Anodot vs Databricks

Databricks has a disclosed valuation of $134B, while Anodot'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 Anodot's $66M — a gap of $20.1B. Combined, the two companies have completed 9 known funding rounds.

Databricks leads with an Awaira Score of 93/100, while Anodot sits at 55/100. That 38-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Anodot was founded by David Drai in 2014. Databricks was founded by Ali Ghodsi in 2013. Visit each company's profile on Awaira for a full founder biography.

Anodot: Anodot provides autonomous business monitoring powered by AI, using time-series anomaly detection algorithms to continuously monitor business metrics including revenue, usage, and operational KPIs across enterprise data sources and alert teams to significant deviations before they become customer-visible incidents. The Herzliya company machine learning models learn seasonality, trends, and normal variance patterns for each metric automatically, reducing alert noise by filtering out expected variation and surfacing only genuine anomalies.\n\nThe company raised approximately $66 million in venture funding including a Series C from investors including Aleph VC, Redline Capital, and Transformative AI. Anodot serves telecommunications, fintech, gaming, and digital media clients including T-Mobile, Pandora, and Wix, deploying monitoring across billions of data points daily across client environments. The platform connects to data warehouses, streaming pipelines, and cloud monitoring sources through native integrations.\n\nAnodot competes in the AIOps and business monitoring market against Datadog, New Relic, and Sumo Logic for infrastructure monitoring use cases, and against business intelligence anomaly detection features in Tableau, Looker, and Power BI for business metric monitoring. Its focus on business-layer metric anomaly detection rather than infrastructure-layer monitoring differentiates it in the revenue operations and product analytics segments, where engineering-focused monitoring tools are insufficient for the metric breadth and business context that business operations teams require. 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 1 year of extra runway. Anodot didn't arrive until 2014. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Anodot has about 100-500 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 Anodot and Databricks compete in AI Data, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.