Five synthetic data generators to consider for 2026

Synthetic data generation is used to create artificial datasets that closely mirror the structure and behavior of real-world data. It captures the same correlations, patterns, and relationships found in real datasets but without exposing sensitive information.

Synthetic data can be thought of as a digital twin of a real dataset – realistic enough to test software, run analytics, and train AI models while being completely free of privacy concerns. 

Organizations increasingly rely on synthetic data generation (SDG) to safely develop and test applications and software, enhance AI training, and speed up innovation. Today, it is a foundational element of secure, responsible data operations. 

What is a synthetic data generator?

A synthetic data generator creates artificial datasets that behave like real ones without exposing sensitive information. By learning the patterns, structure, and relationships of the original data, they then produce new, privacy-safe records that preserve the same analytical value.

They offer, essentially, a safe, realistic stand-in for real-world data. Below, we’re going to take a look at five of the best-in-class synthetic data generators in 2026. 

What are the benefits of using a synthetic data generator?

There are many important benefits of using a synthetic data generator, such as: 

• Protecting sensitive information, by replacing real data with privacy-safe artificial records.

• Negating compliance risks, to ensure organizations don’t contravene data privacy laws and regulations.

• Enabling teams to instantly access realistic data, without having to wait for data masking processes or approvals.

• Improving machine learning and AI performance, by generating diverse, balanced, and bias-free datasets.

• Supporting speedy software testing, with repeatable, consistent datasets that mirror real-world information.

• Reducing dependency on production data, to lower operational risk and speed up development cycles.

• Creating edge cases or rare scenarios, that may not exist in real data but are vital for robust testing.

• Scaling easily, to produce large volumes of high-quality data on demand.

• Making experimentation simpler, by allowing teams to tweak parameters, simulate various conditions, and explore new ideas.

1. K2view

K2view Synthetic Data Generation tools provide an end-to-end solution for creating and managing synthetic data across the entire data lifecycle.

This includes everything from extracting and subsetting source data to building pipelines and generating high-quality synthetic outputs.

The K2view solution produces compliant, realistic data ideal for software development and machine learning training. Its patented entity-based technology generates a schema that acts as a blueprint for the data model to preserve referential integrity.

It features AI- and rules-based generation, built-in data masking, anonymization, and seamless CI/CD integration. K2view offers strong scalability, making it well suited for large organizations with complex data environments. 

What users say: 
Fast, dependable synthetic data delivery, though local support is currently limited to the Americas and Europe. 

2. YData Fabric

YData Fabric combines data profiling with synthetic data generation to enhance the quality and performance of AI models. It works with tabular, relational, and time series data, providing automated data quality checks, integrated ML workflows, and multi-type data generation.

Users can choose no-code tools or an SDK, and the tool excels at handling diverse data and boosting ML readiness. However, it’s worth noting that the tool requires data science expertise and offers limited compliance features. 

YData Fabric is potentially best suited for organizations involved in developing models across multiple domains. 

What users say: 
Produces well-balanced datasets for training AI models, but using it effectively demands solid data science expertise. 

3. Mostly AI

Mostly AI creates high-fidelity synthetic datasets that closely reflect real-world data while maintaining strong privacy protection.

The intuitive interface facilitates fast, accurate data generation for AI development and analytics, while the tool also offers privacy-safe de-identification, multi-relational data support, fidelity metrics, and cloud-based workflows with API integration.

The tool is fast and easy for non-engineers to use, though it offers limited control over hierarchical or highly complex data. Mostly AI is best suited to mid- to large-sized organizations engaged in developing machine learning models. 

What users say: 
Simple to use, but offers limited control over key parameters. 

4. Gretel Workflows

Gretel Workflows is designed to help teams embed synthetic data generation directly into development pipelines. It emphasizes workflow automation and scheduling, and supports both structured and unstructured data.

Gretel Workflows can integrate via API and is commonly evaluated in engineering-led environments where synthetic data needs to fit into CI/CD, Dev/Test, and machine learning workflows. 

It’s worth noting that the approach may rely heavily on cloud infrastructure, which can be a constraint depending on organizational requirements. 

What users say: 
Streamlines development workflows with API support, but relies heavily on cloud infrastructure. 

5. Hazy (now part of SAS Data Maker)

Hazy, now part of SAS Data Maker, focuses on privacy-preserving synthetic data generation, with an emphasis on differential privacy and anonymization for regulated industries.

It supports secure deployment options (including on-prem and cloud) and is often assessed for use cases involving controlled data sharing.

However, setup and implementation can be complex, and it may be less suitable for teams seeking a faster rollout or simpler configuration. 

What users say: 
Ensures data compliance, but setup is complex. 

How to choose a synthetic data generator?

Not all synthetic data generators are built equal, and it’s crucial to choose the best one for your organization’s unique needs and data environment. Here’s what to consider: 

• Identify your primary use case – for example, software testing, or AI model training.

• Check which data types the synthetic data generator supports (tabular, relational, time series, text, images, for example).

• Evaluate its ease of use: consider the quality and usability of the UI, and the steepness of the learning curve required to deploy and use it.

• Assess the incorporated privacy features, such as data masking, de-identification, and compliance safeguards.

• Review the tool’s performance, scalability, and the ease with which it can integrate with your organization’s current data pipelines.

• Consider data fidelity and how well the synthetic data preserves real-world patterns and structures.

• Take a look at customization options, including parameter controls and rules.

• Compare vendor support, pricing, and long-term reliability.

By being very clear about your organization or team’s needs and looking carefully at the features and tools incorporated into each option, you’ll be able to choose the very best tool for both current and future requirements. 

Why your organization needs a reliable synthetic data generator in 2026

Synthetic data generation is quickly becoming a strategic advantage for organizations keen to innovate without compromising privacy.

With real-world data becoming harder to access and more tightly regulated, synthetic data represents a practical means of keeping projects moving while maintaining privacy and compliance.

The tools we’ve reviewed above showcase the breadth of capabilities now available, from enterprise-scale pipelines to user-friendly interfaces designed to facilitate easy experimentation. 

Selecting the right synthetic data generator relies on understanding your data landscape, technical needs, and long-term goals.

By considering factors such as privacy safeguards, data fidelity, integration, and usability, teams can identify and adopt a tool that works for both their current projects and future growth.

Synthetic data is no longer an optional capability or a workaround, but a core enabler of responsible AI and software development, empowering efficient testing and agile decision-making across modern data-driven organizations.