# Best Synthetic Data Tools

  *By [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)*

   Synthetic data software generates artificial datasets, including images, text, and structured data, based on original data, preserving the mathematical characteristics and statistical relationships of the source while protecting privacy-sensitive information, enabling data scientists and ML engineers to build datasets for testing, model training, and simulation.

### Core Capabilities of Synthetic Data Software

To qualify for inclusion in the Synthetic Data category, a product must:

- Generate synthetic data such as images and structured data
- Convert privacy-sensitive data into a fully anonymous dataset while maintaining granularity
- Work out of the box, ensuring the generative model can automatically generate data without being explicitly programmed to do so

### Common Use Cases for Synthetic Data Software

Data scientists, ML engineers, and researchers use synthetic data platforms to overcome data shortages and privacy constraints in AI development. Common use cases include:

- Generating training datasets for [machine learning](https://www.g2.com/categories/machine-learning) models when real-world data is scarce, sensitive, or unavailable
- Testing and validating algorithms in simulated environments that replicate real-world conditions
- Reducing algorithmic bias by supplementing or rebalancing original datasets with synthetic examples

### How Synthetic Data Software Differs from Other Tools

Synthetic data software differs from [data masking software](https://www.g2.com/categories/data-masking), which protects private information by obscuring existing data but does not generate artificial datasets or support large-scale dataset creation. Synthetic data platforms can create entirely new data from scratch using methods such as generative neural networks ([GAN](https://www.g2.com/glossary/gan-definition)s) and CGI, enabling broader use cases in model training and simulation that data masking cannot address. Some synthetic data tools also relate to the [synthetic media](https://www.g2.com/categories/synthetic-media) category but are specifically focused on structured and unstructured datasets rather than media production.

### Insights from G2 on Synthetic Data Software

Based on category trends on G2, data privacy compliance and the ability to generate realistic training datasets at scale stand out as standout capabilities. Accelerated model development timelines and reduced dependency on sensitive real-world data stand out as primary outcomes of adoption.





## Best Synthetic Data Tools At A Glance

- **Leader:** [IBM watsonx.ai](https://www.g2.com/products/ibm-watsonx-ai/reviews)
- **Highest Performer:** [Tumult Analytics](https://www.g2.com/products/tumult-analytics/reviews)
- **Top Trending:** [IBM watsonx.ai](https://www.g2.com/products/ibm-watsonx-ai/reviews)
- **Best Free Software:** [Tonic.ai](https://www.g2.com/products/tonic-ai/reviews)


## Top-Rated Products (Ranked by G2 Score)
  ### 1. [IBM watsonx.ai](https://www.g2.com/products/ibm-watsonx-ai/reviews)
  Watsonx.ai is part of the IBM watsonx platform that brings together new generative AI capabilities, powered by foundation models and traditional machine learning into a powerful studio spanning the AI lifecycle. With watsonx.ai, you can build, train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with ease and build AI applications in a fraction of the time with a fraction of the data.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 133


**Seller Details:**

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Company Website:** https://www.ibm.com/us-en
- **Year Founded:** 1911
- **HQ Location:** Armonk, NY
- **Twitter:** @IBM (708,000 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Consultant
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 41% Small-Business, 31% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (76 reviews)
- Model Variety (31 reviews)
- Features (29 reviews)
- AI Integration (28 reviews)
- AI Capabilities (23 reviews)

**Cons:**

- Difficult Learning (21 reviews)
- Complexity (20 reviews)
- Learning Curve (19 reviews)
- Expensive (17 reviews)
- Improvement Needed (16 reviews)

  ### 2. [Tumult Analytics](https://www.g2.com/products/tumult-analytics/reviews)
  Tumult Analytics is an advanced, open-source Python library designed to facilitate the deployment of differential privacy in data analysis. It enables organizations to generate statistical summaries from sensitive datasets while ensuring individual privacy is maintained. Trusted by institutions such as the U.S. Census Bureau, the Wikimedia Foundation, and the Internal Revenue Service, Tumult Analytics offers a robust and scalable solution for privacy-preserving data analysis. Key Features and Functionality: - Robust and Production-Ready: Developed and maintained by a team of differential privacy experts, Tumult Analytics is built for production environments and has been implemented by major institutions. - Scalable: Operating on Apache Spark, it efficiently processes datasets containing billions of rows, making it suitable for large-scale data analysis tasks. - User-Friendly APIs: The platform provides Python APIs that are familiar to users of Pandas and PySpark, facilitating easy adoption and integration into existing workflows. - Comprehensive Functionality: It supports a wide array of aggregation functions, data transformation operators, and privacy definitions, allowing for flexible and powerful data analysis under multiple privacy models. Primary Value and Problem Solved: Tumult Analytics addresses the critical challenge of extracting valuable insights from sensitive data without compromising individual privacy. By implementing differential privacy, it ensures that the risk of re-identification is minimized, enabling organizations to share and analyze data responsibly. This capability is particularly vital for sectors handling sensitive information, such as public institutions, healthcare, and finance, where maintaining data privacy is both a regulatory requirement and an ethical obligation.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 38


**Seller Details:**

- **Seller:** [Tumult Labs, Inc.](https://www.g2.com/sellers/tumult-labs-inc)
- **Year Founded:** 2019
- **HQ Location:** Durham
- **LinkedIn® Page:** https://www.linkedin.com/company/tmltlabs (3 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 50% Small-Business, 32% Mid-Market


  ### 3. [YData](https://www.g2.com/products/ydata/reviews)
  YData helps data science teams build better datasets for AI


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 12


**Seller Details:**

- **Seller:** [YData](https://www.g2.com/sellers/ydata)
- **Year Founded:** 2019
- **HQ Location:** Seattle, WA
- **Twitter:** @YData_ai (687 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/ydataai (38 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 67% Mid-Market, 25% Small-Business


