Top 10 Synthetic Data Generation Tools: Features, Pros, Cons and Comparison

Posted on February 25, 2026February 25, 2026 | by khushboo

Introduction

Synthetic data generation tools create artificial datasets that behave like real data without exposing the original records. In simple terms, these tools help teams build, test, train, and analyze systems when production data is hard to access because of privacy, security, compliance, or availability limits.

This category matters because organizations need faster AI experimentation, safer data sharing, and better governance across engineering, analytics, and machine learning workflows. Synthetic data is used for software testing, model training, QA environments, sandbox analytics, and proof-of-concept work. Some tools focus on enterprise privacy-safe generation, while others are developer-first or domain-specific.

Common use cases include:

Test data generation for development and QA
Privacy-safe data sharing across teams or partners
ML training and dataset augmentation
Sandbox analytics and internal demos
Healthcare and regulated-domain simulation datasets

What buyers should evaluate before selecting a tool:

Data realism and utility
Privacy protection approach
Relational and multi-table support
Ease of setup and workflow automation
APIs, SDKs, and integration options
Deployment model
Security and access controls
Scalability for large datasets
Validation and quality checks
Team fit and learning curve

Best for: data teams, QA teams, application engineering, AI and ML teams, and regulated industries that need safe non-production data quickly.

Not ideal for: teams that only need basic dummy data for small demos, or teams with no privacy or governance requirement where simple scripts are enough.

Key Trends in Synthetic Data Generation Tools

Synthetic data is becoming a core part of AI and software delivery workflows, not just a privacy project.
Vendors are expanding beyond tabular data into text, documents, and mixed data use cases.
Buyers increasingly expect governance, role-based access, and auditability along with data generation.
Open-source tools remain important for experimentation, but many organizations prefer managed platforms for team collaboration.
Hybrid workflows are becoming common, with local SDK use plus centralized platform management.
Validation is becoming more important, including utility checks and privacy risk review before use.
Domain-specific synthetic data remains highly valuable in healthcare, finance, and regulated sectors.
Test data automation is a major buying driver for QA and engineering teams.
Teams are separating lightweight fake data generators from high-fidelity synthetic data platforms and using both where needed.
Security and compliance claims are reviewed more carefully during evaluation and pilot stages.

How We Selected These Tools (Methodology)

Focused on widely recognized tools used for synthetic, test, or privacy-safe data generation.
Included a balanced mix of enterprise platforms, developer-first tools, and open-source options.
Prioritized tools with strong product visibility, documentation, or community awareness.
Considered fit across testing, analytics, AI and ML, and regulated data use cases.
Reviewed support for different data types and workflow styles.
Considered deployment flexibility where publicly visible.
Assessed integration potential, APIs, SDKs, and extensibility patterns.
Included tools that fit different buyer sizes, from solo developers to enterprises.
Avoided guessing on certifications, ratings, and compliance details.
Used comparative scoring to show relative strengths for decision support.

Top 10 Synthetic Data Generation Tools

1 — Gretel

Gretel is a synthetic data platform used for creating privacy-aware synthetic datasets and data transformation workflows. It is commonly considered by teams working on AI development, testing, and secure data sharing.

Key Features

Synthetic data generation for structured datasets
Privacy-focused workflows for safer data usage
API-driven usage for developers
Data transformation and preparation workflows
Support for AI-related synthetic data use cases
Cloud-oriented platform experience
Designed for scaling beyond simple mock data

Pros

Strong fit for privacy-conscious AI and data teams
Useful for test data and model development scenarios
Developer-friendly approach compared with manual masking workflows

Cons

Enterprise feature depth may require onboarding time
Pricing and packaging vary by plan
Teams may need internal validation for specific schemas

Platforms / Deployment

Cloud
API-driven workflows
Varies / N/A for complete offline deployment details

Security and Compliance

Not publicly stated

Integrations and Ecosystem

Gretel is commonly used in API-centric development workflows and synthetic-data-assisted AI pipelines. Teams often evaluate it for integration into engineering and ML pipelines rather than one-time generation.

