What are the top synthetic data startups?

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The synthetic data market has exploded in 2025, with $763.1 million invested across 42 startups in just two years.

Europe leads innovation with privacy-compliant solutions while North America dominates funding rounds. Major tech acquisitions like Nvidia's $320+ million purchase of Gretel signal mainstream enterprise adoption and validate synthetic data as critical infrastructure for AI development.

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What are the top synthetic data startups in 2025 and what makes them stand out?

Nine startups dominate the synthetic data landscape through specialized domain expertise and strategic positioning rather than broad platform approaches.

MOSTLY AI leads structured data generation with end-to-end GDPR compliance, serving Fortune 100 banks with tabular synthetic datasets. Their Vienna headquarters positions them strategically within EU privacy regulations. Datagen from Tel Aviv specializes in photorealistic computer vision data, creating 3D synthetic humans with generative AI compatibility for autonomous vehicle training.

Synthesis AI in San Diego focuses exclusively on 3D datasets for autonomous systems and robotics, offering photoreal rendering capabilities that competing platforms cannot match. Gretel.ai built native BigQuery integration before their Nvidia acquisition, making them the go-to choice for Google Cloud enterprises. Tonic.ai operates as a full-stack test data platform with vertex AI support, targeting software engineering teams.

Sky Engine AI from Warsaw represents emerging Eastern European innovation with cloud-native architecture and customizable vision datasets. Hazy concentrated on financial services structured data before their SAS acquisition, while Aindo targets regulated healthcare and finance industries from Italy. Advex AI focuses narrowly on manufacturing GenAI data from Detroit, serving industrial vision applications.

Each startup carved out defensible niches through domain specialization rather than attempting to build universal synthetic data platforms.

Which of these startups have raised the most funding, and how much did they raise in 2024 and 2025?

Datagen leads 2025 funding with a $50 million Series B round, while MOSTLY AI secured $25 million in Series B funding.

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Who are the most notable investors backing these synthetic data startups, and what were the conditions or stages of their investments?

Scale Venture Partners and Molten Ventures lead the institutional investor landscape, with Microsoft M12 providing strategic capital plus customer relationships.

Scale Venture Partners led Datagen's $50 million Series B round, demonstrating confidence in computer vision synthetic data applications for autonomous vehicle development. Molten Ventures spearheaded MOSTLY AI's €25 million Series B, focusing on privacy-compliant structured data for European enterprises.

Microsoft M12 invested in Hazy's Series A while simultaneously becoming a customer, validating enterprise use cases for financial services synthetic data. This dual investor-customer relationship provides strategic validation beyond pure capital. United Ventures led Aindo's €6 million Series A, specializing in regulated domain applications across healthcare and finance.

Cogito Capital Partners led Sky Engine AI's $7 million Series A with Edge VC and Taiwania Capital as co-investors, representing international institutional backing for Eastern European innovation. Taiwania Capital's participation demonstrates Asian institutional interest in European synthetic data technology.

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Which synthetic data startups are supported or partnered with major tech giants like Google, Meta, Microsoft, Nvidia, or Amazon?

Google Cloud maintains the strongest partnership ecosystem, while Nvidia executed the sector's largest acquisition to date.

Google Cloud integrated Gretel natively into BigQuery for synthetic data workflows, enabling enterprise customers to generate privacy-preserving datasets directly within their existing data infrastructure. Tonic.ai secured Google Cloud Marketplace availability and partnered on Vertex AI data pipelines, positioning them for enterprise software engineering teams.

Microsoft operates through dual channels: M12 fund invested in Hazy while Microsoft became a customer, creating strategic alignment between capital deployment and product validation. This investor-customer relationship validates enterprise synthetic data use cases for financial services.

Nvidia acquired Gretel for over $320 million, folding their team into Nvidia's AI services division. Nvidia also maintains joint efforts with Databricks on Omniverse-powered synthetic pipelines, leveraging Isaac simulation for 3D motion dataset generation. Amazon Web Services supports integration APIs from Gretel and other providers for synthetic generation workflows.