  ### 4. [Tonic.ai](https://www.g2.com/products/tonic-ai/reviews)
  Tonic.ai frees developers to build with safe, high-fidelity synthetic data to accelerate software and AI innovation while protecting data privacy. Through industry-leading solutions for data synthesis, de-identification, and subsetting, our products enable on-demand access to realistic structured, semi-structured, and unstructured data for software development, testing, and AI model training. The product suite includes: - Tonic Fabricate for AI-powered synthetic data from scratch - Tonic Structural for modern test data management - Tonic Textual for unstructured data redaction and synthesis. Unblock innovation, eliminate collisions in testing, accelerate your engineering velocity, and ship better products, all while safeguarding data privacy. Founded in 2018, with offices in San Francisco, Atlanta, New York, and London, the company is pioneering enterprise tools for data synthesis and de-identification in pursuit of its mission to unblock innovation with usable data. Thousands of developers use data generated with the Tonic.ai platform on a daily basis to build products and train models faster in industries as wide ranging as healthcare, financial services, insurance, logistics, edtech, and e-commerce. Working with customers like Comcast, eBay, UnitedHealthcare, and Fidelity Investments, Tonic.ai builds developer solutions to advance its goals of advocating for the privacy of individuals while enabling companies to do their best work. Be free to build with high-fidelity synthetic data for software and AI development.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 38


**Seller Details:**

- **Seller:** [Tonic.ai](https://www.g2.com/sellers/tonic-ai)
- **Company Website:** https://www.tonic.ai/
- **Year Founded:** 2018
- **HQ Location:** San Francisco, California
- **Twitter:** @tonicfakedata (699 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/18621512 (100 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Financial Services
  - **Company Size:** 45% Mid-Market, 32% Small-Business


  ### 5. [Gretel.ai](https://www.g2.com/products/gretel-ai/reviews)
  Our mission is to enable developers to safely and quickly experiment, collaborate, and build with data.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 13


**Seller Details:**

- **Seller:** [Gretel.ai](https://www.g2.com/sellers/gretel-ai)
- **Year Founded:** 2020
- **HQ Location:** Palo Alto, US
- **LinkedIn® Page:** https://www.linkedin.com/company/51732380 (37 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 77% Mid-Market, 23% Small-Business


  ### 6. [KopiKat](https://www.g2.com/products/kopikat/reviews)
  KopiKat's Sportforma is a comprehensive dataset designed to enhance the development and evaluation of computer vision models in sports analytics. It offers a diverse collection of high-quality images and videos capturing various sports scenarios, enabling researchers and developers to train and test algorithms for tasks such as player detection, action recognition, and event classification. Key Features and Functionality: - Diverse Sports Coverage: Includes a wide range of sports, providing a broad spectrum of scenarios for model training. - High-Quality Visual Data: Offers high-resolution images and videos to ensure detailed analysis and accurate model development. - Annotated Data: Comes with comprehensive annotations, facilitating supervised learning and precise evaluation of models. - Scalable Dataset: Suitable for both small-scale experiments and large-scale model training, accommodating various research needs. Primary Value and User Solutions: Sportforma addresses the challenge of obtaining diverse and annotated sports data for computer vision applications. By providing a rich dataset, it enables users to develop robust models capable of understanding and interpreting complex sports scenes. This is particularly beneficial for applications in sports analytics, performance monitoring, and automated content generation, where accurate visual analysis is crucial.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 13


**Seller Details:**

- **Seller:** [OpenCV.ai](https://www.g2.com/sellers/opencv-ai)
- **Year Founded:** 2023
- **HQ Location:** Palo Alto, US
- **LinkedIn® Page:** http://www.linkedin.com/company/opencv-ai (14 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 69% Small-Business, 23% Mid-Market


  ### 7. [CA Test Data Manager](https://www.g2.com/products/ca-test-data-manager/reviews)
  CA Test Data Manager uniquely combines elements of data subsetting, masking, synthetic, cloning and on-demand data generation to enable testing teams to meet the agile testing needs of their organization. This solution automates one of the most time-consuming and resource-intensive problems in Continuous Delivery: the creating, maintaining and provisioning of the test data needed to rigorously test evolving applications.


  **Average Rating:** 4.0/5.0
  **Total Reviews:** 21


**Seller Details:**

- **Seller:** [Broadcom](https://www.g2.com/sellers/broadcom-ab3091cd-4724-46a8-ac89-219d6bc8e166)
- **Year Founded:** 1991
- **HQ Location:** San Jose, CA
- **Twitter:** @broadcom (62,960 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/broadcom/ (55,707 employees on LinkedIn®)
- **Ownership:** NASDAQ: CA

**Reviewer Demographics:**
  - **Top Industries:** Banking, Accounting
  - **Company Size:** 48% Small-Business, 33% Enterprise


  ### 8. [Syntheticus.ai | Synthetic Data Generator](https://www.g2.com/products/syntheticus-ai-synthetic-data-generator/reviews)
  Syntheticus® is a technology company founded in 2021 and headquartered in Zürich, Switzerland. We are at the forefront of innovation and research in Privacy-Enhancing Technologies, working in collaboration with leading Swiss academic institutions. Backed by prominent investors, we are dedicated to empowering responsible business growth and promoting transparency, trust, and innovation in the data economy. Our vision centers around creating a new era of data exchange that benefits everyone. We believe in data transparency, inclusivity, and accessibility, while maintaining a strong commitment to data privacy and security. With the Syntheticus® platform, we are leading the charge in revolutionizing how businesses utilize and share data in a privacy-preserving way. The Syntheticus® platform seamlessly bridges the gap between data-driven insights and data availability, providing effortless access to high-quality synthetic datasets. Powered by cutting-edge Privacy-Enhancing Technologies, we prioritize data privacy, security, and compliance, ensuring responsible data usage. Trust in the accuracy and quality of the generated datasets with real-time validation tools and features. Safeguard sensitive information and personally identifiable data while leveraging safe, realistic alternatives to enhance privacy and mitigate compliance risks. Designed for seamless integration into sensitive work environments, our platform supports various data types, including structured tabular data, relational databases, geospatial data, time series, open text data, and more. You can also choose from Cloud, On-Premises, or EDGE infrastructure options, catering to your specific data management needs. As a proud member of the "Swiss Made Software" Label, our enterprise-ready framework is hosted on secure Google Cloud servers, providing robust data protection and reliability.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 10


