모델 트레이닝용 합성 데이터 생성 시장 예측(-2032년) : 구성 요소별, 데이터 유형별, 배포 모드별, 기술별, 용도별, 최종 사용자별, 지역별 세계 분석

According to Stratistics MRC, the Global Synthetic Data Generation for Model Training Market is accounted for $419.8 million in 2025 and is expected to reach $3,466.4 million by 2032 growing at a CAGR of 35.2% during the forecast period. Synthetic Data Generation for Model Training refers to the process of creating artificial datasets that mimic real-world data characteristics for use in training machine learning models. These datasets are generated using algorithms such as generative adversarial networks (GANs), simulations, or rule-based systems, ensuring privacy, scalability, and diversity. Synthetic data helps overcome limitations like data scarcity, bias, and regulatory constraints by providing customizable, balanced inputs. It enables faster experimentation, reduces dependency on sensitive or proprietary data, and supports robust model development across industries including healthcare, finance, and autonomous systems, while maintaining compliance with data protection regulations and ethical standards.

Market Dynamics:

Driver:

Growing demand for privacy-preserving data

The rising need for privacy-preserving data is a major driver of synthetic data generation. As organizations face stricter regulations like GDPR and CCPA, synthetic datasets offer a compliant alternative to real data. They enable secure model training without compromising user privacy, especially in sensitive sectors like healthcare and finance. This demand is accelerating adoption across industries, making synthetic data a critical tool for ethical AI development and secure data collaboration in increasingly regulated digital environments.

Restraint:

Limited trust in synthetic data accuracy

Despite its advantages, synthetic data faces skepticism regarding its accuracy and realism. Many organizations question whether artificially generated datasets can truly replicate the complexity and variability of real-world data. This lack of trust can hinder adoption, especially in high-stakes applications like medical diagnostics or financial modeling. Without standardized validation frameworks, synthetic data may be perceived as unreliable, creating barriers to its integration into mission-critical AI workflows and slowing market growth.

Opportunity:

Acceleration of AI and ML adoption

The rapid expansion of AI and machine learning across industries presents a major opportunity for synthetic data generation. As organizations seek scalable, diverse datasets to train models, synthetic data offers a cost-effective and flexible solution. It enables faster experimentation, reduces dependency on proprietary data, and supports innovation in areas like autonomous systems, predictive analytics, and natural language processing. This surge in AI adoption fuels demand for synthetic data, positioning it as a foundational element of modern model development.

Threat:

High computational costs

Generating high-quality synthetic data requires significant computational resources, posing a threat to widespread adoption. Advanced techniques like GANs and simulations demand powerful hardware and specialized expertise, which can be costly for smaller enterprises. These high infrastructure and operational expenses may limit accessibility, especially in emerging markets or resource-constrained sectors. Without affordable solutions, the benefits of synthetic data may remain out of reach for many organizations, slowing market penetration and innovation.

Covid-19 Impact:

The COVID-19 pandemic accelerated digital transformation and highlighted the need for secure, scalable data solutions. With limited access to real-world data and increased privacy concerns, synthetic data emerged as a valuable tool for model training. It enabled continued AI development in healthcare, logistics, and remote services during lockdowns. The pandemic underscored the importance of flexible, privacy-compliant data generation, driving long-term investment in synthetic data technologies to support resilient, future-ready AI infrastructures.

The speech recognition segment is expected to be the largest during the forecast period

The speech recognition segment is expected to account for the largest market share during the forecast period due to its reliance on large, diverse datasets for training voice models. Synthetic data enables the creation of multilingual, accent-rich, and noise-varied speech inputs, enhancing model accuracy and inclusivity. As voice interfaces become mainstream across devices and services, demand for scalable, privacy-compliant training data grows. Synthetic data supports innovation in virtual assistants, transcription tools, and accessibility technologies, securing its leading position in the market.

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

Over the forecast period, the healthcare diagnostics segment is predicted to witness the highest growth rate owing to the need for secure, diverse medical datasets. Synthetic data enables model training without exposing patient information, ensuring compliance with privacy regulations. It supports applications like disease prediction, imaging analysis, and personalized treatment planning. As AI adoption in healthcare accelerates, synthetic data offers a scalable solution to overcome data scarcity and bias, fueling rapid growth in diagnostics and transforming clinical decision-making.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share because of its advanced AI ecosystem, strong regulatory frameworks, and early adoption of synthetic data technologies. Leading tech companies and research institutions in the region are investing heavily in privacy-preserving data solutions. The presence of robust infrastructure, skilled talent, and innovation-friendly policies supports widespread deployment across sectors like healthcare, finance, and autonomous systems, solidifying North America's leadership in synthetic data generation.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR due to rapid digitalization, expanding AI initiatives, and growing awareness of data privacy. Emerging economies like India, China, and Southeast Asia are investing in synthetic data to overcome data access challenges and support scalable model training. Government-backed innovation programs and increasing demand for AI in healthcare, education, and smart cities drive adoption. The region's dynamic growth and tech-forward mindset position it as a high-velocity market for synthetic data.

Key players in the market

Some of the key players in Synthetic Data Generation for Model Training Market include NVIDIA Corporation, Synthera AI, IBM Corporation, brewdata, Microsoft Corporation, Lemon AI, Google LLC, Sightwise, Amazon Web Services (AWS), Simulacra Synthetic Data Studio, Synthetic Data, Inc., Gretel.ai, Hazy, TruEra and Synthesis AI.

