Databricks

According to its about-us page, Databricks was founded in 2013, with origins in academia and the open source community, by the original creators of the lakehouse architecture and the open source projects Apache Spark, Delta Lake, MLflow and Unity Catalog. The company states its mission is to simplify and democratize data and AI, helping data and AI teams solve the world's toughest problems, and that the Data + AI Platform aims to democratize insights to everyone in an organization. Databricks states it is headquartered in San Francisco, with offices around the world. The about-us page states that more than 20,000 organizations worldwide use the platform, naming Block, Comcast, Conde Nast, Rivian and Shell as examples, and states that 70% of the Fortune 500 rely on the Databricks Data + AI Platform. The page also states Databricks has 1,200+ global cloud, ISV and consulting partners that provide data and AI services. The about-us page does not list individual founders by name, a specific founding month or day, current employee headcount, or funding/valuation figures; those are not published on this page.

Databricks Inc. lists its address as 160 Spear Street, 15th Floor, San Francisco, CA 94105, and lists a phone number of 1-866-330-0121 on its official contact page. The company states it is headquartered in San Francisco, with offices around the world, per its about-us page; the contact page separately references office locations worldwide without listing each one individually on that page. To get in touch, the contact page instructs visitors to fill out a form and states that a Databricks team member will reach out; the page does not display the form's specific fields or name which team responds. The site also points existing customers with billing or technical issues to a separate Help Center for Customers, and separately lists Documentation and Training/Certification resources. The contact page does not publish a dedicated sales email address, a support-specific phone line distinct from the main number, or live chat. For product-specific inquiries such as pricing quotes, the pricing page separately offers a contact-sales path; there is no stated appointment-booking or scheduling mechanism on the contact page itself.

Names Unity as a service area. Areas beyond these are not published.

Databricks publishes a pay-as-you-go pricing model on its pricing page, stating customers pay with no up-front costs and only pay for the products they use at per-second granularity. The page describes optional Committed Use Contracts that Databricks says offer opportunities to access discounts for customers who commit to certain usage levels, with the page indicating larger commitments unlock greater savings; the page directs interested parties to contact Databricks for details on these contracts. Pricing is measured using two defined units: a Databricks Unit (DBU), described as a normalized unit of processing power on the Databricks Lakehouse Platform used for measurement and pricing purposes, with the number of DBUs a workload consumes driven by processing metrics including compute resources used; and a Databricks Storage Unit (DSU), a standardized unit for measuring storage usage across Databricks products. The page includes a specific qualifier for one cloud: pricing for Azure Databricks is set by Microsoft and is provided for convenient reference, with a pointer to visit Azure.com for details. The pricing page does not list named pricing tiers or specific dollar figures for AWS or GCP; it instead offers a free trial, a cost calculator, and a request-a-quote path, and directs prospective customers to contact sales for exact rates. Live current rates should be confirmed at databricks.com/product/pricing.

The Databricks product platform page describes the Databricks Data + AI Platform as unifying data engineering, data science and machine learning into one collaborative environment built on top of Apache Spark. It lists an Agentic Data Foundation that unifies data on open formats and automates ingestion and transformation for fast analytics on a single copy of data. It describes Unified Governance through Unity Catalog, which the page says manages data and AI assets with consistent permissions, lineage and auditing, plus a Unity Gateway for controlling AI access, spend and observability. The page describes Genie Ontology as continuously updating enterprise context by learning from actual usage, and Genie itself as a data-smart AI coworker built into the platform and grounded in business context. Agent Bricks is described as offering multi-harness development so teams are not locked into one stack, letting them build using models and frameworks of their choice. The page also describes deploying data and AI applications on serverless compute with built-in governance, connecting apps to governed data. The page does not state specific pricing, service-level guarantees, or numeric performance benchmarks; those are addressed separately on the pricing page.