Synthetic Data Generation Market Size - By Data, By Offering, By Generation Technique, By Application, By End Use, Analysis, Share, Growth Forecast, 2025 - 2034

Synthetic Data Generation Market Size

The global synthetic data generation market size was valued at USD 310.5 million in 2024 and is projected to grow at a CAGR of 35.2% between 2025 and 2034. The growing need for AI and ML model training is driving the expansion of the market. AI and machine learning models require large amounts of diverse and high-quality data for effective training. However, collecting real-world data can be expensive, time-consuming, and challenging due to issues like data scarcity, privacy concerns, and bias.
 

In industries such as healthcare, autonomous vehicles, and finance, obtaining real-world data is often limited, difficult, or restricted by legal and ethical considerations. Synthetic data, which is artificially created to replicate real-world data without using personal or sensitive information, offers a practical solution. It provides high-quality, diverse, and privacy-compliant datasets on demand, enabling companies to train AI and ML models more efficiently and at a lower cost, thus leading to its demand.
 

For instance, in December 2024, Mindtech Global unveiled Chameleon 24.2, the latest iteration of its synthetic data generation platform, designed to enhance the creation of high-quality, labeled training data for AI systems. This next-generation tool aims to address the increasing demand for realistic and diverse datasets necessary for training advanced AI models, particularly in the field of computer vision.
 

Privacy concerns and regulatory compliance are key factors driving the growth of the synthetic data generation market. As businesses in industries such as healthcare, finance, and e-commerce collect increasing amounts of personal and sensitive data, they face growing pressure to comply with strict privacy regulations. Laws such as the GDPR, CCPA, and HIPAA enforce strict rules on how data is used, stored, and shared. These regulations make it challenging to use real-world data, especially when it includes personally identifiable information (PII) or sensitive health data.
 

Synthetic data addresses these challenges by providing privacy-compliant datasets that do not include PII or breach confidentiality agreements. This allows businesses to create large datasets for AI training, testing, and analytics without the risks associated with handling personal information.
 

Synthetic Data Generation Market Trends

The increasing connection of devices to the internet is generating large amounts of data, leading to a growing need for synthetic data. This data is essential for simulating scenarios and improving the performance of edge devices. In industries such as manufacturing and smart cities, synthetic data is used to test AI systems in different conditions, helping to improve decision-making and operational efficiency.
 

Additionally, the use of synthetic data in augmented reality (AR), virtual reality (VR), and gaming is driving market growth. These industries require significant amounts of data to create realistic and engaging experiences. Synthetic data enables companies to develop 3D models, environments, and interactions, which are used to train AI algorithms, enhancing virtual worlds and improving user experiences.
 

Quality and realism challenges are major obstacles to the growth of the synthetic data generation market. The usefulness of synthetic data depends on how well it can replicate real-world data. While synthetic data provides benefits such as cost efficiency, scalability, and privacy protection, ensuring its quality remains a key issue. If synthetic data does not accurately reflect the complexity and diversity of real-world data, it can lead to poor AI training and biased models.
 

Additionally, creating highly realistic synthetic data for complex scenarios, such as rare events, edge cases, or detailed human behaviors, is still a difficult task. For example, in healthcare, where precise data is essential for medical imaging or disease prediction, synthetic data that fails to capture the details of human biology could result in incorrect diagnoses or ineffective treatments.
 

Synthetic Data Generation Market Analysis

Based on application, the market is segmented as AI/ML model training, privacy protection, test data management, data analytics and visualization, and others. In 2024, the AL/ML model training segment held a market share of over 30% and is expected to exceed USD 2 billion by 2034. The AI/ML model training segment leads the application segment of the synthetic data generation market due to the increasing need for large, high-quality datasets to develop and improve AI and ML models. These models require diverse and representative datasets to perform well in real-world scenarios.
 

However, collecting and labeling real-world data is often time-consuming, expensive, and restricted by issues like privacy concerns and limited availability. This is creating a strong demand for synthetic data, which is artificially created to replicate real-world data and address gaps where real data is unavailable or difficult to obtain.
 

Based on the data type, the synthetic data generation market is divided into image & video, tabular, text, and others. The text segment held around 34.5% of the market share in 2024, due to its widespread use in various industries, especially for training AI models in natural language processing (NLP).
 

As businesses increasingly adopt AI for tasks such as customer service, content creation, sentiment analysis, and data analytics, the demand for large volumes of diverse and high-quality text data has grown significantly. Text data is crucial for training AI systems to understand, process, and generate human language, making it essential for modern applications like chatbots, virtual assistants, machine translation, and information retrieval systems.
 

