Synthehol DB for Synthetic Data Generation

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.