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Synthetic Data Generation

Synthetic data powers 46.3% of AI model training and is compounding 39% a year. NVIDIA paid $320M+ for Gretel to own the pipe.

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Growth Overview

39%

CAGR

+45%

YoY Growth

+210%

Search Growth

$3.67B

by 2031

12-Month Trend

Growth Rate

0%25%50%

Market Size Projection

$710M

Current (2025)

$3.67B

by 2031

Overview

Platforms that generate artificial but statistically faithful datasets to train and test AI models without exposing real records. Model training already drives 46.3% of a market that reached $710M in 2026 and is projected to hit $3.67B by 2031 at 38.96% CAGR (Mordor Intelligence). Real-world training data is being exhausted and legally constrained, while privacy rules make synthetic stand-ins the compliant path for regulated-industry machine learning.

What's Driving This Growth?

▸Real-world training data is being exhausted and legally constrained, pushing synthetic data to supplement or replace scraped datasets
▸Privacy regulation (GDPR, HIPAA) makes synthetic stand-ins the compliant path for regulated-industry machine learning
▸Rare events and edge cases (autonomous driving, fraud) are cheaper to generate synthetically than to collect
▸Model training drives 46.3% of demand, the dominant use case as foundation models scale

Market Signals

✓NVIDIA acquired Gretel (Mar 2025) for a reported $320M+, folding synthetic data into its generative-AI developer services
✓Mostly AI raised a $120M Series B led by Insight Partners at a ~$800M valuation for privacy-safe synthetic data
✓Tonic.ai raised a $45M Series B for synthetic test and dev data, serving eBay, Flexport, and Everlywell