Startups > Databricks

Databricks

Databricks helps enterprises unify data, analytics, and AI on a single lakehouse platform.

Equity & Debt Financing $20.2B total Founded 2013 San Francisco, California 14603 employees

Databricks is a unified data intelligence platform built on lakehouse architecture that combines data warehousing and data lake capabilities. It enables organizations to store, process, and analyze structured, semi-structured, and unstructured data while connecting data with AI models to build custom agents. The platform provides data engineering, analytics, and AI/ML workflows in a single environment with built-in governance. Over 70% of Fortune 500 companies use Databricks to build and scale data and AI applications.

Problem solved

Organizations struggle to manage siloed data warehouses and data lakes, and lack unified platforms to connect data infrastructure with AI models for building intelligent applications.

Target customer

Enterprise organizations and Fortune 500 companies (70% adoption rate) needing unified data engineering, analytics, and AI/ML capabilities; teams building data-driven applications and custom AI agents.

Website LinkedIn Crunchbase Twitter / X

Founders

Ali Ghodsi

CEO & Co-Founder

Swedish-American computer scientist specializing in distributed systems and big data; became CEO in January 2016 after Ion Stoica stepped down.

Ion Stoica

Co-Founder

UC Berkeley professor and co-director of AMPLab; co-founded Conviva, a video distribution platform.

Matei Zaharia

Chief Technologist & Co-Founder

Developed Apache Spark as a UC Berkeley Ph.D. student to overcome MapReduce limitations; received ACM Doctoral Dissertation Award in 2014; now Vice President of Apache Foundation.

Patrick Wendell

VP of Engineering & Co-Founder

Played major role in Apache Spark operations and development.

Reynold Xin

Chief Architect & Co-Founder

Technical lead for Apache Spark; won Best Demo Award at VLDB 2011.

Andy Konwinski

VP of AI Operations & Co-Founder

Earlier led Spark Summit creation and market efforts; now oversees AI operations.

Arsalan Tavakoli-Shiraji

SVP of Field Engineering & Co-Founder

Former McKinsey Associate Principal and UC Berkeley Ph.D. student; leads field engineering operations.

Funding history

Series A $13.9M September 2013 Led by Andreessen Horowitz · Unknown

Series B $33M June 2014 Led by New Enterprise Associates (NEA) · Unknown

Series C $60M March 2016 Led by New Enterprise Associates (NEA) · Unknown

Series D $140M December 2017 Led by Andreessen Horowitz · Unknown

Series E $250M February 2019 Led by Andreessen Horowitz · Coatue Management, Microsoft

Series F $400M October 2019 Led by Andreessen Horowitz · Unknown

Series G $1B February 2021 Led by Franklin Templeton · Unknown

Series H $1.6B August 2021 Led by Multiple investors · Unknown

Series I Unknown 2022 Led by Multiple investors · Unknown

Series J $10B December 2024 Led by Thrive Capital · Andreessen Horowitz, DST Global, GIC, Insight Partners, WCM Investment Management

Series K $1B August 2025 Led by Existing investors · Unknown

Series L $4B December 2025 Led by Insight Partners, Fidelity Management & Research Company, J.P. Morgan Asset Management · Unknown

Debt Financing $2B January 2026 Led by JPMorgan Chase · Goldman Sachs, Citi, Barclays

Total raised: $20.2B

Industries

Artificial Intelligence (AI) Analytics Data Integration Data Management Data Storage Information Technology Machine Learning

Pricing

Pay-as-you-go model with no upfront costs. Users are charged per Databricks Unit (DBU) based on computational resources consumed, billed per-second granularity. Tiered editions (Standard, Premium, Enterprise) offer increasing feature access at different DBU price points. Cloud infrastructure (EC2, Azure VMs) billed separately by cloud provider. 14-day free trial available.

Notable customers

adidas, AT&T, Bayer, Block, Mastercard, Rivian, Shell, Unilever; 70% of Fortune 500 companies; 20,000+ organizations worldwide; 9,000+ customers as of 2023

Integrations

Apache Spark, Delta Lake, Apache Foundation projects, Amazon Web Services, Microsoft Azure, Google Cloud Platform, Salesforce, Stripe

Tech stack

React (JavaScript frameworks) Gatsby (Static site generator) Swiper (JavaScript libraries) Tailwind CSS (UI frameworks) Webpack PWA Open Graph Module Federation VWO (Analytics) Naver Analytics (Analytics) Cloudflare Bot Management (Security) reCAPTCHA (Security) HSTS (Security) Apple iCloud Mail (Webmail) Google Workspace (Email) Cloudflare (CDN) Marketo (Marketing automation) Google Tag Manager (Tag managers) Amazon Web Services (PaaS) Vercel (PaaS) OneTrust (Cookie compliance) DigiCert (SSL/TLS certificate authorities) Sendgrid (Email) Amazon SES (Email) Priority Hints (Performance)

Website

databricks.com/

Competitors

Snowflake

Cloud-native data warehouse focused primarily on analytics; lacks integrated AI/ML and lakehouse architecture for unstructured data.

