Steep vs Databricks
Side-by-side comparison of AI visibility scores, market position, and capabilities
Copenhagen Denmark BI platform for modern data teams connecting to Snowflake and BigQuery; metric-centric analytics with fast warehouse-native query execution and clean opinionated UI designed as an alternative to legacy BI paradigms for analytics...
About
Steep is a business intelligence and analytics platform founded in 2021 and headquartered in Copenhagen, Denmark. The company was founded by former product and engineering leaders to build a BI tool optimized for the modern data team workflow — fast, warehouse-native query execution, a clean and opinionated UI, and first-class support for the metric-centric analytics workflows that analytics engineering teams are building. Steep positions itself as an alternative to legacy BI tools that carry the weight of decade-old UI paradigms and to overly complex enterprise platforms.\n\nSteep has raised pre-seed funding and operates as a lean, product-focused startup primarily targeting analytics engineering teams in Europe and growing technology companies. Its platform connects directly to Snowflake, BigQuery, and Redshift as the query engine, ensuring that all analysis runs against live warehouse data without intermediate caching layers that can serve inconsistent results. Steep's metric layer allows teams to define business metrics centrally and build dashboards around those metrics rather than one-off SQL queries, promoting consistency in how the company measures performance.\n\nSteep's dashboard experience is designed for both analysts building data products and business stakeholders consuming them, with a clean viewer mode that removes technical noise for non-technical audiences. The platform supports scheduled email and Slack delivery of dashboard snapshots, data alerting for metric threshold monitoring, and embedding for sharing dashboards in internal tools. Steep's European roots and GDPR-compliant data architecture make it a strong fit for European organizations with data residency requirements.
$4.8B revenue run-rate; 55% YoY growth; $134B valuation (Series L). Mosaic AI for enterprise LLM fine-tuning and inference; Unity Catalog for data governance. DBRX open-source model; every major enterprise AI deployment runs on the lakehouse.
About
Databricks was founded in 2013 by the original creators of Apache Spark — Ali Ghodsi, Matei Zaharia, and five other UC Berkeley researchers — to unify data engineering, analytics, and machine learning on a single platform. The company commercialized the lakehouse architecture, combining the flexibility of data lakes with the reliability of data warehouses. Databricks runs on AWS, Azure, and GCP and leads the commercial distribution of the open-source Delta Lake and MLflow projects.\n\nThe platform includes the Databricks Lakehouse for unified data processing, Unity Catalog for governance and lineage tracking, and Mosaic AI for enterprise LLM fine-tuning, model serving, and generative AI application development. It supports data engineering, SQL analytics, BI, feature engineering, and model training within a single governance perimeter, serving enterprises in financial services, healthcare, manufacturing, and media.\n\nDatabricks achieved a $4.8 billion annualized revenue run-rate in early 2025 with 55% year-over-year growth and a $62 billion valuation from its Series L round — one of the most valuable private software companies globally. Its dual role as the leading commercial lakehouse vendor and steward of influential open-source projects gives it a unique ecosystem advantage as enterprises accelerate investment in AI infrastructure.
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