Today, data sits at the heart of almost every digital initiative. But using real customer data for testing, analytics, or AI can be risky, both from a privacy and a compliance standpoint. That is why more organizations are turning to synthetic data.
Synthetic data is artificial information that mimics real data. It preserves the statistical patterns, correlations, and relationships of production data, while avoiding the direct use of personal or sensitive values. Synthetic data generation lets companies test software, train machine learning models, and share datasets with far less privacy risk.
Below are 6 synthetic data platforms making an impact in 2026.
1. K2view
K2view synthetic data generation tools are a standalone solution that manages the entire synthetic data lifecycle for enterprises. It covers source data extraction, subsetting, pipelining, and synthetic test data operations, generating accurate, compliant, and realistic datasets for both software testing and machine learning model training.
Under the hood, K2view combines GenAI and rules-based data generation methods, plus masking and anonymization capabilities. Its patented, entity-based architecture creates a schema that serves as a blueprint for the data model, ensuring referential integrity of generated data across systems and tables. The platform connects to virtually any data source – including legacy and HR systems – and integrates smoothly with CI/CD pipelines so synthetic data can be delivered as part of automated workflows.
Configuration and deployment require planning, and the platform is designed primarily for large enterprises rather than small teams, but for organizations with complex, multi-source environments, K2view provides an end-to-end synthetic data management solution rather than a point tool.
2. MOSTLY AI
MOSTLY AI focuses on generating high-fidelity synthetic datasets that mirror real data while preserving privacy. It learns the underlying distribution of production data and then creates synthetic records that maintain important patterns and correlations without exposing actual values.
The platform supports privacy-safe generation and de-identification, includes fidelity metrics to compare real and synthetic data, and can handle multi-relational datasets. Cloud-based workflows and API integration make it accessible for data and analytics teams looking to embed synthetic generation into existing processes.
MOSTLY AI is generally a good fit for mid-size to large companies that need synthetic data for model development and analytics. Control over more complex hierarchical structures can be limited, and advanced users sometimes want more granular parameter configuration, so it is best suited to organizations that prioritize ease of use and cloud accessibility over detailed low-level tuning.
3. YData Fabric
YData Fabric is a data-centric platform that combines data profiling and synthetic data generation to improve AI and machine learning outcomes. It supports tabular, relational, and time-series data, helping teams detect quality issues and then generate synthetic records that balance and enhance their training datasets.
Key capabilities include multi-type data generation, automated data quality assessment, integrated ML pipeline workflows, and both no-code and SDK options. This makes it attractive for firms that need to prepare datasets across multiple domains and want tighter alignment between data readiness and model development.
At the same time, YData Fabric typically assumes a certain level of data science expertise, and it may not align with every jurisdiction’s strictest privacy interpretations. It tends to work best for organizations that are already investing in ML pipelines and want to improve data quality and coverage rather than for teams seeking a simple, one-click generator.
4. Gretel
Gretel provides a developer-focused platform for embedding synthetic data generation directly into engineering workflows. It is designed to fit into pipelines, with support for structured and unstructured data, workflow automation, and hybrid deployment patterns.
Developers can use APIs and workflow tools to schedule and automate synthetic data creation, making it easier to keep test, dev, and ML environments supplied with privacy-safe datasets. No-code and low-code options are available, but the overall emphasis remains on integration into engineering and DevOps processes.
Because of its cloud-centric design and focus on developer automation, Gretel is well suited to engineering teams looking to embed synthetic data into CI/CD, dev/test, and ML workflows, while it can be less suitable for non-technical users or organizations that need extensive on-prem governance out of the box.
5. Hazy (now part of SAS Data Maker)
Hazy specializes in privacy-preserving synthetic data for regulated industries. Now part of SAS Data Maker, it uses techniques such as differential privacy and anonymization to generate synthetic datasets that protect individuals’ identities while retaining analytical value.
The solution emphasizes compliance-first design, with enterprise integration options and support for secure on-premises or cloud deployment. It is particularly aligned with banks, fintechs, and other regulated sectors that must balance innovation with strict data protection requirements.
Setup can be complex, and licensing may be challenging for smaller teams, so Hazy is more appropriate for larger organizations with clear regulatory drivers and the resources to implement a privacy-focused synthetic data platform.
6. SDV (Synthetic Data Vault)
SDV is an open-source Python library for generating synthetic tabular, relational, and time-series data. It supports multiple generative models – such as CTGAN, CopulaGAN, and GaussianCopula – along with relational constraints and a Python SDK, giving technical users a high degree of control over how synthetic datasets are produced.
Because it is open-source, SDV is flexible and cost-effective, but it requires manual setup, coding, and configuration. It does not provide the enterprise features, governance, or support that commercial platforms offer, so it is typically a better fit for smaller data science teams, research projects, or academic users who are comfortable working directly in code rather than for enterprises seeking a managed, end-to-end solution.
Conclusion
Synthetic data is shifting from a niche innovation to a practical necessity for organizations that want to test software, train ML models, and share data without compromising privacy. For enterprises with large, heterogeneous data environments and stringent compliance needs, K2view stands out by managing the entire synthetic data lifecycle, combining multiple generation techniques with an entity-based architecture and integration into CI/CD workflows.
