# Synthetic Data Services: The 2025 Guide to Faster, Fairer, and Safer AI Synthetic data services use advanced AI techniques to generate artificial datasets that maintain the statistical properties of real data while eliminating privacy risks. These platforms enable organizations to accelerate AI development, ensure compliance, and unlock insights without exposing sensitive information.

Feature	Option/Tool	Pros	Cons	Price	Best for
Comprehensive Platform	Gretel	Multi-data type support, Navigator AI system, developer-friendly	Higher learning curve for complex features	$1,500/month (Team)	Data scientists and ML teams
Testing Focus	Tonic.ai	Database replication, fast provisioning, RAG testing	Limited to testing use cases	Free tier available	Software development and QA
Regulated Industries	Mostly AI	Strong compliance features, bias reduction, enterprise focus	Higher cost, complex pricing	$3,161/month (Team)	Banking, insurance, healthcare
European Compliance	Syntho	GDPR-native, data residency options	Limited US market presence	$1,363/month (Pro)	EU-based organizations
Computer Vision	Datagen	Specialized image/video generation, industry-specific models	Limited to visual data only	—	Autonomous vehicles, retail AI
Open Source	SDV (Synthetic Data Vault)	Free, customizable, active community	Requires technical expertise, no support	Free	Technical teams with development resources
Privacy-First	Differential Privacy Tools	Mathematical privacy guarantees	May reduce data utility	Varies	High-privacy requirements

The synthetic data platform landscape offers solutions for every use case and budget. Gretel leads in versatility and innovation, Tonic.ai excels for testing environments, and Mostly AI dominates regulated industries. Organizations should match platform capabilities to their specific data types, privacy requirements, and integration needs rather than choosing based on price alone. ## What Are Synthetic Data Services? ### From Data Scarcity to Data Abundance Modern enterprises face a critical paradox: they need vast amounts of data to power AI initiatives, yet accessing real data is increasingly constrained by privacy regulations, compliance reviews, and security concerns. According to Forrester's 2024 State of Data Strategy report, 60% of data and analytics leaders cite data access and integration challenges as the primary barrier to generating value from AI initiatives. Synthetic data services solve this challenge by generating artificial datasets that preserve the statistical relationships and patterns of original data while containing no actual personal information. Unlike traditional data anonymization techniques that modify real records, synthetic data creates entirely new records that never existed but behave statistically like the original dataset. The global synthetic data generation market demonstrates this growing need, valued at $327.5 million in 2024 and projected to reach $2.1 billion by 2030, representing a compound annual growth rate (CAGR) of 36.5% (MarketsandMarkets, February 2025). ### How It Works: A Quick Look at GANs and VAEs Synthetic data generation relies on several core machine learning techniques: **Generative Adversarial Networks (GANs)** use two competing neural networks—a generator that creates synthetic data and a discriminator that attempts to distinguish real from synthetic records. Through this adversarial training process, the generator learns to create increasingly realistic synthetic data. **Variational Autoencoders (VAEs)** compress real data into a lower-dimensional representation, then generate new data points by sampling from this learned distribution. VAEs excel at capturing complex data relationships while providing mathematical guarantees about data privacy. **Diffusion Models**, adapted from image generation in 2024, show promise for tabular data by gradually adding and removing noise to learn data distributions. These models demonstrate superior performance in capturing non-linear correlations compared to traditional GAN-based methods (arXiv, 2024) (Needs 2025 verify). ### Real Data vs. Synthetic Data: A Head-to-Head Comparison | Factor | Real Data | Synthetic Data | |--------|-----------|----------------| | **Cost** | High acquisition, labeling, and storage costs | Lower ongoing costs after initial setup | | **Privacy Risk** | Contains actual personal information | Zero personal information exposure | | **Bias** | Reflects historical biases in collection | Can be rebalanced to reduce bias | | **Availability** | Limited by privacy reviews and approvals | Instantly available on-demand | | **Scalability** | Constrained by real-world data collection | Unlimited generation capacity | **Key Takeaways:** - Synthetic data eliminates the privacy risks inherent in real data while maintaining statistical utility - Organizations can generate unlimited amounts of data on-demand without waiting for privacy approvals - Modern techniques like GANs and VAEs create high-fidelity synthetic datasets suitable for production AI systems - The synthetic data market is experiencing explosive growth, driven by increasing data privacy requirements ## Why Use Synthetic Data? Top 5 Business Drivers in 2025 ### 1. Accelerate AI and ML Development Cycles The most compelling business case for synthetic data is speed. Traditional ML projects face significant delays waiting for data access approvals, privacy reviews, and anonymization processes. An unnamed Canadian bank reduced their fraud detection model development lifecycle from 18 months to 9 months—a 50% acceleration—by eliminating delays associated with accessing sensitive transaction data (Gartner, 2023) (Needs 2025 verify). A global payment provider using Tonic.ai's platform reduced the time required to create production-like test databases from over a week to under an hour, representing a 99% reduction in provisioning time (Tonic.ai, 2024). This acceleration enables data science teams to iterate faster, test more hypotheses, and bring AI solutions to market significantly quicker. Gartner predicts that by 2025, 20% of all