**Seller Details:**

- **Seller:** [Syntheticus Ltd.](https://www.g2.com/sellers/syntheticus-ltd)
- **Year Founded:** 2021
- **HQ Location:** Zurich, CH
- **LinkedIn® Page:** https://www.linkedin.com/company/syntheticus/ (5 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 60% Small-Business, 30% Mid-Market


  ### 9. [Synthesis AI](https://www.g2.com/products/synthesis-ai/reviews)
  Synthesis AI is a pioneering synthetic data technology which builds more capable AI


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 11


**Seller Details:**

- **Seller:** [Synthesis](https://www.g2.com/sellers/synthesis-863e5e7a-d8da-42fd-a274-f85882c524af)
- **Year Founded:** 2019
- **HQ Location:** San Francisco, CA
- **Twitter:** @SynthesisAI_ (649 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/synthesis-ai (14 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 73% Small-Business, 27% Mid-Market


  ### 10. [MOSTLY AI Synthetic Data Platform](https://www.g2.com/products/mostly-ai-synthetic-data-platform/reviews)
  The MOSTLY AI synthetic data platform is the leading synthetic data generator globally. Its platform enables enterprises across industries to unlock, share, fix and simulate data. Thanks to the advances in artificial intelligence ,MOSTLY AI's synthetic data look and feel just like real data, are able to retain the valuable, granular-level information, yet guarantee that no individual is ever getting exposed. This enables businesses to drive innovation and digital transformation, overcome data silos, improve machine learning models as well as application testing capabilities. MOSTLY AI serves customers in a variety of verticals, including banking, insurance and telecommunications.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 17


**Seller Details:**

- **Seller:** [MOSTLY AI](https://www.g2.com/sellers/mostly-ai)
- **Year Founded:** 2017
- **HQ Location:** Vienna, Wien
- **Twitter:** @mostly_ai (488 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/mostlyai/ (60 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 53% Small-Business, 24% Enterprise


  ### 11. [Syntho](https://www.g2.com/products/syntho/reviews)
  Syntho is an Amsterdam-based company revolutionizing the tech industry with AI-generated synthetic data. As the leading provider of synthetic data software, Syntho’s mission is to empower businesses worldwide to generate and leverage high-quality Synthetic Data at scale. Syntho solves 3 main data access problems: 1. 𝗔𝗜-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗱𝗮𝘁𝗮 𝗳𝗼𝗿 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀: Mimic the statistical patterns, relationships, and characteristics of original data in synthetic data with the power of artificial intelligence (AI) algorithms. Clients may share synthetic data and use it for AI modeling. 2. 𝗦𝗺𝗮𝗿𝘁 𝗱𝗲-𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻: De-identification is a process used to protect sensitive information by removing or modifying personally identifiable information (PII) from a dataset or database. 3. 𝗧𝗲𝘀𝘁 𝗱𝗮𝘁𝗮 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Leverage synthetic data in a robust solution for ensuring data privacy, accuracy, and utility in testing environments. By generating realistic synthetic datasets, enables comprehensive testing while safeguarding sensitive information, accelerating development cycles, and optimizing resource allocation.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 16


**Seller Details:**

- **Seller:** [Syntho](https://www.g2.com/sellers/syntho)
- **Year Founded:** 2020
- **HQ Location:** Amsterdam, Noord Holland
- **LinkedIn® Page:** https://www.linkedin.com/company/syntho/ (11 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 69% Small-Business, 19% Mid-Market


  ### 12. [GenRocket](https://www.g2.com/products/genrocket/reviews)
  GenRocket is the technology leader in synthetic data generation for quality engineering and machine learning use cases. We call it Synthetic Test Data Automation (TDA) and it's the next generation of Test Data Management (TDM). GenRocket provides a comprehensive self-service platform to more than 50 of the world's largest organizations who demand superior quality and efficiency in their quality engineering and data science operations. KEY FEATURES SPEED: Data generated at 10,000 rows/second and one billion rows in under two hours QUALITY: Any volume and variety of data (unique, negative, conditioned, permutations) REUSABILITY: Test Data Cases and Test Data Rules can be easily reused SELF-SERVICE: Model, design and deploy test data on-demand into CI/CD Pipelines SECURITY: Secure platform never uses or stores sensitive customer data VERSATILITY: 101+ data formats e.g. SQL, XML, JSON, EDI, PDF, Kafka, Parquet, AWS S3 VALUE FOR MONEY: Attractive license and implementation cost to maximizes value PROVEN BENEFITS ACCELERATION: 100 times faster than creating data in spreadsheets or via scripts COVERAGE: Improve test coverage from less than 50% to more than 90% to maximize quality VALUE: Reduce TCO by 90% when compared to traditional Test Data Management


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 9


**Seller Details:**

- **Seller:** [GenRocket](https://www.g2.com/sellers/genrocket)
- **Year Founded:** 2012
- **HQ Location:** Ojai, CA
- **Twitter:** @GenRocketINC (371 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/genrocket (36 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 73% Enterprise, 27% Small-Business


  ### 13. [Marvin AI](https://www.g2.com/products/marvin-ai/reviews)
  Marvin processes structured data for software development, enhancing your software development process.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 12


**Seller Details:**

- **Seller:** [Askmarvinai](https://www.g2.com/sellers/askmarvinai)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 50% Small-Business, 33% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (7 reviews)
- Simple (3 reviews)
- Useful (3 reviews)
- AI Technology (2 reviews)
- Easy Integrations (2 reviews)

**Cons:**

- AI Limitations (2 reviews)
- Limitations (2 reviews)
- Usage Limitations (2 reviews)
- Complex Implementation (1 reviews)
- Complex Setup (1 reviews)

  ### 14. [AI vision](https://www.g2.com/products/ai-vision/reviews)
  Deep Vision Data specializes in the creation of synthetic training data for supervised and unsupervised training of machine learning systems such as deep neural networks, and also the development of XR environments as reinforcement and imitation learning platforms.


