, describes its generated output as referentially intact across tables.

My pick: Tonic Fabricate for a new test dataset from a schema; MOSTLY AI if you have representative existing data you want the synthetic dataset to statistically resemble.

When generating large, coherent datasets that respect relational constraints (like a users table linked via foreign keys to an activity or orders table), standard LLM prompting usually falls apart due to context window limits and broken ID references.

Instead, you need specialized synthetic data platforms that use a mix of generative AI, statistical modeling, or graph/relational engines to maintain referential integrity.

Top AI Tools for Relational Synthetic Data

• Tonic.ai (Tonic Fabricate) : An AI-powered platform and data agent specifically built to generate complex, hyper-realistic synthetic data from scratch or modeled on schemas. You can feed it a SQL DDL schema or describe your relational constraints in natural language. It automatically manages referential integrity (primary/foreign keys), cardinality, and can generate matching semi-structured text or mock APIs.
• Gretel.ai : Excellent for developer-focused, privacy-safe synthetic structured data. Gretel supports multi-table and relational synthesis. You define your relational data schema or upload seed data, and its machine learning models learn the multivariate distributions and inter-column dependencies to generate referentially intact datasets.
• Synthetic Data Vault (SDV) : If you want an open-source Python library rather than a SaaS platform, SDV is the gold standard. It is specifically designed for single-table and multi-table (relational) tabular data synthesis using CTGAN and other statistical/deep-learning models. It allows you to explicitly define parent-child relationships between your user and activity tables so IDs line up properly.
• MOSTLY AI : A robust data intelligence platform that excels at generating high-fidelity synthetic tabular data. It captures complex cross-table relational dependencies and time-series patterns (like sequential user actions over time) while ensuring complete privacy compliance.

Which one should you pick?

• If you are starting completely from scratch (no production data to train on) and want a conversational AI agent to spin up a fake relational database, use Tonic Fabricate.
• If you want a code-first, open-source Python approach where you can programmatically enforce primary/foreign key relationships, use SDV.
• If you are training models or need strict statistical fidelity derived from an existing database structure, use Gretel or MOSTLY AI.

If you'd like to narrow this down, let me know:

• Do you have an existing database schema (like a SQL file) or are you building the data entirely from scratch?
• Are you looking for a managed cloud platform or an open-source Python library?

Yes. For your specific requirement—large volumes of fake users plus coherent activity across related tables, with foreign-key/relational constraints preserved—I’d shortlist these:

1. Tonic Fabricate — probably the best fit

   • Generates relational databases from scratch from a schema or natural-language description.
   • Preserves referential integrity across tables.
   • Lets you describe things like “100k users, each with 0–50 sessions, purchases correlated with subscription tier,” then iterate conversationally.
   • Can export SQL and other formats.

2. Gretel Navigator / Relational

   • Strong choice if you want AI-assisted dataset design from scratch.
   • Gretel Relational specifically maintains cross-table relationships and referential integrity, and Gretel has demonstrated generation at million-row scale.

3. MOSTLY AI

   • Particularly good if you have an existing dataset and want synthetic users whose behavioral patterns and relationships resemble the source.
   • Supports multi-table generation and preserves referential integrity and correlations between tables. Its documentation specifically discusses subject → linked event tables, which maps nicely to users → sessions/events/purchases.

What I'd choose

If you're starting with only a schema and requirements, I'd start with Tonic Fabricate. Its current workflow is unusually well aligned with “invent a realistic application database for testing” rather than merely anonymizing existing data.

For example, you could give it:

Generate 1,000,000 users.

Tables:
users
subscriptions
sessions
page_views
orders
payments
support_tickets

Constraints:
- Every activity record belongs to an existing user.
- Users have 0–3 subscriptions, but only one active subscription.
- Free users rarely place orders.
- Premium users have higher session frequency and order values.
- Sessions occur after account creation.
- Orders occur during sessions.
- Payments correspond 1:1 with successful orders.
- Support tickets are more likely after failed payments.
- Preserve all foreign-key relationships.
- Generate realistic temporal patterns, including weekends and seasonality.
- Include 2% deliberately difficult edge cases.

