A new market outlook projects synthetic data growing from $485.9M in 2025 to at least $3.1B by 2032, driven by AI adoption and regulatory pressure. For data and privacy teams, that means more budget, more vendors, and more scrutiny on whether “synthetic” actually reduces risk.
Global synthetic data market forecast: $485.9M (2025) to $3.1B+ (2032)
Coherent Market Insights projects the global synthetic data generation market at roughly $485.9 million in 2025, rising to between $3.1 billion and $8.8 billion by 2032. The report frames growth at a 30.6% to 35.2% CAGR, attributing demand to broader AI/ML adoption, autonomous-vehicle development, and regulatory pressure from frameworks including GDPR, CCPA, and HIPAA.
The write-up also breaks out where spend is concentrating: structured/tabular synthetic data is described as the leading data type (36.4% to 50% share), model training is the largest application segment (45.3% of usage in 2025), and North America leads with a 33% share. Asia-Pacific is flagged as the fastest-growth region, tied to initiatives such as China’s AI Development Plan and India’s Digital India program. The brief also notes the average cost of a U.S. data breach at $9.32 million per incident, positioning synthetic data as part of a cost-and-compliance strategy.
- Budget and procurement are shifting from “nice-to-have” to platform decisions. If model training remains the dominant use case (45.3% in 2025), synthetic data tools will be evaluated like core ML infrastructure—alongside feature stores, labeling, and governance—rather than as standalone privacy utilities.
- Compliance teams should expect “prove it” questions. As adoption grows under GDPR/CCPA/HIPAA pressure, buyers will need clearer evidence on utility vs. re-identification risk, including how vendors test privacy leakage and how outputs behave under linkage attacks.
- Regional growth implies different risk postures. North America’s current lead (33% share) suggests near-term enterprise standardization, while faster APAC growth raises practical challenges for multinational teams: cross-border governance, differing regulatory expectations, and operational controls for synthetic pipelines.
- Market expansion increases vendor noise—and consolidation risk. The brief points to enterprise-tier moves by vendors such as Mostly AI and Syntho; data leaders should plan for integration requirements (cloud, MLOps, access controls) and avoid point solutions that can’t survive procurement and security review.