  **Average Rating:** 4.1/5.0
  **Total Reviews:** 7


**Seller Details:**

- **Seller:** [Deep Vision Data](https://www.g2.com/sellers/deep-vision-data)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 38% Mid-Market, 38% Small-Business


  ### 15. [Test Data Generation](https://www.g2.com/products/test-data-generation/reviews)
  Test Data Generation helps automate and accelerate the creation of test data when copies of production data are incomplete, are unavailable, or cannot guarantee data privacy.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 7


**Seller Details:**

- **Seller:** [Informatica](https://www.g2.com/sellers/informatica)
- **Year Founded:** 1993
- **HQ Location:** Redwood City, CA
- **Twitter:** @Informatica (99,861 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/3858/ (5,337 employees on LinkedIn®)
- **Ownership:** NYSE: INFA

**Reviewer Demographics:**
  - **Company Size:** 71% Small-Business, 29% Mid-Market


#### Pros & Cons

**Pros:**

- Automation (1 reviews)
- Ease of Use (1 reviews)
- Efficiency Improvement (1 reviews)
- Integrations (1 reviews)

**Cons:**

- Difficult Learning Curve (1 reviews)
- Integration Issues (1 reviews)
- Limited Customization (1 reviews)
- Slow Performance (1 reviews)

  ### 16. [brudata.ai](https://www.g2.com/products/brudata-ai/reviews)
  - Identifies PII (Personally Identifiable Information) and PHI (Personal Health Information) in corporate data stores (RDBMS, XML, JSON) - Helps de-identify the data so that accidental leak of PII, and PHI is eliminated when sharing the data with internal teams and external organizations. - Profile existing records statistically and generate additional data that fits the inherent statistical properties, thus preserving the semantics. This ensures high-quality data (with biases corrected and such) for downstream ML training.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 5


**Seller Details:**

- **Seller:** [Brudata](https://www.g2.com/sellers/brudata)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 80% Small-Business, 20% Mid-Market


  ### 17. [K2View](https://www.g2.com/products/k2view/reviews)
  K2view Data Product Platform composes and delivers operational context as reusable data products to power use cases such as agentic AI, Customer 360, synthetic data generatio, data privacy and compliance, and test data management. Operational context represents complete, governed, real-time views of business entities such as customers, orders, and products, enabling consistent, trusted data for operational, analytical, and AI use cases. The platform integrates fragmented data from multiple sources into consistent, continuously updated data products, delivered on demand to downstream systems and users. Each data product is a self-contained unit that integrates and organizes multi-source data by entity, persists it in a high-performance Micro-Database, and governs it in-flight. It processes and enriches data in memory, continuously synchronizes it with source systems, and delivers it to authorized systems via APIs, SQL, messaging, CDC, MCP, and RAG. Core capabilities include: • K2Studio: Graphical tool for designing, creating, and deploying data products, accelerated by AI copilots • Universal Connectivity & Integration: Connect to any source or target (structured, semi-structured, unstructured) across cloud and on-prem, supporting batch and real-time, sync/async, and push/pull delivery • Augmented Data Catalog and Governance: AI-driven discovery and classification with in-flight enforcement of data privacy and data quality policies • Advanced Transformation: In-memory (RAM) data transformations and enrichment for near-real-time processing • AI & Agentic Enablement: Built-in MCP server per data product and ability to create data agents with planning, reasoning, and execution capabilities • Flexible Deployment: Cloud, on-prem, hybrid; supports fabric, mesh, hub architectures • K2Cloud Monitoring: Visibility into data product usage and SLAs


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 37


**Seller Details:**

- **Seller:** [K2View](https://www.g2.com/sellers/k2view)
- **Year Founded:** 2009
- **HQ Location:** Dallas, TX
- **Twitter:** @K2View (144 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1012853 (192 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Telecommunications, Computer Software
  - **Company Size:** 40% Small-Business, 38% Enterprise


#### Pros & Cons

**Pros:**

- Data Management (3 reviews)
- Data Sharing (3 reviews)
- Ease of Use (3 reviews)
- Efficiency (3 reviews)
- Organization (3 reviews)

**Cons:**

- Complexity (3 reviews)
- Complex Setup (3 reviews)
- High Technical Requirement (3 reviews)
- Learning Curve (3 reviews)
- Learning Difficulty (3 reviews)

  ### 18. [Subsalt](https://www.g2.com/products/subsalt/reviews)
  Subsalt creates synthetic data that satisfies the anonymized and de-identified data exemptions in major data privacy laws, so valuable data can be shared with internal teams, vendors, and partners without risk of non-compliance, user consent issues, or data breaches.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 5


**Seller Details:**

- **Seller:** [Subsalt](https://www.g2.com/sellers/subsalt)
- **Year Founded:** 2021
- **HQ Location:** Distributed, US
- **LinkedIn® Page:** https://www.linkedin.com/company/getsubsalt/ (7 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 60% Mid-Market, 20% Enterprise


  ### 19. [MDClone](https://www.g2.com/products/mdclone/reviews)
  MDClone offers an innovative, self-service data analytics environment powering exploration, discovery, and collaboration throughout the healthcare ecosystems, cross-institutionally, and globally. The powerful underlying infrastructure of the MDClone ADAMS Platform allows users to overcome common barriers in healthcare in order to organize, access, and protect the privacy of patient data while accelerating research, improving operations and quality, and driving innovation to deliver better patient outcomes. Founded in Israel in 2016, MDClone serves major health systems, payers, and life science customers in the United States, Canada, and Israel. For more information, visit mdclone.com.