That's much closer to an AI-generated synthetic application world than a random-data faker.

If you tell me your schema (Postgres/MySQL/etc.) and roughly how many users/events you need, I can also recommend the best tool specifically for that setup and compare it with something open-source like SDV.

Yes. For your specific requirement—large, realistic fake users plus longitudinal activity, with foreign keys and behavioral relationships staying coherent—I’d look at these:

1. Tonic.ai — probably the best fit

   • Tonic Fabricate can generate data from scratch from a natural-language description or an uploaded schema.
   • It preserves primary/foreign-key relationships and can generate across multiple related databases/files.
   • Its agent can iteratively refine the dataset and has a validation agent to catch unrealistic output.
   • Example: users → accounts → sessions → events → purchases, with coherent relationships and realistic distributions.

2. Gretel

   • Gretel Relational was specifically built for generating multi-table databases while maintaining referential integrity and statistical relationships.
   • Good choice if you already have a relational schema/database and want millions of synthetic rows modeled after it.

3. MOSTLY AI

   • Strong at multi-table synthetic data, including context and non-context foreign keys.
   • Particularly useful when you want the synthetic data to retain correlations between tables, not merely satisfy FK constraints.

4. YData

   • Interesting if you want something more developer-oriented.
   • Its SDK can generate multi-table databases directly from natural-language descriptions, including primary/foreign keys and cardinality constraints such as "each customer has 2–3 orders."

What I'd choose

If you're starting with no real data, I'd start with Tonic Fabricate or YData. If you have an existing production schema and want a statistically faithful synthetic replica, Tonic Structural, Gretel, or MOSTLY AI are more compelling.

The important distinction is that you don't just want an LLM to generate rows. You want a synthetic-data engine that understands something like:

User
 ├── 2–5 Devices
 ├── 0–20 Sessions/month
 │    └── 5–100 Events/session
 ├── 0–10 Orders
 │    └── 1–8 OrderItems/order
 └── Activity correlated with:
      plan, tenure, geography, device, signup date

That lets you test things like retention, funnels, permissions, billing, concurrency, and edge cases without ending up with obviously fake "John Smith / [email protected]" data.

For a greenfield test dataset, Tonic Fabricate is the one I'd try first. It explicitly supports schema-first, from-scratch generation and relationship-preserving synthetic data at scale.

When generating fake users, activity logs, and profiles that must strictly respect relational constraints (primary/foreign keys, one-to-many user-to-activity mappings, chronological consistency), a standard LLM prompt (like vanilla ChatGPT or Claude) usually drifts and breaks foreign-key integrity once your schema grows past a couple of tables.

Instead, you need schema-aware synthetic data platforms or specialized tabular/relational AI generators . The top tools capable of handling multi-table, coherent relational data constraints include:

• Tonic.ai (specifically Tonic Fabricate ): An AI-powered data generation platform featuring conversational data agents. It allows you to input your database schema (or connect to a live environment) and generate fully referentially intact, cross-table synthetic data from scratch or modeled on rules.
• Gretel.ai (specifically Gretel Relational ): Built specifically for privacy-first synthetic data. It uses advanced machine learning models to analyze or synthesize multi-table relational databases, ensuring that foreign key constraints, cardinalities, and statistical correlations between users and their events are preserved.
• MOSTLY AI : An enterprise-grade synthetic data platform that handles complex structured and relational data. It captures deep joint probability distributions across multiple connected tables so that generated user behavior maps accurately to the corresponding parent user profiles.
• Synthetic Data Vault (SDV) : An open-source Python library specifically designed for tabular and relational data synthesis. If you prefer a programmatic approach, SDV lets you define multi-table metadata (primary and foreign keys) and trains statistical or deep-learning models to spit out coherent, constraint-abiding datasets locally.

