Data as of Sep 21, 2026 · Based on 348 AI responses from ChatGPT Search and Google AI Mode · See how Parse measures this
For generating realistic, relational test data, several platforms excel depending on your technical needs. Synthetic Data Vault (SDV) is a top choice for developers seeking an open-source, Python-based framework. For enterprise or high-fidelity privacy requirements, platforms like 
Brands AI recommends here
Named in 65% of answers
Strong choice for high-fidelity, privacy-preserving synthesis. It is well-suited for enterprise and compliance-focused teams who need to generate safe versions of existing data while keeping statistical correlations.
Named in 59% of answers
Best for DevOps and engineering teams needing to mimic complex production database schemas. Tonic Fabricate provides a conversational interface to generate domain-specific, referentially intact data for testing.
Named in 48% of answers
Ideal for data science and development teams requiring an open-source solution. It excels at modeling multi-table relational datasets with custom pipelines, though it lacks the turnkey enterprise support of paid platforms.
Named in 44% of answers
Source | Citation share |
|---|---|
The Complete Guide to Synthetic Test Data Generation for ...https://mostly.ai/blog/synthetic-data-generator-for-healthy-test-data | 3% |
Best synthetic data generation tools for 2026https://www.k2view.com/blog/best-synthetic-data-generation-tools/ | 3% |
Generate Synthetic Databases with Gretel Relational - Mediumhttps://moez-62905.medium.com/revolutionizing-data-privacy-generate-synthetic-databases-with-gretel-relational-3232e534dc58 | 3% |
 Top 6 Synthetic Data Generation Tools [2026]https://averroes.ai/blog/synthetic-data-generation-tools | 2% |
Tonic.ai: Synthetic Data for Software and AI Developmenthttps://www.tonic.ai/ | 2% |
Introducing the Fabricate Data Agent: The AI Agent for Synthetic Data Generation | Bloghttps://www.tonic.ai/blog/hyper-realistic-synthetic-data-via-agentic-ai-has-arrived-meet-the-fabricate-data-agent | 2% |
 Top 14 Synthetic Data Generation Tools in 2024 | JFrog MLhttps://www.qwak.com/post/top-synthetic-data-generation-tools | 2% |
How to Generate Synthetic Data | Tonic.aihttps://www.tonic.ai/guides/how-to-generate-synthetic-data-a-comprehensive-guide | 2% |
 Mediumhttps://medium.com/iam-lab/fake-data-is-safe-data-c30a3731973b | 2% |
 Introducing the Synthetic Data Generator - Build Datasets with Natural Languagehttps://huggingface.co/blog/synthetic-data-generator | 1% |
For this use case, you’re looking for **synthetic data generation** rather than a simple “fake name/email generator.” The important capabilities are: - Understand a **schema** (tables, columns, foreign keys, unique constraints) - Generate **millions of rows**
For this use case, you’re looking for synthetic data generation rather than a simple “fake name/email generator.” The important capabilities are:
order.user_id points to a real user)Some good options:
| Tool | Best for | Notes |
|---|---|---|
| DataCebo / SDV | Enterprise relational databases | Uses generative relational models for complex interconnected databases and synthetic database generation. DataCebo |
| Seedfast | Developers who want production-shaped Postgres test data | Reads schemas and generates linked data with foreign keys, realistic distributions, and large volumes. Seedfast |
| FakerForge | Fast DB seeding and mock environments | Generates schema-aware fake records and exports SQL/JSON/CSV. Faker Forge |
| Syda | Open-source / programmable workflows | Generates structured data while maintaining relationships and constraints; can infer schemas from databases. GitHub |
| IRI RowGen | Very large enterprise test datasets | Focuses on structurally and referentially correct test data across databases and files. IRI |
| Synthehol DB | Prompt-to-database workflows | Generates linked rows with constraints and exports such as CSV, SQL, and Parquet. Synthehol DB |
For a realistic SaaS/app dataset, I’d usually model it like this:
users
├── accounts
├── subscriptions
├── sessions
├── events
├── purchases
├── support_tickets
└── notifications
Then add behavioral rules:
A pure LLM prompt like “make me 10 million users and events” usually produces inconsistent tables. A better pattern is:
users, accounts).If you already have a database schema, I’d pick:
Seedfast GitHub DataCeboIf you tell me your stack (Postgres/MySQL/Snowflake/etc.) and approximate size (10k users vs 10M users), I can narrow it down.
To generate large, coherent datasets of fake users and activity that respect relational constraints (like foreign keys, timestamps, and realistic behavioral distributions across multiple tables), you need a [**Generative AI-powered synthetic data platform**…
To generate large, coherent datasets of fake users and activity that respect relational constraints (like foreign keys, timestamps, and realistic behavioral distributions across multiple tables), you need a Generative AI-powered synthetic data platform rather than basic random mock generators (like standard Faker libraries, which only do column-by-column randomization).
The top-tier AI platforms built specifically for multi-table, relationally constrained data generation include:
If you'd like, let me know:
I can help you outline how to structure your prompt or configure the data generation plan.