  **Average Rating:** 4.9/5.0
  **Total Reviews:** 4


**Seller Details:**

- **Seller:** [MDClone](https://www.g2.com/sellers/mdclone)
- **Year Founded:** 2015
- **HQ Location:** Beer-Sheva, IL
- **Twitter:** @MDCloneHQ (300 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/mdclone/ (132 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 75% Small-Business, 25% Mid-Market


  ### 20. [DATAMIMIC](https://www.g2.com/products/datamimic/reviews)
  DATAMIMIC is a deterministic test data platform specializing in enterprise-grade synthetic generation, policy-based anonymization, and complex JSON and XML handling. Teams define data requirements as reusable models — not brittle scripts — and generate reproducible, PII-safe datasets on demand. Built for regulated industries, every generation run is logged, replayable, and aligned with GDPR, DORA, BCBS 239, and PCI DSS requirements. Founded in Hamburg in 2019, rapiddweller builds tools that help engineering teams accelerate delivery without exposing production data. From our offices in Germany and Vietnam, we serve banks, insurers, payment processors, and public-sector organizations across Europe and beyond — combining deep domain expertise with a platform engineered for the most demanding compliance environments. DATAMIMIC puts your team in control: define your data model once, generate across any environment, test with confidence. Model. Generate. Test.


  **Average Rating:** 4.1/5.0
  **Total Reviews:** 5


**Seller Details:**

- **Seller:** [rapiddweller](https://www.g2.com/sellers/rapiddweller-1f2f7004-87af-448c-bde0-c8a67062cda1)
- **Year Founded:** 2019
- **HQ Location:** Hamburg, DE
- **Twitter:** @rapiddweller (8 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/rapiddweller/ (15 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 40% Small-Business, 40% Enterprise


#### Pros & Cons

**Pros:**

- Data Management (1 reviews)
- Performance (1 reviews)

**Cons:**

- Data Restrictions (1 reviews)
- Expensive (1 reviews)
- Integration Issues (1 reviews)

  ### 21. [SyntheticAIdata](https://www.g2.com/products/syntheticaidata/reviews)
  syntheticAIdata is your partner in creating synthetic data that enables you to craft diverse datasets effortlessly and at scale. Utilising our solution doesn’t just mean significant cost reductions; it means ensuring privacy, regulatory compliance, and expediting your AI products' journey to the market. Let syntheticAIdata be the catalyst that transforms your AI aspirations into achievements.


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 3


**Seller Details:**

- **Seller:** [SyntheticAIdata](https://www.g2.com/sellers/syntheticaidata)
- **Year Founded:** 2021
- **HQ Location:** Copenhagen, DK
- **LinkedIn® Page:** https://www.linkedin.com/company/syntheticaidata (6 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 100% Small-Business, 33% Mid-Market


  ### 22. [BENERATOR](https://www.g2.com/products/benerator/reviews)
  BENERATOR is a leading solution for generating synthetic data, anonymizing, and obfuscating production data, leveraging a model-driven approach for safe, GDPR-compliant use in development, testing, and training. Founded in Hamburg in 2019, our global team at rapiddweller is equipping developers with the tools they need to accelerate development cycles while ensuring data privacy. From our offices in Vietnam and Germany, we've become a front-runner in the fields of Data Masking Software, Data De-Identification Tools, and Synthetic Data Software, serving customers across diverse industries. Experience the power of BENERATOR and "Shape Your Test Data Universe" — secure, useful data that fuels efficient delivery, syncing perfectly with your developers' pace.


  **Average Rating:** 3.0/5.0
  **Total Reviews:** 2


**Seller Details:**

- **Seller:** [rapiddweller](https://www.g2.com/sellers/rapiddweller-1f2f7004-87af-448c-bde0-c8a67062cda1)
- **Year Founded:** 2019
- **HQ Location:** Hamburg, DE
- **Twitter:** @rapiddweller (8 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/rapiddweller/ (15 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 100% Small-Business


#### Pros & Cons

**Pros:**

- Features (1 reviews)

**Cons:**

- Complex Setup (1 reviews)
- Expensive (1 reviews)

  ### 23. [DATPROF Privacy](https://www.g2.com/products/datprof-privacy/reviews)
  Data masking and synthetic data generation consistently across any supported databases or systems: Oracle, DB2, PostgreSQL, Microsoft SQL Server, MySQL, MariaDB and many more.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 6


**Seller Details:**

- **Seller:** [DATPROF](https://www.g2.com/sellers/datprof)
- **Year Founded:** 2003
- **HQ Location:** Groningen, NL
- **Twitter:** @DATPROF (168 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/datprof/ (17 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 50% Small-Business, 33% Mid-Market


  ### 24. [Perforce Delphix](https://www.g2.com/products/perforce-delphix/reviews)
  Enterprises around the world choose Perforce Delphix to automate compliant data for DevOps. The Delphix DevOps Data Platform provides integrated data masking and virtualization to rapidly deploy compliant data into non-production environments. With Delphix, customers automate test data management and CI/CD, delivery compliant data for AI, and swiftly recover from downtime events, while ensuring data privacy and security. For more information, visit www.perforce.com/products/delphix


  **Average Rating:** 4.1/5.0
  **Total Reviews:** 11


**Seller Details:**

- **Seller:** [Perforce](https://www.g2.com/sellers/perforce)
- **Year Founded:** 1995
- **HQ Location:** Minneapolis, MN
- **Twitter:** @perforce (5,087 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/perforce/ (2,032 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 55% Enterprise, 36% Mid-Market


#### Pros & Cons

**Pros:**

- Database Management (2 reviews)
- Data Management (2 reviews)
- Data Security (2 reviews)
- Ease of Use (2 reviews)
- Features (2 reviews)

**Cons:**

- Expensive (3 reviews)
- Expensive Pricing (3 reviews)
- Complexity (2 reviews)
- Complex Setup (2 reviews)
- Integration Issues (2 reviews)

