Atlan vs Databricks

By valuation, Databricks is the larger company at $134B versus $750M — a 178.7x difference. Size can also be measured by team: Atlan employs 200-500 people while Databricks has 6,000 employees.

Databricks has raised more in total funding at $20.2B, compared to Atlan's $206M — a gap of $20B. Combined, the two companies have completed 7 known funding rounds.

Databricks leads with an Awaira Score of 93/100, while Atlan sits at 68/100. That 25-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Atlan was founded by Prukalpa Sankar in 2018. Databricks was founded by Ali Ghodsi in 2013. Visit each company's profile on Awaira for a full founder biography.

Atlan: Atlan is an active metadata platform and data workspace that enables data teams to discover, understand, and govern data assets across cloud warehouses, lakes, BI tools, and pipelines through an AI-powered catalog interface. The platform automatically crawls data infrastructure to build lineage graphs, enables natural language search over data assets, and enforces data governance policies through automated tagging and access controls.\n\nThe company raised approximately $78M in Series C funding from investors including Insight Partners and Sequoia India, and has built a global customer base of data-forward technology companies and enterprises. Atlan has emerged as a recognized leader in the data catalog market, competing with Alation, Collibra, and DataHub in the modern data stack ecosystem.\n\nAs cloud data infrastructure has proliferated, data discoverability and governance have become critical operational problems for engineering teams managing hundreds of data sources. Atlan's modern metadata-first approach and deep integrations with the contemporary data stack — Snowflake, dbt, Fivetran, Looker — position it well in a market where legacy catalog vendors have struggled to keep pace with infrastructure evolution. 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. Atlan didn't arrive until 2018. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Atlan has about 200-500 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 Atlan and Databricks compete in AI Data, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.