As organizations collect more data than ever before, the pressure to protect user privacy has reached an all-time high. From healthcare records to financial transactions and customer behavior logs, sensitive information now sits at the core of innovation—yet sharing or using it unsafely can lead to regulatory fines and loss of trust. This is where synthetic data generation tools step in, offering a way to create realistic, statistically accurate datasets without exposing real individuals.
TLDR: Synthetic data tools like Mostly AI help organizations generate artificial datasets that mirror real-world data without compromising privacy. These platforms are ideal for training AI models, testing applications, and sharing data securely. In this article, we explore six powerful alternatives to Mostly AI, along with their key features and ideal use cases. A comparison chart is included to help you choose the right solution for your needs.
Synthetic data is artificially generated information that maintains the statistical properties and patterns of real datasets but contains no direct ties to actual individuals. With growing regulations like GDPR and HIPAA, businesses increasingly rely on synthetic data to balance innovation and compliance.
Why Synthetic Data Matters
Before diving into the tools, it’s important to understand why synthetic data is becoming indispensable:
- Privacy Compliance: Reduces exposure of personally identifiable information (PII).
- Safer Data Sharing: Enables collaboration without risking sensitive data.
- Faster AI Development: Generates additional samples to improve model training.
- Bias Control: Allows developers to balance datasets intentionally.
- Cost Efficiency: Reduces dependency on expensive or limited real-world datasets.
Now let’s explore six leading synthetic data platforms like Mostly AI that are helping organizations innovate responsibly.
1. Synthesia
Synthesia specializes in generating high-quality synthetic data for structured datasets, especially in finance and insurance. It focuses on replicating complex tabular data with high fidelity while embedding privacy-preserving safeguards.
Key features:
- High-fidelity structured data generation
- Built-in privacy risk assessment tools
- Support for large enterprise deployments
- Advanced statistical validation reports
Best for: Enterprises that need synthetic financial, insurance, or customer datasets with strong governance requirements.
2. Gretel AI
Gretel AI is a developer-friendly platform for generating synthetic structured and text data. It is designed with APIs that make integration into machine learning workflows seamless.
One of Gretel’s standout capabilities is privacy engineering controls, allowing teams to measure and tune the balance between realism and anonymity.
Key features:
- APIs for structured and unstructured data
- Privacy tuning controls
- Cloud-native deployment
- Data labeling and transformation tools
Best for: AI teams and developers building pipelines that require synthetic text, logs, and tabular data.
3. Tonic.ai
Tonic.ai focuses heavily on synthetic data for software development and testing. Instead of simply masking data, it generates entirely new datasets that maintain real-world characteristics.
Key features:
- Developer-first interface
- Automated schema mapping
- Realistic data relationships preservation
- Integration with CI/CD pipelines
Best for: Engineering teams who need safe production-like data for staging and QA environments.
4. Hazy
Hazy is designed with financial institutions and highly regulated industries in mind. It uses advanced generative models to ensure synthetic datasets preserve behavioural patterns while reducing disclosure risk.
Hazy is particularly known for maintaining high data utility—meaning models trained on synthetic data often perform similarly to those trained on real data.
Key features:
- Strong privacy metrics reporting
- Utility benchmarking
- On-premise deployment options
- Scalable for enterprise workloads
Best for: Banks, fintech firms, and healthcare providers operating under strict regulatory oversight.
5. MDClone
MDClone specializes in healthcare and clinical data. Unlike many generalized synthetic data tools, MDClone was built specifically to handle complex medical records while preserving clinical accuracy.
This makes it especially valuable for hospitals and life sciences researchers who need to collaborate across departments or institutions without exposing patient data.
Key features:
- Healthcare-optimized synthetic generation
- Self-service data exploration tools
- Comprehensive compliance support
- Scalable cross-institutional collaboration
Best for: Hospitals, research institutions, and pharmaceutical companies.
6. Synthea + Synthetic Data Vault (SDV)
Synthea and SDV (Synthetic Data Vault) represent powerful open-source options for teams that want flexibility and customization.
Synthea generates realistic synthetic health records, while SDV offers a broader toolkit capable of modeling diverse structured datasets. While these solutions require more technical expertise than commercial platforms, they provide extensive control.
Key features:
- Open-source flexibility
- Customizable generative models
- Community support
- No licensing fees
Best for: Data scientists and researchers with strong technical backgrounds.
Comparison Chart
| Tool | Primary Focus | Best For | Deployment Options | Ease of Use |
|---|---|---|---|---|
| Mostly AI | Structured enterprise data | Compliance driven organizations | Cloud and on premise | High |
| Synthesia | Financial data | Insurance and banking | Enterprise cloud | Medium |
| Gretel AI | Structured and text data | ML developers | Cloud native | High |
| Tonic.ai | Software testing data | Engineering teams | Cloud and integration pipelines | High |
| Hazy | Regulated industries | Finance and healthcare | Cloud and on premise | Medium |
| MDClone | Healthcare datasets | Clinical research | Enterprise deployment | Medium |
| Synthea + SDV | Open source modeling | Researchers and data scientists | Self hosted | Technical |
What to Look for in a Synthetic Data Tool
Choosing the right tool depends heavily on your goals. Here are several factors to consider:
- Data Type Support: Does the tool handle structured, unstructured, time-series, or relational data?
- Privacy Guarantees: Are disclosure risk assessments included?
- Data Utility: How well does the synthetic data perform compared to real datasets?
- Deployment Flexibility: Cloud, hybrid, or on-premise?
- Ease of Integration: Does it fit into your existing pipeline?
The Future of Privacy-Safe Data
Synthetic data generation is rapidly evolving thanks to breakthroughs in generative models, including advanced neural networks and diffusion-based systems. As these techniques improve, the gap between real and synthetic data performance continues to shrink.
In the near future, we can expect:
- Greater automation in privacy evaluation
- Industry-specific synthetic data models
- Integration with federated learning systems
- Improved explainability metrics
What makes tools like Mostly AI and its alternatives so compelling is their ability to unlock innovation safely. Organizations no longer need to choose between compliance and progress—they can achieve both.
Final Thoughts
Synthetic data is no longer a niche concept reserved for academic research. It has become a practical solution for enterprises seeking privacy-safe innovation across finance, healthcare, software development, and beyond.
Whether you need high-fidelity financial modeling, secure clinical datasets, AI-ready text data, or scalable developer testing environments, there is now a mature ecosystem of synthetic data generation platforms ready to assist.
By carefully evaluating your data types, regulatory requirements, and technical capacity, you can select the solution that best fits your organization’s needs—and move forward confidently into a privacy-first future powered by artificial intelligence.