Every company wants to build smarter systems that can understand data and act on it. To do that, they need large amounts of high-quality data. Because production data is often sensitive or limited, synthetic data has become an important way to train and test AI safely.
Synthetic data is artificial information that resembles real data but does not contain actual personal details. Synthetic data generation tools learn from real datasets, reproduce their patterns and relationships, and then generate new, privacy-safe data that can be used for development, testing, and analytics.
Here are 5 synthetic data generation tools to consider for your AI projects in 2026.
1. K2view
K2view provides a standalone synthetic data generation tool that manages the entire lifecycle of synthetic data for enterprises. It handles source data extraction, subsetting, pipelining, and synthetic test data operations, so teams can go from production data to usable synthetic datasets within a single platform.
The K2view solution combines GenAI and rules-based generation methods to produce accurate, compliant, and realistic synthetic data for both software testing and machine learning model training. A patented, entity-based architecture creates a schema that acts as a blueprint for the data model, helping maintain referential integrity across related tables and systems.
K2view also includes built-in masking and anonymization capabilities and integrates with CI/CD pipelines, so synthetic data can be provisioned as part of automated development and testing workflows. Configuration and deployment require planning, and the platform is geared mainly toward larger enterprises rather than very small teams, but for organizations with complex, multi-source data environments it offers an end-to-end approach rather than a narrow point tool.
2. Mostly AI
Mostly AI generates high-fidelity synthetic datasets that closely mirror real data while protecting privacy. It uses deep learning to learn the distributions and relationships in production data, then creates new records that preserve that structure without including real values.
The platform supports privacy-safe generation and de-identification, offers fidelity metrics to compare real and synthetic datasets, and works with multi-relational data. It runs in the cloud and exposes APIs, which makes it accessible for companies that want to incorporate synthetic data into analytics and model development without managing heavy infrastructure.
Mostly AI is generally well suited to mid-size and larger companies that need synthetic data for AI and analytics but do not require extremely fine-grained control over complex hierarchical structures, and it is often chosen by teams that prefer a straightforward interface over highly tuned configuration.
3. YData Fabric
YData Fabric focuses on improving AI and machine learning outcomes by combining data profiling and synthetic data generation in one environment. It can work with different data types, including tabular, relational, and time-series data, and it emphasizes data quality and balance before models are trained.
Key capabilities include multi-type data generation, automated data quality assessment, integration into ML pipelines, and both code and no-code options. This makes it useful for teams that need to prepare and enrich training datasets across several domains while keeping them aligned with downstream AI projects.
Because YData Fabric assumes some level of data science and engineering expertise, it tends to fit best with organizations that already have ML workflows in place and want to refine and balance their training data, rather than teams looking for a simple generator aimed at non-specialists.
4. Gretel Workflows
Gretel offers a developer-focused platform that embeds synthetic data generation directly into engineering workflows. It supports structured and unstructured data, and its workflow and scheduling features help automate the creation and refresh of synthetic datasets.
Developers can use APIs, no-code, and low-code options to connect Gretel to CI/CD pipelines and other software delivery processes, allowing test, dev, and ML environments to receive privacy-safe data with minimal manual effort. By keeping synthetic data generation close to development and DevOps, it helps teams reduce friction around test data provisioning.
Because Gretel is cloud-centric and oriented toward engineering teams, it is usually a good fit for organizations that already have pipeline-driven development and want to add synthetic data to those pipelines, while it may be less appropriate for non-technical users or organizations that require extensive on-premises deployment by default.
5. Hazy
Hazy specializes in privacy-focused synthetic data, particularly for regulated industries where adherence to strict data protection requirements is critical. Now part of SAS Data Maker, it uses techniques such as differential privacy and anonymization to generate synthetic data that protects individuals while retaining value for analysis and modeling.
The platform is designed with compliance and integration in mind, supporting secure deployment in the cloud or on premises and aligning with existing enterprise ecosystems. It is often adopted in sectors such as banking and financial services, where demonstrating strong privacy controls is a key part of technology decision-making.
Hazy typically requires more effort to configure and can be expensive for smaller organizations, so it is better suited to larger companies with clear regulatory drivers and the need for synthetic data capabilities that emphasize privacy guarantees and governance.
Conclusion
Synthetic data generation tools are becoming a core part of how organizations build and scale AI projects, allowing teams to work with realistic datasets without exposing sensitive information.
K2view stands out for enterprises that need to manage the full synthetic data lifecycle across complex, multi-source data landscapes, combining entity-based modeling, multiple generation techniques, masking, and CI/CD integration in one platform. Mostly AI, YData Fabric, Gretel, and Hazy each offer different balances of usability, data-type coverage, developer focus, and privacy emphasis, giving companies a range of options depending on their AI maturity, regulatory environment, and internal skills.
