Databricks

Databricks is a unified Data Intelligence Platform built around the lakehouse architecture, combining data engineering, data warehousing, real-time analytics, and AI/ML development into a single system. The company was founded in 2013 by seven UC Berkeley PhD researchers and AMPLab alumni, later nicknamed the "Apache Spark Seven": Ali Ghodsi, Ion Stoica, Matei Zaharia (the original creator of Apache Spark), Patrick Wendell, Reynold Xin, Andy Konwinski, and Arsalan Tavakoli-Shiraji. The founders had already built and open-sourced Apache Spark at Berkeley before starting the company, and Andreessen Horowitz led their $13.9 million Series A in 2013 after valuing the young company at $50 million. Ali Ghodsi became CEO in January 2016 and remains in that role today, with Databricks reaching a $5.4 billion annual recurring revenue run-rate by January 2026 and a valuation exceeding $100 billion following a September 2025 funding round.

At its core, Databricks pioneered the "lakehouse" concept, a data architecture that combines the low-cost, flexible storage of a data lake with the performance and reliability guarantees of a traditional data warehouse. This is powered by Delta Lake, Databricks' open-source storage layer that adds ACID transaction support to data lakes, alongside MLflow for managing the machine learning lifecycle. The platform has since expanded well beyond its Spark-processing origins into a broader suite: Unity Catalog provides unified governance, access control, and lineage tracking across all data and AI assets; Lakeflow handles data engineering and ETL pipeline orchestration; Agent Bricks lets teams build and deploy custom AI agents; and Lakebase is an autoscaling operational database built natively on the platform.

Databricks operates natively across AWS, Azure, and Google Cloud Platform, giving enterprises flexibility to run workloads on their existing cloud infrastructure rather than requiring a migration. More than 20,000 organizations use the platform today, including Adidas, AT&T, Bayer, Block, Mastercard, Rivian, and Unilever, with over 60% of the Fortune 500 among its customer base. The platform's open-source contributions, particularly Apache Spark, Delta Lake, and MLflow, have become foundational infrastructure across the broader data engineering industry well beyond Databricks' own paying customers.

Pricing follows a consumption-based model measured in Databricks Units (DBUs), with rates varying by workload type (data engineering, data warehousing, AI/ML, and more) and by edition (Standard, Premium, Enterprise), plus separately billed underlying cloud infrastructure costs. This makes Databricks best suited for organizations with real data engineering and data science maturity who need a unified platform spanning ingestion through AI deployment, rather than smaller teams looking for a simple, fixed-price analytics tool; the DBU-based pricing model rewards careful workload monitoring and can become expensive quickly without governance around cluster usage and compute type.

Databricks uses consumption-based pricing measured in Databricks Units (DBUs), with rates varying by workload type and platform edition (Standard, Premium, Enterprise). Representative per-DBU rates include Data Engineering at approximately $0.15/DBU, Data Warehousing at $0.22/DBU, Interactive/All-Purpose compute at up to $0.40/DBU, and AI/ML workloads at around $0.07/DBU. Premium edition costs roughly 37% more per DBU than Standard but adds role-based access control, Unity Catalog governance, audit logging, and SQL Serverless warehouses; Enterprise tier pricing is typically higher still or governed by custom committed-use agreements with added compliance features like HIPAA support and customer-managed encryption keys. Underlying cloud infrastructure costs (compute instances, storage, networking) from AWS, Azure, or GCP are billed separately from DBU charges, except for certain bundled serverless SKUs. Databricks offers a 14-day free trial with usage credits and a permanently free, quota-limited Free Edition for learning and experimentation, but no free tier for production use. Committing to 1-3 year contracts can reduce DBU costs by up to 37%, and using Jobs Compute instead of All-Purpose Compute can cut costs up to 4x for eligible workloads. Note that Azure Databricks' Standard tier is being retired in October 2026, requiring affected customers to migrate to Premium.