8 Best Synthetic Data Generation Tools in 2026

Synthetic data is becoming increasingly useful for teams that need realistic data for AI development, software testing, analytics, and data-sharing workflows without relying entirely on sensitive production datasets. The technology can help organizations create data for scenarios where obtaining, exposing, or modifying real-world data is difficult or restricted.

Gartner has identified synthetic data as an important technique for AI and data applications, particularly where organizations need useful training or testing data while reducing reliance on sensitive information. Gartner previously projected that by 2026, 75% of businesses would use generative AI to create synthetic customer data, compared with less than 5% in 2023.

Synthetic data generation tools are software platforms, libraries, or frameworks that create artificial datasets for specific business, development, or analytical purposes. Depending on the tool, they can generate realistic test data from scratch, reproduce important characteristics of existing datasets, maintain relationships across database tables, or create data for training and evaluating machine learning models.

These tools are not all designed for the same job. Some focus on generating realistic test and development data, while others specialize in privacy-preserving synthesis, machine learning datasets, relational databases, or highly customizable open-source workflows.

For this list, we evaluated tools based on data types supported, generation capabilities, data realism, privacy features, database and schema support, deployment options, integrations, developer experience, pricing, and open-source availability.

Why Do You Need Synthetic Data Generation Tools?

Synthetic data generation tools help teams create realistic datasets without depending entirely on sensitive, incomplete, or difficult-to-access production data. They are useful across software development, machine learning, analytics, testing, and privacy-focused data workflows.

Key reasons to use synthetic data generation tools include:

• Protect sensitive data: Create usable datasets without exposing real customer, employee, financial, or healthcare records during development and testing.
• Generate data on demand: Produce datasets in the volume and format required for a specific project instead of manually creating records.
• Accelerate software testing: Generate realistic test data for applications, APIs, databases, and integration environments.
• Support AI and machine learning: Create additional training or evaluation data when real-world datasets are limited, expensive, imbalanced, or difficult to obtain.
• Handle rare scenarios: Generate examples for unusual events or edge cases that may be difficult to capture in real-world datasets.
• Create realistic relational data: Preserve relationships between tables and fields when generating synthetic versions of complex databases.
• Improve data availability: Give development, QA, analytics, and data-science teams access to usable datasets without waiting for production-data extraction and approval.
• Reduce privacy risks: Use synthetic datasets as an alternative to distributing raw personal or confidential information across teams and environments.
• Support data sharing: Share representative datasets with partners, vendors, researchers, or development teams without directly providing the underlying production records.
• Test data pipelines: Generate controlled datasets with different volumes, formats, distributions, and edge cases to validate data pipelines and transformations.
• Address data imbalance: Create additional examples for underrepresented classes or scenarios in machine learning and analytical workloads.
• Create repeatable test environments: Generate consistent datasets that allow teams to reproduce application, database, and pipeline testing scenarios.
• Reduce manual data preparation: Automate the creation of large and structured datasets instead of building test records manually.
• Enable faster development: Provide developers and data teams with realistic data earlier in the development lifecycle.

The right synthetic data approach depends on the intended use. Test-data generation, privacy-preserving synthesis, machine-learning data augmentation, and large-scale enterprise data generation have different requirements, so teams should evaluate tools based on the type of data they need, required realism, privacy expectations, deployment model, and integration with their existing data stack.

Top 8 Synthetic Data Generation Tools: Comparison

Best 8 Synthetic Data Generation Tools in 2026

The 8 synthetic data generation tools below cover different requirements, including privacy-preserving data synthesis, AI training data, test data, relational databases, data science, and open-source synthetic data workflows. Each tool is evaluated based on its generation capabilities, supported data types, integrations, pricing, deployment options, and open-source availability.

#1 Tonic.ai

Tonic.ai is a synthetic data and test data management platform designed to help organizations generate realistic datasets for software development, testing, AI model development, and data workflows. Its product portfolio includes Tonic Fabricate for generating synthetic data, Tonic Structural for transforming production data into safe test data, and Tonic Textual for working with sensitive unstructured data.

Tonic is particularly focused on complex enterprise environments where data may span multiple databases, tables, and relationships. Its current Fabricate product also uses an AI-powered Data Agent to generate relational and unstructured datasets from natural-language instructions.

