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Best AI Synthetic Data Solutions

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What are AI Synthetic Data?

AI Synthetic Data offers a robust solution for generating realistic, artificial data. It ensures data privacy and eliminates the need for actual data collection, making it essential for machine learning and analytics projects.

To learn more, read our DATPROF vs. Snowflake Buyer's Guide (Updated: January 2025).
The top 5 AI Synthetic Datum solutions are DATPROF, Broadcom Test Data Manager, Snowflake, K2View and Tonic.ai, as ranked by PeerSpot users in February 2025. Broadcom Test Data Manager received the highest rating of 9.3 among the leaders. Snowflake is the most popular solution in terms of searches by peers, and K2View holds the largest mind share of 18.2%.

AI Synthetic Data provides an innovative way to develop and test machine learning models without compromising privacy or security. It is particularly useful in scenarios where gathering real-world data is impractical or risky. By using sophisticated algorithms, it replicates the statistical properties of real data, providing a valuable resource for training and validating AI systems.

What are the critical features?
  • Data Privacy: Ensures that sensitive information remains secure by generating artificial datasets.
  • Cost Efficiency: Reduces the need for expensive data collection processes.
  • Flexibility: Can be tailored to replicate a wide range of data types and distributions.
  • Scalability: Easily produces large volumes of data to meet extensive analytical requirements.
What benefits or ROI can users expect?
  • Enhanced Data Security: Eliminates risks associated with using real, sensitive data.
  • Improved Model Performance: Provides diverse datasets to train more robust AI models.
  • Faster Development Cycles: Speeds up the process of data preparation and model training.
  • Compliance: Simplifies adherence to data protection regulations.

Industries like finance, healthcare, and retail benefit greatly from AI Synthetic Data. In finance, it helps in developing fraud detection systems without accessing real client data. In healthcare, it enables research while maintaining patient confidentiality. Retail companies use it for customer behavior analysis and personalizing marketing strategies.

A valuable tool for organizations, AI Synthetic Data mitigates privacy risks, reduces costs, and accelerates project timelines, making it indispensable for modern AI applications.

Buyer's Guide
DATPROF vs. Snowflake
January 2025
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AI Synthetic Data solutions mindshare

As of March 2025, in the AI Synthetic Data category, the mindshare of Broadcom Test Data Manager is 16.6%, down from 35.0% compared to the previous year. The mindshare of DATPROF is 9.9%, down from 33.6% compared to the previous year. The mindshare of K2View is 18.2%, down from 31.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Synthetic Data

