If you are considering MOSTLY AI Synthetic Data Platform, you may also want to investigate similar alternatives or competitors to find the best solution. Other important factors to consider when researching alternatives to MOSTLY AI Synthetic Data Platform include ease of use and reliability. The best overall MOSTLY AI Synthetic Data Platform alternative is Tonic.ai. Other similar apps like MOSTLY AI Synthetic Data Platform are GenRocket, IBM watsonx.ai, Gretel.ai, and Syntho. MOSTLY AI Synthetic Data Platform alternatives can be found in Synthetic Data Tools but may also be in Data Masking Software or Large Language Model Operationalization (LLMOps) Software.
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.
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.
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.
Syntho provides deep learning software for generating synthetic data 'twins' which can be used and shared without privacy and GDPR concerns
Mask privacy sensitive information and generate synthetic test data to comply with privacy rules and regulations like GDPR, PCI and HIPAA.
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.
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.
Elevating data and privacy in a world moving towards AI. Great experiences do not need to come at the expense of users' privacy and security. Rather, privacy and security can help support great experiences. We provide privacy and synthetic data tools.
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.