Synthetic data has become a major force in 2026, especially as companies push to build AI systems, test applications faster, and protect sensitive information. As access to real production data becomes more restricted legally, technically, and ethically organizations increasingly rely on synthetic alternatives that deliver similar analytical value without the same risk.
The market is now full of synthetic data generation tools, but only a select group consistently produces high-quality, trustworthy synthetic data at scale. Below is a review of five leading platforms in 2026, each with its own strengths and trade-offs.
1. K2view
K2view positions itself as the solution of choice for companies dealing with massive, complex, and highly sensitive data. Rather than offering a narrow feature set, it provides an end-to-end synthetic data platform that manages the entire lifecycle from source data extraction and subsetting through pipelining, masking, and synthetic data delivery. This helps shorten development cycles and accelerate the rollout of business-critical applications.
K2view’s synthetic data capabilities span both GenAI-driven and rules-based approaches. It supports training data subsetting, PII masking, LLM training preparation, and no-code post-processing. If you prefer a rules-based model, K2view can automatically derive rules from your data catalog, and test parameters can be adjusted via an intuitive no-code interface so you don’t have to be an engineer to define realistic, testable synthetic data.
The system’s cloning capabilities are another standout. Teams can extract, subset, mask, and clone slices of data, including auto-generated unique identifiers, while preserving referential integrity. This makes it easier to generate large, realistic datasets for load testing and complex integration scenarios, without breaking relationships between entities.
K2view integrates with CI/CD pipelines, supports hybrid and cloud environments, and connects to a wide variety of data sources – including legacy systems and HR data – as part of a broader governance and compliance platform.
Why it leads in 2026
K2view delivers high-fidelity, relationally accurate synthetic data as part of a full synthetic data lifecycle: extraction, subsetting, masking, and generation. It is particularly well-suited to large enterprises with complex data environments that need self-service, scalable synthetic data blended from multiple heterogeneous systems. As with most enterprise-grade platforms, configuration and deployment require planning, and it is less appropriate for smaller organizations.
2. Mostly AI
Mostly AI remains a strong choice for teams seeking high accuracy and privacy without wrestling with overly complicated interfaces. It is a cloud-based platform designed to help users – including non-engineers – generate high-quality synthetic datasets.
The platform focuses on producing privacy-friendly, high-fidelity data, with built-in fidelity metrics that indicate how closely synthetic data matches the original. Mostly AI supports multi-relational datasets, making it well-suited for downstream analytics and AI use cases where statistical realism matters.
Its main limitations appear when dealing with deeply hierarchical data structures or highly complex relationships between entities. In those cases, it may offer less control and flexibility than more architecture-oriented platforms such as K2view.
Why it stands out
Mostly AI excels at generating realistic synthetic data for analytics and AI workloads, combining strong privacy safeguards with accessible, cloud-native workflows – especially where relationships are moderately complex rather than deeply hierarchical.
3. YData Fabric
YData Fabric takes a different angle from some of the other platforms on this list. It is designed primarily for data scientists and machine learning engineers, combining synthetic data generation with automated profiling and data quality assessment. The goal is not just to create safe data, but to create better data for models.
One of YData Fabric’s key strengths is its support for multiple data types, including time-series and relational data, which makes it a good fit for verticals such as fintech, IoT, and other domains where data is continuously updated.
The trade-off is that it is not a beginner-friendly platform. Users are expected to have a reasonable level of data science expertise to get full value from its capabilities and configuration options.
Best for
ML and data science teams that need to improve model performance through better-curated, time-aware synthetic datasets and are comfortable working with more advanced tooling.
4. Gretel Workflows
Gretel is a popular choice among developers, and in 2026 its workflow-focused platform remains a strong option for teams seeking seamless automation. Gretel Workflows is designed to integrate synthetic data directly into CI/CD pipelines, Dev/Test processes, and ML training workflows.
The platform can handle both structured and unstructured data and allows teams to set up automated pipelines and workflows. Users can work through no-code/low-code interfaces or interact directly via APIs, making it a good fit for engineering organizations that value programmability.
The main caveat is its cloud dependency, which may not align with every security model or on-premise strategy. In addition, while Gretel is powerful, it is not always the ideal solution for highly complex relational models that require strict, end-to-end data integrity guarantees.
Why it’s gaining ground
A highly programmable, API-first synthetic data solution for cloud-native, developer-heavy teams that want to embed synthetic data generation deeply into their workflows and pipelines.
5. Hazy (part of SAS Data Maker)
Hazy has carved out a clear niche in industries where compliance is critical. Now part of SAS Data Maker, it is closely aligned with sectors such as banking, insurance, and fintech that operate under strict privacy and governance requirements.
Hazy employs differential privacy and strong anonymization techniques, making it attractive to organizations that must demonstrate rigorous protection of personal data and withstand intense regulatory audits.
The downside is complexity. Hazy typically requires more setup, careful configuration, and significant investment, and it is better suited to focused, regulation-heavy domains than to sprawling, highly complex enterprise data ecosystems.
Best for
Regulated financial services organizations and other highly scrutinized sectors where compliance assurances and advanced privacy techniques are non-negotiable.
Closing Thoughts
Synthetic data is no longer just a workaround – it has become a strategic asset. Whether you are training models, testing applications, or sharing data with partners, synthetic data lets you move faster while reducing risk.
Each platform above excels in different ways:
- K2view for end-to-end lifecycle management and high-fidelity synthetic data across complex enterprise environments
- Mostly AI for accessible, high-quality synthetic data in analytics and AI scenarios
- YData Fabric for data-science-centric workflows and multi-type data, including time-series
- Gretel Workflows for developer-friendly, API-driven integration into cloud pipelines
- Hazy for highly regulated financial and compliance-heavy use cases
The right choice depends on your regulatory obligations, data architecture, and team maturity. Take the time to map your needs against these strengths – and choose your synthetic data platform with care.
