Research alternative solutions to MDClone on G2, with real user reviews on competing tools. Other important factors to consider when researching alternatives to MDClone include reliability and ease of use. The best overall MDClone alternative is IBM watsonx.ai. Other similar apps like MDClone are Tonic.ai, CA Test Data Manager, YData, and Tumult Analytics. MDClone alternatives can be found in Synthetic Data Tools but may also be in Large Language Model Operationalization (LLMOps) Software or Data Masking Software.
IBM Watsonx.ai is an advanced AI and machine learning platform designed to accelerate enterprise AI adoption, offering a comprehensive suite of tools for businesses to build, deploy, and scale AI applications. The product is part of IBM's broader Watsonx ecosystem, which aims to democratize AI by providing accessible, powerful solutions tailored for organizations of all sizes and industries.
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.
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.
Tumult Analytics is an open-source Python library making it easy and safe to use differential privacy; enabling organizations to safely release statistical summaries of sensitive data. Tumult Analytics is running in production at institutions such as the U.S. Census Bureau, the Wikimedia Foundation, and the Internal Revenue Service. It is easy to use, it can scale to datasets with billions of rows, it integrates with common data science tools, and it supports advanced features to maximize the value extracted from protected data.
GenRocket provides hundreds of intelligent data generators and dozens of database formats enabling testers to design and generate any variety of test data they need in any volume. Use GenRocket to maximize test coverage with complete control over generating test data for a variety of use cases, like positive and negative testing, specific boundary conditions and edge cases, data combinations and permutations, testing complex workflows with dynamic data, and new applications that have no data when introduced. It’s easy to integrate GenRocket’s Test Data Automation platform with all test automation tools and frameworks to accelerate the development and delivery of high-quality software in your CI/CD pipeline.
MOSTLY GENERATE is an enterprise-grade Synthetic Data Platform that preserves significantly more information and data value than any other data anonymization technique on the market. It enables you to overcome the barriers to AI and Big Data adoption. All while securely protecting your customers’ privacy.
K2View is an end-to-end solution that delivers the data speed and agility the digital world demands, while working seamlessly within the complex technology environments of large enterprises.
Statice allows you to freely work with your customers data by securely anonymizing it. This protects your customers and opens up new data-driven opportunities.
Syntho provides deep learning software for generating synthetic data 'twins' which can be used and shared without privacy and GDPR concerns