At the doorstep of 2026, Synthetic Data Generation (SDG) has shifted from a niche capability to a central pillar of enterprise AI outlook. It now powers model training, supports safe product testing, and protects sensitive data across heavily regulated environments.
Gartner estimates that three out of four businesses will use generative AI to generate synthetic customer data by 2026. This clearly underscores the critical role of synthetic datasets. Add to it the growing compliance pressures and accelerating AI adoption, organizations are now turning to platforms that can deliver high-quality, privacy-safe datasets at scale.
Here are the Top 5 Synthetic Data Generation products of 2026, followed by a strong lineup of tools driving the next wave of synthetic data innovation.
1. K2view – The Benchmark for Enterprise-Scale SDG
In 2026, K2view shall remain an undisputed leader in this league.
The standalone solution redefined the life cycle of synthetic data across creation, governance and consumption. As a holistic solution, K2view manages everything from source extraction and subsetting to PII discovery, masking, and AI-powered rule-based generation. K2view gained popularity for its entity-based micro-database approach, which proved highly successful. It ensures trustworthiness, analysis readiness and referential integrity for structured and unstructured datasets.
Their Synthetic Data Generation tool provides an intuitive, no-code interface that enables testers to generate data for real-time scenarios rapidly. Thus, it supports data subsetting, LLM data preparation, cloning and performance testing datasets.
Unlike traditional tools, K2view integrates seamlessly with enterprise ecosystems and automates CI/CD pipelines, enabling quick provisioning of synthetic data into any target system. Consistently rated a Visionary in Gartner’s Data Integration MQ, K2view is the go-to choice for enterprises demanding accuracy, scale, and compliance.
2. Mostly AI
Mostly AI provides high-fidelity synthetic twins for AI training. It remains one of the most adopted SDG tools for its ability to mirror real-world distributions while offering built-in privacy protection. It provides fidelity scoring, support for multi-relational datasets, and an intuitive UI accessible to non-technical users.
Best for: companies prioritizing fast dataset creation for AI and analytics.
3. YData Fabric
YData provides unified data profiling and SDG for AI systems. Its fabric strengthens AI development workflows by combining data profiling, quality assessment, and multi-type synthetic data generation. It caters well to enterprises building ML models across structured, relational, and time-series data sources. Its no-code + SDK options offer flexibility for both business users and data scientists
Best for: ML-driven organizations.
4. Gretel Workflows
Engineering teams widely prefer Gretel for its strong automation capabilities, which allow synthetic data to plug directly into CI/CD processes and ML pipelines. It works well with both structured and unstructured data, and its no-code and low-code orchestration options make it a natural fit for developer-driven environments.
Best for: DevOps teams embedding SDG into automated workflows.
5. Hazy (SAS Data Maker)
Hazy focuses on generating privacy-safe synthetic data using differential privacy, making it a strong fit for sectors such as banking, insurance, and fintech. It provides enterprise-level integration features and secure deployment choices, including on-premise environments. Organizations often select Hazy when compliance and governance are absolute requirements.
Best for: highly regulated sectors.
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