  ### 25. [Statice](https://www.g2.com/products/statice/reviews)
  An enterprise-ready platform to generate privacy-preserving synthetic data from structured data types. ✅ High utility and privacy guarantees ✅ Use the synthetic data as a drop-in replacement for any type of behavior, predictive, or transactional analysis in compliance with data protection laws. ✅ Possible trial. More at www.statice.ai


  **Average Rating:** 4.1/5.0
  **Total Reviews:** 4


**Seller Details:**

- **Seller:** [Statice](https://www.g2.com/sellers/statice)
- **Year Founded:** 2018
- **HQ Location:** Berlin, DE
- **LinkedIn® Page:** https://www.linkedin.com/company/staticeberlin/ (6 employees on LinkedIn®)
- **Total Revenue (USD mm):** $1,869

**Reviewer Demographics:**
  - **Company Size:** 75% Small-Business, 25% Mid-Market




## Parent Category

[Artificial Intelligence Software](https://www.g2.com/categories/artificial-intelligence)




---

## Buyer Guide

### What You Should Know About Synthetic Data

Synthetic data software refers to tools and platforms designed to generate artificial datasets that replicate the statistical properties and patterns of real-world data. Unlike traditional data sources, synthetic data is entirely artificial, created to mimic the characteristics of actual data without containing sensitive or [personally identifiable information (PII)](https://www.g2.com/glossary/personally-identifiable-information-definition). This approach helps organizations adhere to various privacy regulations, such as the [General Data Protection Regulation (GDPR)](https://www.g2.com/glossary/gdpr-definition).

These software tools are commonly used to augment datasets, simulate events, and address class imbalances, providing a cost-effective solution to data scarcity. By using synthetic data, businesses can safely test algorithms, [predictive models](https://www.g2.com/articles/predictive-analytics), applications, and systems without the risks associated with real data. This not only protects privacy but also enhances compliance with data protection laws.

### What is synthetic data generation?

Synthetic data generation is the process of creating artificial data that reflects the statistical properties of real datasets. This method is particularly useful when developing a dataset from scratch would be too time-consuming and costly, often resulting in incomplete or inaccurate data. Synthetic data generation tools make this process easier, allowing developers to quickly create accurate and detailed datasets with the required variables.

Synthetic dataset generation serves several key purposes, such as enhancing data privacy, improving [machine learning (ML) models](https://www.g2.com/articles/machine-learning-models), supporting legal research, detecting fraud, and testing software applications. It empowers organizations to innovate and analyze while minimizing the risks associated with using real data.

### How to generate synthetic data

Below is a general overview of the steps involved in generating synthetic data.

- **Define the data requirements:** Start by identifying your needs (training machine learning models, testing algorithms, or validating data pipelines), data type (like images, text, or numerical), and required data characteristics (size, format, and distribution). Also, establish the required volume of synthetic data.
- **Choose a generation method:** Select a generation method. There are three main approaches you can choose from:

-[Statistical modeling](https://www.g2.com/articles/statistical-modeling) **:** By analyzing real data, data scientists identify its underlying statistical patterns (for example: normal or exponential). They then generate synthetic data that follows these distributions, creating a dataset that mirrors the original.

**-Model-based:** Machine learning models are trained on real data to learn its characteristics. Once trained, these models can generate synthetic data that mimics the statistical patterns of the original. This approach is useful for creating hybrid datasets.

**-Deep learning methods:** Advanced techniques like GANs and variational autoencoders (VAEs) generate high-quality synthetic data, especially for complex data types like images or time series.



- **Prepare the training data:** Gather a representative dataset to simulate real-world scenarios. Ensure this data is cleaned and preprocessed for effective training.
- **Train the model:** Choose a suitable algorithm and train your model by feeding it the prepared data, allowing it to learn the relevant patterns.
- **Generate synthetic data:** Input the desired attributes and volume into the trained model to produce new synthetic data that mimics real-world patterns.
- **Evaluate and refine:** Evaluate the quality of the generated data to ensure it meets standards. If necessary, refine the model or retrain it to improve results.
- **Additional considerations:** Ensure the synthetic data generation process adheres to privacy regulations and ethical guidelines and protects individual identities. Address any biases to ensure fair representation, and strive for realism, especially when the data is used for training AI or testing software.

### Key features of synthetic data generation tools

Here are the key features found in some of the best synthetic data tools. Note that specific features may vary from product to product.

- **Data generation algorithms:** Synthetic data software creates realistic and statistically relevant data sets that aim to imitate the behavior of real-world data.
- **Privacy preservation:** These tools make sure the generated data doesn’t contain any personal information in order to safeguard user privacy.
- **Data augmentation:** This feature enhances existing data sets with synthetic data. Data augmentation addresses issues like class imbalance or data scarcity.
- **Data type support:** This software type can generate a wide variety of data types, including [structured data](https://www.g2.com/articles/structured-vs-unstructured-data#structured) (tables), [unstructured data](https://www.g2.com/articles/structured-vs-unstructured-data#unstructured) (text and images), and time-series data.
- [Scalability](https://www.g2.com/glossary/scalability) **:** Synthetic data generator allows for the creation of large volumes of data, which makes it a flexible and scalable solution that meets the varying data demands an organization has.

### Types of synthetic data tools

You can choose from four types of synthetic data tools, all explained below.

