Synthetic data platforms came of age in a world where the hardest problem was getting enough tabular data to train models. Tools like YData were built for that world: they focus on single tables, model‑ready datasets, and data‑centric AI workflows.
Today, the teams buying synthetic data are dealing with a different reality:
AI systems embedded deep inside products and microservices
Regulated environments where PII and auditability are critical
Dev, QA, and staging environments that must behave like production without using production
In that reality, SyntheholDB is simply a better choice than YData. It is designed for synthetic data as infrastructure, not just as an experiment.
This article explains why.
1. YData Solves Yesterday’s Synthetic Data Problem
YData focuses on datasets:
Load a table
Profile columns and correlations
Train a generator
Produce a synthetic dataset that approximates the original statistically
That’s useful when your main question is: “How do we get more training data for our model without exposing real records?”
It is less useful when your questions become:
“How do we get a realistic, safe database for our app, pipelines, and agents?”
“How do we keep our non‑prod environments compliant without cloning production?”
“How do we give auditors synthetic environments that mirror production behavior, not just column stats?”
YData doesn’t really try to solve those; it stays in the dataset lane. That was enough five years ago. It isn’t enough for where synthetic data is now headed.
2. SyntheholDB Treats Synthetic Data as Infrastructure, Not Just a Dataset
SyntheholDB starts from a different premise: synthetic data is no longer just a file you hand to a data scientist. It is infrastructure your whole stack relies on.
That shows up in three fundamental design choices.
a) Databases, not just tables
SyntheholDB is built to generate complete databases, not isolated tables:
You start from a schema (your own, a template, or a natural‑language description).
It generates all tables together, keeping primary keys, foreign keys, and relationships coherent.
Customers, accounts, transactions, claims, events, and logs all line up and tell consistent stories.
YData can give you synthetic rows for a table. SyntheholDB gives you a synthetic backend your application can actually run against.
b) Behavioural realism, not only statistical similarity
YData optimises for column‑level and table‑level statistics. SyntheholDB optimises for system behaviour:
Customers sign up, transact, and churn over realistic timelines.
Policies start, renew, lapse.
Loans are disbursed, repaid, or defaulted, with probabilities that vary by segment and product type.
Events and logs follow plausible sequences tied to business flows.
If you plug YData’s outputs into a complex application, you often get data that looks fine in a profile but doesn’t behave like your system. Plug SyntheholDB’s outputs in, and integration tests, microservices, and agents experience something that feels like production.
c) Privacy and governance by construction
Cloning and masking production databases for non‑prod is the quiet risk most organisations carry.
SyntheholDB eliminates that risk by design:
Every row is generated from models, not copied from production.
There is no dependency on raw customer data for dev, QA, staging, or demos.
You can attach governance layers (audit trails, scenario labels, temporal constraints) directly to synthetic databases.
YData’s outputs still leave you figuring out what to do with the rest of the database. SyntheholDB replaces the database entirely, with privacy baked in, not bolted on.
3. One Product Family for Datasets and Databases
There is another practical reason SyntheholDB is a better choice: Synthehol already gives you both layers.
Synthehol.ai covers synthetic datasets for AI/ML training and analytics.
SyntheholDB covers synthetic databases for applications, pipelines, and governance.
That means:
You don’t have to pick a separate vendor for dataset work and another for database work.
You get a consistent privacy, quality, and governance philosophy across both.
You can move from “we need a synthetic dataset for a model” to “we need a synthetic database for a product” without leaving the ecosystem.
With YData, you still have to solve the database problem elsewhere. With Synthehol, you solve both with one stack.
4. Where SyntheholDB Is Clearly the Better Choice
If you ask a blunt question — “In which situations is SyntheholDB simply the better option than YData?” — the list is very straightforward:
You need to seed dev, QA, and staging databases with realistic data that matches your schema.
You run microservices that depend on shared relational data and need synthetic environments for integration testing.
You build AI products or agents that operate across entities and tables, not just single datasets.
You are in a regulated industry and cannot justify cloning production databases into non‑prod, even with masking.
You want synthetic audit environments that mirror production schemas and behaviour for governance and replay.
YData does not meaningfully address those needs. SyntheholDB is purpose‑built for them.
For teams operating in BFSI, insurance, healthtech, RegTech, and enterprise SaaS, those are not edge cases; they are daily realities. In that context, SyntheholDB isn’t just an alternative — it is the right tool.
5. The Strategic Difference: Experiments vs Systems
The simplest way to summarise the difference is this:
YData is a good tool for experiments. It helps data scientists generate and explore synthetic datasets.
SyntheholDB is a better tool for systems. It helps engineering, AI, and compliance teams run real applications and governance processes on synthetic databases.
If your organisation is still at the “we need synthetic data for model experiments” stage, YData can be adequate.
If you are at the “synthetic data is part of our infrastructure, and we need to trust it for apps, tests, and regulators” stage, SyntheholDB is a better choice.
That is the reality the article should convey: not that YData is bad, but that for modern, production‑grade synthetic data in regulated and complex environments, SyntheholDB is the superior option.
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.
Sign up here: https://db.synthehol.ai/#/login
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.
SyntheholDB: The Best Choice For Engineering and Clinical Data Teams
MDclone serves a specific and well-defined purpose inside large health systems. If your team needs a self-service clinical analytics platform for clinicians and researchers to explore institutional patient data, MDclone was built for that use case.
But if your team is an engineering team, a clinical data engineering team, a platform architecture team, or a developer-facing data infrastructure team, you are being asked to use a tool that was designed for a fundamentally different buyer. And that mismatch costs you time, flexibility, and budget every week.
This post breaks down what MDclone actually does, where it falls short for technical teams, and why SyntheholDB is the right choice for the teams that need production-realistic synthetic databases built for engineering workflows.
What MDclone Actually Is
MDclone is a healthcare analytics and synthetic data platform built around its ADAMS Platform, a self-service environment that allows clinicians, researchers, and operational staff at health systems to explore patient data, generate insights, and share findings using synthetic data to protect privacy.
The platform centers on a specific workflow: a user connects to an institutional data lake, explores a real patient population using a no-code query interface, generates a synthetic version of that cohort for safe sharing and external collaboration, and switches between synthetic and original data as the research matures.
