Synthetic data has moved well beyond the “interesting experiment” phase. In 2026, it is becoming essential for teams that need to move fast without exposing sensitive information.
Using real production data for application testing, AI training, or analytics is getting harder and riskier. Privacy regulations are stricter, access is more limited, and copying large datasets can slow everything down. Synthetic data addresses this by giving teams data that behaves like the real thing, without exposing real customer or business information.
That said, not every synthetic data tool works the same way. Some are built for large enterprises that need control and scale. Others are better for data scientists or quick experiments.
Below are 6 synthetic data generation tools to watch in 2026, based on realism, security, ease of use, and fit for real-world use cases.
1. K2view
K2view is more than a synthetic data generator. It supports the full lifecycle, pulling source data, subsetting it, applying masking and anonymization where needed, generating synthetic datasets, and delivering them across environments.
A key strength is how it maintains inter-system relationships. Customer, account, and transaction data remain related, even when data is masked or synthetically generated. This helps tests behave like production, which matters in enterprise testing, AI training, and validation of complex, multi-system processes.
K2view supports GenAI and rules-based generation, includes built-in masking/anonymization capabilities, and integrates cleanly with CI/CD pipelines. For teams that need realistic data at scale, with governance and control – it is designed for enterprise delivery.
Best for: Large enterprises with complex data spread across multiple systems
Good to know: Setup and deployment require planning, but value increases significantly at scale
User feedback: Quick, reliable synthetic data delivery, though local support can be limited to Europe and the Americas
2. MOSTLY AI
MOSTLY AI focuses on producing high-fidelity synthetic datasets that closely mirror real data while maintaining privacy. It is widely associated with AI and analytics use cases, and it includes fidelity metrics that help quantify how closely the synthetic output matches the original dataset.
The interface is approachable, which makes it usable even for non-engineers. It supports multi-relational datasets, cloud-based workflows, and API access.
However, teams working with highly complex hierarchical relationships may find it less flexible than enterprise-oriented platforms.
Best for: Mid-size to large teams building AI models or analytics pipelines
Good to know: Very easy to use, but offers limited control for hierarchical data and complex relationships
User feedback: Simple and fast, but lacks adequate parameter controls
3. YData Fabric
YData Fabric is built with machine learning teams in mind. It combines data profiling, data quality assessment, and synthetic data generation to help teams improve model readiness and reduce bias risks.
It supports tabular, relational, and time-series data, and it can fit well into ML workflows. Teams can use no-code tooling or SDK-based approaches depending on their skill level and workflow needs.
The trade-off is complexity. YData is powerful, but it assumes users are comfortable with data science concepts. It may also be a concern in highly regulated markets, since it does not comply with all data privacy laws.
Best for: Data science teams training and improving ML models
Good to know: Very capable, but not beginner-friendly
User feedback: Helps create balanced datasets for AI model training, but requires strong data science skills to use effectively
4. Gretel
Gretel is a developer-focused platform designed to embed synthetic data generation directly into engineering workflows. It is a strong fit when synthetic data needs to be part of CI/CD pipelines, Dev/Test setups, or ML automation.
It supports structured and unstructured data, includes scheduling and automation, and offers no-code/low-code workflow options alongside an API-first experience.
The main limitation is usability for non-developers. It is also dependency-heavy on cloud infrastructure, which can be a consideration for teams with strict deployment constraints.
Best for: Engineering teams automating synthetic data in pipelines
Good to know: Strong for workflow integration and automation, but best suited to developer-led teams
User feedback: Streamlines development workflows with API support, but relies heavily on cloud infrastructure
5. SDV (synthetic data vault)
SDV is an open-source Python library that gives data scientists a high level of control over synthetic data generation. It supports tabular, relational, and time-series data, and it includes multiple generative models such as CTGAN, CopulaGAN, and GaussianCopula.
SDV is flexible and cost-effective, but it requires strong technical skills to configure models, tune parameters, and manage outputs. It also lacks enterprise features and support that larger organizations often need for governance and operational workflows.
Best for: Small data science teams, research projects, or academic use
Good to know: Powerful, but requires hands-on setup and advanced configuration
User feedback: Generates realistic data with strong parameter control, but requires significant technical skill
6. Hazy (now part of SAS Data Maker)
For highly regulated environments, Hazy is often associated with differential privacy, anonymization, and compliance-first deployment options (including on-prem). The trade-off is typically a more complex, time-consuming setup.
Best for: Regulated industries such as financial services
Good to know: Compliance-first approach, but heavier implementation effort
Final take
Synthetic data is no longer optional. It is becoming a practical foundation for safe testing, AI development, and analytics.
Today’s leading tools reflect a clear split in the market: enterprise platforms emphasizing governance, scale, and integration, and developer or data science options emphasizing speed and flexibility. The right choice depends on your data complexity, compliance requirements, and how your teams actually work.
One thing is consistent: the most useful synthetic data platforms in 2026 are the ones that are realistic, private, and operationally useful – not just impressive algorithms.