- **Generative adversarial networks (GANs) based software:** GANs are a type of [artificial intelligence (AI)](https://www.g2.com/articles/what-is-artificial-intelligence) model whereby two neural networks – the generator and the discriminator – are trained together through a process of competition. The generator creates synthetic data, and the discriminator evaluates how close the generated data measures up against the real thing. 
- **Statistical modeling software:** This synthetic data tool uses mathematical models to generate data based on the statistical properties found in real-world information. It relies on statistical techniques and algorithms to build synthetic data sets that maintain the same overall patterns as the original data.
- **Rule-based synthetic data software:** This refers to tools and platforms that make synthetic data that depends on predefined rules and conditions. Unlike data generated through statistical models or machine learning techniques like GANs, rule-based synthetic data is created by applying specific rules and algorithms that define how data should be structured and what values it should contain. For example, a rule might state that a person's age must be between 21 and 35 or that a transaction amount must be greater than one.
- [Deep learning](https://www.g2.com/categories/deep-learning) **and autoencoder software:** [Deep learning techniques](https://www.g2.com/articles/deep-learning), particularly autoencoders, generate synthetic data. Autoencoders are [neural networks](https://www.g2.com/glossary/artificial-neural-network-definition) used to learn codings of data, typically for dimensionality reduction or feature learning. They can also be used to build synthetic data by reconstructing input data with added variability.

### Benefits of synthetic test data generation tools

No matter how a business plans to use synthetic data software, there are several benefits to doing so. Some are:

- [Reduced algorithmic bias](https://www.g2.com/glossary/algorithmic-bias-definition) **.** Synthetic data software helps diminish biases that are sometimes present in real-world data. By designing the synthetic data generation process, developers can check that underrepresented groups or scenarios are adequately represented, leading to more balance. 
- **Enhanced data sharing.** Synthetic data facilitates data sharing between organizations without compromising privacy or proprietary information. Since it doesn’t contain authentic personal or sensitive information, users can freely share it for collaboration, research, and development purposes. 
- **Risk-free testing and development.** Synthetic data constructs a safe environment for testing and development processes. Developers can use synthetic data to try out new systems, algorithms, and applications without the risk of exposing or damaging real data. This eliminates the risk of [data breaches](https://www.g2.com/articles/data-breach) or leaks since the high-quality data used in testing is phony.
- **Cost-effective and scalability.** Generating synthetic data is often more cost-effective than collecting and labeling real-world data, with the added advantage of easily scaling to produce large datasets.

### Who uses synthetic data software?

Several types of individual developers and teams within organizations can benefit from employing synthetic data software. The most common users are detailed here.

- **Data scientists** may use synthetic data generation tools to research new ideas without the need for access to real-world data sets and without spending a lot of time assembling sets from different sources.
- **Compliance managers** may use synthetic data software to create non-identifiable data sets for testing and validating compliance with data protection regulations. Doing so promises privacy and security without exposing real personal information or sensitive data.
- **Software developers** turn to generation tools to speed up [debugging](https://www.g2.com/glossary/debugging-definition) and software creation processes by giving developers realistic data sets to complete. This type of software can also be useful for prototyping applications when real data may not be available yet.

### Synthetic data software pricing

Synthetic data software is typically broken into three different pricing models.

- **Subscription-based model:** Users pay a recurring fee to access all features at regular intervals, such as monthly or annually.
- **Pay-per-use model:** This model allows users to pay based on their usage, data storage, seats, or consumption. 
- **Tiered model:** This type of model offers multiple pricing levels or "tiers," each with a different set of features or usage limits. Users can choose a tier that best fits their needs and budget, often ranging from basic to premium options.

Like most software, the price changes depending on factors such as the complexity of the program and the features it offers. Before investing in a synthetic data tool, companies need to figure out their specific needs and the features on their must-have list for more clarity.

### Alternatives to synthetic data generation tools

Before choosing a synthetic data tool, you can also consider one of the following alternatives for your needs.

- [Data masking solutions](https://www.g2.com/categories/data-masking) protect an organization’s important data by disguising it with random characters or other information so that it’s still usable by everyone in the organization, but not by anyone outside of it.
- **Data augmentation solutions** use techniques to artificially expand the size and range of a data set without collecting new data. Most commonly used in image and text processing, it mitigates issues like class imbalance and data scarcity. By deepening the diversity and volume of training data, they also help models generalize better to unseen data, leading to more accurate and reliable predictions.
- **Mock data generation software** create simulated data sets that impersonate the structure and properties of real data without containing actual information. It’s usual domain is testing, development, and training purposes to make certain that applications can handle real-world data scenarios. 

### Software and services related to synthetic data software

Certain tools related to synthetic data software have similar functionalities. They can be of use depending on a business's needs. Some examples of such tools are as follows.

- **Data simulation software** generates artificial data sets to replicate real-world scenarios for testing and analysis. It helps model complex systems, predict outcomes, and evaluate performance under various conditions without real data. 
- **Data modeling software** creates visual representations of data structures and relationships within a [database](https://www.g2.com/articles/what-is-a-database). It helps design, organize, and document the data architecture to maintain integrity and consistency. Some use cases are database design, enabling efficient management, improved quality, and clear communication among [stakeholders](https://www.g2.com/glossary/stakeholder-definition).
- [Machine learning frameworks](https://www.g2.com/categories/machine-learning) automate tasks for users by applying an algorithm to produce an output. Machine learning models improve the speed and accuracy of desired outputs by constantly refining them as the application digests more training data.

### Challenges with synthetic data solutions

Despite the numerous benefits users experience from synthetic data software, some challenges exist, too.

- **Data growth:** As the volume of data grows, the process of synthetic data generation via generative AI needs to scale appropriately. This process can be intensive and may require a variety of resources in terms of processing power and storage. Additionally, sustaining the quality of synthetic data as the dataset grows becomes more complex. Larger data sets require more sophisticated models to keep up accuracy and relevance.
- [Data security](https://www.g2.com/glossary/data-security-definition) **and compliance** : If the generated data is not properly handled, it can lead to potential security breaches where sensitive information may be leaked. Moreover, some synthetic data generation tools don’t adhere to existing privacy regulations such as GDPR or the[California Consumer Privacy Act (CCPA)](https://learn.g2.com/california-consumer-privacy-act). 
- **Data preservation:** Ensuring that synthetic data preserves and maintains the original’s essential properties, patterns, and relationships over time can be difficult, but it has to be done in order for synthetic data to remain useful and relevant for its intended applications.
- [Data storage](https://learn.g2.com/data-storage) **and retrieval cost:** Synthetic data generation tools may incur additional costs for storage and retrieval due to the use of [cloud computing](https://www.g2.com/articles/cloud-computing) or ML algorithms. Companies end up going over budget because they fail to account for these costs during the planning process.
- **Data accessibility and format compatibility:** Keeping synthetic data easily accessible across different systems and applications requires consistent, standardized formats. However, diverse software environments and varying data storage solutions can lead to compatibility issues. Further, as data standards evolve, maintaining compatibility with new formats while preserving accessibility to historical data becomes complicated. 