MDclone has earned genuine recognition for this model. It won the MedTech Breakthrough Award for Best Healthcare Big Data Platform in 2021, 2022, and 2023. The World Economic Forum recognized it as a Technology Pioneer in 2021. Health systems in the United States, Canada, and Israel use it to accelerate research, enable cross-institutional collaboration, and give clinical staff direct access to data exploration without requiring programming expertise.
That is a real value proposition for its intended audience: non-technical clinical and research users at health systems who need governed access to institutional patient data for exploration and hypothesis generation.
It is not the right tool for engineering teams building clinical software, data pipelines, or infrastructure that needs realistic relational databases to develop against.
Where MDclone Falls Short for Technical Teams
It Is Built on Your Production Data
MDclone works by connecting to your institutional data lake, organizing patient data longitudinally, and generating synthetic versions of real patient cohorts. The synthetic data output is derived from real patient records in your system.
This means the quality and fidelity of the synthetic output depends entirely on the data that exists in your production environment. If you are building a new product line, launching in a new therapeutic area, prototyping a feature for a patient population you have not yet served, or developing a system that requires data your institution does not currently collect, MDclone cannot generate realistic test data for you. There is no production source to derive it from.
SyntheholDB generates synthetic databases from statistical models, not from production sources. You can generate production-realistic clinical data for any schema, any population, and any data model, whether or not your organization has ever collected that type of data.
It Requires a Healthcare Organization as the Host
MDclone is sold to and deployed within health systems and payer organizations. The ADAMS Platform is institutional infrastructure, not a developer tool. To use MDclone, your organization needs to be a licensed health system customer with a deployed instance and a configured data lake.
Healthtech companies, digital health startups, medtech product teams, CROs, pharma engineering teams, and independent clinical data engineers building systems that interact with healthcare data do not have an MDclone instance. They cannot get one without an enterprise sales cycle and a health system partnership.
SyntheholDB is available to any team, at any organization, starting free with no credit card required, in under 60 seconds.
The Procurement Process Is the Product
MDclone pricing is not published. Prospective buyers must engage in an enterprise sales cycle to receive a quote. For teams that need synthetic clinical data this week, this is not a path that leads anywhere useful.
SyntheholDB starts free. The pricing is transparent and public. The free tier delivers your first synthetic database in under 60 seconds with no sales call, no procurement cycle, and no contract negotiation.
It Is Not Built for Relational Database Engineering Workflows
MDclone generates synthetic datasets for clinical research and analytics exploration. Its output is designed for cohort analysis, hypothesis testing, and feasibility studies by clinical users.
It is not designed to generate a complete relational database with enforced foreign keys, composite unique keys, cross-table temporal ordering, and domain-aware field correlations that your application can actually run queries against.
The gap between a synthetic cohort dataset and a production-realistic relational database is the gap between data that looks correct in a spreadsheet and data that behaves correctly when your application joins it across five tables at three in the morning during a CI run.
SyntheholDB closes that gap by design.
No Self-Service for Developers
MDclone is designed so that clinicians and researchers can explore data without needing a data team or programming expertise. That self-service model works well for its intended audience.
For engineering teams, the MDclone workflow is inverted. A developer who needs a synthetic clinical database for a staging environment does not need a no-code analytics interface. They need a schema definition, a generation endpoint, and an export they can wire into their pipeline. MDclone does not offer that workflow.
SyntheholDB offers exactly that workflow. Describe your schema in plain English, import a CSV, or start from a pre-built clinical template. Generate. Export as CSV, SQL dump, or Parquet. Pull from the API for CI automation. No no-code interface required, no clinical analyst required, no health system license required.
What SyntheholDB Does That MDclone Cannot
Complete Relational Database Generation From Scratch
SyntheholDB generates complete relational databases without requiring a production data source. You describe your data model, and a multi-agent generation pipeline builds it: schema architecture, constraint planning, domain-aware correlation modeling, generation, and privacy labeling, in that order, with live progress at every step.
The Healthcare EHR starter schema covers patients, providers, encounters, diagnoses, prescriptions, procedures, and outcomes at production scale: 500,000 encounters, 750,000 diagnoses, fully linked across every table with enforced referential integrity and temporal consistency.
Domain-Aware Clinical Correlations
Blood pressure correlates with age and comorbidity burden. Medication dosing correlates with diagnosis severity. Lab values stay within clinically plausible ranges for each patient profile. Adverse event frequency correlates with treatment intensity.
These relationships are built into the generation models. You do not define them manually. They emerge from domain knowledge embedded in the generation pipeline.
Temporal Consistency Across Clinical Sequences
Diagnosis dates precede treatment dates. Treatment dates precede outcome dates. A resolved condition does not recur without a new diagnosis event. A deceased patient does not have subsequent encounters.
These constraints are enforced across all linked tables simultaneously, not table by table.
Referential Integrity That Holds on Every Join
Foreign keys, composite unique keys, non-overlapping time windows, and monotonic timelines are validated and repaired before export. Every join works. Every constraint holds. Every query that runs in production runs in your synthetic database.
Privacy by Architecture
SyntheholDB never ingests production data. All values are generated from statistical models. There is no real patient data in the system to protect, mask, or audit. A sensitive-field pattern scan labels every export before it leaves the platform.
This is not a compliance layer applied after generation. It is the architecture.
Enterprise Compliance Out of the Box
SyntheholDB is SOC 2 Type II certified, ISO 27001 certified, HIPAA compliant, and GDPR compliant. Enterprise deployments run fully on-premises with no external calls in the generation or validation path. Every generation produces a published fidelity, privacy, and utility score that is auditable end to end.
Head-to-Head Comparison
Dimension
SyntheholDB
MDclone
Primary persona
Clinical data engineers, healthtech developers, pharma platform teams
Clinicians, clinical researchers, health system analysts
Core output
Synthetic relational databases for engineering workflows
Synthetic cohort datasets for analytics and research exploration
Production data required
Never
Yes, as the source for synthetic generation
No-code analytics interface
Not applicable
Core feature of the ADAMS Platform
Developer API access
Available on Pro and above
Not a developer-facing tool
New product lines with no data history
Fully supported
Not applicable, requires existing production data
CI/CD integration
API-driven, deterministic seeds
Not designed for engineering pipelines
Self-service without health system license
Yes, free tier available
Requires institutional deployment
Transparent public pricing
Yes, from free to $99 per month
Not published, requires sales engagement
Compliance certifications
SOC 2 Type II, ISO 27001, HIPAA, GDPR
Designed for HIPAA and PHIPA regulated environments
Time to first output
Under 60 seconds
Weeks to months for deployment and configuration
Air-gapped on-premises deployment
Available on Enterprise tier
Available as part of enterprise contracts
The Use Cases Where SyntheholDB Is the Clear Choice
Building and Testing Clinical Applications
Your team is building a digital health product that integrates with EHR data. You need a realistic multi-table clinical database for local development, staging, and CI. MDclone is institutional infrastructure that your healthtech company does not have access to. SyntheholDB generates that database in under 60 seconds.