North America synthetic data generation market accounted for 34% of the revenue share in 2024, owing to its strong position in technology innovation, AI development, and data-driven industries. The country hosts major tech companies that are deeply involved in AI and machine learning research. These companies rely on large and diverse datasets to train and test their AI models, driving the demand for synthetic data. Additionally, government agencies and research institutions are increasingly funding AI and ML technologies, encouraging advancements in synthetic data generation methods.
 

The demand for synthetic data generation is increasing rapidly in APAC due to technological advancements, economic growth, and regulatory developments. Countries such as China, India, Japan, and South Korea are experiencing significant digital transformation and are heavily investing in AI and ML technologies. Industries such as healthcare, automotive, finance, and manufacturing are using AI models to improve efficiency, streamline processes, and offer innovative solutions. Since AI and ML models require large amounts of high-quality data for training, synthetic data has become essential to address issues like limited data availability, privacy concerns, and high data collection costs.
 

The synthetic data generation market in Europe is growing quickly due to regulatory, technological, and industry-specific factors. A major driver is the strict data privacy regulations, especially the GDPR, which sets the standard for data protection across Europe. Industries such as healthcare, finance, and retail increasingly use AI and machine learning to process large amounts of personal data. This has created a rising need for privacy-focused solutions like synthetic data.
 

Synthetic data enables businesses to train AI models, test algorithms, and perform analytics without using real personal data. This helps them comply with strict data privacy laws while gaining useful insights and improving the accuracy of AI models.
 

Synthetic Data Generation Market Share

DataGen and Gretel collectively held a substantial market share of over 10% in the synthetic data generation industry in 2024. DataGen and Gretel are leading companies in the synthetic data generation industry, recognized for their innovative approaches and strong presence in areas like AI/ML model training, privacy protection, and data scalability.
 

DataGen specializes in creating high-quality synthetic data for AI model training, particularly in fields such as computer vision and 3D simulations, helping businesses overcome the challenges of real-world data limitations. Gretel provides tools that enable companies to generate privacy-focused synthetic data, ensuring compliance with strict regulations while enhancing machine learning model performance.
 

Sagemaker and Sogeti have taken strategic actions to strengthen their presence in the growing synthetic data generation market by leveraging their technology and expanding their services. Sagemaker has incorporated synthetic data generation into its AI/ML tools, enabling organizations to efficiently create and use synthetic datasets for training, testing, and improving AI models at scale.
 

Meanwhile, Sogeti has focused on consulting and developing customized synthetic data solutions for industries such as healthcare, automotive, and finance. By combining their industry expertise with a focus on data privacy, compliance, and AI innovation, both companies have enhanced their market positions and expanded their customer base in this growing field.
 

Synthetic Data Generation Market Companies

Major players operating in the synthetic data generation industry are:

• Aetion
• Anylogic
• Anyverse
• Bifrost
• Cvedia
• DataGen
• GenRocket
• Gretel
• Hazy
• K2View
   

Synthetic Data Generation Industry News

• In November 2024, SAS acquired the principal software assets of Hazy, a pioneer in synthetic data technology, to enhance its capabilities in artificial intelligence (AI) development. This strategic acquisition aims to integrate Hazy's innovative synthetic data generation tools into SAS's offerings, particularly within the SAS Data Maker platform
   
• In October 2024, Mostly AI launched a new synthetic text functionality designed to help enterprises overcome the challenges associated with AI training, particularly the limitations posed by the availability of public data. This innovation allows organizations to leverage their proprietary text data—such as emails, customer support transcripts, and chatbot conversations—safely and effectively to train large language models (LLMs) without compromising privacy
   

The synthetic data generation market research report includes in-depth coverage of the industry with estimates & forecasts in terms of revenue ($Bn) from 2021 to 2034, for the following segments:

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Market, By Data

• Image & video
• Tabular
• Text
• Others

Market, By Offering

• Fully synthetic
• Partially synthetic

Market, By Generation Technique

• Statistical methods & models
• Rule-based system
• Agent-based system
• Deep learning methods
• Others

Market, By Application

• AI/ML model training
• Privacy protection
• Test data management
• Data analytics and visualization
• Others

Market, By End Use

• BFSI
• Healthcare & life sciences
• Manufacturing
• Technology & telecommunications
• Automotive & transportation
• Others

The above information is provided for the following regions and countries:

• North America
  • U.S.
  • Canada
• Europe
  • UK
  • Germany
  • France
  • Italy
  • Spain
  • Russia
  • Nordics
• Asia Pacific
  • China
  • India
  • Japan
  • Australia
  • South Korea
  • Southeast Asia 
• Latin America
  • Brazil
  • Mexico
  • Argentina 
• MEA
  • UAE
  • South Africa
  • Saudi Arabia