Synthesized vs Databricks

Databricks has a disclosed valuation of $134B, while Synthesized's valuation is not publicly available, making a direct size comparison difficult. Databricks employs 6,000 people.

Databricks has raised more in total funding at $20.2B, compared to Synthesized's $12M — a gap of $20.2B. Combined, the two companies have completed 7 known funding rounds.

Databricks leads with an Awaira Score of 93/100, while Synthesized sits at 43/100. That 50-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Synthesized was founded by Nicolai Baldin in 2018. Databricks was founded by Ali Ghodsi in 2013. Visit each company's profile on Awaira for a full founder biography.

Synthesized: Synthesized builds synthetic data generation software for financial services and enterprise organisations that need to share, test, or analyse sensitive data without exposing real customer information. The London company platform generates statistically representative synthetic datasets that preserve the analytical properties of production data while eliminating personally identifiable information, enabling data science, software testing, and regulatory compliance workflows to proceed without privacy risk.\n\nThe company raised approximately $12 million in a Series A round with investors including Lakestar and Playfair Capital. Synthesized counts financial institutions and data-intensive enterprises among its clients, deploying synthetic data capabilities for use cases including fraud model training, application testing with production-like data, and safe cross-border data sharing under GDPR and similar privacy regulations. The platform supports both tabular structured data synthesis and time-series data relevant to financial transaction modelling.\n\nSynthesized operates in the synthetic data market alongside Mostly AI, Gretel, and Tonic.ai, competing for data science teams seeking privacy-preserving alternatives to data anonymisation. The synthetic data market has expanded significantly as GDPR enforcement and financial regulatory requirements around data privacy have increased the cost and complexity of working with real customer data in development and analytics environments. The company financial services focus aligns with one of the highest-value segments for synthetic data adoption, where the sensitivity of transaction and identity data makes synthetic alternatives particularly valuable. Databricks: Databricks is an AI and data platform founded in 2013 that provides a unified analytics workspace for data engineering, data science, and machine learning. The company developed Databricks Lakehouse, which combines data lake and data warehouse capabilities, built on Apache Spark technology. Its platform enables organizations to process large-scale data, build machine learning models, and deploy AI applications through a single interface. The company offers several core products: Databricks SQL for analytics, Databricks Machine Learning for model development, and Databricks Jobs for workflow automation. The platform supports multi-cloud deployment across AWS, Azure, and Google Cloud. Databricks serves enterprises across various industries, with customers including organizations in financial services, technology, and healthcare sectors. As of its latest funding round, Databricks has raised $11.2 billion in total funding and maintains a valuation of $134 billion, positioning it among the highest-valued private AI and data companies. The company achieved Series J funding status, indicating significant capital accumulation and investor confidence. Databricks competes with platforms like Snowflake, Teradata, and cloud-native data solutions from major hyperscalers. The company's growth trajectory reflects strong market demand for integrated data and AI infrastructure, driven by increasing enterprise adoption of machine learning and data-driven decision-making. Databricks unified the traditionally separate data warehouse and data lake approaches through its Lakehouse architecture, creating a single platform for analytics and AI workflows.

Databricks got there first, launching in 2013 — that's 5 years of extra runway. Synthesized didn't arrive until 2018. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Synthesized has about 1-50 employees; Databricks has about 6,000. A bigger team usually means more revenue or heavier VC backing, but in AI, small teams can build at massive scale.

Yes — they're direct rivals. Both Synthesized and Databricks compete in AI Data, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.