AI in Synthetic Data Generation Market Size, Share, Opportunities, And Trends By Component (Services, Solutions), By Data Type (Structured Data, Unstructured Data), By Deployment (On-Premise, Cloud-Based), By End-User (Banking, Financial Services, and Insurance (BFSI), Retail and E-Commerce, Healthcare, IT & Telecommunication, Automotive, Others), And By Geography – Forecasts From 2025 To 2030

AI in Synthetic Data Generation Market Size:

The AI in the synthetic data generation market is anticipated to expand at a high CAGR over the forecast period.

 The market for artificial intelligence (AI) in synthetic data generation is witnessing steady growth. This is because businesses look for privacy-preserving substitutes for real-world data. Machine learning models are increasingly being trained and tested using synthetic data produced by AI techniques. These techniques include generative adversarial networks (GANs), particularly in situations where data privacy and labelling costs are significant concerns. Synthetic data has multiple applications in a variety of industries, including healthcare and retail. It allows businesses to produce sizable and well-balanced datasets.  The need to reduce bias in algorithms and new AI regulations is driving the market growth.  Generative AI advances, enabling safer and more scalable AI development, are also propelling the market.

AI in Synthetic Data Generation Market Overview & Scope:

The AI in the synthetic data generation market is segmented by:

• Component: Services hold a significant share of AI in the synthetic data generation market. This is because many organisations require expert support to implement, customise, and scale synthetic data solutions effectively. Services provide many capabilities, like consulting and data strategy development. There has been of increase in demand for synthetic data-as-a-service.
• Data Type: Structured Data holds a substantial share of AI in the synthetic data generation market. This is because it is widely used in industries such as finance, healthcare, and retail, where tabular data formats are common.
• Deployment: Cloud-based solutions hold a substantial share of AI in the synthetic data generation market. It is due to their scalability and ease of deployment. Cloud platforms help in using advanced techniques like GANs and diffusion models to generate synthetic data models. Cloud-based synthetic data tools allow organisations to generate, store, and manage large datasets.
• End User:.Retail and e-commerce hold a considerable share of the AI in the synthetic data generation market. It is due to their heavy reliance on data-driven personalisation and customer behaviour analysis. The sector has a large amount of sensitive data, such as purchase history, browsing behaviour, and payment information. They cannot be shared without violating privacy regulations like GDPR and CPRA.
• Region: The Asia-Pacific AI in synthetic data generation market is witnessing strong growth. This is due to rapid digital transformation and increasing enterprise data generation. Countries like India and China are adopting synthetic data in sectors such as AI in the synthetic data generation market. Synthetic data helps in training AI while avoiding privacy violations

Top Trends Shaping the AI in Synthetic Data Generation Market:

1. Integration of Generative AI models: A trend in the AI in the synthetic data generation market is the integration of generative AI models with synthetic data generator platforms. These models are improving the realism, diversity, and quality of synthetic datasets.

2. Growing Use in Regulated Industries- Another significant trend is the growth of the use of synthetic datasets in industries such as healthcare and finance. This is to ensure regulatory compliance and preserve privacy during data sharing.

3. Expansion of Synthetic Data-as-a-Service: There has been an increase in the expansion of synthetic data-as-a-service. It has made it easier for companies to access, customise, and scale synthetic datasets without in-house expertise.

AI in Synthetic Data Generation Market Growth Drivers vs. Challenges:

Drivers:

•  Increasing Demand for Data Privacy and Regulatory Compliance: One of the key drivers of AI in the synthetic data generation market is the increase in demand for data privacy and regulations. Laws like GDPR, HIPAA, and CPRA are forcing organisations to find privacy-preserving alternatives to using real-world data. According to Secureframe.It is estimated that the global cost of cybercrime is expected to reach $10.5 trillion by the year 2025. In 2024, the average cost of a data breach was $4.88 million, which was a 10% increase from 2023.
• Growing Adoption of AI and Machine Learning: Another key driver of AI in the synthetic data generation market is the growth in adoption of AI and machine learning. Sectors such as finance and healthcare require large, diverse, and unbiased datasets for model training and validation. According to a 2025 report by the Ministry of Electronics & IT, in the IndiaAI mission 2024 by the Indian government, the government has invested in RS. 10,300 crore over five years to improve AI and its capabilities.

Challenges:

• High-Quality and Realistic Synthetic Data: One of the major challenges of AI in the synthetic data generation market is ensuring that synthetic datasets are of high quality, realistic, and statistically representative of real-world data. Maintaining privacy and compliance is another requirement of the market. Synthetic data should accurately capture the complexity, correlations, and distributions of real data. These characteristics are required for multiple applications, like computer vision or tabular data analytics. Poorly generated data leads to biased or inaccurate AI/ML models, undermining their performance in real-world scenarios

AI in Synthetic Data Generation Market Regional Analysis:

• USA: The U.S. is the global leader in the synthetic data generation market. The US market is growing because of factors such as a strong tech ecosystem, investment in AI/ML, a startup ecosystem and a regulatory environment.
• China: China is another market with a huge synthetic data generation. It is a key player in the Asia-Pacific region, which is the fastest-growing region globally. The key drivers for this market are AI and digital transformation, consumer electronics and tech and government support.
• Germany: Germany is the leading country in Europe, which is expected to grow at a high CAGR. The key drivers of this market are stringent regulations, electronics and automotive and the research ecosystem.
• Japan: Japan is a significant contributor to the Asia-Pacific market. Japan’s leadership in robotics and autonomous vehicles drives demand for synthetic data for simulation and training

AI in Synthetic Data Generation Market Competitive Landscape:

The market has many notable players, including Amazon Web Services, MOSTLY AI, and Gretel Labs. Synthesis AI, K2view, GenRocket, Tonic AI, YData, Datagen, Anyverse, NVIDIA, Microsoft, Google, IBM, among others

• Partnership: In June 2024, NVIDIA announced the launch of a synthetic data generator, Nemotron-4 340B.It can create synthetic data that has the characteristics of real-world data. This will help improve data quality to increase the performance and robustness of data.
• Product Launch: In February 2024, Mostly AI announced the launch of MOSTLY AI Synthetic Data Platform v200.One of the best features of this is that the maximum timer could be for the training phase of a generator.

AI in Synthetic Data Generation Market Segmentation:

By Component

• Services
• Solutions

By Data Type

• Structured Data
• Unstructured Data

By Deployment

• On-Premise
• Cloud-Based

By End-User

• Banking, Financial Services, and Insurance (BFSI)
• Retail and E-Commerce
• Healthcare
• IT & Telecommunication
• Automotive
• Others

By Region

• North America
  • USA
  • Canada
  • Mexico
• South America
  • Brazil
  • Argentina
  • Others
• Europe
  • United Kingdom
  • Germany
  • France
  • Italy
  • Spain
  • Others
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • Others
• Asia Pacific
  • China
  • India
  • Japan
  • South Korea
  • Thailand
  • Others

1. EXECUTIVE SUMMARY 

2. MARKET SNAPSHOT

2.1. Market Overview

2.2. Market Definition

2.3. Scope of the Study

2.4. Market Segmentation

3. BUSINESS LANDSCAPE 

3.1. Market Drivers

3.2. Market Restraints

3.3. Market Opportunities 

3.4. Porter’s Five Forces Analysis

3.5. Industry Value Chain Analysis

3.6. Policies and Regulations 

3.7. Strategic Recommendations 

4. TECHNOLOGICAL OUTLOOK 

5. AI IN SYNTHETIC DATA GENERATION MARKET BY COMPONENT 

5.1. Introduction

5.2. Services

5.3. Solutions

6. AI IN SYNTHETIC DATA GENERATION MARKET BY DATA TYPE

6.1. Introduction

6.2. Structured Data

6.3. Unstructured Data

7. AI IN SYNTHETIC DATA GENERATION MARKET BY DEPLOYMENT

7.1. Introduction

7.2. On-Premise

7.3. Cloud-Based

8. AI IN SYNTHETIC DATA GENERATION MARKET BY END-USER

8.1. Introduction

8.2. Banking, Financial Services, and Insurance (BFSI)

8.3. Retail and E-Commerce

8.4. Healthcare

8.5. IT & Telecommunication

8.6. Automotive

8.7. Others

9.  AI IN SYNTHETIC DATA GENERATION MARKET BY GEOGRAPHY

9.1. Introduction

9.2. North America

9.2.1. USA

9.2.2. Canada

9.2.3. Mexico

9.3. South America

9.3.1. Brazil 

9.3.2. Argentina

9.3.3. Others

9.4. Europe

9.4.1. United Kingdom

9.4.2. Germany

9.4.3. France

9.4.4. Italy

9.4.5. Spain

9.4.6. Others

9.5. Middle East & Africa

9.5.1. Saudi Arabia

9.5.2. UAE

9.5.3. Others

9.6. Asia Pacific

9.6.1. China

9.6.2. India

9.6.3. Japan

9.6.4. South Korea

9.6.5. Thailand

9.6.6. Others

10. COMPETITIVE ENVIRONMENT AND ANALYSIS

10.1. Major Players and Strategy Analysis

10.2. Market Share Analysis

10.3. Mergers, Acquisitions, Agreements, and Collaborations

10.4. Competitive Dashboard

11. COMPANY PROFILES

11.1. Amazon 

11.2. MOSTLY AI

11.3. Synthesis AI 

11.4. K2view

11.5. GenRocket

11.6. Tonic AI

11.7. YData

11.8. Datagen

11.9. Anyverse

11.10. NVIDIA

11.11. Microsoft 

11.12. Google 

11.13. IBM 

12. APPENDIX

12.1. Currency 

12.2. Assumptions

12.3. Base and Forecast Years Timeline

12.4. Key benefits for the stakeholders

12.5. Research Methodology 

12.6. Abbreviations 

Amazon 

MOSTLY AI 

Synthesis AI  

K2view  

GenRocket 

Tonic AI 

YData 

Datagen 

Anyverse

NVIDIA 

Microsoft

Google

IBM

Sorry, the research methodology content could not be loaded.