APIs for programmatic generation
Workflow compatibility with data engineering pipelines
AI use case alignment
Automation potential for developers

Support and Community

Documentation and ecosystem visibility are present, but support tiers and service expectations vary by plan and should be validated during evaluation.

2 — MOSTLY AI

MOSTLY AI is an enterprise-focused synthetic data platform for generating privacy-safe synthetic datasets with platform workflows and SDK usage. It is often evaluated by teams that need repeatable synthetic data operations across environments.

Key Features

Synthetic dataset generation workflows
Generator-based training and reuse
Data rebalancing and imputation capabilities
Connectors for databases and cloud storage
Platform plus SDK usage modes
Delivery of generated data to target destinations
Team collaboration features

Pros

Strong enterprise usability with UI and SDK flexibility
Good fit for repeatable generation pipelines
Connectors and delivery workflows reduce manual handoffs

Cons

Enterprise orientation may be too much for small teams
Advanced setup may require data expertise
Full compliance details must be confirmed directly

Platforms / Deployment

Cloud / Self-hosted / Hybrid
SDK supports local and client usage patterns

Security and Compliance

Not publicly stated

Integrations and Ecosystem

MOSTLY AI stands out for connecting data sources, generation steps, and delivery workflows. It is useful for teams that want governed collaboration and local experimentation.

Database connectors
Cloud object storage connectors
SDK and CLI support
Shared platform workflows
Import and export capabilities

Support and Community

Documentation is structured and product-oriented. Enterprise support strength appears solid, but exact support tiers and response commitments vary.

3 — Tonic.ai

Tonic.ai focuses on synthetic and de-identified data for development, testing, and AI workflows. It is often considered by teams that need support across structured and unstructured data workflows.

Key Features

Structured and semi-structured data synthesis workflows
De-identification support for sensitive datasets
Text and unstructured data workflows
From-scratch synthetic data generation for relational data
Product-specific modules for different use cases
API and SDK support
Strong test-data and AI development positioning

Pros

Broad coverage across structured and unstructured workflows
Strong fit for software testing and AI feature development
Modular approach helps teams choose what they need

Cons

Product portfolio can feel complex for new buyers
Better value at team or enterprise scale
Security and compliance specifics should be validated

Platforms / Deployment

Cloud
Varies by product and deployment arrangement

Security and Compliance

Not publicly stated

Integrations and Ecosystem

Tonic.ai supports integration into engineering and data workflows through APIs and SDKs. It is strongest where teams need recurring test data operations and privacy-safe data preparation.

APIs
SDK support
Product modules for different data types
Workflow integration for QA, staging, and AI pipelines

Support and Community

Documentation is mature and product-specific. Enterprise onboarding is typically a key factor, but support details should be confirmed directly.

4 — Syntho

Syntho is an all-in-one synthetic data platform focused on privacy-safe data generation and realistic dataset creation for analytics, AI, and testing use cases.

Key Features

Privacy-safe synthetic data generation platform
Multiple synthetic generation methods in one platform
Workflow-oriented user experience
Analytics and AI modeling use cases
Data connection guidance
Guided onboarding resources
Enterprise-ready collaboration approach

Pros

Clear platform focus on privacy-safe synthetic data
Good fit for organizations seeking guided implementation
Strong practical positioning for analytics and AI teams

Cons

Platform adoption may be heavier than lightweight tools
Technical depth should be validated through a pilot
Public compliance details should not be assumed

Platforms / Deployment

Cloud / Self-hosted / Hybrid
Varies by package and deployment model

Security and Compliance

Not publicly stated

Integrations and Ecosystem

Syntho is designed for operational workflows with data connections and guided deployment paths. It is best evaluated as a platform component in broader data programs.