Prototyping in New Therapeutic Areas
Your organization is expanding into oncology, rare disease, or behavioral health. No production data exists yet for these populations in your systems. MDclone cannot generate synthetic data your institution has not collected. SyntheholDB generates production-realistic databases for any clinical domain from day one.
CRO and Vendor Onboarding
Your CRO partner needs a complete, realistic clinical database to build and test their EDC integration. You cannot share real patient data. MDclone requires the CRO to work within your institutional platform. SyntheholDB generates a shareable database you can hand off immediately with no legal review, no access provisioning, and no compliance exception.
Engineering Teams in Pharma and Medtech
Your team is building data pipelines, model training infrastructure, or regulatory submission systems. You need realistic clinical databases for pipeline validation and QA. MDclone is not available to pharma or medtech engineering teams outside of health system partnerships. SyntheholDB is available to any team with an email address.
Staging and CI Environments
Your release pipeline requires a realistic clinical database in staging and a deterministic fixture set in CI. MDclone does not offer an API for engineering automation. SyntheholDB offers API access on Pro and above for deterministic, reproducible generation on every pipeline run.
The Compliance Posture Is Built In
Every SyntheholDB generation is documented end to end. Fidelity scores, privacy scan results, and referential integrity reports are published with every export. When a regulator, auditor, or enterprise customer asks how your test and development environments are governed, the answer is complete before the question is finished.
SR 11-7, the EU Act effective in 2026, and HIPAA tightening all require auditable, governed inputs for systems operating in regulated contexts. SyntheholDB generates that audit trail automatically. Four published papers on arXiv and SSRN back the statistical engine.
Start Free Today
MDclone is a well-built platform for its intended audience: clinical and research users at health systems exploring institutional patient data. If that describes your team, it may be exactly what you need.
If your team is building clinical software, developing data infrastructure, running a healthtech product, or working in pharma or medtech engineering, SyntheholDB gives you production-realistic synthetic clinical databases with no institutional dependency, no enterprise sales cycle, and no configuration overhead.
Free tier, no credit card required. First synthetic database in under 60 seconds.
Synthetic data has moved from an experimental technology to a strategic requirement for organizations building AI systems, testing software, enabling secure data sharing, and meeting increasingly strict privacy regulations.
Among the established players in this space, MOSTLY AI has earned a strong reputation, particularly within European banking and financial services. Its platform has helped organizations generate privacy-preserving synthetic datasets while maintaining analytical utility for a wide range of use cases.
However, as synthetic data adoption expands beyond pilot projects into enterprise-wide deployments, organizations are beginning to ask different questions.
Can the platform support multiple privacy and utility objectives simultaneously?
Can it handle large-scale relational databases efficiently?
Can it operate inside fully air-gapped environments?
Can governance teams validate every generation run without additional tooling?
These are some of the reasons enterprises are increasingly evaluating Synthehol DB as an alternative to MOSTLY AI.
The Synthetic Data Market Is Evolving
The first generation of synthetic data platforms focused primarily on proving that realistic artificial datasets could be created without exposing sensitive information.
Today’s enterprise buyers have different priorities.
They need synthetic data platforms that support:
Complex relational databases
Multiple privacy requirements
AI development workflows
Software testing environments
Regulatory compliance
Internal governance controls
On-premises deployments
As organizations mature their synthetic data strategies, flexibility becomes just as important as data generation quality.
Why MOSTLY AI Became Popular
MOSTLY AI established itself as a recognized leader in synthetic data, particularly across European banking and financial services organizations.
The platform became known for:
Strong privacy-preserving data generation
Banking-focused use cases
Synthetic customer data creation
GDPR-conscious workflows
Enterprise adoption within regulated industries
For organizations seeking a proven synthetic data platform with experience in financial services, MOSTLY AI remains a respected option.
However, many enterprises are now looking for greater flexibility, deployment control, and operational scalability.
Synthehol DB’s Approach: More Control, More Flexibility
One of the biggest differentiators between Synthehol DB and traditional synthetic data platforms is flexibility during generation.
Most synthetic data projects involve competing priorities.
Sometimes privacy matters most.
Sometimes analytical utility is critical.
Sometimes speed is the primary objective.
Instead of forcing teams into a single generation approach, Synthehol DB provides multiple generation profiles within the same platform.
Five Generation Profiles for Different Objectives
Synthehol DB allows users to select from five optimization profiles:
Quick
Balanced
Utility-Preserving
High-Fidelity
Privacy-Focused
This allows teams to generate datasets aligned with specific project goals.
For example:
A QA team may prioritize speed.
A data science team may prioritize utility.
A compliance team may require maximum privacy protection.
Rather than maintaining multiple tools, organizations can address these requirements from a single platform.
Built for High-Volume Enterprise Workloads
As synthetic data initiatives scale, performance becomes increasingly important.
Generating a few thousand records is one thing.
Generating large relational datasets with preserved relationships is another.
Synthehol DB was designed to support banking-class workloads and can generate approximately 100,000 linked rows in just a few minutes while maintaining relational consistency.
For organizations supporting:
Enterprise software testing
User acceptance testing
AI model development
Vendor validation
Analytics environments
Generation speed directly impacts productivity.
Faster synthetic data generation means faster development cycles and shorter project timelines.
Air-Gapped Deployment for Maximum Security
Many synthetic data discussions assume cloud-based deployment.
In reality, some industries cannot operate that way.
Financial institutions.
Healthcare organizations.
Government agencies.
Defense contractors.
Critical infrastructure operators.
These organizations often require complete infrastructure isolation.
Synthehol DB supports fully air-gapped, on-premises deployment models designed for organizations with strict security requirements.
Perhaps more importantly, enterprises maintain full control over data processing without relying on external LLM calls during generation workflows.
For security-conscious organizations, this architectural choice can significantly reduce operational and compliance concerns.