Key Features

• AI-powered synthetic data generation: Fabricate allows users to describe the dataset they need and use an AI Data Agent to generate and refine it.
• Relational data generation: The platform can generate data across multiple related tables while maintaining relationships needed for realistic application testing.
• Test data management: Structural helps teams create high-fidelity test datasets from production sources without distributing sensitive production records.
• Data subsetting: Teams can create smaller datasets from larger databases while maintaining referential integrity.
• Privacy controls: Tonic provides privacy scanning, privacy reports, masking, and other controls for sensitive datasets.
• Multiple database sources: Tonic supports databases and data platforms including PostgreSQL, MySQL, SQL Server, Snowflake, BigQuery, Databricks, Salesforce, and others.
• Unstructured data protection: Tonic Textual supports sensitive text processing across formats such as PDF, DOCX, CSV, XLS, HTML, and images.
• API and automation: REST APIs, webhooks, and automated workflows allow synthetic and test-data processes to become part of engineering pipelines.

Pricing: Fabricate has a Free plan at $0/month, a Plus plan at $29/month, and Enterprise custom pricing. Plus and Enterprise can incur additional AI-usage charges based on consumption.

G2 Rating: 4.2/5.

Also Read: Best Tonic.ai Alternatives and Competitors in 2026

#2 MOSTLY AI

MOSTLY AI is a synthetic data platform focused on generating high-quality artificial datasets while preserving useful statistical relationships and reducing exposure to sensitive source data. It is designed for applications such as AI and machine learning, analytics, testing, and privacy-sensitive data sharing.

The platform combines a managed synthetic-data environment with open-source technology, giving data scientists and developers options for both hosted and programmatic workflows. Its focus on measurable data quality and privacy makes it particularly relevant when teams need more than simple randomly generated test records.

Key Features

• Synthetic data generation: Generates artificial datasets based on source data while attempting to retain important statistical characteristics and relationships.
• Tabular data synthesis: Supports structured datasets for analytics, machine learning, testing, and experimentation.
• Time-series support: Synthetic data workflows can be applied to sequential datasets where temporal relationships are important.
• Privacy evaluation: Provides privacy-oriented measurements to help teams assess the risk associated with generated datasets.
• Data-quality evaluation: Teams can assess how closely synthetic data reflects important characteristics of the original dataset.
• Open-source SDK: MOSTLY AI provides open-source technology that allows developers to integrate synthetic data generation into technical workflows.
• AI assistant: The platform includes AI-assisted capabilities for working with synthetic data and exploring datasets.
• Enterprise deployment: Enterprise environments can use customized usage configurations and deployment approaches based on organizational requirements.

Pricing: A free tier provides 2 credits per day. Heavier usage and enterprise configurations use additional credits or organization-specific pricing.

G2 Rating: 4.5/5.

Also Read: Best Mostly AI Alternatives and Competitors in 2026

#3 NVIDIA NeMo Data Designer

NVIDIA NeMo Data Designer is a synthetic data generation framework designed for creating structured and domain-specific datasets for AI development. It originated from the technology developed by Gretel, which NVIDIA acquired, and is now part of the NVIDIA NeMo ecosystem. The current platform can orchestrate complex generation workflows using different model providers and data-generation techniques.

Rather than functioning only as a browser-based fake-data generator, Data Designer is built for developers and AI teams that need configurable, reproducible synthetic-data pipelines. It can generate datasets from scratch or use existing datasets as seeds, making it useful for building specialized AI training and evaluation data.

Key Features

• Synthetic dataset generation: Generates structured datasets using configurable columns, samplers, constraints, and language models.
• Seed datasets: Existing datasets can be used as input to guide synthetic-data generation and introduce real-world context into generated records.
• LLM-powered generation: Teams can use supported language models to generate text and other complex fields within datasets.
• Column dependencies: Data Designer allows generated fields to reference other fields, helping create more coherent synthetic records.
• Validation: The framework validates generated datasets against configured specifications before or during generation.
• Batch processing: The system handles batching and parallelization for larger synthetic-data workloads.
• Python SDK: Developers can configure and execute synthetic-data workflows programmatically.
• Image generation: Data Designer also supports synthetic image-data workflows using supported image-generation models.

Pricing: NVIDIA provides a free trial for NeMo Data Designer. Usage through NVIDIA services, models, or infrastructure can have separate costs depending on the deployment and compute configuration.

G2 Rating: N/A.

#4 SAS Data Maker

SAS Data Maker is an enterprise synthetic data platform designed to generate high-fidelity datasets for AI development, analytics, modeling, testing, and other data-intensive workflows. SAS incorporated technology from Hazy after acquiring its principal software assets, bringing Hazy’s synthetic-data capabilities into the SAS ecosystem.