Meta and other major tech companies have not yet announced significant synthetic data startup partnerships or acquisitions, leaving potential opportunities for future strategic relationships.

Which countries or regions are leading in synthetic data startup activity, and where are the top companies based?

Europe leads innovation through privacy regulations while North America dominates funding volume, with emerging activity in Eastern Europe.

Western Europe hosts four major startups across four countries: MOSTLY AI operates from Vienna leveraging Austria's GDPR expertise, Hazy established London operations before SAS acquisition, Aindo operates from Trieste with Italian government backing, and Sky Engine AI represents Warsaw's emerging Eastern European innovation hub.

North America concentrates activity in California and Israel: San Diego hosts both Gretel.ai and Synthesis AI, San Francisco bases Tonic.ai, while Tel Aviv positions Datagen for Middle Eastern and European market access. Detroit's Advex AI represents specialized manufacturing focus outside major tech hubs.

Europe's privacy regulations create natural competitive advantages for synthetic data startups, while North America's robust venture ecosystem provides superior funding access. Eastern Europe emerges as a cost-effective innovation center with Sky Engine AI demonstrating international VC appetite for regional talent.

Geographic distribution reflects regulatory environments: EU-based startups lead privacy-compliant solutions while US startups focus on cloud integration and enterprise adoption. Israel's computer vision expertise positions Datagen strategically for autonomous vehicle applications.

Which startups received significant awards, recognition, or media coverage in 2024 or 2025 that increased their credibility?

Acquisition coverage and government grants provided the strongest credibility signals, while CES showcasing elevated European startups.

Hazy received extensive SAS acquisition coverage, underscoring synthetic data market maturation and enterprise validation. This acquisition demonstrated that established analytics companies view synthetic data as essential infrastructure rather than experimental technology.

Gretel.ai gained significant Wired and CRN coverage following their Nvidia acquisition, positioning them as leaders in cloud partnerships and enterprise synthetic data integration. The $320+ million valuation provided market validation for privacy-preserving synthetic data platforms.

Aindo showcased at CES 2024 as Italy's government-backed deeptech startup, demonstrating European regulatory support for synthetic data innovation. Government backing provides credibility signals for regulated industry applications in healthcare and finance.

Westat and Tumult Labs received nearly $1 million in U.S. DHS grants to develop large-scale synthetic data toolkits, validating government interest in privacy-enhancing technologies. These grants signal synthetic data's importance for national security and privacy applications.

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What major breakthroughs in synthetic data R&D were announced or released in 2025, and which startups were responsible for them?

Google Research led algorithmic breakthroughs while Nvidia advanced 3D motion data generation through OpenUSD pipelines.

Google Research unveiled differentially private synthetic data generation using off-the-shelf large language models, enabling enterprises to create privacy-preserving datasets without specialized infrastructure. This breakthrough democratizes differential privacy for synthetic data applications across industries.

Nvidia developed OpenUSD motion data pipelines for humanoid robotics, accelerating 3D motion dataset generation through Isaac simulation environments. This advancement directly benefits robotics startups requiring high-fidelity training data for autonomous systems development.

The Omniverse and Databricks integration created scalable vision-centric synthetic pipelines leveraging Delta Lake for enterprise data management. This partnership enables computer vision teams to generate massive synthetic datasets using familiar data infrastructure.

These breakthroughs focus on enterprise accessibility rather than pure research advancement, indicating synthetic data's transition from academic research to production infrastructure. Startups benefit from these platform improvements without requiring internal R&D investment in foundational algorithms.

What are the most anticipated synthetic data innovations or launches expected in 2026, and which startups are behind them?

Private end-to-end LLM-driven data generation and cross-cloud marketplace expansion represent the highest-impact anticipated developments.

Startups will likely ship turnkey differential privacy pipelines integrated with enterprise large language models, enabling organizations to generate synthetic data using their existing AI infrastructure. This reduces technical barriers for enterprise adoption while maintaining privacy compliance.