Auditability Built Into Every Run
Synthetic data generation is only part of the equation.
Enterprise governance teams also need proof.
Proof that privacy has been protected.
Proof that utility remains intact.
Proof that generated data meets internal standards.
Synthehol DB automatically generates audit artifacts for every run, including:
Fidelity Scores
Measure how closely synthetic data reflects source characteristics.
Utility Scores
Validate whether generated datasets remain useful for downstream applications.
Privacy Scores
Help organizations assess privacy preservation outcomes.
These metrics are generated automatically and attached to each run, simplifying governance and compliance reviews.
Instead of treating validation as a separate workflow, Synthehol DB makes it part of the generation process itself.
More Than a Single Product
Many synthetic data vendors focus on a single product experience.
Synthehol takes a broader approach.
The platform includes:
Synthehol DB
For relational synthetic database generation.
Synthehol Dataset
For synthetic datasets and machine learning workflows.
Synthehol Shield
For privacy-focused data protection and governance initiatives.
This three-product ecosystem allows organizations to address multiple synthetic data use cases while maintaining a consistent operational experience.
Rather than assembling multiple vendors, enterprises can standardize on a unified synthetic data platform.
Which Organizations Are Choosing Synthehol DB?
Synthehol DB is increasingly being evaluated by organizations that require:
Multi-table synthetic database generation
High-volume relational workloads
On-premises deployment
Air-gapped environments
Audit-ready synthetic data workflows
Flexible privacy and utility controls
Enterprise governance capabilities
These requirements are particularly common across:
Banking
Insurance
Healthcare
Life Sciences
Government
Large Enterprise Software Teams
Why Synthehol DB Is a Strong Alternative to MOSTLY AI
MOSTLY AI remains a well-established synthetic data platform with significant experience supporting financial services organizations.
However, enterprises evaluating modern synthetic data solutions are increasingly looking beyond generation quality alone.
They want flexibility.
They want control.
They want governance.
They want deployment freedom.
Synthehol DB delivers these capabilities through:
Five generation profiles per run
High-throughput relational data generation
Air-gapped deployment support
No external LLM dependency during generation
Built-in fidelity, utility, and privacy validation
A broader synthetic data product ecosystem
For organizations building long-term synthetic data strategies, these capabilities can provide meaningful operational advantages.
Get Started with Synthehol DB
Synthetic data should help organizations move faster, not introduce new complexity.
Whether you’re building secure test environments, enabling AI development, accelerating analytics, or supporting enterprise-scale software delivery, Synthehol DB provides the flexibility, governance, and deployment options required by modern enterprises.
Start free today and generate your first multi-table synthetic database in under a minute.
Because the future of synthetic data isn’t just about creating realistic records.
It’s about giving enterprises complete control over how synthetic data is generated, governed, and deployed.
If you landed here, you’re probably either evaluating Tonic.ai and wondering if there’s a better option, or you’re already using it and something isn’t working the way you expected. Either way, this is the honest breakdown you need before making a decision.
What Tonic.ai Does and Where It Falls Short
Tonic.ai built its reputation as a data de-identification and subsetting platform. Its core workflow takes a production database, runs it through masking and transformation rules, and outputs a safer copy for non-production use. More recently, the Fabricate product added AI-driven generation from scratch.
That sounds like it solves the test data problem. In practice, for most engineering teams, it solves a different problem — and the gap creates real friction.
The production data dependency. Tonic.ai’s foundational workflow requires connecting to a production database and processing real records through a masking pipeline. The output is de-identified, but real PII still travels through the pipeline to get there. Under GDPR, HIPAA, and CCPA, that pipeline still needs a data processing agreement, a third-party risk assessment, and ongoing audit documentation. For teams who thought they were eliminating compliance risk around test data, this is the moment they realize they’ve reorganized it instead.
Pricing built for enterprise procurement. Tonic.ai is priced for compliance departments with formal procurement cycles. For a small engineering team that needs to seed a staging environment or a CI pipeline, the budget conversation becomes a blocker for what should be a developer productivity decision. There is no free tier, no self-serve start, and no path to getting started without a sales conversation.
Complexity that doesn’t match the use case. Teams that want to generate realistic relational test data from scratch — without de-identifying anything, without connecting to production, without configuring masking rules — consistently find Tonic.ai heavier than what they actually need. The platform was built for a different problem and it shows in the workflow.
Relational generation gaps. Tonic.ai’s de-identification workflow preserves relationships from a source database because those relationships already exist in the data it’s masking. When generating from scratch, maintaining foreign key integrity across multiple linked tables with correct cardinality and realistic value distributions across those relationships is not what the platform was primarily designed for.
Why SyntheholDB Is the Right Answer
SyntheholDB was designed from the ground up for the problem Tonic.ai doesn’t cleanly solve: generating realistic, relationally consistent synthetic databases from scratch, with zero real data at any step in the workflow.
There is no production database connection. No source data required. No masking rules to configure. No real records processed anywhere in the pipeline. You describe what you need and the platform generates it.
Plain English schema input. Describe your data model conversationally — “a Users table, an Orders table, and a Products table, with orders linking to users, and order values scaling with customer tenure” — and the AI schema builder creates the full structure. No SQL, no YAML, no configuration files. The schema builder infers column types, proposes foreign key relationships, and lets you review and adjust before generation runs.
Native relational integrity. Foreign keys resolve correctly across every linked table. Cardinality is tunable. Value distributions reflect the business logic you specify — not the distributions of a production database you may not want to touch. If 15% of your users should have incomplete profiles and 8% should be in a failed payment state, you specify that and the generator respects it across the entire relational structure.
Starter schema blueprints. Not starting from scratch? The platform includes pre-built schema blueprints for e-commerce, SaaS, fintech, healthcare EHR, banking ledger, global workforce HRIS, and IoT device fleet — each with realistic row count proportions already configured and ready to generate from immediately.
Zero PII in the workflow — by architecture, not by policy. Because SyntheholDB generates from schema descriptions rather than processing real data, the compliance posture is categorically different. There is no data transfer to document, no third-party processing to assess, and no production records to track through a non-production pipeline. Your legal team’s conversation about test data environments goes from ongoing audit work to a one-time architectural sign-off.
A free tier that actually lets you start. The free tier is live at db.synthehol.ai with no credit card required. Upload a schema CSV, pick a starter blueprint, or describe your first table in plain English and have a seeded relational database in under five minutes. Paid plans start at $19/month for teams that need higher row counts, API access, or advanced export formats.
