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SyntheholDB vs Synthesized: Why Engineering Teams Are Making the Switch

Synthesized is a well-engineered platform. If you are looking for a tool to mask, subset, or scale up data that already exists in a production database you can connect to, it handles that workflow with genuine sophistication. The TDK CLI is solid. The YAML configuration model is expressive. The subsetting engine is one of the better implementations in the market.

But the moment you step outside that workflow, the gaps become structural, not cosmetic.

This post is a direct, honest comparison of Synthesized and SyntheholDB for engineering teams who need production-realistic synthetic databases and want to understand which tool actually fits their situation.

What Synthesized Actually Does

Synthesized operates as a transformation pipeline between a source database and a destination database. The core workflow is: connect to production, analyze schema and data patterns, apply a transformation (mask, generate, or subset), write to a destination.

The platform offers three core capabilities:

Database masking replaces sensitive values with realistic but fake alternatives using 35-plus built-in transformers. Names, emails, SSNs, credit card numbers, and other PII fields are replaced at the column level while preserving row-level continuity and referential integrity.

Database generation uses the source database schema and data distribution as the model. The TDK learns statistical distributions from your production data and generates additional rows that match those distributions. The target ratio configuration lets you scale a production dataset to any multiple.

Database subsetting extracts a representative slice of production data, 10%, 25%, or 50%, while preserving referential integrity across linked tables. For teams that need a smaller but structurally valid version of production data, this is a genuinely useful capability.

The SDK adds a Python-native layer for ML teams who need data rebalancing, imputation, and augmentation for model training pipelines. The claimed uplift of up to 15% model performance improvement from data rebalancing is the kind of claim that resonates with data science teams running classification models on imbalanced datasets.

All of this is coherent and well-executed for its intended use case.

The Structural Dependency That Changes Everything

The Synthesized workflow has one requirement that shapes every other decision: it needs a source database to connect to.

Every generation mode in the TDK, whether masking, subsetting, or statistical generation, begins with a connection to a source. The platform analyzes that source, learns its schema and distributions, and uses that learning to produce the output.

This means:

If you are a healthtech startup building a clinical product and you do not have a production clinical database yet, Synthesized cannot generate data for you.

If you are a pharma engineering team building a pipeline for a new therapeutic area that your institution has not collected data for, Synthesized cannot generate that population.

If you are a CRO technical team that needs to give a vendor partner a realistic clinical database for integration testing without sharing any production data, Synthesized’s transformation pipeline starts from a source you cannot share.

If you are building a new product line in fintech, insurance, or healthcare and need realistic data for demos, staging, and CI before your first customer is onboarded, the tool that requires a production source cannot help you.

SyntheholDB generates complete, production-realistic databases from statistical models without ever touching production data. There is no source connection. There is no production dependency. There is no data transfer to configure, secure, or audit.

The YAML Configuration Layer

Synthesized uses a declarative YAML configuration model that the company calls “Data as Code.” For teams with strong DevOps culture and experience managing infrastructure-as-code, this is a genuine workflow advantage. Configurations are version-controlled, reviewable, and reproducible.

For teams that are not primarily infrastructure engineers, the YAML layer is overhead that sits between the team and the synthetic database they need.

Consider the typical onboarding path for a new engineer who needs a realistic test database for a feature branch. With Synthesized TDK, the path is:

Install the TDK CLI. Clone the tutorial databases repository. Configure Docker Compose to start local PostgreSQL instances. Write or generate a YAML configuration file defining the transformation mode, target ratios, table-level overrides, transformer specifications for each column type, and safety mode settings. Run the TDK with source and destination JDBC connection strings. Debug configuration errors against the schema validation output.

That path is manageable for a senior data engineer who owns the test data infrastructure. It is a meaningful barrier for a backend developer who needs a database this afternoon.

SyntheholDB takes a different approach. Describe your schema in plain English or import a SQL DDL file. Configure the population size. Generate. The entire interaction is a conversation with a generation pipeline, not a YAML authoring exercise.

Domain Knowledge vs Statistical Learning

Synthesized learns distributions from production data. When it generates additional rows, it is sampling from the statistical model it built from your source database. This produces output that faithfully reflects the patterns in your actual production data.

This is a strength when your production data is the right reference. It is a limitation when:

Your production data has historical biases you do not want to reproduce in your test environment. A model trained on your production distribution will faithfully reproduce underrepresented populations, historical coding errors, and whatever anomalies your production system accumulated over time.

Your production data does not yet contain the scenarios you need to test. Edge cases, rare disease presentations, atypical transaction patterns, low-frequency but high-severity events: if they are rare in production, they will be proportionally rare in the Synthesized output unless you manually configure conditional overrides.

Your production data does not exist for the use case you are building. No production source means no learned distribution means no output.

SyntheholDB uses domain-specific statistical models built from clinical, financial, and enterprise data standards. These models understand that a Type 2 diabetes patient should have a correlated prescription history, that an HbA1c result should fall within a range consistent with their diagnosis severity, that a high-value transaction should trigger a correlated risk event. This domain knowledge is built into the generation engine, not learned from whatever production data you happen to have available.

Edge Case Coverage as a First-Class Feature

Clinical, financial, and regulated industry data is defined by its edge cases. The patient with 14 overlapping prescriptions. The transaction that matches three different fraud pattern signatures simultaneously. The insurance claim where the procedure code is valid but the diagnosis code pairing is clinically implausible.

Synthesized generates data that reflects the distribution of your production database. If those edge cases appear in production at 0.3% frequency, they will appear in the Synthesized output at approximately 0.3% frequency. At a test dataset size of 10,000 records, that is 30 edge case instances, which may be enough to catch the obvious failures but not the subtle ones.

SyntheholDB generates edge cases by design. The generation pipeline includes anomalous but domain-valid patterns as a configurable first-class feature, not as a low-probability byproduct of statistical sampling. You define the edge case coverage you need. The engine generates it.

The Cold Start Problem for New Products

There is a specific scenario where the production-dependency of Synthesized creates a complete blocker: building a product before you have customers.

Every healthtech company, fintech startup, and enterprise software team building in a regulated vertical faces this moment. You need realistic data to build the product. You need the product to get the first customer. You need the customer to get the data.

Synthesized cannot break this cycle. Its value proposition requires a source database to analyze. No source, no generation.

SyntheholDB breaks this cycle on day one. Start from a pre-built domain template, import a schema definition, or describe your data model in plain English. Generate a production-realistic database before your first customer conversation. Build your entire product on realistic data from the first line of code.

This is not a minor convenience. For regulated industry products where the difference between a realistic demo and a toy demo determines whether you get the pilot, the ability to generate realistic data without a production source is a competitive advantage.

CI/CD Integration: Two Different Models

Both platforms support CI/CD integration. The practical experience is meaningfully different.

Synthesized CI/CD integration works by running the TDK transformation pipeline in your pipeline, connecting to a source database, applying the configured transformations, and writing to the destination. This means your CI pipeline needs network access to a source database, a configured and maintained YAML transformation file, and a destination database to write to. The pipeline is fast, but the dependencies are real.

SyntheholDB on Pro and above supports API-driven generation with a deterministic seed. Your CI pipeline calls the generation API with a schema identifier and a seed value. The same seed produces the same database on every run. No network access to a source database. No transformation configuration to maintain. No production dependency in your pipeline at any point.

Head-to-Head Comparison

Dimension	SyntheholDB	Synthesized
Production data required	Never	Required as source for generation and masking
Generation from scratch	Full schema, from plain English or DDL	Requires source database to learn distributions from
Domain-aware clinical correlations	Built into generation models	Learned from production data if available
Edge case generation by design	Configurable, first-class feature	Proportional to production distribution
CI/CD integration	API-driven, deterministic seed, no source DB needed	TDK CLI with source DB connection required
Configuration model	Conversational, plain English or DDL	Declarative YAML with 35-plus transformer types
Cold start for new products	Fully supported on day one	Not applicable without production source
Domain templates	Healthcare EHR, financial, custom	Not available, requires source schema
Compliance certifications	SOC 2 Type II, ISO 27001, HIPAA, GDPR	Enterprise plans, certifications not publicly listed
Fidelity and privacy reports on every export	Standard across all tiers	Available via platform reporting
Time to first database	Under 60 seconds, free tier	Requires TDK setup, Docker, source DB configuration
Pricing transparency	Free tier, $99/month Pro, Enterprise published	Not publicly listed, requires contact
SAP integration	Not applicable	Native SAP TDM support
ML data rebalancing and imputation	Not the primary use case	Available via SDK

When Synthesized Is the Right Choice

This comparison exists to help teams make the right decision, not to dismiss a tool that serves a real need.

Synthesized is the right choice when your team has an existing production database, needs to mask it for compliance, subset it for developer environments, or scale it for load testing. If your primary workflow is transforming production data rather than generating data without a production source, the TDK is well-suited to that workflow.

Synthesized is also the right choice for teams with SAP infrastructure who need native SAP TDM support. SyntheholDB does not serve that use case.

If your ML pipeline needs data rebalancing, imputation, and augmentation for model training on tabular data, the Synthesized SDK is purpose-built for that use case in a way that SyntheholDB is not.

When SyntheholDB Is the Right Choice

SyntheholDB is the right choice for every scenario where you need production-realistic data without a production source.

Building a new product before your first customer. Generating clinical data for a therapeutic area you have not served yet. Creating a realistic database for a CRO or vendor partner without sharing production data. Running a CI pipeline that cannot depend on a live source database connection. Testing edge cases at a coverage level that production data distribution cannot provide. Onboarding a developer in under 60 seconds without a TDK setup, Docker configuration, or YAML authoring exercise.

SyntheholDB is also the right choice for teams in regulated industries where the compliance posture of a tool that never touches production data simplifies audit conversations significantly. Every generation is documented: fidelity score, privacy label scan, and referential integrity report included with every export. SOC 2 Type II certified, ISO 27001 certified, HIPAA and GDPR compliant.

Start Free Today

SyntheholDB is free to start. No credit card required. Your first synthetic database is ready in under 60 seconds.

If you have been running Synthesized for production transformation workflows and are looking for a complementary tool for the new product, new data domain, or no-production-dependency use cases, drop a comment below. These two tools solve different problems and the teams getting the most value from SyntheholDB often have both in their stack for different purposes.

July 16, 2026

The Best Delphix Alternative for Modern Engineering Teams

SyntheholDB Vs Delphix

The way engineering teams access test data is changing. Teams that have spent years managing Delphix infrastructure are asking a simple question: is there a better way? The answer is yes, and it is called SyntheholDB.

This post is for engineering leads, data teams, and DevOps practitioners who are evaluating their test data stack and want to understand why a growing number of teams are moving away from production-derived virtualization platforms toward synthetic database generation.

The Old Approach Has Run Its Course

For years, the dominant model for test data management was virtualization: connect to production, compress a copy, mask the sensitive fields, and spin up virtual environments for development and QA teams. Delphix built a business on this approach and served large enterprises well in an era when the main concern was storage cost and provisioning speed.

That era is over.

The compliance environment has fundamentally shifted. SR 11-7, HIPAA tightening, the EU AI Act taking effect Q3 2026, and GDPR enforcement have all moved in one direction: any architecture that starts with production data carries increasing legal, reputational, and operational risk over time. The question is no longer how fast you can mask production data. It is whether you should be using production data as your starting point at all.

SyntheholDB answers that question cleanly: you should not, and you do not have to.

What Delphix Requires That SyntheholDB Does Not

Delphix is built on a dependency that limits every team that adopts it: a live, persistent connection to production systems.

To create a virtual database, Delphix must first ingest a compressed source copy of your production environment. That source copy becomes the master replica from which all virtual copies are derived. Every masked environment, every developer sandbox, every QA refresh ultimately traces back to real production data living inside the Delphix engine.

This creates four structural constraints that no amount of configuration can remove:

Production connectivity is mandatory. If your security policy, data residency requirements, or compliance program prohibits connecting production systems to lower environments, the entire Delphix model is unavailable to you without architectural exceptions and ongoing risk acceptance.

New product lines cannot be served. If you are building a greenfield product, launching in a new market, or prototyping a feature that does not yet have production history, there is nothing for Delphix to virtualize. You are back to handcrafted seed data or empty schemas.

Sharing data with third parties requires legal overhead. Every dataset handed to a vendor, contractor, auditor, or external QA partner carries the question of whether masked production data has been sufficiently de-identified. Legal review on every export is not a process problem. It is an architecture problem.

NoSQL and modern data stacks are largely unsupported. Delphix was designed for conventional relational databases. Teams running MongoDB, Cassandra, DynamoDB, or mixed polyglot architectures find the virtualization model either limited or entirely inapplicable.

SyntheholDB: Built for the Way Modern Teams Work

SyntheholDB generates complete, relationally consistent synthetic databases from scratch. No production connection. No master replica. No masking pipeline to maintain. No compliance exception to justify.

You describe your data model, and SyntheholDB builds it. The result is a fully populated, production-realistic database where every table connects to every other table the way it would in a real system, every join works, every constraint holds, and not a single real customer record was ever involved.

The Multi-Agent Generation Pipeline

SyntheholDB uses a team of specialized AI agents working in sequence to produce results that no single model can achieve alone:

Schema Architect identifies entities, infers relationships, and maps your data model from a plain-English description
Constraint Planner adds domain-aware correlations: lifetime value scales with order count, salary scales with tenure, claim severity correlates with treatment cost
Reviewer surfaces only genuine ambiguities and asks targeted clarifying questions before generation begins
Generator populates all tables simultaneously with statistically faithful, relationally consistent rows
PII Labeler scans every export for sensitive-shaped fields and labels them before the data leaves the system

You see live progress at every step. You know exactly what each agent decided and why.

Domain-Aware Correlations That Reflect Reality

Most synthetic data tools generate independent rows that look plausible in isolation but fall apart the moment you run a real query. SyntheholDB generates data where the relationships between fields reflect how data actually behaves in production environments.

Blood pressure correlates with age. Order frequency correlates with customer lifetime value. Incident severity correlates with resolution time. Product return rate correlates with category. These are not rules you have to define. They are baked into domain-specific statistical models that ship with the platform.

Coherent Business Logic You Can Trust

A fatal adverse event is never labeled mild. A resolved support ticket always has a close date. A completed transaction never has a negative balance without a corresponding reversal. Temporal sequences respect real-world causality.

These are the failure modes that appear in production when teams build on bad test data. SyntheholDB eliminates them by enforcing domain rules at the generation layer, before a single row reaches your environment.

Referential Integrity That Survives a Join

The single most common failure point in synthetic data is broken relationships between tables. Foreign keys that reference nonexistent rows. Many-to-many tables that produce duplicate joins. Timestamps that violate parent-child sequences.

SyntheholDB validates and repairs foreign keys, composite unique keys, non-overlapping time windows, and monotonic timelines before export. Every relationship holds. Every query that works in production works in your synthetic database.

Privacy That Is Architecture, Not Process

With Delphix, privacy is a process applied to real data. Masking runs after ingestion. The compliance posture depends on the masking configuration being correct, complete, and consistently applied across every data source and every refresh cycle.

With SyntheholDB, privacy is architectural. There is no real data in the system to begin with. All values are sampled from statistical models. The compliance posture does not depend on a masking configuration being perfect, because there is nothing to mask.

This distinction matters enormously at audit time. The answer to the question “where does your test data come from” is either “we masked production data and here is our masking configuration” or “we generate synthetic data from statistical models and production data is never involved.” One answer invites follow-up questions. The other closes the conversation.

SyntheholDB is SOC 2 Type II certified, ISO 27001 certified, HIPAA compliant, and GDPR compliant. Enterprise deployments run fully air-gapped on-premises with no external LLM calls in the generation or validation path.

Production-Scale Starter Schemas Ready on Day One

SyntheholDB ships with proven, production-scale starter schemas for the industries where realistic relational data matters most.

Schema	Scale	Key Entities
Banking and Ledger	1B transactions, 5M accounts	Transactions, Accounts, KYC, built-in fraud patterns
Healthcare EHR	500K encounters, 750K diagnoses	Patients, Providers, Encounters, Diagnoses, Prescriptions
E-Commerce Platform	200K orders, 500K items	Customers, Products, Orders, Reviews, Inventory
Global Workforce HRIS	10M employees, 240M payroll logs	Employees, Payroll, Leave History
B2B Subscription CRM	50K companies, 200K subscriptions	Companies, Contacts, MRR History
IoT Device Fleet	10M telemetry readings	Devices, Sensors, Telemetry, Alerts, Firmware
University LMS	150K enrollments, 200K assignments	Students, Instructors, Courses, Grades

Start from a proven schema and customize it to your exact data model, or describe your own from scratch in plain English. Your first synthetic database is ready in under 60 seconds.

