SyntheholDB vs Gretel.ai

If you’ve been evaluating synthetic data platforms, you’ve probably come across Gretel.ai. It’s well-funded, well-known, and has built a solid reputation in the privacy and ML training data space. So why are engineering teams in regulated industries increasingly choosing SyntheholDB instead?

The answer comes down to one fundamental difference in philosophy: Gretel.ai was built to synthesize data you already have. SyntheholDB was built to generate data you need — from scratch, with full relational integrity, in minutes, without touching a single real record.

That distinction matters more than it sounds.

What Gretel.ai Does Well

Gretel.ai is a mature, capable platform with a strong focus on differential privacy, PII detection, and ML model training data[cite:99][cite:103]. Its core workflow takes an existing dataset as input, learns its statistical properties, and outputs a synthetic version that preserves those patterns while protecting individual privacy.

For teams that have data and need a privacy-safe version of it, Gretel.ai does that job well. It supports tabular data, text, and time-series formats, offers a Python SDK and API for integration, and has enterprise-grade infrastructure for large-scale generation[cite:108]. Reviews consistently highlight the quality of its synthetic output and the depth of its privacy tooling[cite:103].

But that workflow — input real data, get synthetic data back — carries a hidden assumption that limits its usefulness for a significant portion of what engineering teams actually need synthetic data for.

The Problem Gretel.ai’s Workflow Creates

To use Gretel.ai, you need to feed it real data first.

That means real customer records, real patient data, or real transaction histories have to travel through your pipeline, get uploaded to a third-party platform, be processed through their models, and then come back out the other side as synthetic output. Even if the end result is private, the journey involves real PII at every step.

For teams operating under HIPAA, GDPR, or CCPA, this creates a compliance question that many organisations would rather not have to answer[cite:99]. You’re not eliminating PII exposure from your data pipeline — you’re adding a step to it. Your security team still has to evaluate the third-party risk. Your legal team still has to review the data processing agreement. Your engineers still have to handle, transfer, and manage real records before any synthetic data is generated.

This is a real friction point, especially for teams in healthcare, fintech, and any regulated B2B SaaS product where moving production data to an external platform triggers a formal review process.

There’s a second limitation that surfaces in developer workflows specifically. Gretel.ai’s pricing starts at $295 per month for team plans[cite:99], with usage-based costs layered on top. For an individual developer or a small engineering team that needs realistic test data for a staging environment or a CI pipeline, that price point is a significant barrier to adoption — especially when the use case doesn’t require privacy preservation of existing data, just realistic generation of new data.

What SyntheholDB Does Differently

SyntheholDB starts from a completely different place. There is no input data. No real records. No upload, no transfer, no third-party processing of sensitive information.

You describe your schema — in plain English, or by uploading a CSV — and SyntheholDB generates a fully synthetic relational database from scratch. The output reflects the statistical distributions and business logic you specify, not the patterns of an existing real dataset. Foreign keys resolve correctly across linked tables. Value distributions reflect the parameters you set. Edge cases are built into the generation, not discovered later in production.

The built-in PII scan runs before every export — not to detect PII you uploaded, but to catch any generated value that accidentally resembles a real-world identifier before it ever leaves the tool. The compliance posture is fundamentally different because the architecture is fundamentally different. There is nothing to breach, nothing to audit, and nothing to disclose.

Head-to-Head: Where Each Platform Wins

Dimension	Gretel.ai	SyntheholDB
Core workflow	Synthesize from existing real data	Generate from schema description, no real data required
Relational integrity	Limited — primarily flat tabular datasets	Native — foreign keys resolve across linked tables by design
PII exposure in workflow	Real data must be uploaded and processed	Zero real data at any step
Plain English input	No — requires structured data input or SDK	Yes — describe your schema conversationally
Time to first dataset	Hours to days (model training required)	Under 5 minutes
Pricing entry point	$295/month for team plans[cite:99]	Free tier, no credit card required
Primary use case	ML training data privacy, data sharing	Dev/staging/CI seed data, demo environments, ML evaluation data
Compliance posture	Reduces PII in output	Eliminates PII from entire workflow
Differential privacy	Yes — built-in DP mechanisms[cite:96]	Built-in PII detection scan pre-export
Enterprise infrastructure	Yes — cloud-scale, GCP partnership[cite:108]	Free tier to paid, focused on developer workflow

The Use Case Gap Nobody Talks About

Gretel.ai’s documentation, pricing, and product design all point toward a specific buyer: a data science or ML team that needs a privacy-safe version of an existing dataset for model training or sharing with external partners[cite:104][cite:108].

That is a real and valuable use case. But it’s not the use case most engineering teams face day-to-day.

The majority of synthetic data problems in production engineering teams aren’t about privacy-preserving copies of real datasets. They’re about:

• Seeding a staging environment with realistic data that doesn’t come from production
• Generating test data for a CI pipeline that breaks as soon as it uses real records
• Building a demo environment that looks convincing without carrying any compliance risk
• Stress-testing an ML model against edge cases that never appear in the training distribution

For all of these use cases, starting from real data is not just unnecessary — it’s the wrong approach entirely. The whole point is to avoid real data at every step. SyntheholDB’s schema-first, generation-first architecture is purpose-built for exactly these workflows.

The Relational Integrity Difference

This is worth addressing specifically because it’s where the technical gap between the two platforms is most pronounced.

Gretel.ai’s core synthetic generation capability is designed primarily for tabular data — flat, single-table datasets where statistical fidelity to an original source is the primary objective[cite:104][cite:108]. Generating multi-table relational structures with consistent foreign key relationships across linked tables is not what the platform was designed to do.

SyntheholDB’s generation engine is built around relational integrity as a first principle. When you describe a schema with Users, Orders, and Products tables, the generator maintains referential integrity across all three — order foreign keys resolve to valid user IDs, product references are consistent, and value distributions across linked tables reflect the business logic you specified. This isn’t a feature layered on top of a tabular generator. It’s the core of how the generation engine works.

For any team working with a relational database — which is most teams — this distinction directly affects how useful the synthetic data is in practice.

Who Should Use Which Platform

Gretel.ai is the right choice if:

• You have existing datasets and need privacy-safe synthetic versions for model training or external sharing
• Your primary concern is differential privacy guarantees on data that already exists
• You need enterprise-scale infrastructure with GCP integration and formal privacy compliance tooling
• Your team has the budget for a $295/month starting point and the data science expertise to work with the SDK

SyntheholDB is the right choice if:

• You need realistic relational test data without touching any production records
• Your use case is staging environments, CI pipelines, demo environments, or ML evaluation datasets
• You want to describe your schema in plain English and get usable data back in minutes, not days
• You’re working in a regulated industry and need the compliance posture of zero real data in the workflow
• You want to start free and scale as your needs grow

The Bottom Line

Gretel.ai and SyntheholDB are solving adjacent but meaningfully different problems. Gretel.ai is a privacy-preservation platform for teams that have real data and need a safer version of it. SyntheholDB is a relational data generation platform for teams that need realistic data without ever touching real records.

For engineering teams in regulated industries who are tired of the compliance conversation that comes with every staging environment, every demo setup, and every test data request — SyntheholDB’s architecture eliminates the problem at the source rather than managing it downstream.

The free tier is live at db.synthehol.ai. No credit card, no model training, no real data required. Describe your first schema and have a seeded relational database in under five minutes.