For generating **large, coherent fake users + activity data with relational constraints** (foreign keys, realistic distributions, user journeys, events, transactions, etc.), you’re looking for a **synthetic relational data generator**, not just an LLM that spits out CSVs. Good options: Tool | Best for | Why it fits
For generating large, coherent fake users + activity data with relational constraints (foreign keys, realistic distributions, user journeys, events, transactions, etc.), you’re looking for a synthetic relational data generator, not just an LLM that spits out CSVs.
Good options:
| Tool | Best for | Why it fits |
|---|---|---|
| Tonic.ai | Enterprise test environments | Generates synthetic databases while preserving relationships and realistic distributions across tables. Tonic AI |
| Synthetic Data Vault (SDV) | Developers / Python workflows | Open-source framework for modeling and generating multi-table synthetic datasets with relationships. DataCebo |
| DataCebo | Enterprise relational databases | Focuses on generative relational models for complex interconnected databases. DataCebo |
| IRI RowGen | Very large volumes | Designed for high-volume relational test data with referential correctness and database/file targets. IRI |
| Seedfast | Postgres dev/test databases | Reads schemas and generates linked tables with foreign keys, uniqueness constraints, and realistic distributions. Seedfast |
For a typical SaaS/product database, I’d think about it this way:
Example:
users
├── accounts
├── subscriptions
├── sessions
├── events
├── purchases
└── support_tickets
Use a schema-aware generator:
Feed it your schema/migrations.
Tell it scale:
10M users
200M events
3.5 sessions/user/month
realistic churn patterns
Generate in dependency order:
users first
child tables second
activity streams last
Tools like SDV, Tonic, Seedfast, and RowGen are built around this workflow.
For example:
Pair a relational generator with an LLM-generated specification:
Generate:
- 1M users
- 60% mobile-only
- 20% inactive after signup
- premium users average 4.2 sessions/week
- create correlated events over 18 months
The LLM writes the generation rules; the synthetic data engine enforces the database integrity.
A practical setup:
For very large production-like environments (millions/billions of rows), I’d lean toward a dedicated synthetic data platform rather than prompting an LLM directly, because LLMs are not naturally good at maintaining millions of cross-table invariants.
If you tell me your stack (Postgres/MySQL, schema size, rows needed, and whether you need realistic behavior sequences), I can narrow this to a specific tool and architecture.
To generate realistic test data that respects **relational constraints** (like foreign keys, user-to-activity mappings, and temporal consistency), traditional column-by-column fake data generators like standard *Faker* libraries or *Mockaroo* usually fall short because they lack cross-table…
To generate realistic test data that respects relational constraints (like foreign keys, user-to-activity mappings, and temporal consistency), traditional column-by-column fake data generators like standard Faker libraries or Mockaroo usually fall short because they lack cross-table awareness.
Instead, you need AI-powered synthetic data platforms or relational-aware synthesizer libraries that understand statistical distributions and referential integrity.
If you'd like to narrow this down, let me know:
Yes. What you’re looking for is **relational synthetic-data generation**: an AI system that can generate users, events, transactions, etc. while keeping foreign keys, cardinalities, correlations, and timelines coherent. ### My top picks 1. **Tonic.ai — probably the best fit**
Yes. What you’re looking for is relational synthetic-data generation: an AI system that can generate users, events, transactions, etc. while keeping foreign keys, cardinalities, correlations, and timelines coherent.
Tonic.ai — probably the best fit
users → sessions → events → purchases → subscriptions → support_tickets
where all the entities describe the same underlying fictional users.MOSTLY AI — excellent for existing relational schemas
Gretel.ai — strong API/developer option
If you're starting with only a schema + requirements, I'd try Tonic Fabricate first. Its current positioning is unusually close to "give me a fictional world with users and activity that all makes sense together," rather than merely "make realistic-looking rows."
For example, you could specify something along the lines of:
Generate 10 million fictional users. Each user may have 0–20 sessions per month, sessions contain timestamped events, purchases are correlated with sessions, subscriptions constrain which features can be used, and support tickets reference the appropriate user and product. Preserve all primary/foreign-key relationships and create realistic temporal and behavioral correlations.
The important distinction is coherence beyond foreign keys. A basic faker can ensure event.user_id exists; a good synthetic-data system can make the events, purchase frequency, subscription status, timestamps, demographics, and behavior statistically/semantically consistent with one another.
If you tell me your stack (e.g. Postgres + Python) and roughly how many rows/tables you need, I can compare Tonic vs. MOSTLY vs. Gretel specifically for that workload, including pricing/API/self-hosting considerations.
To generate realistic test data that respects relational constraints (primary keys, foreign keys, logical value distributions like matching order totals to line items, etc.), a basic script using a library like Faker won't cut it because it operates column-by-column without…
To generate realistic test data that respects relational constraints (primary keys, foreign keys, logical value distributions like matching order totals to line items, etc.), a basic script using a library like Faker won't cut it because it operates column-by-column without context.
Instead, you need schema-aware AI data generators or AI data agents that read your database DDL/schema first, understand the entity relationships, and then write/execute relationally consistent generation logic.
