세계의 합성 데이터 생성 시장 예측 - 제공 제품 및 서비스별, 컴포넌트별, 데이터 유형별, 모델링 유형별, 전개 모드별, 용도별, 최종 사용자별, 지역별 분석(-2032년)

According to Stratistics MRC, the Global Synthetic Data Generation Market is accounted for $0.62 billion in 2025 and is expected to reach $7.93 billion by 2032 growing at a CAGR of 43.9% during the forecast period. Synthetic data generation produces artificial datasets that mirror statistical properties of real data while protecting privacy, enabling AI training, testing, and analytics without using sensitive production records. It helps alleviate labeling scarcity, reduce bias, and accelerate model iteration across regulated sectors. Growth is propelled by AI/ML uptake, privacy regulation, and demand for diverse, large labeled datasets.

Market Dynamics:

Driver:

Rising demand for data for AI/ML training amidst privacy regulations

The growing adoption of artificial intelligence (AI) and machine learning (ML) solutions has significantly increased the need for large, high-quality datasets for model training. Organizations face strict privacy regulations such as GDPR and CCPA, which limit access to real-world sensitive data. Synthetic data generation addresses this gap by providing realistic, privacy-compliant datasets that preserve statistical properties. Furthermore, it enables scalable experimentation, testing, and algorithm improvement without breaching regulations. Additionally, enterprises across healthcare, finance, and autonomous systems increasingly rely on synthetic datasets to accelerate innovation while maintaining compliance.

Restraint:

Concerns about synthetic data quality and fidelity

Despite its advantages, synthetic data is often scrutinized for its quality and fidelity compared to real-world data. If synthetic datasets fail to accurately replicate statistical distributions, edge cases, or correlations, AI/ML models trained on them may underperform or exhibit bias. Moreover, ensuring data validity across diverse applications requires sophisticated generation techniques and domain expertise, increasing cost and complexity.

Opportunity:

Growing adoption in data-sensitive industries

Synthetic data presents significant opportunities in industries where privacy, security, and compliance constraints restrict access to real datasets. Sectors such as healthcare, banking, insurance, and defense can leverage synthetic datasets to train AI models without exposing personal or classified information. Furthermore, adoption is expanding for testing autonomous vehicles, robotics, and IoT systems, where real-world data collection is costly or hazardous. Additionally, enterprises increasingly use synthetic data for scenario simulation, algorithm validation, and data augmentation, unlocking new revenue streams for vendors offering robust, customizable solutions tailored to highly regulated environments.

Threat:

Competition from emerging data solutions like data marketplaces

Synthetic data providers face competitive pressure from alternative data acquisition solutions, such as commercial data marketplaces, federated learning frameworks, and anonymized datasets. These alternatives offer ready-made or collaborative access to real-world data, sometimes at lower costs or with simpler implementation. Moreover, organizations may perceive marketplace datasets as more reliable for certain analytics or model training, limiting synthetic data uptake. Additionally, emerging technologies in privacy-preserving AI, like homomorphic encryption or differential privacy, could further reduce reliance on synthetic datasets, creating a competitive landscape that challenges market growth.

Covid-19 Impact:

The Covid-19 pandemic accelerated the adoption of digital technologies and remote operations, highlighting the importance of accessible, privacy-compliant datasets for AI/ML development. Lockdowns and restrictions made real-world data collection challenging, particularly in healthcare and mobility sectors. This situation increased reliance on synthetic data for model training, simulation, and predictive analytics. Additionally, organizations prioritized data-driven decision-making while adhering to privacy laws, which strengthened the use of synthetic data generation solutions. Consequently, the pandemic acted as a catalyst for broader awareness, adoption, and investment in synthetic data technologies across multiple industries.

The partially synthetic data segment is expected to be the largest during the forecast period

The partially synthetic data segment is expected to account for the largest market share during the forecast period. By offering a blend of real and synthetic data, this segment mitigates risks associated with fully synthetic datasets while maintaining privacy and regulatory compliance. Organizations benefit from enhanced model performance, reduced bias, and accelerated deployment cycles. Additionally, partially synthetic datasets are increasingly adopted for research, testing, and enterprise analytics applications, reinforcing their dominance. Vendor investments in generation algorithms, validation tools, and industry-specific solutions further strengthen adoption, ensuring this segment continues to capture the largest share of the synthetic data generation market.