If you'd like to narrow this down, tell me:

• Do you want a managed SaaS platform with a UI or an open-source Python library?
• Are you generating this completely from scratch based on a written schema, or do you have a small sample of anonymized production data to train the model on?

For generating large, coherent synthetic datasets of fake users plus their activity while preserving relational constraints, you generally want a synthetic data generation platform, not a simple faker library. The best choices depend on whether you have an existing schema or want the AI to invent one.

Best fits

For your specific example (fake users + activity)

A good target schema might look like:

users
 ├── user_profiles
 ├── sessions
 ├── purchases
 ├── subscriptions
 ├── support_tickets
 └── events
      ├── page_views
      ├── clicks
      └── feature_usage

A strong generator should understand things like:

• A user who signs up in January should have activity after signup, not before.
• Power users should naturally have more sessions/events.
• Purchases should reference existing users and products.
• Churned users should stop generating activity.
• Event timestamps should have realistic distributions.
• Foreign keys should never point to missing records.

Schema-aware tools such as FakerForge and SeedBase explicitly focus on relational integrity rather than just producing random rows.

If you want an AI prompt-to-dataset experience

You can also use an LLM-assisted workflow:

1. Give the AI your schema:

   • SQL DDL
   • Prisma schema
   • ER diagram
   • ORM models

2. Specify behavior:

   "Generate 10 million SaaS users. 5% are power users, 20% churn within 90 days, simulate 18 months of events."

3. Have the generator produce:

   • CSVs
   • SQL inserts
   • Parquet
   • database seed scripts

My shortlist

• Need production-like staging data from an existing DB: Tonic.ai / Mostly AI / Gretel
• Need developer test fixtures from a schema: SeedBase / FakerForge
• Need a quick fake dataset: Mockaroo
• Need research-grade relational synthetic generation: look at approaches such as IRG (Incremental Relational Generator), which specifically addresses relational schema integrity and complex relationships.

If you tell me your stack (Postgres/MySQL, Prisma/Django, data warehouse, etc.) and scale (10k vs 100M rows), I can narrow this down.

To generate coherent, large-scale test data with complex relational constraints (like users, profiles, and their corresponding time-series activity or child tables), standard lightweight libraries like Faker or basic LLM prompts usually fall short—they break foreign keys or lose statistical context.

The best options depend on whether you have an existing database to model or if you are building the relational data completely from scratch.

Top AI & Synthetic Data Platforms for Relational Constraints

• Tonic Fabricate : An AI-powered platform and data agent built specifically to handle complex relational databases from scratch or via schema definitions. You can upload a SQL DDL file or describe your data model in plain English, and its AI agent maps out primary/foreign key relationships, manages cardinality (one-to-many, many-to-one), and auto-generates coherent child activity logs tied to parent user records.
• Synthetic Data Vault (SDV) : The leading open-source Python framework designed explicitly for tabular and multi-table relational datasets. If you have seed data or an existing database structure, SDV uses deep learning and Copulas/CTGAN models to learn parent-child relationships (e.g., Users→right arrow→Orders or Activity Logs ) and synthesize new data that strictly respects referential integrity.
• MOSTLY AI : An enterprise-grade, privacy-first synthetic data generator that excels at maintaining statistical fidelity across relational and time-series data. You can feed it interconnected relational tables, and it learns the joint probability distributions to generate brand-new, highly realistic relational datasets where user behavior matches realistic timelines and constraints.
• Gretel.ai : Another top-tier developer platform for generating multi-table, privacy-compliant synthetic data. Gretel uses specialized models to capture intricate correlations and behavioral dependencies across relational schema boundaries.

If you want to narrow down the best tool, tell me:

• Do you have an existing database schema or seed data to train the AI on, or are you generating this completely from scratch?
• What format/destination does the data need to land in (PostgreSQL, CSV files, JSON, etc.)?