Amazon Redshift

AWS proprietary data warehouse; tightly integrated with AWS ecosystem but less emphasis on unified data and AI workflows.

Google BigQuery

Google's data warehouse offering; strong on analytics but lacks lakehouse capabilities and native AI integration.

Delta Lake (Open Source)

Open-source project (created by Databricks); provides lakehouse foundation but requires more engineering to implement full platform.

Why this matters: Databricks has achieved remarkable scale with 70% Fortune 500 adoption and $20.2B in total funding, positioning it as a foundational AI infrastructure company. The company's timing is exceptional—it uniquely bridges the data infrastructure and generative AI revolution, enabling enterprises to connect proprietary data with AI models at scale when the market is rapidly shifting toward AI-driven applications.

Best for: Large enterprises and Fortune 500 companies that need to unify data storage, analytics, and AI/ML workflows while managing diverse data types and building intelligent applications at scale.

Use cases

Unified Data Analytics

Organizations consolidate data from multiple silos into a single lakehouse, eliminating redundant data warehouse and data lake infrastructure. A financial services firm uses Databricks to combine structured transaction data with unstructured documents and logs for comprehensive risk analysis.

AI/ML Model Development and Deployment

Data science teams build, train, and deploy machine learning models on the same platform where data lives, eliminating ETL complexity. An automotive company develops computer vision models on Databricks using structured inspection data alongside images and sensor feeds from manufacturing.

Custom AI Agents

Enterprises connect their proprietary data with large language models to build domain-specific AI agents. A retail company builds a custom agent that accesses real-time inventory, customer behavior, and supply chain data to provide inventory optimization recommendations.

Data Governance and Compliance

Organizations manage metadata, lineage, and access controls across all data assets in one place. A healthcare provider ensures HIPAA compliance by implementing unified governance across patient records, clinical workflows, and research datasets.

Alternatives

Snowflake Choose Snowflake if you primarily need a high-performance cloud data warehouse for structured analytics without native support for unstructured data and AI/ML integration.

Amazon Redshift Choose Redshift if you want tight AWS ecosystem integration and prefer AWS-native services for data warehousing and analytics.

Google BigQuery Choose BigQuery if you're deeply integrated with Google Cloud Platform and need strong analytics capabilities without lakehouse functionality.

FAQ

What does Databricks do? +

Databricks is a unified data intelligence platform that combines data warehousing and data lake capabilities (lakehouse architecture) to enable organizations to store, process, analyze, and govern all types of data while building and deploying AI/ML models and agents. It unifies data engineering, analytics, and AI workflows in a single platform accessible to data engineers, analysts, and data scientists.

How much does Databricks cost? +

Databricks uses a pay-as-you-go pricing model with no upfront costs. You pay for Databricks Units (DBUs) based on computational resources consumed, billed per second. Pricing varies by tier (Standard, Premium, Enterprise). Cloud infrastructure costs (EC2, Azure VMs) are billed separately by your cloud provider. A 14-day free trial is available.

What are alternatives to Databricks? +

Key alternatives include Snowflake (cloud data warehouse focused on analytics), Amazon Redshift (AWS-native data warehouse), and Google BigQuery (Google Cloud analytics platform). Delta Lake is an open-source alternative that provides lakehouse foundation but requires more engineering investment.

Who uses Databricks? +

70% of Fortune 500 companies, including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Shell, and Unilever. Over 20,000 organizations worldwide and 9,000+ customers use Databricks, primarily large enterprises needing unified data and AI platforms.

How does Databricks compare to Snowflake? +

Databricks offers lakehouse architecture supporting structured, semi-structured, and unstructured data with integrated AI/ML capabilities, while Snowflake is primarily a cloud data warehouse optimized for structured analytics. Databricks emphasizes unified data and AI workflows; Snowflake focuses on analytics performance and ecosystem integrations.

What is a lakehouse? +

A lakehouse combines the flexibility and cost-effectiveness of data lakes (supporting diverse data types) with the structure and governance of data warehouses. Databricks' lakehouse architecture allows organizations to store and analyze all data types in one place while maintaining data quality and governance.