The way companies deal with data has been changing rapidly, and synthetic data is becoming one of the most exciting frontiers. Businesses today are under pressure to innovate, protect privacy, and still feed their hungry machine learning models with enough high-quality information to perform well. That’s where synthetic data steps in. Instead of always relying on real-world data—often messy, sensitive, or limited—organizations are creating artificial datasets that are just as useful, sometimes even more so.
Let’s walk through some of the top synthetic data generation tools worth paying attention to this year:
K2view
K2view has become something more than just another synthetic data generator. It’s a standalone system that manages all aspects of the synthetic data lifecycle, from extraction up through testing and training. What’s different in 2025 with K2view is how it pairs AI-driven functionality with rules-based creation and gives data scientists and testers a flexible way in which they can produce the exact type of datasets they’re interested in.
On the AI side, K2view can subset training data, mask sensitive personal data, and even support large-language model training. It also has no-code post-processing, which means teams don’t need deep technical skills to refine the output.
Another area in which K2view stands out is data cloning. It can mask, extract, and clone data and auto-generate unique identifiers within a single step and thus ensure data integrity. This is especially useful in the case of performance and load testing, where test data on a massive scale is needed.
In 2025, K2view is no longer considered a supplement, but as a significant contributor in the way organizations protect, manage, and evolve their data strategies.
Mostly AI
Mostly AI has established itself by striking a balance between privacy and realism. In industries such as insurance or banking that rely on sensitive customer data for running business, corporations cannot afford to handle data in the wrong way. Mostly AI provides a platform through which it creates mock datasets that resemble and function similar to the original ones but hold no real personal data. This makes it easier in complying with regulations such as GDPR or HIPAA.
The real strength in Mostly AI is in how it preserves statistical properties. That is, if you’re building a machine learning model, the inferences you’d make out of the generated data would be identical to what you’d make out of the original data. You essentially have a virtual twin of your dataset—safe, controllable, and yet robust for analysis.
Gretel AI
Gretel AI became popular since it speaks directly to data scientists and developers who require flexibility. The platform is cloud-based and can be used easily to generate synthetic text, tabular data, and time-series data. In 2025, Gretel will be most popular among teams working on natural language processing projects since it is capable of generating exceedingly realistic synthetic text data.
Another reason why Gretel remains appealing is that it is seamless to integrate. You can plug it into your workflow without revamping the existing systems. Startups and agile teams love it especially since they can test and experiment quickly without having to wait on lengthy approval cycles or IT revamps.
Synthea
When discussing synthetic data in the field of healthcare, Synthea is usually the first recommendation that comes up. It is an open-source tool that is solely dedicated to creating electronic health records, which are highly realistic in nature. You can mimic patient history, the treatment, prescriptions, and results through Synthea without ever employing actual patient data.
Synthea has also been a game-changer among researchers, hospitals, and health tech startups. It allows teams to experiment with algorithms, make simulations, and even test public health hypotheses without ever crossing into the ethical minefield of collaborating with private medical data.
Even today, Synthea is a time-honored favorite, both in the US and globally, as healthcare systems around the world strive for safer innovations.
Hazy
Hazy has become an enterprise-scale platform for synthetic data. Where smaller tools focus on fast adoption, Hazy is designed for firms that need synthetic data at scale. Its value is most achieved in financial services, where legacy systems and inflexible compliance regulations make working with sensitive data a never-ending headache.
The beauty of Hazy is that it’s scalable and automated. The platform does something more than creating dummy data—it integrates itself into the entire data lifecycle, and it gives companies a routine way to update and supplement their datasets, all while staying in compliance. In 2025, companies serious about incorporating synthetic data usually end up picking Hazy first.
Final Thoughts
Synthetic data is no longer just a buzzword. In 2025, it’s proving to be a practical, safe, and often superior alternative to using sensitive real-world datasets. The tools we’ve discussed are leading the charge, each in their own way. Choosing the right one depends on what you’re trying to achieve, whether it’s compliance, flexibility, scalability, or fairness.
