Syntho
AI-powered synthetic data platform for privacy-safe testing, analytics, and AI training
- Category
- Data Analysis
- Pricing
- Custom/enterprise pricing based on data volume and deployment; Syntho offers a free trial or demo, with quote-based subscription plans (on-premise, private cloud, or SaaS deployment options).
- Best for
- Syntho is built for mid-size to large enterprises, especially in banking, insurance, healthcare, and other regulated sectors, that need realistic but privacy-safe data for QA, analytics, or AI model training.
- Official site
- www.syntho.ai
- Last updated
- August 2026
Syntho is a synthetic data generation platform designed to solve a common enterprise problem: teams need realistic data for development, testing, and AI training, but using real production data raises privacy, compliance, and security risks. Syntho's generative models analyze an organization's actual databases and produce synthetic datasets that preserve statistical distributions, correlations, and referential relationships between tables, without containing any real individual's data.
The platform is aimed heavily at regulated industries such as financial services, insurance, and healthcare, where regulations like GDPR, HIPAA, and DORA restrict how real customer data can be used outside production systems. By generating synthetic data that behaves like the original in aggregate but cannot be traced back to real people, Syntho lets QA, analytics, and data science teams work with realistic datasets in lower environments.
Syntho positions itself less as a point tool and more as an enterprise data platform, offering deployment flexibility (SaaS, private cloud, or fully on-premise) to accommodate strict data residency and security requirements, along with built-in data masking and subsetting capabilities alongside its core synthetic data generation engine.
Syntho is built for mid-size to large enterprises, especially in banking, insurance, healthcare, and other regulated sectors, that need realistic but privacy-safe data for QA, analytics, or AI model training. Data engineering, platform, and compliance teams are the typical buyers, since implementation involves connecting source databases and configuring generation rules. It's a strong fit for organizations that have hit friction using real production data in lower environments due to privacy regulations. Smaller teams with simple test-data needs may find lighter-weight or open-source synthetic data tools more cost-effective.
Key features
Generative Synthetic Data Engine
Uses AI models trained on source data structure and distributions to produce new, non-identifiable synthetic records.
Multi-Table Referential Integrity
Preserves foreign-key relationships and correlations across complex relational databases, not just isolated tables.
Data Anonymization and Masking
Includes classic masking techniques alongside generative synthesis to support layered privacy strategies.
Compliance-Focused Design
Built with GDPR, HIPAA, and financial services regulations (like DORA) in mind for regulated-industry customers.
Test Data Automation for QA
Provisions realistic, production-like datasets to lower environments for software testing without real customer data.
Flexible Deployment
Available as SaaS, private cloud, or on-premise to meet varying data residency and security requirements.
Pricing breakdown
Trial/Demo
- Guided demo or trial access
- Sample synthetic data generation on limited datasets
Business
- Core synthetic data generation
- Multi-table support
- Standard deployment options
Enterprise
- Full compliance tooling
- Dedicated deployment (on-prem/private cloud)
- Priority support and integration assistance
Pros and cons
Pros
- Purpose-built for regulated industries, with explicit support for GDPR, HIPAA, and financial-sector compliance frameworks
- Maintains referential integrity across complex, multi-table schemas, which many simpler synthetic data tools struggle with
- Deployment flexibility (SaaS, private cloud, on-prem) makes it viable for organizations with strict data residency rules
- Reduces risk and turnaround time for provisioning test/dev data compared to manual anonymization processes
- Can support both software testing use cases and AI/ML training data generation from the same platform
Cons
- Lack of transparent, published pricing makes early-stage cost comparison difficult for smaller buyers
- Initial setup requires meaningful data engineering effort to connect and map source schemas correctly
- Best suited to larger organizations with dedicated data or platform teams; overkill for small teams needing simple test data
- Smaller market presence and community/support ecosystem compared to more established data testing vendors
- Quality of synthetic data still depends on the quality and representativeness of the source data provided
Alternatives to Syntho
BooleanMaths
Server-side attribution and ROAS analytics purpose-built for Shopify D2C brands, queryable by AI assistants via MCP.
Compare →Akkio
No-code AI platform for building predictive analytics and machine learning models fast
Compare →Alegion AI
Managed data labeling and annotation platform for training production-grade AI and ML models
Compare →Frequently asked questions
What is synthetic data used for with Syntho?
Common use cases include software testing and QA, analytics/BI development, and training AI/ML models, all without exposing real personal or sensitive data.
Does synthetic data from Syntho still resemble real data statistically?
Yes, the platform is designed to preserve statistical distributions and relationships from the source data while ensuring individual records are not identifiable or traceable.
Can Syntho handle multiple related database tables?
Yes, one of its core strengths is maintaining referential integrity (foreign keys, relationships) across complex, multi-table schemas.
Is Syntho GDPR and HIPAA compliant?
Syntho is designed with these regulatory frameworks in mind, and its synthetic data approach is intended to help customers meet privacy requirements, though compliance ultimately depends on the customer's own implementation.
What deployment options does Syntho support?
Syntho can be deployed as SaaS, in a private cloud, or fully on-premise, which is important for organizations with strict data residency requirements.
Is there a free version of Syntho?
Syntho typically offers a trial or guided demo rather than a permanent free tier; full access requires a custom subscription.
Ready to try Syntho?
Head to the official site to explore pricing and start a free trial where available.
Visit Syntho →