Fasal vs Databricks

Databricks has a disclosed valuation of $134B, while Fasal'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 Fasal'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 Fasal sits at 55/100. That 38-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Fasal was founded by Ananda Verma in 2018. Databricks was founded by Ali Ghodsi in 2013. Visit each company's profile on Awaira for a full founder biography.

Fasal: Fasal is a precision farming platform that deploys IoT sensors in agricultural fields to collect real-time micro-climate, soil moisture, and pest pressure data, which is then analyzed by AI models to deliver actionable crop management advisories directly to farmers' smartphones. The platform helps farmers make data-driven decisions about irrigation, fertilization, and pest management to improve yields and reduce input costs.\n\nThe company raised approximately $12M in Series A funding from investors including Omnivore, and has deployed its sensor networks and AI advisory platform across thousands of farms in India covering crops including grapes, tomatoes, pomegranate, and rice. Fasal's AI models are trained on crop science and agronomic data to translate sensor readings into specific, actionable recommendations.\n\nIndian agriculture faces significant productivity gaps relative to global benchmarks, with irrigation overuse, input inefficiency, and pest losses representing measurable economic costs at the national scale. Fasal's data-driven precision agriculture approach addresses these inefficiencies in a market where increasing farm profitability and resource efficiency are national policy priorities. 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. Fasal didn't arrive until 2018. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Fasal has about 50-200 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 Fasal and Databricks compete in AI Data, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.