test data for consumer-facing use cases will be synthetically generated, reflecting this shift toward speed and agility (Gartner, October 2024). ### 2. De-Risk Innovation with Bulletproof Privacy With the average cost of a US data breach reaching $9.95 million in 2025—the highest globally—and an average cost of $205 per compromised record (IBM Security and Ponemon Institute, July 2025), synthetic data represents a powerful risk mitigation strategy. Properly generated synthetic data contains zero personal information, making it exempt from most privacy regulations. Under HIPAA's de-identification requirements, synthetic data can satisfy both Expert Determination and Safe Harbor methods by generating data that contains none of the 18 specific identifiers required for de-identification. The California Privacy Rights Act (CPRA), effective since January 2023, defines personal information in ways that exclude properly generated synthetic data, providing legal clarity for California-based operations. ### 3. Combat Algorithmic Bias and Improve Fairness Synthetic data provides unprecedented control over dataset composition, enabling organizations to proactively address algorithmic bias. In a 2024 fairness benchmark, Gretel's conditional data generation improved the F1 score for minority classes from 0.53 to 0.72—a 35% improvement—while simultaneously enhancing fairness metrics (Gretel AI, May 2024). This capability is increasingly critical as the Federal Trade Commission (FTC) has stated its authority to take enforcement action against companies for unfair or deceptive practices related to AI, including biased algorithms that result in discrimination (FTC, May 2023). Synthetic data enables organizations to create balanced datasets that promote fair outcomes across demographic groups. ### 4. Supercharge Software Testing and QA Traditional software testing relies on sampled production data, which often lacks edge cases and rare scenarios critical for robust applications. A Fortune 500 financial institution increased software testing coverage for edge cases by 25% using synthetically generated data representing rare but critical transaction scenarios not present in their production samples (Hazy, 2024). Synthetic data generators can create specific edge cases, stress test scenarios, and boundary conditions that rarely occur in real-world data but are essential for comprehensive testing. This capability is particularly valuable for financial services, healthcare, and other high-stakes applications where edge case handling is critical. ### 5. Securely Share Data Internally and Externally Synthetic data enables secure data sharing across organizational boundaries without privacy concerns. Teams can share datasets with external partners, vendors, or research institutions without legal reviews or data sharing agreements. Internal teams can access production-like data without security clearances or access controls. In healthcare research, synthetic patient data reduced data provisioning time from 6 months (due to privacy reviews and ethics board approvals) to under one week, enabling researchers to begin model training significantly faster (MDClone, 2024). **Key Takeaways:** - Synthetic data can reduce ML development cycles by 30-50% by eliminating data access delays - The cost of synthetic data platforms is often lower than the potential cost of a single data breach - Organizations can proactively address algorithmic bias through balanced synthetic dataset generation - Edge case coverage for software testing improves by 25% or more with synthetic data - Data sharing becomes frictionless when using privacy-safe synthetic datasets ## How to Implement a Synthetic Data Strategy: A 4-Step Workflow ### Step 1: Identify the High-Value Use Case Begin with use cases where synthetic data provides the highest return on investment. Priority areas include: **AI/ML Development**: Projects blocked by data access restrictions or privacy concerns **Software Testing**: Applications requiring comprehensive edge case coverage **Data Sharing**: Scenarios requiring external collaboration or vendor access **Compliance Testing**: Environments needing production-like data without regulatory risk Evaluate each use case based on current data access delays, privacy risks, and potential business impact. Start with projects where data access is the primary bottleneck rather than data quality or model architecture challenges. ### Step 2: Select the Right Generation Tool and Technique Different synthetic data techniques excel with different data types: **Tabular Data**: GANs and VAEs work well for structured datasets with complex relationships. Mostly AI specializes in structured data for banking and insurance, while Gretel offers broad tabular data support. **Time-Series Data**: Specialized techniques handle temporal dependencies. Gretel and Syntho both offer time-series capabilities with their enterprise plans. **Text Data**: Large language models adapted for synthetic generation. Gretel provides natural language synthetic data capabilities. **Image/Video Data**: Diffusion models and specialized GANs. Datagen focuses specifically on computer vision applications. Consider pricing models carefully. Gretel's Team plan starts at $1,500/month (August 2025), Mostly AI's Team Edition begins at approximately $3,161 USD/month, and Syntho's Pro plan starts at approximately $1,363 USD/month when billed annually. ### Step 3: Generate, Evaluate, and Iterate for Quality Synthetic data quality requires rigorous evaluation across multiple dimensions: **Statistical Fidelity**: Measure how well synthetic data preserves the statistical properties of the original dataset using metrics like Kolmogorov-Smirnov tests and correlation preservation. **Privacy Protection**: Verify that synthetic data doesn't memorize or leak information from the training set through privacy auditing techniques. **Utility Preservation**: Test whether models trained on synthetic data perform comparably to those trained on real data for your specific use case. **Bias Assessment**: Evaluate whether synthetic data maintains or reduces biases present in the original dataset. Gretel introduced Gretel Navigator in March 2024, a compound AI system that automates the selection of optimal models and privacy protections for specific datasets, reducing the complexity of this evaluation process. ### Step 4: Integrate into Your MLOps and CI/CD Pipelines Successful synthetic data adoption requires integration with existing workflows: **MLOps Integration**: Incorporate synthetic data generation into model training pipelines, enabling automatic dataset refresh and bias monitoring. **CI/CD Integration**: Automate synthetic test data generation for software testing environments, ensuring consistent and comprehensive test coverage. **Data Governance**: Establish policies for synthetic data usage, quality thresholds, and approval processes that align with existing data governance frameworks. **Monitoring and Alerting**: Implement monitoring for synthetic data quality drift and privacy protection effectiveness. Forrester projects that by 2027, synthetic data will be a standard component of over 50% of enterprise MLOps pipelines (Forrester, December 2024). **Key Takeaways:** - Start with high-impact use cases where data access is the primary bottleneck - Match generation techniques to data types and specific business requirements - Implement rigorous quality evaluation covering statistical fidelity, privacy, and utility - Integrate synthetic data generation into existing MLOps and CI/CD workflows for maximum impact ## Key Risks and Mitigation Strategies ### The Fidelity Challenge: Ensuring Real-World Accuracy Synthetic data must maintain sufficient fidelity to real data while avoiding exact replication. Poor fidelity can lead to models that fail in production when encountering real-world data patterns not captured in the synthetic training set. **Risk**: Models trained on low-fidelity synthetic data may exhibit degraded performance, poor generalization, or unexpected behavior when deployed. **Mitigation**: Implement comprehensive quality assessment frameworks that measure statistical similarity, correlation preservation, and downstream model performance. Use holdout real data for final model validation before production deployment. Establish minimum fidelity thresholds based on your specific use case requirements. ### Avoiding Model Collapse and Memorization Generative models can suffer from mode collapse (generating limited data variety) or memorization (reproducing exact training examples, compromising privacy). **Risk**: Collapsed models produce synthetic data lacking diversity, while memorizing models leak sensitive information from the training set. **Mitigation**: Use differential privacy techniques to add mathematical privacy guarantees. Implement privacy auditing tools to detect potential memorization. Monitor generated data for diversity and coverage of the original data distribution. Consider ensemble approaches that combine multiple generation techniques. ### Navigating the US Regulatory Landscape (NIST & FTC) The NIST AI Risk Management Framework (AI RMF 1.0), published in January 2023, emphasizes data quality, integrity, and provenance in its MAP and MEASURE functions. Organizations must demonstrate that synthetic data meets these requirements for trustworthy AI systems. **Risk**: Regulatory bodies may question the validity of AI systems trained primarily on synthetic data, particularly in high-stakes applications like healthcare or finance. **Mitigation**: Document synthetic data generation processes thoroughly, including quality metrics and validation procedures. Maintain clear provenance records showing the relationship between synthetic and original data. Implement the NIST AI RMF guidelines for data governance and risk management. Consider hybrid approaches that combine synthetic and real data for critical applications. The FTC's increased scrutiny on AI bias and discrimination makes synthetic data both an opportunity and a responsibility. While synthetic data can help reduce bias, organizations must ensure their generation processes don't inadvertently amplify existing biases or create new ones. ### When NOT to Use Synthetic Data Synthetic data isn't appropriate for all use cases: **Insufficient Training Data**: Synthetic data quality depends on having adequate real data for training generation models. With very small datasets, synthetic generation may not be feasible. **Highly Regulated Domains**: Some regulatory frameworks may not yet accept synthetic data for compliance purposes. Healthcare clinical trials, financial stress testing, and safety-critical systems may require real data. **Novel Pattern Discovery**: If your goal is discovering entirely new patterns or relationships not present in historical data, synthetic data generated from that historical data won't help. **Real-Time Streaming**: Current synthetic data generation techniques work best with batch data rather than real-time streaming scenarios. **Key Takeaways:** - Implement comprehensive quality assessment to ensure synthetic data maintains real-world accuracy - Use differential privacy and auditing tools to prevent model memorization and privacy leaks - Document generation processes thoroughly to meet NIST AI RMF requirements - Recognize that synthetic data isn't suitable for all use cases, particularly those requiring novel pattern discovery ## Evaluating Top Synthetic Data Platforms in 2025 ### Key Features to Look For When evaluating synthetic data platforms, prioritize these critical capabilities: **Data Type Support**: Ensure the platform handles your specific data types (tabular, time-series, text, images). Most enterprise use cases involve tabular data, but multimedia applications require specialized capabilities. **Privacy Guarantees**: Look for mathematical privacy protections like differential privacy, not just claims about "privacy-safe" data. Platforms should provide configurable privacy budgets

Synthetic Data Services: 2025 Guide to Faster AI

Ready to transform your business?