Compliance certifications that cover regulated industries. SOC 2 Type II, ISO 27001, HIPAA, and GDPR compliance are built into the platform — not roadmap items. For teams in healthcare, fintech, and regulated SaaS, this is the certification stack that makes procurement conversations fast.
The Workflow Comparison
In Tonic.ai, every generation starts with a production database connection. You configure masking rules, run the pipeline, and export a transformed copy. When your schema changes, you reconfigure the masking rules and rerun. The workflow is repeatable but it always starts from real data.
In SyntheholDB, every generation starts with a description. You describe the schema, configure the distributions, and generate. When your schema changes, you update the description and regenerate. The output drops directly into your staging database, CI pipeline, or demo environment as a CSV or SQL dump. Nothing in the pipeline ever touches a real customer record.
For developer environments, the change is friction — seeding a local database goes from waiting on a de-identification pipeline to having data in under a minute. For CI pipelines, the change is consistency — deterministic seed configuration means every test run starts from the same realistic, relational dataset without manual fixture maintenance. For demo environments, the change is confidence — demo data tuned to look like a prospect’s industry without any compliance risk attached to how it got there.
The Migration Path
If you’re currently using Tonic.ai and want to move your test data workflow to SyntheholDB, the migration is straightforward.
Export your schema structure from your current database — column names and types, no data values needed. Import the CSV into SyntheholDB. Review the inferred foreign key relationships and adjust anything that didn’t infer correctly. Configure the distributions and edge case populations you want in your test data. Export as CSV or SQL dump and drop it into the seed step that previously consumed a Tonic.ai output.
Once your seed scripts run cleanly against SyntheholDB output, the production database connection that fed the previous workflow can be removed entirely. That removal is the moment the compliance posture changes permanently — there is no longer any pipeline that processes real records for non-production purposes.
Most engineering teams complete the full migration in two to four days. The longest part is the distribution configuration step, not because it’s technically difficult, but because it’s the first time most teams have explicitly thought about what their test data should actually look like.
Start Here
The free tier is live at db.synthehol.ai. No credit card, no sales call, no enterprise procurement process. Upload your schema CSV or describe your first table in plain English and have a seeded relational database in under five minutes.
The workflow you’ve been looking for doesn’t start with a production database connection. It starts with a description.
Last week, I had a deeply insightful conversation with a group of seasoned life sciences professionals—CROs, clinical trial auditors, quality leads, and physicians with decades of experience across leading pharma and biotech companies spanning Phase I through Phase III trials.
The conversation wasn’t about technology for technology’s sake. It was about a real, recurring operational problem that every healthcare and pharma organization faces:
You know the use case. You have the hypothesis. You can see the value. But you can’t access the data.
The Data Gridlock in Healthcare
Here’s what we heard, condensed into three painful realities:
1. Data scarcity when you need it most
Clinical trials often begin with limited patient enrollment. Imagine running a Phase I trial targeting 20 subjects, but only 4 have enrolled. Your analytics, modeling, and monitoring workflows are starved for data. You can’t validate your assumptions. You can’t test your systems. You can’t demonstrate proof-of-concept to stakeholders.
2. Regulatory and privacy barriers that delay everything
HIPAA. IRB approvals. Internal governance layers. Cross-organizational data sharing agreements. Even when data exists, accessing it for development, testing, vendor demos, or AI training can take months—if it’s approved at all.
The reality? Teams often know exactly what they need. But compliance frameworks make “just get me the data” nearly impossible.
3. Data movement challenges across organizations
For vendor teams, CROs, or product builders working across multiple sponsors, moving data between environments is blocked by governance. Teams end up rebuilding the same logic over and over because data can’t travel safely.
What We Learned: The Real Demand Signal
During the call, one participant asked a clarifying question that crystallized the entire conversation:
“If we have a trial with only 4 enrolled subjects but need to simulate 20 or 30 for feasibility analysis—can synthetic data do that?”Yes. That’s exactly the kind of problem synthetic data solves.
But more importantly, the question revealed something deeper: the market needs a way to generate enterprise-grade, relational, statistically realistic datasets without requiring production data access at all.
How SyntheholDB Solves This
This is where SyntheholDB becomes strategically valuable for healthcare, pharma, and med-tech organizations.
1. Generate data from prompts, schemas, or limited samples
SyntheholDB gives teams three flexible starting points:
Extrapolate from small samples (like those 4 enrolled patients) to create larger, realistic cohorts
Generate from schema alone when you have the structure but can’t access live data
Build from natural language prompts when designing new trials, monitoring workflows, or analytics use cases from scratch
2. Preserve referential integrity across tables
SyntheholDB understands table relationships, foreign keys, dependencies, and generation order.
When you generate a synthetic EHR database with patients, encounters, diagnoses, prescriptions, and trial eligibility tables, the system ensures:
Encounters only exist when patients exist
Prescriptions correctly link encounters, medications, and patients
The result? Data that behaves like real data when you query it, join it, or run analytics on it.
3. Tune correlations for domain realism
SyntheholDB includes a correlation studio where teams can inspect and adjust relationships between columns and across tables.
Healthcare data is full of domain-specific relationships: age correlates with comorbidity count, state influences treatment access, encounter type affects prescription frequency.
Teams can see those relationships, adjust them, and regenerate until the synthetic data matches the real-world behavior they need to model.
4. Catalog-level transparency for audit readiness
Every generation comes with a detailed catalog:
Row, column, and table counts
Table reference mappings
Distribution summaries and relationship validation
Version history for complete traceability
For regulated environments where auditability and transparency are non-negotiable, this built-in governance layer accelerates adoption.
Real Use Cases Healthcare Teams Are Solving
Clinical Trials & Research
Trial feasibility analysis when enrollment is incomplete
Synthetic control arms for faster comparisons
Protocol simulation to test scenarios across demographics, sites, or treatment variations
Imbalance detection and bias mitigation before trials scale
Operational & Monitoring Workflows
CRO reporting and analytics dashboards that can’t use live sponsor data
Audit scenario testing for quality, compliance, and site monitoring teams
Vendor product development when clients can’t share production PHI
AI, Analytics & Product Innovation
EHR-like sandbox data for training AI models, testing algorithms, and validating hypotheses
Faster pilot cycles because teams don’t wait months for data access approvals
Client-facing demos that look real without exposing sensitive information
Cross-Organizational Collaboration
Data sharing between sponsors, CROs, and health systems when governance blocks live data movement
Multi-site analytics where pooling real data is legally or operationally impossible
Why This Matters Now
The healthcare and life sciences industry is at an inflection point. AI, real-world evidence, precision medicine, and patient-centric trials all depend on faster access to better data.
