Today, synthetic data is no longer a shrewd trick; it is integrated into the way institutions use AI models, develop software, and remain compliant with data protection laws that are becoming more stringent by the day.
Whether you are a fast-growing startup that would like to scale quickly without compromising customer data or an international business that processes or stores thousands of sensitive data sets, synthetic data is your secret weapon.
Let’s explore some of the biggest synthetic data generation tools of this year:
K2view
If there’s one platform that’s set a new benchmark in 2025, it’s K2view. While many products focus on certain parts of the synthetic data lifecycle, K2view delivers an end-to-end, AI-powered, enterprise-grade solution that covers it all.
From data source extraction and subsetting to pipelining and AI-driven processing, it’s a one-stop standalone shop.
When it comes to accessibility, that’s where K2view really shines. Even non-tech testers and QA groups can use its no-code user interface to set up rules, apply logic, and come up with exactly the sort of synthetic data they need for a particular test scenario.
The AI engine doesn’t just generate data—it creates brilliant subsets of training datasets, performs automatic masking of sensitive values, and even manages LLM training with rich, fully context-aware synthetic datasets.
No wonder that K2view is the trusted choice of world-class enterprises. It is also worth noting that Gartner named it a Visionary in the 2024 Data Integration Magic Quadrant and called it one of the most mature and capable synthetic data products in the market today.
Mostly AI
To a large extent, Mostly AI has moved on to become one of the most trusted synthetic data platforms in the world. Now, it’s the go-to preference for organizations seeking data that doesn’t just appear real but also acts like real data.
That implies behaviors, patterns, and correlations are retained such that it can be best used for training machine learning models or testing systems within production-like settings.
Mostly AI is also very easy to use. You don’t need to be a data scientist to get started and onboarding is effortless. The tool caters to structured datasets from industries such as banking, insurance, and health care and thus is easy to integrate into other workflows.
Gretel
Gretel has always been a developer-first platform, and in 2025 it’s embracing that persona more than ever. Its APIs are adaptable, documentation precise, and you can get a model into production in minutes—all of it without needing you to jump through hoops.
Where Gretel impresses the most is on-the-fly production of synthetic data. It handles structured and semi-structured data and produces output that is statistically valid and ready for use immediately.
This makes it an excellent fit for dynamic data systems or CI/CD pipelines where synthetic data generation needs to happen on the go.
YData
Ydata’s strength has always been in ensuring that the synthetic data it produces isn’t only realistic but also machine learning–friendly also. This year, YData is doubling down on products that allow teams to spot gaps in their datasets and synthesize data for them automatically.
This creates more balanced training datasets that result in higher-performing AI models. For businesses that grapple with incomplete or imbalanced datasets, YData is undoubtedly an automated data engineering sidekick.
RAIC Labs
Not all synthetic data comes in rows and columns. In industries like self-driving vehicles, robotics, and medical imaging, the real challenge is visual. RAIC Labs specializes in this space, focusing on synthetic video and image generation, where other tools stop at tabular datasets.
Their methodology combines generative AI and human-in-the-loop feedback, so that the visuals do not look random but realistic, diverse, and consistent with particular use cases.
They recently added 3D scene synthesis and domain adaptation, so we can train AI on data that would otherwise be infeasible, or even unethical, to collect in the real world.
Hazy
Hazy came into 2025 with one big goal: allowing organizations to develop and deploy machine learning models without employing a single record of true personal data. And they’ve remained single-minded about it.
With a solid presence in finance, insurance, and telecom, Hazy provides hyper-realistic synthesized datasets that mathematically hold up and are machine learning ready.
Powered by the deep generative models, Hazy preserves statistical integrity of original data sets and offers an explainability layer on top—a fast-emerging requirement as organizations demand more transparency in AI-driven processes.
The Future Ahead
Synthetic data is more than a specialist tool—it’s an innovation enabler sans the headaches of privacy, security, and compliance. In 2025, you’re not deciding if you will use synthetic data but which of the best-of-breed platforms you will rely on for delivering it.
All of the products we’ve reviewed have their strengths and weaknesses and so it will be helpful to compare them thoroughly before determining the best one for you. One thing’s for sure: in the modern AI era, synthetic data isn’t just part of the game—it’s how you win it.