Top AI Synthetic Data products

Rankings through
PeerSpot #1 Ranked
#1 Ranked
DATPROF
DATPROF is used for subsetting and masking test data, ensuring GDPR compliance and protecting sensitive information. Users reduce storage costs, facilitate regression testing, and generate synthetic data. Major use cases include saving disk space, refreshing test databases, and scrambling sensitive information. Integration with SAP and non-SAP applications is supported, offering excellent UI and reusable masking templates.
9.0
Rating
8
Reviews
1,281
Words/ Review
1,224
Total Views
70
Category Comparisons
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Broadcom Test Data Manager
Broadcom Test Data Manager manages test data, creates and masks data, and ensures data privacy. Users appreciate data subsetting, generating high-quality data sets, automating test data creation, and the self-service portal. Enhancements needed include reducing import times, adding mining capabilities, supporting cloud-native SaaS, and integrating with data warehousing and Jira.
9.3
Rating
32
Reviews
685
Words/ Review
1,562
Total Views
94
Category Comparisons
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Buyer's Guide
Broadcom Test Data Manager vs. Snowflake
January 2025
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Snowflake
Snowflake is a cloud-based data warehousing solution for storing and processing data, generating reports and dashboards, and as a BI reporting source. It is used for optimizing costs and using financial data, as well as for migrating data from on-premises to the cloud. The solution is often used as a centralized data warehouse, combining data from multiple sources.
8.2
Rating
99
Reviews
452
Words/ Review
18,275
Total Views
5
Category Comparisons
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K2View
Users implement K2View for data integration and real-time access, managing and synchronizing data across diverse sources. They praise K2View's capabilities in handling large data volumes, ensuring data privacy, enhancing data quality, and optimizing ETL processes. Challenges include complex setup, insufficient support resources, and performance issues with high data volumes.
489
Total Views
93
Category Comparisons
Popular comparisons
Tonic.ai
Tonic.ai generates realistic synthetic data for testing, development, and data privacy, ensuring confidentiality by masking sensitive information. It supports rapid application deployment and testing efficiency with high-quality data. Users value data masking, flexibility, integration, multiple data sources, automation, and high accuracy. Improvement areas include stability, documentation, customer support, performance, and user experience.
107
Total Views
30
Category Comparisons
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GenRocket
Users primarily use GenRocket for generating test data for software testing, ensuring data privacy compliance. Praised for its flexibility, efficiency, scalability, and seamless integration with other tools, users appreciate the data generation speed, customization, and user-friendly interface. They suggest refining documentation, making setup more intuitive, improving integration, and optimizing performance speed for large datasets.
37
Total Views
24
Category Comparisons
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Gretel.ai
Gretel.ai is used for generating synthetic data, enhancing data privacy and security, enabling safe data sharing and collaboration. It creates sample datasets for machine learning models, testing, and analytics. Valued for data augmentation and anonymization, it provides realistic yet artificial data. Users appreciate its advanced synthetic data creation, robust privacy protection mechanisms, and seamless API integration.
50
Total Views
21
Category Comparisons
MOSTLY AI Platform
MOSTLY AI Platform is used for synthetic data generation, privacy preservation during analysis, and safe data sharing. Users appreciate its realistic data, ease of integration, and scalability. Challenges include initial setup complexity and pricing flexibility. Suggestions include improved documentation, faster support, enhanced real-time processing, and performance optimization.
24
Total Views
16
Category Comparisons
Syntho
Syntho enhances data privacy and compliance through synthetic data generation, supporting machine learning model training and software testing while enabling secure data sharing. Users value its straightforward design, seamless integration, and real-time analytics. However, improvements in stability, performance, and customer support responsiveness are needed. Navigation can be challenging for new users.
16
Total Views
10
Category Comparisons
Hazy
Hazy is used for synthetic data generation, enabling safe testing and training of machine learning models without compromising privacy. Valued features include strong data anonymization, privacy regulation compliance, and ease of integration. However, users noted slower data processing speed, complexities in setup, integration difficulties, and the need for better documentation and support.
14
Total Views
7
Category Comparisons
SDV
SDV generates synthetic data to train machine learning models while preserving data privacy. It aids in data augmentation, testing algorithms, and enhancing data diversity. Users value its data generation capabilities, flexibility, and integration ease. They desire better documentation, improved performance with large datasets, enhanced integration, and advanced customization options.
15
Total Views
6
Category Comparisons
Synthesized SDK
Synthesized SDK generates realistic synthetic data for testing, development, and machine learning model training. Users value high-quality, privacy-compliant datasets, seamless integration, and flexible handling of data types. Users note documentation clarity, integration issues, limited customization, and performance concerns. Enhancements in support response time are also needed.
12
Total Views
6
Category Comparisons
Synthesis AI
Synthesis AI creates photorealistic synthetic data for training machine learning algorithms. Companies use it to enhance facial recognition systems, improve computer vision models, and test autonomous driving systems. It generates diverse datasets rapidly, reducing biases and the need for real-world data. Users praise high-quality data, quick dataset generation, and ease of integration. Some users note improvement areas in data accuracy, response speed, and documentation clarity.
11
Total Views
5
Category Comparisons
YData
YData excels in data preprocessing by streamlining data cleaning tasks and enhancing data quality. Users commend its efficiency with large datasets, synthetic data generation, and seamless pipeline integration. They appreciate its powerful processing, automation features, and robust security. Areas for improvement include customer support, documentation, performance during peak operations, and integration options.
11
Total Views
4
Category Comparisons
Lexset
Lexset augments virtual staging and enhances 3D modeling in interior design with accurate object placement and realistic renderings. It streamlines furniture catalog curation with high-quality digital assets. Users value its asset generation, customization, AI technology, and scalability. Improvements needed in documentation, performance, and customer service response time are noted. Setup process is challenging.
10
Total Views
4
Category Comparisons
DATAMIMIC
DATAMIMIC provides a robust, AI- and model-driven approach to test data generation, enabling you to define your requirements at an abstract level and create synthetic data to match these specifications. This ensures comprehensive and secure testing, essential for business success in competitive industries such as banking and finance. Companies in these sectors have utilized DATAMIMIC to overcome slow development cycles caused by data dependencies and strict compliance regulations.