The services segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the services segment is predicted to witness the highest growth rate. The surge in AI/ML adoption, combined with the complexity of generating high-quality, domain-specific synthetic datasets, fuels demand for specialized services. Additionally, organizations increasingly prefer managed or subscription-based models that reduce operational overhead and technical risks. Vendors offering end-to-end support from data generation to validation and integration are better positioned to capture emerging opportunities. Furthermore, as awareness of regulatory compliance and model accuracy grows, services play a critical role in accelerating adoption, making this segment the fastest-growing component of the synthetic data generation market.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share. The region benefits from strong AI/ML adoption, robust R&D infrastructure, early technology deployment, and substantial investment in privacy-compliant solutions. Additionally, the presence of major vendors, startups, and leading research institutions fosters innovation in synthetic data generation. Regulatory frameworks such as HIPAA and CCPA drive demand for privacy-preserving datasets, particularly in healthcare, finance, and defense sectors. Furthermore, high cloud penetration, advanced IT infrastructure, and strong enterprise budgets enable rapid implementation of synthetic data solutions, sustaining North America's dominant market position globally.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR. Rapid digital transformation, increasing AI/ML adoption, rising cloud infrastructure, and supportive government initiatives drive regional growth. Additionally, expanding industrial and healthcare sectors are investing in privacy-compliant data solutions, while startups and local vendors offer cost-effective synthetic data services. Increasing smartphone penetration, internet access, and digital literacy further facilitate adoption. Moreover, multinational corporations entering the region create collaboration opportunities, fueling competitive growth. Collectively, these factors contribute to Asia Pacific emerging as the fastest-growing market.

Key players in the market

Some of the key players in Synthetic Data Generation Market include Amazon.com, Inc., Mostly AI, Synthesis AI, Gretel.ai, Tonic.ai, Meta Platforms, Inc., Microsoft Corporation, NVIDIA Corporation, OpenAI, Datagen Technologies, CVEDIA Inc., IBM Corporation, Databricks Inc., Sogeti (Capgemini Group), and Synthesia Ltd.

Key Developments:

In August 2025, AWS enhanced its Amazon Bedrock generative AI service with new foundational models, improved data processing, prompt caching to reduce costs and latency, and intelligent prompt routing for optimized AI task handling. AWS is also advancing its Knowledge Bases for richer AI applications by enabling structured data retrieval and graph modeling integration, useful for synthetic data applications. These tools are aimed at improving synthetic data use and inference efficiency in AI workloads.

In June 2024, NVIDIA announced Nemotron-4 340B, a family of open models that developers can use to generate synthetic data for training large language models (LLMs) for commercial applications across healthcare, finance, manufacturing, retail and every other industry.

Offerings Covered:

• Fully Synthetic Data
• Partially Synthetic Data
• Hybrid Synthetic Data

Components Covered:

• Solution (Software/Platform)
• Services

Data Types Covered:

• Tabular Data (Structured)
• Image & Video Data (Unstructured)
• Text Data (Unstructured)
• Other Data Types

Modeling Types Covered:

• Generative Adversarial Networks (GANs)
• Variational Autoencoders (VAEs)
• Statistical Methods
• Agent-Based Modeling (ABM)
• Transformer Models

Deployment Modes Covered:

• Cloud-based
• On-premise

Applications Covered:

• AI/ML Model Training & Development
• Test Data Management (TDM) and Quality Assurance (QA)
• Data Analytics & Visualization
• Enterprise Data Sharing & Monetization
• Privacy Protection & Compliance

End Users Covered:

• Banking, Financial Services, and Insurance (BFSI)
• Healthcare & Life Sciences
• Automotive & Transportation
• IT & Telecommunication
• Retail & E-commerce
• Government & Defense
• Manufacturing & Industrial
• Media & Entertainment
• Other End Users

Regions Covered:

• North America
  • US
  • Canada
  • Mexico
• Europe
  • Germany
  • UK
  • Italy
  • France
  • Spain
  • Rest of Europe
• Asia Pacific
  • Japan
  • China
  • India
  • Australia
  • New Zealand
  • South Korea
  • Rest of Asia Pacific
• South America
  • Argentina
  • Brazil
  • Chile
  • Rest of South America
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • Qatar
  • South Africa
  • Rest of Middle East & Africa

What our report offers:

• Market share assessments for the regional and country-level segments
• Strategic recommendations for the new entrants
• Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
• Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
• Strategic recommendations in key business segments based on the market estimations
• Competitive landscaping mapping the key common trends
• Company profiling with detailed strategies, financials, and recent developments
• Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

• Company Profiling
  • Comprehensive profiling of additional market players (up to 3)
  • SWOT Analysis of key players (up to 3)
• Regional Segmentation
  • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
  • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

• 2.1 Abstract
• 2.2 Stake Holders
• 2.3 Research Scope
• 2.4 Research Methodology
  • 2.4.1 Data Mining
  • 2.4.2 Data Analysis
  • 2.4.3 Data Validation
  • 2.4.4 Research Approach
• 2.5 Research Sources
  • 2.5.1 Primary Research Sources
  • 2.5.2 Secondary Research Sources
  • 2.5.3 Assumptions