The platform provides a low-code/no-code experience for organizations that want to generate or augment datasets without building synthetic-data workflows entirely from scratch. It can use existing data or user-defined parameters and includes evaluation capabilities for assessing the quality of generated datasets.

Key Features

• Synthetic data generation: Creates artificial datasets that retain important patterns and relationships from real-world data.
• Data augmentation: Can expand existing datasets to address limited or imbalanced data.
• Low-code/no-code interface: Allows users to build synthetic-data workflows without extensive programming.
• Statistical evaluation: Provides visual metrics for evaluating the quality and realism of generated data.
• Privacy protection: Supports synthetic-data workflows intended to reduce exposure of personally identifiable and sensitive information.
• Rare-event generation: Can generate additional examples for scenarios where real-world observations are limited.
• Multi-table capabilities: Technology integrated from Hazy supports generation involving multiple related tables and more complex data structures.
• Enterprise integration: SAS Data Maker is designed to operate within broader enterprise data and AI environments, including Microsoft Azure Marketplace deployment.

Pricing: Custom pricing. SAS does not publicly list standard subscription pricing for Data Maker.

G2 Rating: N/A.

#5 Synthesized

Synthesized is a data platform focused on generating, masking, and subsetting production-like data for software development, testing, data engineering, and AI workflows. Its approach combines synthetic data generation with test-data management, allowing teams to create realistic datasets while maintaining database relationships and applying privacy rules.

The platform is particularly suited to organizations working with complex enterprise databases where realistic test data needs to preserve schema, foreign-key relationships, business logic, and other structural characteristics. It also provides a “Data as Code” approach that lets teams define data transformations and compliance requirements as reusable configurations.

Key Features

• Synthetic data generation: Creates production-like datasets with configurable distributions, patterns, and relationships.
• Data masking: Replaces sensitive values with realistic alternatives while maintaining useful data structures.
• Database subsetting: Creates smaller representative datasets from production databases while preserving referential integrity.
• Relational data support: Maintains primary-key and foreign-key relationships across connected tables.
• Data as Code: Teams can define data requirements, transformations, and policies through configuration files and Python-based workflows.
• CI/CD integration: Synthetic and transformed datasets can be incorporated into automated development and testing pipelines.
• Cloud and self-hosted deployment: Supports cloud, Docker, Kubernetes, and other deployment approaches.
• Enterprise database support: Supports environments including PostgreSQL, MySQL, Oracle, SQL Server, SAP HANA, Salesforce, and other enterprise systems.

Pricing: The Synthesized SDK has a free self-service option. Enterprise platform pricing is available through the vendor and varies by deployment and requirements.

G2 Rating: N/A.

#6 YData

YData provides data-science and data-quality tooling with capabilities for generating synthetic data, profiling datasets, and preparing data for machine learning workflows. Its synthetic-data functionality is particularly relevant to data scientists who want to work with artificial datasets as part of broader data preparation and quality processes.

The platform is also relevant to technical users who prefer Python-based workflows. Rather than positioning synthetic data as an isolated application, YData connects data profiling, quality assessment, preparation, and synthetic-data generation within the broader data-science lifecycle.

Key Features

• Synthetic data generation: Creates artificial datasets for machine learning, analytics, experimentation, and data-science workflows.
• Tabular data synthesis: Supports structured datasets and synthetic-data generation for common data-science scenarios.
• Data profiling: Helps users inspect distributions, data types, missing values, and other dataset characteristics before synthesis.
• Data-quality workflows: Allows teams to identify and address data-quality issues that can affect synthetic-data generation and downstream models.
• Python integration: Provides developer-oriented tooling for incorporating data preparation and synthetic generation into Python workflows.
• Privacy-focused workflows: Synthetic datasets can be used to reduce the need to distribute raw sensitive data during development and experimentation.
• Open-source capabilities: YData provides open-source components that technical users can run and integrate into their own environments.
• Machine-learning workflows: Synthetic datasets can be incorporated into experimentation, model development, data augmentation, and evaluation workflows.

Pricing: YData provides open-source capabilities that can be used without a paid subscription. Commercial offerings and enterprise requirements are priced separately.

G2 Rating: 4.6/5.

#7 Mockaroo

Mockaroo is a browser-based data generator and API mocking platform that helps developers create realistic test datasets without manually entering large numbers of records. Users can define schemas, choose data types, add formulas, and generate datasets in formats such as CSV, JSON, SQL, and Excel.