Synthetic data infrastructure—when built for enterprise needs—decouples innovation speed from data access friction.
It lets teams:
Build before they have full data access
Test before they have scale
Prove value before they expose risk
Collaborate across organizational boundaries safely
Final Thought: Solving for Clinical Data Specifically
During the call, a seasoned auditor emphasized:
“Can we focus the discussion on clinical data specifically? That’s where we see the most friction.” That singular focus—clinical data, not just any healthcare data—is where real commercial value lives.
Because clinical data is:
The hardest to access
The most regulated
The most valuable for innovation
The highest priority for AI and analytics initiatives
If you’re leading innovation, analytics, AI, or product development in healthcare, pharma, or med-tech—and data access is your bottleneck—SyntheholDB is built to solve this.
SyntheholDB provides enterprise-grade synthetic data infrastructure designed specifically for regulated environments where privacy, speed, and statistical fidelity can’t be compromised.
Most Synthetic Data Platforms Stop at Datasets. Your AI Needs Databases.
Why AI teams that care about production reality are moving from synthetic CSVs to synthetic systems.
The Real Bottleneck Is Not Models. It Is Test Environments.
If you are running an AI product in finance, insurance, or healthcare, you already know the ugly truth. The hard part is not training another model. The hard part is keeping a production-like environment where data, schemas, queues, and services behave like the real world without violating privacy.
You can get a synthetic CSV from almost any tool. It looks statistically plausible in isolation. But when your backend expects 30 tables stitched together with foreign keys, slowly changing dimensions, event streams, and authorization rules, a nice-looking dataset is useless. Your team hacks together one-off scripts, breaks referential integrity, and spends weeks debugging test failures that have nothing to do with the model itself.
Datasets vs Databases Is Not Semantics. It Is Why Pilots Die.
Most synthetic data platforms were designed for analytics teams. They give you a table. Maybe a handful of tables. That is enough if your use case is a one-off model experiment in a notebook.
Your world is different:
Your product reads from an OLTP database, not a single CSV.
Your pipelines assume consistent primary and foreign keys across dozens of tables.
Your compliance team will not let you clone production into dev any more.
Your incident history is full of bugs that only show up when the whole system runs together, not in a lab dataset.
So you get stuck in a bind:
Use basic synthetic tables and hope your integration tests do not lie.
Or keep “golden copies” of real data in hidden dev environments and hold your breath.
Neither scales. Both are risky. And both ignore what actually matters to you: can we safely recreate our production system so we can move faster without breaking things or leaking PII.
What SyntheholDB Actually Does (In Terms That Matter to You)
SyntheholDB does not start from “generate a table with N rows.” It starts from “recreate the behavior of this system.”
Practically, that means:
You define or import your real schema: 10, 30, 100+ tables.
SyntheholDB learns the joint behavior of entities across those tables from safe samples or aggregated patterns.
It generates a complete synthetic database that preserves:
Full schema fidelity
Referential integrity and key constraints
Cross-table and temporal correlations
Business rules that actually matter (for example: no claim without a policy, no transaction without a KYC-ed account)
The outcome is not just “fake data.” It is a drop-in, production-safe database you can load into Postgres or your cloud warehouse and start running your pipelines, services, and tests against.
You can see the overall Synthehol platform here: <https://synthehol.ai>
What Excites Serious Data Leaders (And How SyntheholDB Delivers)
When we talk with heads of data and ML at banks, insurers, and healthtech companies, they are excited by features, but they buy for different reasons:
You finally get realistic dev and staging environments without begging Legal. Synthetic databases from SyntheholDB are non-identifiable by design, so infra and ML teams can self-serve test environments.
Your integration and regression tests stop lying to you. You can simulate month-end loads, high-cardinality edge cases, and multi-entity workflows that only emerge when the whole graph of tables is in play.
You can safely share realistic data beyond your walls. Vendors, SIs, offshore dev teams, and internal hackathons can work on data that behaves like production without anyone losing sleep over re-identification.
You compress months of “data plumbing” into minutes. Instead of your senior engineers writing fragile generation scripts, they give SyntheholDB a schema and constraints and get a database back.
SyntheholDB is not exciting because it is another AI tool. It is exciting because it unlocks engineering work you currently cannot do at all under your regulatory and operational constraints.
A Concrete Example from Your World
Imagine you are a fintech with:
18 core tables in production
6 services hitting the same database
3 regions with slightly different regulatory rules
Today, spinning up a new environment means:
Coordinating with security to get a scrubbed snapshot
Running brittle anonymization scripts that break joins
Hand-fixing foreign keys for days
Telling your team “staging is flaky, do not trust the data beyond basic flows”
With SyntheholDB:
You give us your schema and a representative profile of the real system.
You define constraints and policies once.
You click Generate and get a synthetic database that:
Respects your schemas
Obeys your business rules
Is safe to ship to any region or vendor
You can start this same journey from the hosted platform: Generate your first synthetic database at https://db.synthehol.ai/.
Who SyntheholDB Is For (And Who It Is Not For)
SyntheholDB is built for teams who:
Run complex transactional systems, not just dashboards
Need to prove privacy protection to regulators and auditors
Treat non-production environments as first-class citizens, not afterthoughts
Are tired of treating test data as a one-off script instead of a platform capability
It is probably not for you if:
You just want a sample CSV for a tutorial
You do not care about schemas, constraints, or end-to-end flows
You are okay with copying production into dev and accepting the risk
If that is you, simpler tools will do.
Ready To See Your Own System, Synthetic and Safe?
You do not need a six-month project to know if this fits your world.
Start with one system: your core transactional database.
Point SyntheholDB at the schema, define your constraints, and generate your first synthetic environment.
Run your existing tests and pipelines on it. See what still fails and what suddenly becomes possible.
If you are responsible for keeping AI and data products from breaking in production, SyntheholDB gives you something you do not currently have: a realistic, defensible, fully synthetic copy of your world to build in.
See what your production system looks like, fully synthetic and safe. Start a SyntheholDB trial and generate your first database in under an hour.