14
Total Views
3
Category Comparisons
brewdata.ai
Users primarily utilize brewdata.ai for data analytics and business intelligence, appreciating its capability to handle large datasets efficiently, derive insights, and generate comprehensive reports. Favored for predictive analytics and real-time data analytics, its seamless integration, intuitive design, scalability, robust security, and trend analysis are highlighted, though users suggest enhancing speed, performance, and documentation.
9
Total Views
4
Category Comparisons
Benerator
Users appreciate Benerator for generating large volumes of test data, anonymizing sensitive information, and maintaining data privacy. They value its customizable options, scalability, and integration capabilities. While users find Benerator's documentation helpful and support team responsive, they note stability and performance issues, occasional crashes, slow processing times, and a steep learning curve.
9
Total Views
4
Category Comparisons
TESTINT
Most users utilize TESTINT for comprehensive software testing and automation. Praised for its robust features, it catches bugs efficiently and integrates with tools seamlessly. Users emphasize its effectiveness in regression testing and executing multiple test cases, improving productivity. They value its analytics, reporting tools, integration with processes, test coverage, and intuitive setup. Criticism includes slow performance during high-traffic periods, delayed customer service, confusing navigation, and outdated features.
9
Total Views
3
Category Comparisons
Datawizz
Datawizz is used for data analytics and business intelligence, handling large datasets, generating comprehensive reports, and providing actionable insights. Users appreciate its advanced visualization tools, real-time data processing, integration capabilities, and customizable dashboard. Enhanced data security, automation features, scalable capabilities, and responsive customer support boost satisfaction. Issues include processing speed, occasional technical glitches, and customer support responsiveness.
13
Total Views
2
Category Comparisons
CVEDIA
Users employ CVEDIA for developing custom AI models, conducting data annotation, and obtaining synthetic data. They value its ability to generate realistic simulations, testing and validating AI models, and deploying them without GPU infrastructure. Users appreciate its versatility, scalability, cost-effectiveness, excellent technical support, and fast performance, though they seek better customer support, documentation, and stability.
9
Total Views
2
Category Comparisons
watsonx.ai
8
Total Views
2
Category Comparisons
Neuromation
Users employ Neuromation for data labeling, model training, and dataset generation in AI projects. Appreciated for robust synthetic data capabilities and high-quality training datasets, it enhances AI workflows with scalable computing power and automation. Users highlight deep learning features, scalability, and ease of use, though they seek improved uptime consistency, support availability, and comprehensive documentation.
8
Total Views
2
Category Comparisons
syntheticAIdata
Users use syntheticAIdata for creating realistic training and test datasets to improve algorithm accuracy and ensure privacy compliance. It mimics real-world scenarios for software testing, data modeling, and analysis. Users appreciate real-time data generation, accuracy, easy integration, customizable features, and robust security. However, users find data processing speed and integration challenging, needing better documentation.
8
Total Views
2
Category Comparisons
Tumult Analytics
Marketing teams track engagement metrics. Data analysts visualize complex datasets. E-commerce businesses monitor sales performance. Financial institutions forecast market movements. Enterprise organizations optimize operational efficiency. Users appreciate data visualization, integration capabilities, and real-time analytics. Users suggest improving navigation, load times, and customization options. Enhancing these areas improves satisfaction.
7
Total Views
2
Category Comparisons
Statice
Statice is used for data anonymization, ensuring privacy compliance in data analytics and sharing. Users appreciate its ability to handle sensitive information securely while maintaining data utility for analysis and machine learning. They favor its integration capabilities and effectiveness in providing data privacy without compromising analytical power, scalability, and automated compliance with data regulations.
10
Total Views
1
Category Comparisons
Subsalt
Many users utilize Subsalt for efficient data management, secure storage, quick retrieval of large datasets, real-time data streaming, and analytics. They appreciate seamless integration, scalability, intuitive design, customization capabilities, automated reporting, and strong security. Users have suggested enhanced documentation, better customer support, performance optimization, frequent updates, and usability improvements.
8
Total Views
1
Category Comparisons
Syntheticus Suite
Syntheticus Suite enhances AI-driven data analysis by automating workflows, providing predictive analytics, and managing large datasets efficiently. Users value its integration capabilities, design, scalability, reporting features, and customer support. However, they note performance issues, complex navigation, insufficient documentation, slower support response, occasional bugs, and integration challenges.
7
Total Views
1
Category Comparisons
KopiKat
Users utilize KopiKat for automating data entry tasks, reducing manual labor and errors. It generates reports quickly, integrates with existing systems, and enhances productivity. Bulk data processing is a key feature, ensuring data accuracy. Users praise its easy setup, functionality, and performance, but suggest faster processing speeds, better peak-time support, and clearer documentation.
6
Total Views
Anyverse
Anyverse is used for creating high-quality synthetic data for training autonomous driving systems. Users appreciate its ability to generate complex datasets, extensive customization options, and seamless integration. The platform's reliability and robust data analytics capabilities receive praise, though some note slowdowns, lag, and a steep learning curve. Customer support response times need improvement.
8
Total Views
MDClone
MDClone is used for healthcare data analytics. Users create synthetic data for research, enhance data accessibility, and streamline operations. It supports cohort building, accelerates clinical research, and ensures data privacy. Valued for data anonymization and user-friendly tools, some seek improvements in data integration, query response times, and navigation. Security and support are highly praised.
5
Total Views

AI Synthetic Data experts

Sushrit Moundekar - PeerSpot reviewer
Snehasish Das - PeerSpot reviewer
Padmanesh NC - PeerSpot reviewer
DominicMackenzie - PeerSpot reviewer
Amit-Rastogi - PeerSpot reviewer
KumarRajeev - PeerSpot reviewer
VivekSingh 1 - PeerSpot reviewer
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Related categories

Data Warehouse
Cloud Data Warehouse
Test Data Management
Data Masking
Data Integration
Master Data Management (MDM) Software

Popular comparisons

Broadcom Test Data Manager vs. K2View
Gretel.ai vs. Tonic.ai
GenRocket vs. MOSTLY AI Platform

AI Synthetic Data Q&As

Read answers to top AI Synthetic Datum questions. 839,319 professionals have gotten help from our community of experts.
Sep 5, 2024
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Best AI Synthetic Data Solutions
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