Top-tier tools built specifically for this include:
Users ), parse the returned IDs, and sequentially prompt or programmatically compute children records (e.g., Activity/Logs ) to guarantee foreign key integrity.If you want to narrow this down, let me know:
I can give you a more specific workflow or sample setup.
For this use case, you want a **synthetic data generation** tool rather than a simple “fake name/email generator.” The key capability is **schema-aware generation**: it should understand tables, foreign keys, cardinality, distributions, and user behavior patterns so that the data tells a coherent story. [BlueGen…
For this use case, you want a synthetic data generation tool rather than a simple “fake name/email generator.” The key capability is schema-aware generation: it should understand tables, foreign keys, cardinality, distributions, and user behavior patterns so that the data tells a coherent story.
Some strong options:
| Tool | Best for | Why it fits |
|---|---|---|
| Tonic.ai | Production-like app test data | Generates synthetic data while preserving relationships and constraints across databases, files, and APIs. Tonic.ai |
| Synthetic Data Vault (SDV) | Developers/data scientists | Open-source Python tooling for modeling and generating synthetic tabular and relational data. BlueGen AI |
| MOSTLY AI | High-fidelity synthetic datasets | Focuses on statistically realistic synthetic data while protecting privacy. Guideflow |
| Gretel.ai | API-driven generation pipelines | Good if you want synthetic data generation integrated into engineering workflows. Guideflow |
| Seedfast | Database seeding | Reads schemas and generates large relational datasets with foreign keys and constraints preserved. Seedfast |
| IRI RowGen | Enterprise-scale database testing | Generates structurally and referentially correct test data from database metadata. IRI |
For your specific example — “millions of fake users plus their activity” — I’d look for something that can model:
users - demographicssessions - login frequencyevents - page viewsrelationships - every event belongs to a real userA typical workflow:
users, orders, events, etc.).If you want a developer-first/local approach, I’d start with SDV or a schema-aware seeding tool like Seedfast. If you need something closer to a production data replacement platform, look at Tonic.ai or MOSTLY AI.
For an application test environment specifically, I would avoid plain Faker-style libraries alone: they can make realistic-looking rows, but they generally do not understand that a user, their orders, payments, sessions, and events need to form one consistent history.
**Tonic Fabricate** (by [Tonic.ai](https://www.tonic.ai/synthetic-data/best-synthetic-data-tools) ) and specialized AI-driven synthetic data agents are designed to generate large, coherent relational datasets that respect complex foreign keys and…
Tonic Fabricate (by Tonic.ai ) and specialized AI-driven synthetic data agents are designed to generate large, coherent relational datasets that respect complex foreign keys and constraints.
If you can share what database engine or format (PostgreSQL, MySQL, JSON, CSV) and the scale of the records you need, I can recommend the exact tool or provide a starter script to build your dataset.
Yes. For **large, realistic fake users + longitudinal activity + relational integrity**, I’d look first at **Tonic.ai Fabricate**. ### Best fit: Tonic Fabricate [tonic.ai](https://www.tonic.ai/capabilities/data-synthesis?utm_source=chatgpt.com) can generate relational synthetic data **from scratch**, based on a schema…
Yes. For large, realistic fake users + longitudinal activity + relational integrity, I’d look first at Tonic.ai Fabricate.
tonic.ai can generate relational synthetic data from scratch, based on a schema or natural-language description. It specifically handles:
For example, you could describe something like:
"Generate 10 million users for a SaaS application. Each user has an account, organization membership, subscription, and login history. Generate 6–18 months of activity per user, with realistic churn, upgrades, failed payments, support tickets, and feature usage. Heavy users should generate disproportionately more events. Preserve all foreign-key relationships." That's much closer to what you're asking for than simply asking an LLM to spit out JSON.
If your priority is specifically "create an entire fake population and simulate what those people do over time", I'd rank them:
The distinction matters: traditional synthetic-data tools are often optimized to reproduce distributions in existing data, whereas Tonic Fabricate is particularly geared toward generating a new relational world from a schema and behavioral description.
If you tell me your schema (e.g. users, orders, sessions, events, messages) and roughly how many users/events you need, I can also suggest the best tool + generation architecture for producing it at scale.
When you need fake users and user activity that respect strict relational constraints (like foreign keys, timestamps that make chronological sense, or transactions tied to actual existing users), standard basic tools like Faker or random scripts fall short because they break relational integrity. A few specialized…
When you need fake users and user activity that respect strict relational constraints (like foreign keys, timestamps that make chronological sense, or transactions tied to actual existing users), standard basic tools like Faker or random scripts fall short because they break relational integrity.
A few specialized AI-powered and agentic synthetic data platforms handle relational constraints natively:
Faker combined with SQLAlchemy or pandas ) to generate the data locally. Because LLMs can write the relational loop logic themselves, they can enforce ID mapping across parent-child tables before outputting the final CSVs or JSON files.(Note: If you just need quick, non-AI rule-based mock data for a couple of tables without complex statistical behavior, Mockaroo is great for small batches, though it lacks deep AI relational inference.)
To help narrow down the best choice, tell me:
