ByWith more and more companies embracing AI, machine learning, and advanced analytics across industries, synthetic data has grown very strong as an enabler of innovation.
Synthetic data helps organizations create, train, and test models securely and efficiently since it actually replicates real-world data without breaching privacy. In this article, I will review the top synthetic data generation tools of 2025. So, let’s get started!
What is Synthetic Data?
Synthetic data acts as data which is completely simulated or random, while it still upholds structural, behavioral and statistical resemblance to the actual data. This type of data is invaluable for the items below.
- Training AI Models: Improve the performance of machine learning without jeopardizing sensitive information.
• Testing and Development: The emulation of the edge cases or real-world scenarios while testing any software.
• Compliance: Ensuring compliances such as GDPR, HIPAA, and many others by not exposing real data in the world.
Now, let’s look at the top synthetic data generation tools of recent times.
Popular Artificial Synthetic Data Generating Tools of 2025
1. K2View Synthetic Data Generator
Key Features
• Entity-based data generation for unmatched levels of granularity and accuracy.
• Real time generation of good synthetic data for training and evaluating artificial intelligence systems.
• It supports structured data, semi structured data and unstructured data.
• Integration with the other existing data pipelines and other platforms.
Why K2View Leads the Market
The synthetic data produced by K2View is very realistic and is the most secure in regard to information security. This makes it the best solution for industries which needs such solutions as finance, health care and telecommunications among others through integration with cloud, on premise solutions and everything in between.
2. Gretel.ai
Among these, Gretel.ai is the primary driver in the synthetic data market with features for generating, converting, and masking datasets for AI and ML.
Key Features
• API-driven platform-easy to work with both for developers and data scientists.
• Advanced differential privacy control to comply with regulations.
• It supports the generation of time-series and sequential data for various complex use cases.
Gretel.ai is just the right choice for startups and medium-sized businesses wanting to access synthetic data at lower costs.
3. Synthesized.io
Synthesized.io is a flexible synthetic data generation platform; this means that the tools required to create quality datasets on a large scale are quite easy to use.
Key Features
• Automated creation of synthetic data with the use of AI.
• Bias detection and mitigation toward ethical AI applications.
• Integration with major cloud platforms: AWS and Azure.
Synthesized.io will be a good fit for organizations looking to accelerate and automate their data pipelines.
4. MOSTLY AI
MOSTLY AI is one of the pioneers in synthetic data generation. It focuses on the generation of synthetic data that is customer-centric, using the most advanced privacy engines. It is well-renowned for the replication of customer behaviors, demographics, and preferences.
Key Features
• AI-driven simulation of highly realistic customer data.
• Advanced features of bias elimination and fairness.
• Industry-leading, privacy-preserving technology.
• Scalable solutions for big data environments.
MOSTLY AI is in demand for providing better customer experiences with data-driven insights to retail, telecom, and insurance firms.
5. Sogeti
Sogeti is another player that is powerful in synthetic data generation, servicing enterprise needs for security and scalability in testing and AI training.
Key Features
• Automatically generated synthetic data at high statistical accuracies.
• Multi-domain support: financial, healthcare, retail, and many more.
• Real-time generation for agile development cycles.
Sogeti offers a great way for an organization to enable the quick reduction of time-to-market in the case of data-intensive applications.
6. Synthea
Synthea is an open-source tool for generating synthetic data that has been designed mainly for healthcare organizations.
Key Features
• Healthcare-specific data generation in demographics, diseases, treatments.
• An open-source framework for customization.
• HL7 FHIR, among other Healthcare data standards, is supported.
• Very ideal for the training of AI models, both clinical and pharmaceutical.
The focus of Synthea on healthcare makes it irreplaceable in medical research for organizations and the construction of AI solutions in healthcare.
7. Hazy
Hazy is a synthetic data platform built to meet enterprise needs in highly regulated finance and healthcare industries. It specializes in generating privacy-preserving data for analytics and AI applications.
Key Features
• Data synthesis for large and complex sets using Artificial Intelligence.
• Standard connectors for the leading enterprise applications and data repositories.
• High levels of compliance especially in the General Data Protection Regulation and the California Consumer Privacy Act.
• Brings focus on to the banking and financial services application areas.
Hazy’s focus on domain-specific applications makes it stand out from the rest, when it comes to the selection of most organizations requiring depth in their customization.
Choosing the Right Synthetic Data Tool
When selecting a tool for generating synthetic data, consider the aspects below.
- Does the tool offer a free trial, did you tried it? And how satisfied are you with the tool?
- Is the tool relevant to the industry you are in and to the particular need you have?
- Can it remain compliant with GDPR, HIPAA, or other such regulations as might apply?
- Is it still relevant to the current strategy for implementing data and analytics?
- What is the licensing, and is it within your budget?
Final Words
Synthetic data is a game-changing factor for organizations trying to innovate within the frames set by strict privacy standards. Powered by tools such as K2View, Gretel.ai, and MOSTLY AI, businesses have at their disposal various powerful platforms that combine scalability with compliance and accuracy.
As AI and Big Data continue to reshape industries, selecting the right synthetic data generator can position your organization as a leader—not just in growth and efficiency but also in ensuring top-tier security and ethical AI practices.
Governance plays a crucial role in achieving this balance. For instance, Cognizant recently became the first global IT services company to earn the ISO/IEC 42001:2023 certification for its Artificial Intelligence Management System (AIMS). This milestone sets a new benchmark for ethical and responsible AI development.
By leveraging top synthetic data tools alongside robust governance frameworks, businesses can drive AI advancements that are both innovative and ethically sound.