Data connections
Workspace and project workflows
Guided onboarding resources
Enterprise process alignment

Support and Community

Documentation is clear and accessible. Vendor-led onboarding is often stronger than community-led support, which is common in enterprise platforms.

5 — YData

YData provides synthetic data capabilities through a platform and SDK ecosystem, with focus on data quality, AI-ready datasets, and synthetic generation for analytics and ML workflows.

Key Features

Synthetic data generation for tabular and time-series data
SDK-based programmatic workflows
Platform support for data preparation and evaluation
Generative approaches for dataset augmentation
Data quality and synthetic workflow alignment
Community and enterprise usage paths
AI-focused positioning for data teams

Pros

Strong fit for data science and ML teams
Useful mix of SDK and platform experiences
Good for teams wanting synthetic data plus data quality context

Cons

Platform breadth can increase learning effort
Enterprise features may exceed simple testing needs
Security and compliance specifics should be verified directly

Platforms / Deployment

Cloud / SDK-based workflows
Varies / N/A for complete deployment matrix

Security and Compliance

Not publicly stated

Integrations and Ecosystem

YData offers both platform and package-based approaches, which helps teams move from experimentation to more governed workflows.

SDK and package workflows
Platform-based data management
AI and pipeline compatibility
Community and enterprise usage options

Support and Community

Developer visibility is good through SDK materials and product presence. Enterprise support details vary by engagement.

6 — Hazy

Hazy is known as a synthetic data platform focused on privacy-preserving data generation and enterprise use cases, especially in regulated environments.

Key Features

Privacy-preserving synthetic data generation
Enterprise and regulated-industry alignment
Representative synthetic data generation workflows
Data sharing and development acceleration use cases
Governance-oriented platform positioning
Enterprise integration potential
Platform-led synthetic data operations

Pros

Strong fit for enterprise privacy and governance discussions
Recognized synthetic data brand in regulated use cases
Useful for teams prioritizing controlled data sharing

Cons

Product packaging and roadmap may require direct validation
Public product detail availability may be limited
Buyers should confirm deployment and support model carefully

Platforms / Deployment

Varies / N/A

Security and Compliance

Not publicly stated

Integrations and Ecosystem

Hazy is best evaluated with attention to current packaging, deployment, and integration capabilities. Enterprise buyers should confirm current ecosystem support directly.

Enterprise workflow integration potential
Privacy-focused data sharing use cases
Regulated domain alignment
Platform-based enterprise adoption path

Support and Community

Support and onboarding should be treated as vendor-confirmed items during evaluation. Community visibility is lower than open-source alternatives.

7 — GenRocket

GenRocket is a synthetic test data automation platform focused on generating high-volume, format-specific test data for QA, testing, and enterprise software delivery.

Key Features

Design-driven synthetic test data generation
Enterprise-scale test data automation workflows
High-volume generation across formats
QA and regression testing alignment
Support for complex application test scenarios
Domain-focused testing support
Centralized test data operations approach

Pros

Excellent fit for QA-heavy enterprise organizations
Built for repeatability and coverage
Strong operational value in testing pipelines

Cons

Less focused on analytics or ML synthetic workflows
Can be too specialized for small app teams
Rollout may require process maturity

Platforms / Deployment

Cloud / Varies by enterprise deployment arrangement

Security and Compliance

Not publicly stated

Integrations and Ecosystem

GenRocket is strongest when integrated into testing operations and delivery pipelines. It is best viewed as a test data automation platform rather than a general synthetic analytics tool.

Testing workflow compatibility
Enterprise QA process integration
High-volume format generation support
Domain-oriented testing workflows

Support and Community

Vendor-led support is important for successful deployment. Community footprint is lower than open-source tools, but enterprise enablement is a major part of the value.

8 — SDV

SDV is a well-known open-source Python library for synthetic data generation, especially for tabular and relational datasets. It is a strong developer-first choice for custom workflows.