Key Developments:

In September 2025, Keepler and AWS have entered a strategic collaboration to accelerate the adoption of Generative AI in Europe. Keepler, as an AWS Premier Tier Partner, will harness its AI/data expertise with AWS infrastructure to build autonomous AI agents and bespoke enterprise solutions-spanning supply chain, customer experience, and more.

In April 2025, EPAM is deepening its strategic collaboration with AWS to push generative AI across enterprise modernization efforts. The expanded agreement enables EPAM to integrate AWS GenAI services like Amazon Bedrock into its AI/Run(TM) platform to help clients build specialized AI agents, automate workflows, migrate workloads, and scale applications efficiently and securely.

Components Covered:

• Tools/Platforms
• Services

Data Types Covered:

• Tabular Data
• Time-Series Data
• Image & Video Data
• Audio Data
• Text Data
• Other Data Types

Deployment Modes Covered:

• On-Premises
• Cloud-Based

Technologies Covered:

• Machine Learning
• Predictive Analytics
• Deep Learning
• Speech Recognition
• Natural Language Processing (NLP)
• Computer Vision

Applications Covered:

• Data Privacy & Security
• Autonomous Systems
• Data Augmentation
• Robotics
• Simulation & Testing
• Healthcare Diagnostics
• Algorithm Validation
• Fraud Detection
• Other Applications

End Users Covered:

• Media & Entertainment
• Manufacturing
• Government & Defense
• Retail & E-commerce
• IT & Telecommunications
• Automotive & Transportation
• Energy & Utilities
• 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 Technology Analysis
• 3.7 Application Analysis
• 3.8 End User Analysis
• 3.9 Emerging Markets
• 3.10 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 for Model Training Market, By Component

• 5.1 Introduction
• 5.2 Tools/Platforms
• 5.3 Services
  • 5.3.1 Consulting
  • 5.3.2 Training & Support
  • 5.3.3 Managed Services

6 Global Synthetic Data Generation for Model Training Market, By Data Type

• 6.1 Introduction
• 6.2 Tabular Data
• 6.3 Time-Series Data
• 6.4 Image & Video Data
• 6.5 Audio Data
• 6.6 Text Data
• 6.7 Other Data Types

7 Global Synthetic Data Generation for Model Training Market, By Deployment Mode

• 7.1 Introduction
• 7.2 On-Premises
• 7.3 Cloud-Based

8 Global Synthetic Data Generation for Model Training Market, By Technology

• 8.1 Introduction
• 8.2 Machine Learning
• 8.3 Predictive Analytics
• 8.4 Deep Learning
• 8.5 Speech Recognition
• 8.6 Natural Language Processing (NLP)
• 8.7 Computer Vision

9 Global Synthetic Data Generation for Model Training Market, By Application

• 9.1 Introduction
• 9.2 Data Privacy & Security
• 9.3 Autonomous Systems
• 9.4 Data Augmentation
• 9.5 Robotics
• 9.6 Simulation & Testing
• 9.7 Healthcare Diagnostics
• 9.8 Algorithm Validation
• 9.9 Fraud Detection
• 9.10 Other Applications

10 Global Synthetic Data Generation for Model Training Market, By End User

• 10.1 Healthcare & Life Sciences
• 10.2 Media & Entertainment
• 10.3 Manufacturing
• 10.4 Government & Defense
• 10.5 Retail & E-commerce
• 10.6 IT & Telecommunications
• 10.7 Automotive & Transportation
• 10.8 Energy & Utilities
• 10.9 Other End Users

11 Global Synthetic Data Generation for Model Training Market, By Geography

• 11.1 Introduction
• 11.2 North America
  • 11.2.1 US
  • 11.2.2 Canada
  • 11.2.3 Mexico
• 11.3 Europe
  • 11.3.1 Germany
  • 11.3.2 UK
  • 11.3.3 Italy
  • 11.3.4 France
  • 11.3.5 Spain
  • 11.3.6 Rest of Europe
• 11.4 Asia Pacific
  • 11.4.1 Japan
  • 11.4.2 China
  • 11.4.3 India
  • 11.4.4 Australia
  • 11.4.5 New Zealand
  • 11.4.6 South Korea
  • 11.4.7 Rest of Asia Pacific
• 11.5 South America
  • 11.5.1 Argentina
  • 11.5.2 Brazil
  • 11.5.3 Chile
  • 11.5.4 Rest of South America
• 11.6 Middle East & Africa
  • 11.6.1 Saudi Arabia
  • 11.6.2 UAE
  • 11.6.3 Qatar
  • 11.6.4 South Africa
  • 11.6.5 Rest of Middle East & Africa

12 Key Developments

• 12.1 Agreements, Partnerships, Collaborations and Joint Ventures
• 12.2 Acquisitions & Mergers
• 12.3 New Product Launch
• 12.4 Expansions
• 12.5 Other Key Strategies

13 Company Profiling

• 13.1 NVIDIA Corporation
• 13.2 Synthera AI
• 13.3 IBM Corporation
• 13.4 brewdata
• 13.5 Microsoft Corporation
• 13.6 Lemon AI
• 13.7 Google LLC
• 13.8 Sightwise
• 13.9 Amazon Web Services (AWS)
• 13.10 Simulacra Synthetic Data Studio
• 13.11 Synthetic Data, Inc.
• 13.12 Gretel.ai
• 13.13 Hazy
• 13.14 TruEra
• 13.15 Synthesis AI