By admin 
If there’s one thing that every tech company has learned by now, it’s that real data is messy, sensitive, or – you guessed it – stuck behind approval processes that drag on for what feels like forever. It’s exactly this kind of problem that’s made synthetic data go from “that’s a niche idea!” to something that’s absolutely necessary.
Inching towards 2026, some synthetic data generation platforms have emerged as leaders within this segment. Let’s discuss the top five solutions that will dominate this year.
1. K2view
Let’s begin with the tool that’s really setting the bar.
K2view is the type of platform you can depend on when you need your synthetic data to be perfect from start to finish. It’s not a side feature or an add-on; it’s a full standalone solution that handles the entire synthetic data lifecycle by itself. That means from extracting your original data to sub-setting, to pipelining, to creating your finished datasets—you get to take care of all of that from one system.
What really differentiates K2view is the flexibility of its generation engine. On the AI front, it can subset your training data, conceal any identified PII, prep your data for training an LLM, or even let you clean things up with simple no-code post-processing. And on the rules-based front, it offers its users the ability to create entire sets of data that can be tested for functionality, all without the aid of a developer.
And on top of all that, K2view’s patented entity-based technology ensures your synthetic data keeps its relational integrity. In simple terms: everything stays connected the way it’s supposed to, which is something a lot of other platforms still struggle to get right.
2. Mostly AI
Mostly AI easily comes in as a very solid choice, especially for teams that simply want a smooth, welcoming experience. You can use it to create high-quality, privacy-compliant synthetic data without requiring a strong technical background. This is a huge plus for analysts.
Mostly AI mirrors real-world data to a remarkable extent, provides intrinsic metrics for fidelity, and is quite adept at handling multiple relational datasets. It is cloud-native and API-friendly, making it perfect for simple workflows.
However, one area where Mostly AI starts to feel a bit limited is where you’re working with a more complex or hierarchical data structure. It’s a good tool for most mid-to-large-sized businesses, but its lack of available control and scalability truly can’t be matched by what is offered by the architecture of a product like K2view.
3. YData Fabric
If you’re working with a team that’s composed of lots of ML engineers and data scientists, you’re most likely to hear about YData Fabric. It integrates synthetic data generation, data profiling, and quality checks so you can better understand your data’s effect on your model’s performance.
YData Fabric differentiates itself because of its compatibility with a broad number of data types, such as time-series, which is a type of data that is not always supported by every platform. It also integrates into ML processes, both for no-code and SDK platforms.
The catch with YData is that you would require some expertise in data science to be able to make the most of this tool. Although YData is fantastic for models, it’s not the best for complete compliance, which is a serious drawback for, let’s say a banking company.
4. Gretel Workflows
Gretel continues to shine as a developer-friendly platform geared toward automation. It’s designed for teams that want synthetic data to be woven directly into CI/CD or Dev/Test pipelines. Supporting both structured and unstructured data, it enables automated workflows, and additionally, it also offers incredible flexibility through no-code, low-code, or API-based development.
Gretel is also cloud-based, which can be a strength or a weak point, depending on your environment. And although Gretel is good for a lot of applications, its system isn’t optimal for complex data that requires strong relational integrity.
Overall, Gretel is appreciated for its seamless workflow integration and good API, although its cloud dependency can be a hassle sometimes.
5. Hazy (now a part of SAS Data Maker)
Hazy is known to be a privacy-oriented synthetic data platform, and now, under SAS Data Maker, it is again relevant to industries that have to work under stringent rules, such as banking, fintech, or insurance.
Hazy relies on differential privacy and strong anonymization methods to ensure that the generated dummy data is secure. It supports secured on-premises and cloud deployment, which is a requirement for companies that have strict policies for data governance.
For small-scale teams, Hazy’s setup complexity and costs could be a barrier to entry though. That said, for companies that must pass audits with zero risk tolerance, Hazy is a name that’s earned its place.
Final Thoughts
By 2026, synthetic data will no longer be something that companies experiment with, but it will rather become a vital component of a company’s data strategy. So, go through all the synthetic data solutions we’ve covered above and see which one suits your requirements. If you don’t want to get into the intricacies of the tools, you can go with K2view. It’s an all-rounder which you can’t go wrong with.