Cross-cloud synthetic marketplaces should expand beyond Google Cloud to include broader AWS and Azure Marketplace listings. Current Google Cloud concentration limits enterprise choice, creating opportunities for platform-agnostic providers to capture multi-cloud customers.

Auto-ML synthetic data modules will embed synthetic generation into AutoML workflows, enabling rapid proof-of-concept to production deployment. This integration reduces time-to-value for data science teams experimenting with synthetic datasets for model training.

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Which synthetic data startups are already generating strong revenue or have secured key enterprise customers?

Financial services and software engineering represent the strongest revenue-generating verticals, with banking clients providing highest customer lifetime value.

Hazy generated $2.9 million in revenue pre-acquisition with Fortune 100 banking clients as primary customers, demonstrating enterprise willingness to pay premium prices for financial services synthetic data. Banking compliance requirements create high switching costs and long-term customer relationships.

MOSTLY AI secured contracts with multiple global banks and reported robust annual recurring revenue in 2025, leveraging GDPR compliance as a competitive advantage. Their end-to-end platform approach generates higher customer lifetime value than point solutions.

Datagen and Synthesis AI operate pilot deployments with autonomous vehicle and robotics OEMs, indicating enterprise interest in computer vision synthetic data. These pilots typically convert to production contracts as autonomous systems move toward commercial deployment.

Tonic.ai and Gretel achieved broad adoption among software engineering teams for quality assurance and test data generation, creating high-frequency usage patterns that drive consistent revenue growth. Developer-focused products benefit from viral adoption within engineering organizations.

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What's the total amount of capital invested across the synthetic data sector in 2024 and so far in 2025?

$763.1 million flowed into 42 synthetic data startups across 2024-2025, averaging $18.2 million per startup.

This investment volume represents significant institutional confidence in synthetic data as essential AI infrastructure rather than experimental technology. The average funding amount indicates Series A and B stage maturity across the sector.

Investment concentration in computer vision and structured data applications reflects enterprise demand for privacy-compliant training datasets. Financial services and autonomous vehicle applications drive the highest valuations due to regulatory requirements and technical complexity.

Geographic distribution favors North American startups for funding volume while European startups achieve higher valuations relative to revenue due to privacy regulation advantages. Eastern European startups access international capital at favorable valuations compared to Western counterparts.

Are there any startups building full-stack synthetic data platforms, and how do they compare in terms of scalability and accuracy?

Three startups operate full-stack platforms with distinct architectural approaches and enterprise integration strategies.

Which synthetic data startups have been acquired recently, or are rumored to be acquisition targets for 2026?

Two major acquisitions completed in 2024-2025, while vision-focused startups represent potential 2026 targets.

Nvidia acquired Gretel for over $320 million, integrating their privacy-preserving synthetic data capabilities into Nvidia's AI services division. This acquisition validates synthetic data as strategic infrastructure for AI model training and deployment.

SAS acquired Hazy through a strategic exit, folding their financial services synthetic data expertise into SAS's analytics suite. This acquisition demonstrates established software companies' interest in synthetic data capabilities for existing enterprise customers.

Synthesis AI represents a potential 2026 acquisition target due to their specialized 3D dataset capabilities for autonomous systems. Their photoreal rendering technology could attract interest from automotive OEMs or autonomous vehicle platform companies. Eastern European startups like Sky Engine AI offer attractive valuations for international acquirers seeking cost-effective computer vision capabilities.

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Conclusion

Sources

1. AI Superior - Synthetic Data Generation for AI Companies
2. Quick Market Pitch - Synthetic Data Funding
3. Gretel AI - Google Cloud Partnership
4. Tonic AI - Google Cloud Marketplace
5. Tonic AI - Google Cloud Partnership
6. CRN - Nvidia Buys Gretel
7. Wired - Nvidia Gretel Acquisition
8. Databricks - Synthetic Data Pipelines
9. Innovation Open Lab - Aindo at CES 2024
10. Americas Data Hub - DHS Grants
11. Google Research - Differential Privacy LLM
12. LinkedIn - Nvidia OpenUSD Pipeline