Mockaroo differs from statistical synthetic-data platforms because it is primarily designed around configurable test-data generation rather than learning complex distributions from production datasets. That makes it particularly practical for developers who need predictable mock records for application development, API testing, demonstrations, and prototypes.

Key Features

• Custom data schemas: Users can define fields, data types, relationships, and generation rules for their test datasets.
• Multiple export formats: Generated data can be exported as CSV, JSON, SQL, Excel, and other supported formats.
• Large library of data types: Mockaroo provides numerous built-in types for names, addresses, dates, identifiers, financial values, geographic data, and other fields.
• Formula-based generation: Developers can use formulas to create customized values and more complex data-generation logic.
• Generate API: The API allows applications and development workflows to generate datasets programmatically.
• Mock APIs: Developers can simulate backend APIs and generate dynamic responses before the real backend is available.
• Private deployment: Enterprise customers can deploy Mockaroo as a Docker image in a private cloud or data center.
• AI-assisted field generation: The platform also provides AI-assisted options for generating fields and data schemas.

Pricing: Free plan supports up to 1,000 rows per file. Silver starts at $60/year, Gold at $500/year, and Enterprise at $7,500/year.

G2 Rating: 4.2/5.

#8 SDV

SDV, or Synthetic Data Vault, is an open-source synthetic-data ecosystem designed primarily for generating artificial tabular, relational, and time-series data. It provides Python libraries that allow developers and data scientists to build synthetic-data workflows programmatically rather than relying on a proprietary graphical platform.

SDV is useful for teams that want direct control over their synthetic-data workflow and the ability to experiment with different synthesis models. Its ecosystem includes tools for generating data, evaluating synthetic datasets, and working with different structured-data modalities.

The project is especially relevant for data scientists and developers who are comfortable working in Python and want to integrate synthetic data into existing machine-learning or data-engineering pipelines.

Key Features

• Tabular data synthesis: Generates synthetic versions of structured datasets using configurable synthesis models.
• Relational data synthesis: Supports datasets containing multiple related tables and relationships between records.
• Time-series data: Provides capabilities for generating synthetic sequential and temporal datasets.
• Multiple synthesis models: Users can select models based on the characteristics and requirements of their data.
• Python SDK: Synthetic-data generation can be integrated directly into Python applications, notebooks, and data pipelines.
• Synthetic-data evaluation: The broader SDV ecosystem includes tools for assessing the quality and statistical similarity of generated datasets.
• Metadata management: Users can define and manage the metadata needed to describe dataset structures and relationships.
• Open-source access: The SDV project can be installed and used programmatically, giving technical teams greater control over implementation.

Pricing: SDV is available as an open-source project. Enterprise or commercial support options can have separate pricing.

G2 Rating: N/A.

How to Choose the Best Synthetic Data Generation Tool

The best synthetic data generation tool depends primarily on what kind of data you need to create and how closely it needs to represent a real production environment.

• For enterprise test data: Tonic.ai and Synthesized are strong options when relational databases, production-like data, referential integrity, and automated test-data workflows are important.
• For privacy-preserving synthetic data: MOSTLY AI and SAS Data Maker are worth evaluating when privacy, statistical fidelity, and enterprise data workflows are major requirements.
• For AI training data: NVIDIA NeMo Data Designer is particularly relevant when teams need configurable, domain-specific datasets for AI development.
• For data science: YData and SDV are useful for teams that prefer Python-based workflows and want to incorporate synthetic data into broader machine-learning processes.
• For simple test data: Mockaroo is a practical choice when developers need quickly generated records, API responses, or application mock data without building a complex synthesis pipeline.
• For open-source workflows: SDV, YData, NVIDIA NeMo Data Designer, and the open-source components of MOSTLY AI provide options for teams that want greater technical control.
• For relational data: Look for tools that preserve primary-key and foreign-key relationships rather than simply generating independent rows.
• For sensitive data: Review privacy metrics, masking capabilities, deployment options, access controls, and the vendor’s approach to handling source data.
• For AI datasets: Consider whether the platform supports text, images, time series, structured data, or other modalities required by your models.
• For production integration: Check APIs, SDKs, CI/CD support, database connectors, automation, and deployment options before selecting a platform.

The most important consideration is data utility for the intended use case. A tool that works well for generating simple application test records may not be appropriate for training a machine-learning model or reproducing a complex enterprise database.

Conclusion

Synthetic data generation tools have become useful for organizations that need realistic datasets without relying entirely on sensitive or production data. They can support software testing, AI and machine learning, analytics, development, privacy-focused workflows, and controlled data sharing.