Key Features

Open-source Python library for synthetic data generation
Tabular and relational dataset support
Metadata-driven modeling for tables and relationships
Multiple synthesis approaches
Transparent and customizable workflows
Good fit for experimentation and prototyping
Community-driven ecosystem

Pros

Strong developer control and transparency
Excellent for experimentation and custom workflows
No vendor lock-in for core usage

Cons

Requires technical skill for effective use
Managed governance features are limited compared with commercial tools
Support depends on community or internal expertise

Platforms / Deployment

Python / Local / Cloud where Python runs
Self-hosted workflow by nature

Security and Compliance

Varies / N/A

Integrations and Ecosystem

SDV integrates naturally with Python-based data science stacks and custom pipelines. It is a strong building block for teams wanting full control over generation logic.

Python ecosystem compatibility
Notebook and script workflows
Custom pipeline integration
Metadata-based multi-table modeling

Support and Community

SDV has strong documentation and open-source visibility. Community support is valuable for technical teams, but organizations needing guaranteed vendor support may prefer commercial options.

9 — Mockaroo

Mockaroo is a popular random data generator and API mocking tool used for creating realistic test and demo datasets quickly. It is best for fast schema-based data generation rather than high-fidelity synthetic replication.

Key Features

Fast generation of realistic mock datasets
Multiple export formats
API mocking and generated APIs
Schema-based field generation
Browser-based ease of use
Useful for demos, testing, and prototyping
Lightweight adoption path

Pros

Very easy to start with for non-experts
Great for quick test and demo data
Useful API mocking support for app development

Cons

Not a high-fidelity privacy-safe synthetic platform
Limited fit for complex relational privacy workflows
Governance capabilities are not its primary focus

Platforms / Deployment

Web / Cloud

Security and Compliance

Not publicly stated

Integrations and Ecosystem

Mockaroo is more of a practical utility than a deep platform. It fits developer workflows needing fast generated records and mock APIs.

Browser-based schema creation
Generated API endpoints
Common file exports
Lightweight development integration

Support and Community

Documentation is straightforward and practical. It is widely used by developers, but enterprise-grade support expectations should be checked directly.

10 — Synthea

Synthea is an open-source synthetic patient population simulator used for healthcare research, interoperability testing, and health IT development. It generates realistic but artificial patient records for domain-specific use cases.

Key Features

Open-source synthetic patient population generation
Healthcare and EHR-focused data generation
Longitudinal medical-history-style patient records
Useful for interoperability and health IT testing
Large dataset simulation outputs
Strong health IT and research relevance
Domain-specific synthetic data generation

Pros

Excellent for healthcare-specific synthetic data needs
Open-source and widely recognized in health IT contexts
Strong value for standards testing and demos

Cons

Domain-specific and not general purpose
Requires healthcare data understanding for best results
Commercial support is not the primary model

Platforms / Deployment

Open-source / Self-hosted / Local generation workflows

Security and Compliance

Varies / N/A

Integrations and Ecosystem

Synthea fits healthcare developer and research ecosystems where synthetic patient data is needed for standards testing, integration development, and educational simulation.

Healthcare workflow compatibility
Research toolchain support
Open-source customization
Population simulation workflows

Support and Community

Synthea has strong community relevance in healthcare informatics and health IT development. Support is mainly community and documentation based.

Comparison Table (Top 10)