There is a moment every enterprise AI team dreads. The model looked perfect in staging. The synthetic data passed every quality check. The distributions were right, the privacy review was clean, and the QA team signed off. Then the model ships to production and starts making decisions nobody can explain.
Fraud cases get missed. Risk scores drift after two weeks. A healthcare model misrepresents rare patterns in ways that only become apparent after a compliance review. The instinct is to question the model architecture, the feature engineering, the hyperparameters. But the architecture wasn’t the problem. The training data was.
Specifically, the synthetic training data.
The Assumption That Breaks Everything
Most enterprise AI teams approach synthetic data the same way: generate a table, validate it, move to training. The distributions match the original. The privacy risk score is low. The univariate fidelity looks strong. On paper, the dataset is clean.
The problem is that AI products don’t run on tables. They run on databases — interconnected systems where a user’s transaction history actually belongs to that user, where claims link to valid policies with realistic timestamps, where event sequences follow allowed state transitions, and where foreign keys, constraints, and referential integrity hold together under real query loads.
When you generate synthetic tables in isolation and assume they will behave like a production database when joined, you are not creating a test environment. You are creating a structurally coherent-looking lie. And your model will learn from that lie with complete confidence.
What the Data Is Actually Getting Wrong
The failure modes are predictable once you know what to look for. Referential integrity breaks first. Synthetic transactions get generated without valid user records to link to. Claims appear without corresponding policies. Events reference entities that don’t exist in the user table. Your model trains on these phantom relationships and learns correlations that have no grounding in reality.
Temporal consistency breaks next. In real production systems, a user’s transaction timestamps follow logical sequences — account creation, first login, first transaction, repeat behavior. Synthetic data generated at the table level ignores these sequences entirely. You end up with transactions timestamped before the accounts they belong to were created. Anomaly detection models trained on this data learn that impossible timelines are normal. Then they encounter real impossible timelines in production and have no calibrated response.
Cross-table correlations collapse last, and most quietly. An individual synthetic table might show statistically correct distributions. But the relationship between a user’s income bracket and their transaction frequency, or between a policy type and the claims pattern it generates — these joint distributions disappear when tables are generated independently. Your model sees a world where those relationships don’t exist, and it builds its logic accordingly.
The Three Levels of Synthetic Data Maturity
To understand why this keeps happening, it helps to think about synthetic data capability in levels rather than as a single yes-or-no question.
At Level 1, platforms handle dataset generation. They produce single-table outputs with correct univariate distributions, pass privacy checks, and generate statistically plausible rows. This is genuinely useful for early prototyping, notebook experiments, and proofs of concept. The overwhelming majority of synthetic data platforms today operate at this level, and for a notebook demo, it is sufficient. For production AI, it is not.
At Level 2, platforms handle multi-table coherence. They preserve cross-table correlations, maintain foreign key relationships, and ensure that joint distributions match production rather than just within-table distributions. A meaningful subset of platforms attempt this. Fewer do it well. This level is sufficient for model training pipelines and integration testing environments where compliance scrutiny is light.
At Level 3, platforms handle synthetic systems. This means full schema fidelity — preserving constraints, triggers, indexes, and all relational structure. It means temporal consistency across entities, so that user journeys, transaction sequences, and event flows follow the logic of real production behavior. It means audit-ready generation logs with full reproducibility, so that a dataset generated six months ago can be recreated exactly on demand. This is the level that enterprise AI teams in regulated industries need to operate at. Almost no platform is genuinely built here.
Why Regulated Industries Face a Higher Standard
For AI teams in banking, insurance, and healthcare, the requirement to operate at Level 3 is not optional. It is imposed from outside by the regulatory environment in which these organizations operate.
Model risk teams under SR 11-7 and similar frameworks need to know that the data used to train and validate a model preserves the statistical properties of the real population it represents. That includes joint distributions across variables, not just marginal distributions of individual columns. It includes rare event representation. It includes the correlation structure that defines how risk actually behaves.
Compliance officers under GDPR, HIPAA, and equivalent frameworks need to see evidence that no sensitive information leaked through the generation process — not just an assertion that PII was removed, but a quantified risk score that demonstrates re-identification probability was minimized. They also need traceability: who generated this dataset, from which source version, with which parameters, and when.
Internal and external auditors need reproducibility. If a model decision is challenged twelve months after training, the team needs to produce the exact training data used. If the synthetic data platform cannot reproduce a specific dataset from a logged seed and parameter set, that audit trail is broken.
These requirements are not technical edge cases. They are baseline expectations for any AI system operating in a regulated environment. And they cannot be met by platforms operating at Level 1 or even Level 2.
The Questions That Separate Production-Ready From Not
Before any synthetic dataset enters a production AI pipeline, every team should be able to answer six questions clearly.
First: does the synthetic database preserve the full schema, including all foreign keys, constraints, and relational structure from the source? Not approximately. Exactly.
Second: does referential integrity hold across all tables? If you join users to transactions to events, do the records connect to real counterparts?
Third: do cross-table correlations match production? Not just within a single table, but across entities and relationships?
Fourth: are temporal sequences logically valid? Do timestamps follow real-world event ordering? Do state transitions respect allowed workflows?
Fifth: can the platform generate at production scale without structural degradation? Millions of rows across dozens of tables should produce the same integrity guarantees as a small test set.
Sixth: can the exact dataset be reproduced on demand, with a logged audit trail that includes the source schema version, generation parameters, and timestamp?
If the answer to any of these is no, or more concerning, if the platform doesn’t measure it at all, the data foundation is not ready for production.
How SyntheholDB Addresses This
SyntheholDB was built to operate at Level 3 from the ground up. The platform generates complete synthetic databases — not isolated tables — with full schema fidelity preserved automatically. Foreign keys hold. Referential integrity is enforced across every generated record. Cross-table correlations are modeled from the source database structure, not inferred independently per table.
Temporal consistency is handled at the generation layer, not as a post-processing check. User journeys, transaction sequences, and event flows follow the behavioral logic encoded in the source data. State transitions respect allowed workflows. Timestamps follow real-world ordering.
Every generation run produces an immutable audit log recording the source schema version, the generation parameters, the seed, and the output metadata. Any dataset can be reproduced exactly from that log. Compliance teams, model risk reviewers, and auditors receive the documentation they need without requiring the team to reconstruct anything manually.
