Top Synthetic Data Platforms Ranked for 2025

The data game is changing fast. And in 2025, synthetic data has become central to the way corporations train their A.I. models, build software, and keep in compliance with increasingly stringent data protection laws. Whether you’re a startup aiming to scale without sacrificing customer privacy or an enterprise managing thousands of sensitive datasets, synthetic data is the secret weapon you never knew you needed.

Let’s dive into the greatest synthetic data generation tools building the future this year.

K2view

If there’s a platform that truly raised the bar in 2025, it’s K2view. While other products focus on the facets of the synthetic data lifecycle, K2view offers an end-to-end, AI-powered, enterprise-grade solution that covers the entire synthetic data lifecycle.

What makes K2view so powerful is its standalone synthetic data generation offering that can handle the whole spectrum of events from data source extraction and subsetting to pipelining and AI-driven synthetic data processing.

When it comes to usability, K2view takes it up a notch. Testers and quality assurance teams, even those without a technical background, get to use its easy, no-code user interface to define rules, apply logic, and generate the very kind of synthetic data they’re searching for in the test scenarios at hand. The AI-driven engine not only performs data generation but also allows for the intelligent subsetting of the training data, automated masking of sensitive values, and even LLM training scenarios through the use of context-rich synthetic datasets.

K2view is trusted by many global enterprises, and for a very good reason. So much so, that Gartner positioned K2view as a Visionary in the 2024 Data Integration Magic Quadrant for being the most mature and effective synthetic data solution in the market.

Mostly AI

Mostly AI has come a long way from being an up-and-coming player to being one of the most reliable synthetic data platforms in the world. In 2025, they’re in general considered the go-to solution if you want synthetic data that not only appears realistic but also behaves that way. You’re not simply substituting out names and addresses with made-up ones. You’re building out datasets that retain patterns, behaviors, and correlations, and that makes the synthetic data ideal for machine learning models or for testing systems in production-like environments.

Another benefit of Mostly AI is that it is very user-friendly. You do not need to be a data scientist to get started, and the onboarding is smooth. The platform also supports structured datasets across a variety of industries like banking, insurance, and healthcare.

Gretel

Gretel has been mostly promoted as a developer-first platform, and in 2025, that’s truer than ever. It is crafted in a way that seems to be done only by people who actually know how developers function. APIs are flexible, the documentation is descriptive, and you can put a model into production in a matter of minutes—without jumping through hoops.

Gretel excels most in synthetic data generation in real time. It supports both structured and semi-structured data, and the output generated is statistically highly accurate. It is most preferred if you are working with dynamic data systems or if synthetic data generation has to be a part of your CI/CD pipeline.

RAIC Labs

All synthetic data is not about rows and columns. In the majority of industries, including the self-driving, robots, and health imaging sectors, the problem is pictorial. Here is where RAIC Labs excels. Where the majority of products in the synthetic data market handle tabular data, in synthetic video and image generation, the answer is RAIC Labs.

They’ve built a unique edge by combining generative AI with human-in-the-loop feedback systems, so that the generated views are not random concoctions but realistic, varied, and use-case suitable. In 2025, they also developed 3D scene synthesis and domain adaptation that is helping teams to train AI models with data that would not be practical—or even ethical—to capture in the physical world.

Hazy

Hazy entered 2025 with only one mission: to empower corporations to build and release machine learning models without using a single record of real personal data. And they are doing that very well. With a strong emphasis on banking, insurance, and telcos, Hazy offers a platform that not only anonymizes but actually creates hyper-realistic synthetic data that is mathematically accurate and machine learnable.

Hazy is based on deep generative models trained to preserve the statistical content of datasets, and they’ve doubled down on explainability in 2025—something businesses are increasingly demanding.

The Future Ahead

Synthetic data is increasingly becoming critical for firms seeking to innovate without the risk of privacy, security, or compliance breaches. In 2025, the issue isn’t whether you should use synthetic data—it’s what platform you should rely on to get it done right. All the tools we mentioned here come with their own advantages. So, compare them well and make the best decision.

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Adam Regan

Deputy Editor

Features and account management. 3 years media experience. Previously covered features for online and print editions.

Email Adam@MarkMeets.com