Tool Name	Best For	Platform(s) Supported	Deployment	Standout Feature	Public Rating
Gretel	Privacy-aware synthetic data for AI and engineering teams	Web / API	Cloud	Developer-friendly synthetic data workflows	N/A
MOSTLY AI	Enterprise synthetic datasets with platform and SDK workflows	Web / SDK	Hybrid	Generator workflows with connectors and delivery	N/A
Tonic.ai	Test data, de-identification, and AI data prep	Web / APIs / SDK	Cloud / Varies	Multi-product approach for structured and unstructured use cases	N/A
Syntho	Privacy-safe synthetic data platform for analytics and AI	Web	Cloud / Self-hosted / Hybrid	All-in-one synthetic platform positioning	N/A
YData	Synthetic data plus data quality workflows	Web / Python	Cloud / Varies	Platform and SDK approach for AI teams	N/A
Hazy	Enterprise privacy-preserving synthetic data in regulated use cases	Varies / N/A	Varies / N/A	Enterprise privacy-focused synthetic generation	N/A
GenRocket	Enterprise synthetic test data automation for QA	Web / Enterprise tooling	Cloud / Varies	Design-driven synthetic test data automation	N/A
SDV	Open-source tabular and relational synthetic generation	Python	Self-hosted	Metadata-driven open-source synthesis	N/A
Mockaroo	Fast mock data and API mocking for dev and test	Web	Cloud	Rapid schema-based generation and mock APIs	N/A
Synthea	Healthcare synthetic patient records and interoperability testing	Open-source / Local	Self-hosted	Synthetic patient population simulator	N/A

Evaluation and Scoring of Synthetic Data Generation Tools

Tool Name	Core (25%)	Ease (15%)	Integrations (15%)	Security (10%)	Performance (10%)	Support (10%)	Value (15%)	Weighted Total (0–10)
Gretel	8.8	7.8	8.2	7.8	8.1	7.8	7.5	8.03
MOSTLY AI	9.0	8.2	8.6	8.1	8.4	8.3	7.4	8.31
Tonic.ai	9.2	8.0	8.7	8.3	8.5	8.2	7.2	8.34
Syntho	8.6	8.1	8.0	7.9	8.0	7.8	7.6	8.00
YData	8.7	7.7	8.4	7.6	8.0	7.9	8.0	8.07
Hazy	8.4	7.2	7.6	8.2	8.0	7.3	7.0	7.73
GenRocket	8.8	7.0	8.3	7.8	8.6	7.9	7.1	7.95
SDV	8.3	6.8	7.8	6.8	7.8	8.1	9.0	7.88
Mockaroo	6.9	9.2	6.5	6.2	7.4	7.2	9.1	7.56
Synthea	7.8	6.9	7.3	7.0	8.0	8.4	9.2	7.86

How to interpret these scores:

These scores are comparative and scenario-based, not benchmark test results.
A higher total does not mean a universal winner for every team.
Enterprise platforms and open-source tools solve different problems, so scores reflect fit across common buying criteria.
Open-source options may score lower on ease or managed support but higher on flexibility and value.
Always validate shortlisted tools with your own dataset patterns, privacy needs, and delivery workflows.

Which Synthetic Data Generation Tool Is Right for You

Solo / Freelancer

If you are a solo developer, consultant, or prototype builder, start with tools that are fast and lightweight. Mockaroo is excellent for quick mock datasets and API testing. SDV is a strong choice if you need more realistic tabular synthesis and can work in Python. If your work is in health IT demos, Synthea can be very useful.

Recommended shortlist: Mockaroo, SDV, Synthea (for healthcare-specific work)

SMB

SMBs usually need speed, lower setup effort, and enough realism for QA or analytics pilots. YData and Syntho are attractive when your team wants a platform experience without building everything internally. Tonic.ai can also be a strong fit if privacy-safe test data is a recurring engineering bottleneck.

Recommended shortlist: YData, Syntho, Tonic.ai

Mid-Market

Mid-market teams often need repeatability, connectors, access control, and cross-team data delivery. MOSTLY AI and Tonic.ai are strong candidates for operational synthetic data workflows. Gretel is also worth evaluating if your organization is AI-heavy and wants developer-centric capabilities.

Recommended shortlist: MOSTLY AI, Tonic.ai, Gretel

Enterprise

Enterprise buyers should prioritize governance, scalability, deployment flexibility, privacy validation, and integration with existing data and security processes. MOSTLY AI, Tonic.ai, Syntho, GenRocket, and Hazy are strong candidates depending on whether the core need is AI and analytics, test data automation, or regulated data sharing.