### What kind of companies should buy synthetic data tools?

Any company with a development team could benefit from synthetic data tools, but these specific organizations should consider buying this type of software to add to their tech stack.

- **Financial institutions:** Synthetic financial data can be used for risk modeling and fraud detection.
- **Healthcare organizations:** These tools can create synthetic patient records for research and testing without compromising patient privacy.
- **Tech firms and startups:** It’s common for synthetic data software to be used to test data and validate applications and ML models.
- **Government agencies:** These institutions may use synthetic data software for policy testing, public health simulations, and data privacy in research initiatives.
- **Educational organizations:** These tools can make realistic datasets for training, research projects, and new edification practices and policies.
- **Retail and manufacturing companies:** A synthetic data platform can simulate customer data about behavior and sales data to improve marketing strategies and [inventory management](https://www.g2.com/articles/inventory-management).
- **Automotive companies:** Synthetic scenarios allow autonomous systems to be tested under various conditions that would be difficult or risky to replicate in real life.
- **Security and cyber defense organizations:** Creating synthetic attack scenarios helps train security systems and enhance their threat detection capabilities.

### How to choose the best synthetic data generation tool

The following explains the step-by-step process buyers can use to find suitable synthetic data tools for their businesses. 

#### Identify business needs and priorities

Before choosing a synthetic data tool, companies should identify their top priorities for a tool and what exactly they’ll be using it for. Clear goals and requirements make the selection process easier and more efficient, especially as more options hit the market. Because to consider factors like data quality, compliance and security, customization, and scalability.

#### Choose the necessary technology and features

Next, companies work on narrowing down the features and functionalities they need most. Some essential technology and features a company may be looking for are discussed here.

- **Generative adversarial networks** for creating highly realistic synthetic data by training models to generate data that closely mimics real data.
- **Customizable parameters** that allow users to tailor data generation to specific needs, such as adjusting distributions, correlations, and noise levels.
- [APIs](https://www.g2.com/articles/what-is-an-api) **and** [SDKs](https://www.g2.com/articles/sdk) that provide easy integration with existing systems, databases, and workflows.
- [Regulatory compliance](https://www.g2.com/glossary/regulatory-compliance-definition) to ensure software adheres to data protection regulations such as GDPR and [Health Insurance Portability and Accountability Act (HIPAA)](https://www.g2.com/glossary/hipaa-definition).
- **Scenario simulation** for the ability to simulate various hypothetical scenarios for testing and analysis.
- **Quality assurance** features to validate the accuracy and quality of data.

When companies have a short list of services based on their requirements and must-have functionalities, it’s easier to refine which options best suit their needs.

#### Review vendor vision, roadmap, viability, and support

In this stage, you can start vetting the selected synthetic data software vendors and conduct demos to determine if a product meets your requirements. For the best outcome, a buyer should share detailed requirements in advance so providers know which features and functionalities to showcase. 

Below are some meaningful questions buyers can ask synthetic data generation companies as a part of the decision process.

- What kind of data does the tool generate? Is it exclusively structured data or can it generate unstructured data, like images and videos?
- How accurately does the software replicate the statistical properties and complexity of real data?
- Can the solution handle large-scale data generation and maintain performance and quality as data volumes grow?
- How does the tool handle missing values? Is there an option to fill in missing values with realistic replacements?
- Is the output format customizable? Can you specify a preferred output format for your dataset?
- How does the software ensure compliance with data protection regulations like GDPR and HIPAA?
- How does security and privacy fit into synthetic data generation? To avoid security breaches, does the tool offer any safeguards against unauthorized access of generated data sets?
- Is there a support system to help users if they encounter or discover any issues? Are tutorials, FAQs, or customer service provided if necessary? 

#### Evaluate the deployment and purchasing model

Once you’ve received answers to the above questions and are ready to move on to the next stage, loop in your key stakeholders and at least one employee from each department who will be using the software. 

For example, with synthetic data software, it’s best that the buyer loops in the developers who will be using the software to ensure it covers the core features your business is looking for in synthetic data sets.

#### Put it all together

The buyer makes the final decision after getting buy-in from everyone on the selection committee, including [end users](https://www.g2.com/glossary/end-user-definition). The buy-in is essential for getting everyone on the same page regarding implementation, onboarding, and potential use cases. 

### Synthetic test data generation software trends

Some recent trends that were recently seen in the field of synthetic data software are as follows.

- **Integration with the machine learning pipeline:** Synthetic data tools are increasingly designed to automatically generate and ingest data directly into machine learning pipelines. Automation like this reduces the time and effort required to prepare training data, which lets data scientists focus on model development and optimization.
- **Automated data generation platforms:** Automated synthetic data generation tools are becoming popular for their ability to quickly and accurately make large amounts of realistic data. They permit users to create realistic data sets with minimal effort, enabling them to come up with intricate scenarios and test new models efficiently.
- **Generative AI in synthetic data:** The use of Generative AI, using techniques like GANs and VAEs, is transforming the synthetic data field by creating high-quality artificial datasets that mimic real data. It enhances data quality, automates generation, and allows for diverse, customizable datasets while protecting privacy. 

_Researched and written by_ [_Shalaka Joshi_](https://learn.g2.com/author/shalaka-joshi)

_Reviewed and edited by_ [_Aisha West_](https://learn.g2.com/author/aisha-west)