3 Market Trend Analysis

• 3.1 Introduction
• 3.2 Drivers
• 3.3 Restraints
• 3.4 Opportunities
• 3.5 Threats
• 3.6 Application Analysis
• 3.7 End User Analysis
• 3.8 Emerging Markets
• 3.9 Impact of Covid-19

4 Porters Five Force Analysis

• 4.1 Bargaining power of suppliers
• 4.2 Bargaining power of buyers
• 4.3 Threat of substitutes
• 4.4 Threat of new entrants
• 4.5 Competitive rivalry

5 Global Synthetic Data Generation Market, By Offering

• 5.1 Introduction
• 5.2 Fully Synthetic Data
• 5.3 Partially Synthetic Data
• 5.4 Hybrid Synthetic Data

6 Global Synthetic Data Generation Market, By Component

• 6.1 Introduction
• 6.2 Solution (Software/Platform)
  • 6.2.1 AI-Based Generation Platforms
  • 6.2.2 Simulation Software
  • 6.2.3 Data Masking and Anonymization Tools
  • 6.2.4 APIs and Integration Modules
• 6.3 Services
  • 6.3.1 Professional Services
  • 6.3.2 Managed Services

7 Global Synthetic Data Generation Market, By Data Type

• 7.1 Introduction
• 7.2 Tabular Data (Structured)
  • 7.2.1 Time-Series Data
  • 7.2.2 Relational/Transactional Data
• 7.3 Image & Video Data (Unstructured)
• 7.4 Text Data (Unstructured)
• 7.5 Other Data Types

8 Global Synthetic Data Generation Market, By Modeling Type

• 8.1 Introduction
• 8.2 Generative Adversarial Networks (GANs)
• 8.3 Variational Autoencoders (VAEs)
• 8.4 Statistical Methods
• 8.5 Agent-Based Modeling (ABM)
• 8.6 Transformer Models

9 Global Synthetic Data Generation Market, By Deployment Mode

• 9.1 Introduction
• 9.2 Cloud-based
• 9.3 On-premise

10 Global Synthetic Data Generation Market, By Application

• 10.1 Introduction
• 10.2 AI/ML Model Training & Development
• 10.3 Test Data Management (TDM) and Quality Assurance (QA)
• 10.4 Data Analytics & Visualization
• 10.5 Enterprise Data Sharing & Monetization
• 10.6 Privacy Protection & Compliance

11 Global Synthetic Data Generation Market, By End User

• 11.1 Introduction
• 11.2 Banking, Financial Services, and Insurance (BFSI)
• 11.3 Healthcare & Life Sciences
• 11.4 Automotive & Transportation
• 11.5 IT & Telecommunication
• 11.6 Retail & E-commerce
• 11.7 Government & Defense
• 11.8 Manufacturing & Industrial
• 11.9 Media & Entertainment
• 11.10 Other End Users

12 Global Synthetic Data Generation Market, By Geography

• 12.1 Introduction
• 12.2 North America
  • 12.2.1 US
  • 12.2.2 Canada
  • 12.2.3 Mexico
• 12.3 Europe
  • 12.3.1 Germany
  • 12.3.2 UK
  • 12.3.3 Italy
  • 12.3.4 France
  • 12.3.5 Spain
  • 12.3.6 Rest of Europe
• 12.4 Asia Pacific
  • 12.4.1 Japan
  • 12.4.2 China
  • 12.4.3 India
  • 12.4.4 Australia
  • 12.4.5 New Zealand
  • 12.4.6 South Korea
  • 12.4.7 Rest of Asia Pacific
• 12.5 South America
  • 12.5.1 Argentina
  • 12.5.2 Brazil
  • 12.5.3 Chile
  • 12.5.4 Rest of South America
• 12.6 Middle East & Africa
  • 12.6.1 Saudi Arabia
  • 12.6.2 UAE
  • 12.6.3 Qatar
  • 12.6.4 South Africa
  • 12.6.5 Rest of Middle East & Africa

13 Key Developments

• 13.1 Agreements, Partnerships, Collaborations and Joint Ventures
• 13.2 Acquisitions & Mergers
• 13.3 New Product Launch
• 13.4 Expansions
• 13.5 Other Key Strategies

14 Company Profiling

• 14.1 Amazon.com, Inc.
• 14.2 Mostly AI
• 14.3 Synthesis AI
• 14.4 Gretel.ai
• 14.5 Tonic.ai
• 14.6 Meta Platforms, Inc.
• 14.7 Microsoft Corporation
• 14.8 NVIDIA Corporation
• 14.9 OpenAI
• 14.10 Datagen Technologies
• 14.11 CVEDIA Inc.
• 14.12 IBM Corporation
• 14.13 Databricks Inc.
• 14.14 Sogeti (Capgemini Group)
• 14.15 Synthesia Ltd.