The tools covered in this guide serve different requirements. Tonic.ai is focused on enterprise synthetic and test data, while MOSTLY AI provides synthetic-data capabilities for privacy-sensitive and machine-learning workflows. NVIDIA NeMo Data Designer is designed for configurable synthetic datasets and AI development, while SAS Data Maker brings enterprise synthetic-data generation into the broader SAS data and AI ecosystem.

Synthesized focuses heavily on production-like test data, data masking, subsetting, and data engineering, while YData combines synthetic data with broader data-science and data-quality workflows. Mockaroo provides a simpler approach for generating test records and mocking APIs, making it useful when teams do not need a sophisticated statistical synthesis platform.

SDV provides an open-source alternative for teams that want programmatic control over synthetic tabular, relational, and time-series data. Its approach is particularly relevant for data scientists and developers who want to build synthetic-data workflows directly into Python-based environments.

There is no single best synthetic data generation tool for every use case. A team generating application test records may have very different requirements from a machine-learning team creating training datasets or an enterprise handling sensitive relational data. The right choice depends on the type of data, required realism, privacy requirements, scale, deployment model, and existing data infrastructure.

Open-source options are particularly useful when customization, local execution, or control over the underlying workflow is important. However, managed enterprise platforms can reduce the technical effort required for deployment, governance, evaluation, and ongoing synthetic-data management.

Before selecting a platform, evaluate how well it handles your actual datasets rather than relying only on sample demonstrations. Test the quality and usefulness of generated data, check privacy and re-identification controls where applicable, review supported databases and data types, and confirm that the pricing and deployment model fit your requirements.

Ultimately, synthetic data is most valuable when it solves a specific data-access problem while retaining enough realism for the intended workload. The best tool is the one that provides the right balance of data utility, privacy, control, scalability, and ease of integration.

Frequently Asked Questions

#1. What are synthetic data generation tools?

Synthetic data generation tools are platforms, libraries, or frameworks that create artificial datasets for testing, AI development, analytics, machine learning, privacy-sensitive workflows, and other data use cases.

#2. What are the best synthetic data generation tools in 2026?

Some of the leading options include Tonic.ai, MOSTLY AI, NVIDIA NeMo Data Designer, SAS Data Maker, Synthesized, YData, Mockaroo, and SDV. The right choice depends on whether the primary requirement is test data, AI training, privacy, relational data, or developer-focused data generation.

#3. Is synthetic data the same as anonymized data?

No. Synthetic data is newly generated rather than simply having identifying fields removed from an existing dataset. However, synthetic data is not automatically anonymous or risk-free, so privacy and re-identification risks should still be evaluated.

#4. What is the best open-source synthetic data tool?

SDV is one of the strongest open-source options for structured synthetic data, particularly for tabular, relational, and time-series workflows. YData and NVIDIA NeMo Data Designer also provide open-source capabilities for technical users.

#5. Can synthetic data be used for AI training?

Yes. Synthetic data can be used to augment or create datasets for machine-learning and AI development. NVIDIA NeMo Data Designer, MOSTLY AI, YData, and other platforms support workflows designed for AI and machine-learning use cases.

#6. Can synthetic data be used for software testing?

Yes. Synthetic data can provide realistic records for application, API, database, integration, regression, and load testing. Tonic.ai, Synthesized, and Mockaroo are particularly relevant to development and testing workflows.

#7. Does synthetic data protect personal information?

Synthetic data can reduce the need to distribute real personal information, but protection depends on how the data is generated and evaluated. Organizations should review privacy metrics, disclosure risks, masking methods, deployment controls, and the possibility of records being too similar to source data.

#8. What is the difference between synthetic data and mock data?

Mock data generally refers to artificially created records used to simulate inputs during development or testing. Synthetic data can be broader and may use statistical or machine-learning techniques to reproduce meaningful patterns, distributions, relationships, or other characteristics of real datasets.

#9. Can synthetic data replace real data?

Not in every situation. Synthetic data can be valuable for testing, development, AI training, data augmentation, and privacy-sensitive workflows, but some applications still require real-world data for validation and final performance evaluation.

#10. How do I choose a synthetic data generation tool?

Start with the intended use case and data type. Then evaluate generation quality, privacy capabilities, relational-data support, supported modalities, deployment options, integrations, APIs, open-source availability, pricing, and how well the generated data performs in the actual workflow where you plan to use it.