Use Cases Where SyntheholDB Outperforms Virtualization

Local Development Environments

Seed a realistic, multi-table dataset on day one. Every new engineer on your team gets a working database that behaves like production, without waiting for a DBA to provision an environment or a compliance team to approve a data request.

CI and CD Test Fixtures

Pull deterministic, relationally consistent fixtures from the SyntheholDB API. Replace brittle seed scripts and stale data snapshots with generated databases that rebuild from scratch on every pipeline run. Available on Pro and above.

Sales Demos and Staging Environments

Show a fully populated product without exposing real customers. Regenerate fresh, client-specific data for every pitch. No legal review. No anonymization checklist. No risk.

AI and ML Model Development

Train baseline models and validate pipelines on production-shaped synthetic data without any data-access approval process. The statistical properties that make training data useful are preserved. The compliance risks of using real data are eliminated entirely.

External Vendors and Contractors

Hand off complete, realistic datasets to partners, auditors, or QA contractors without a legal review on every share. The data is synthetic by construction. There is nothing to protect and nothing to disclose.

Greenfield Products and New Market Launches

Generate production-realistic data for systems that do not exist yet. Prototype dashboards, stress-test APIs, and train models on data your product will generate six months from now. Delphix cannot do this. SyntheholDB was built for it.

Performance and Load Testing

Generate up to 500,000 linked rows to stress query plans, indexes, and data pipelines before launch. All relational constraints hold at scale.

Pricing Built for Engineering Teams

Delphix licenses per terabyte of ingested source data with pricing available on request. For most engineering teams, that means a procurement process before a single test runs.

SyntheholDB starts free with no credit card required and scales transparently as your usage grows.

Plan	Price	Rows per Generation	Key Features
Free	$0	1,000	10 projects, 5 daily generations
Plus	$19 per month	10,000	25 projects, 25 daily generations
Pro	$49 per month	50,000	Unlimited generations, API access
Max	$99 per month	100,000	Priority support, 500 GB storage
Enterprise	Custom	Unlimited	Air-gapped on-prem, SSO, dedicated SLA

You can evaluate SyntheholDB, generate your first database, and decide whether it fits your stack before spending a dollar. The onboarding experience is under 60 seconds, not weeks.

The Compliance Tailwind Is Only Getting Stronger

Every major regulatory development in 2025 and 2026 has moved in the same direction. SR 11-7 requires auditable, governed model training inputs. The EU AI Act mandates documented data provenance for AI systems. HIPAA enforcement is expanding its scope for health data used in AI development. Tier-1 banks and healthcare organizations are repatriating AI workloads on-premises and demanding air-gapped synthetic data pipelines.

In this environment, an architecture that begins with production data accumulates regulatory surface area over time. An architecture that never touches production data has nothing to defend.

SyntheholDB publishes per-run fidelity, privacy, and utility scores with every export. Every generation is auditable end-to-end. Four published papers on arXiv and SSRN back the statistical engine. When your compliance team, your auditor, or your enterprise customer asks how your test data is governed, the answer is complete and defensible.

The Choice Is Clear

Delphix served a generation of enterprise teams well in a world where the primary constraint was storage and provisioning speed. That world has changed.

The teams moving fastest in 2026 are the ones that have removed production data from their testing, prototyping, and AI development workflows entirely. They are not masking real data and hoping the masking is complete. They are generating synthetic databases that behave exactly like production, sharing them freely, and shipping with confidence.

SyntheholDB is the platform built for that world. It is faster to start, simpler to operate, more compliant by design, and accessible to every team regardless of budget.

Your first synthetic database is ready in under 60 seconds. No credit card required.

Start free at db.synthehol.ai

June 29, 2026

SyntheholDB vs Gretel.ai: Why Relational-First Synthetic Data Changes Everything

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.

May 22, 2026