The platform runs on-premise, in a private VPC, or in controlled cloud environments — meeting the deployment requirements of security and compliance teams across banking, insurance, and healthcare without requiring production data to leave a controlled environment.
Teams upload their schema, configure their generation parameters, and produce a structurally coherent synthetic database ready for end-to-end AI testing, model training, QA, load simulation, and product demonstration — without touching a single real customer record.
The Shift That Needs to Happen
The enterprise AI industry has spent years treating synthetic data as a privacy tool — a way to avoid using real data while still training models. That framing is incomplete. Synthetic data is not just a privacy solution. It is a data infrastructure problem.
The teams that recognize this distinction are the ones moving from pilot to production. They are not asking whether their synthetic data looks real. They are asking whether their synthetic database behaves like production — structurally, statistically, and temporally. They are treating data generation with the same engineering rigor they apply to the models trained on top of it.
The AI landscape is moving from novelty to defensibility. Generating data is easy. Generating data you can defend to a model risk committee, a compliance officer, and an external auditor is hard. It requires infrastructure, not just generation. It requires Level 3, not Level 1.
If your current synthetic data workflow cannot answer the six questions above, the foundation your AI is built on is not production-ready. And no amount of model optimization will fix a broken foundation.
Try SyntheholDB at db.synthehol.ai — upload your schema and generate your first production-safe synthetic database today
Why realistic, privacy‑safe databases are the missing piece in reliable testing pipelines.
Introduction: the real cost of “works on my machine”
Every engineering team has a version of the same story: a feature passes all tests in dev, sails through QA, and then explodes in production in the first 10 minutes. The root cause almost always traces back to data. The code path was fine; the test database was not.
Most lower environments are powered by one of three options:
A stale copy of production from “some time last quarter”
A heavily masked subset that no one fully understands
A hand‑crafted dummy dataset that looks nothing like reality
None of these are good enough if you care about reliability, privacy, or speed. That’s the gap SyntheholDB is built to close.
The core problem: environment drift is a data problem
We talk about environment drift as if it’s just configuration: different feature flags, different infra, different versions of a service. But underneath that, there’s a quieter, nastier drift happening in the data itself.
Over time:
New edge cases show up only in production
Distributions shift (a field that was “sometimes null” is now “almost always null”)
New tables and relationships get added without making it into test datasets
Your test database slowly stops representing the real world. The result is predictable: bugs only show up when real users are on the line.
SyntheholDB’s job is to keep your test databases statistically close to production, structurally correct, and completely free of real user data.
What a synthetic test database actually is
When we say “synthetic test database” with SyntheholDB, we mean something very specific:
The same schema as production (tables, columns, constraints).
The same relationships (foreign keys, many‑to‑many, cascades) enforced.
Data that matches real‑world distributions and edge cases, but is generated, not copied.
Zero direct link back to any real person or account.
You keep all of the behavior that matters for testing—joins, aggregations, tricky edge cases—without the risk and overhead of copying production data around.
How SyntheholDB changes day‑to‑day engineering work
Here’s what changes once teams start using SyntheholDB as their default for staging and test:
New services don’t block on “getting data” Spinning up a new environment no longer means begging ops for a sanitized dump. You define the schema or connect to an existing one, tell SyntheholDB how big you want it, and generate a fresh database on demand.
Repro steps actually reproduce When a production bug is tied to a weird combination of values, you can encode that pattern into the generation config and regenerate the environment. Now that “impossible” state is part of your standard test data.
CI becomes less flaky Instead of a single shared test DB that’s constantly being mutated, you can generate isolated synthetic databases per test run, per branch, or per suite. Tests stop stepping on each other’s data.
Security stops being the bottleneck No more long review cycles around “Can we use this prod dump for this vendor / hackathon / POC?” The data is synthetic by design, so you can move faster without negotiating exceptions every time.
A concrete example: onboarding a new microservice
Imagine you’re introducing a new billing microservice that relies on:
Customer profiles
Subscription plans
Invoices and payments
Feature flags and discounts
In a traditional setup, you would:
Request a masked subset of prod
Wait days or weeks for it to be prepared and approved
Discover late that important edge cases were removed by masking
With SyntheholDB, the flow looks different:
Point SyntheholDB at your existing schema (or define it via the UI / config).
Describe a few critical scenarios in plain language or via templates:
“Customers with overlapping subscriptions”
“Invoices with partial payments and chargebacks”
“Long‑tail currencies and tax rules”
Generate a synthetic database that includes those patterns at the frequency you want.
Spin up as many identical or variant environments as you need across dev, QA, and CI.
The billing team ends up testing against a rich, realistic dataset from day one, without ever touching real payment data.
Why not just mask production data?
Masking sounds attractive because it starts from something “real.” In practice, it introduces its own set of problems:
Masking often breaks referential integrity, especially when done in a hurry.
Clever attackers (or just bad luck) can still expose patterns that are too close to real users.
You’re still copying production records into places they don’t belong.
Most teams doing masking end up with data that’s neither fully safe nor fully realistic. Synthetic data flips the model: we start from privacy and realism as requirements, not as afterthoughts.
Where SyntheholDB fits in your stack
SyntheholDB is not meant to replace your production database, your observability tools, or your data warehouse. It plugs into the parts of your stack where you need realistic behavior without real users:
Developer sandboxes
Shared QA / UAT environments
CI pipelines and ephemeral test environments
Demo and sales environments that can show “real” flows without real PII
In each case, you get a database that feels like prod in all the ways that matter for testing, while being safe to share, reset, and experiment with.
What to measure after adopting synthetic databases
If you roll out SyntheholDB, here are a few metrics worth tracking over the next few months:
Number of prod incidents caused by data assumptions
Time taken to spin up a fully functional test environment
Number of data‑related security exceptions or review cycles needed
Flaky test rate in CI (especially for integration tests)
Teams that take this seriously usually see fewer “surprise” bugs, faster release cycles, and happier security reviewers.
Closing: making “works on my machine” rare
“It worked on my machine” is not a law of nature. It’s a symptom of unrealistic, inconsistent, and unsafe test data.
By treating the test database as a first‑class product and generating it synthetically instead of copying prod you give engineers a shared, reliable view of reality they can safely break, reset, and iterate on.
That’s exactly what SyntheholDB is designed for: realistic test databases that help you ship faster, avoid incidents, and keep real user data where it belongs.