Recommended shortlist: MOSTLY AI, Tonic.ai, GenRocket, Syntho, Hazy

Budget vs Premium

Budget-friendly or open-source-first: SDV, Synthea, Mockaroo for lighter use cases
Premium enterprise platforms: MOSTLY AI, Tonic.ai, Syntho, Gretel, GenRocket
Enterprise strategic evaluation: Hazy, especially for regulated workflows

If budget is limited, start with one lightweight tool plus one open-source library before committing to a full platform rollout.

Feature Depth vs Ease of Use

Highest ease of use: Mockaroo
Strong developer depth: SDV
Strong platform depth: Tonic.ai, MOSTLY AI
Balanced platform usability: Syntho, YData

Many teams fail by selecting maximum feature depth when they actually need faster adoption. Match the tool to team maturity and workflow complexity.

Integrations and Scalability

If you need connectors, repeatable workflows, and delivery into enterprise data systems, lean toward MOSTLY AI, Tonic.ai, YData, or GenRocket. If you only need local generation inside notebooks or scripts, SDV may be enough to start.

Security and Compliance Needs

For regulated workflows, treat vendor claims as the start of due diligence. Ask for:

Access control details
Encryption practices
Audit logging
Deployment options
Privacy risk evaluation methods
Compliance documentation and attestations

If these requirements are critical, run a controlled proof-of-value with your governance team involved from the beginning.

Frequently Asked Questions

1. What is the difference between fake data and synthetic data?

Fake data tools usually create random or rule-based placeholder values for demos and simple testing. Synthetic data tools aim to preserve patterns and relationships from real datasets while reducing privacy risk.

2. Can synthetic data fully replace production data?

Not always. It can replace production data for many testing, sandbox, and model-development tasks, but some edge-case validation still benefits from controlled checks using real data.

3. Is synthetic data automatically privacy-safe?

No. Privacy safety depends on the generation method, evaluation process, and governance controls. Teams should validate re-identification risk and leakage risk before sharing data.

4. Which tool is best for software testing teams?

For quick test and demo data, Mockaroo is very practical. For enterprise-grade test data automation and repeatable QA workflows, GenRocket and Tonic.ai are often stronger choices.

5. Which tool is best for AI and machine learning teams?

It depends on workflow maturity. SDV is great for developer-led Python work, while MOSTLY AI, YData, Gretel, and Syntho are stronger when teams need managed workflows and collaboration.

6. Are open-source tools enough for enterprise use?

They can be, especially for teams with strong internal engineering skills. However, many enterprises prefer commercial platforms for governance, support, and cross-team operational control.

7. How long does implementation usually take?

Lightweight tools can be used quickly. Enterprise platform adoption takes longer because schema mapping, validation, integration setup, and governance review all take time.

8. What is a common mistake when evaluating synthetic data tools?

A common mistake is checking only data realism and ignoring privacy controls, integration effort, and operational repeatability. Another mistake is testing only on simple datasets.

9. Can these tools handle relational or multi-table datasets?

Some can, and some are much better than others. Always confirm support for relationships, metadata handling, and consistency rules during your pilot.

10. How should I choose between platform tools and libraries?

Choose libraries when you want coding flexibility, control, and lower cost. Choose platforms when you need collaboration, automation, governance, and repeatable workflows across teams.

Conclusion

Synthetic data generation tools now play an important role in software testing, analytics, AI development, and safer internal data sharing. The best choice depends on your actual use case, team skills, privacy requirements, and operational maturity. Some teams need fast mock data for development, while others need governed enterprise platforms for repeatable privacy-safe workflows. A smart approach is to shortlist a few tools that match your environment, run a focused pilot, compare utility and workflow fit, and then select the option that performs well in real daily use rather than only looking strong in product messaging.