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Designing AI-First Systems for Customer Access
Key Takeaways
- Databricks achieved over 65% YoY growth and a $5.4B run-rate.
- The company secured $7B+ in funding from leading global investors.
- The $7B+ raise is aimed at speeding up AI-native database and analytics development.
Databricks has closed a $7 billion funding round at a $134 billion valuation.
The company announced on February 9, 2026, that it surpassed a $5.4 billion revenue run-rate, delivering more than 65% year-over-year growth in Q4. The investment includes approximately $5 billion in equity financing and roughly $2 billion in additional debt capacity.
According to company officials, the funding will accelerate development of Lakebase, its serverless Postgres database designed for AI agents, and Genie, a conversational AI assistant that lets employees interact with data through natural language. The financing drew participation from JPMorganChase, Glade Brook Capital, Goldman Sachs, Microsoft, Morgan Stanley, Neuberger, Qatar Investment Authority and UBS.
"With this new capital, we'll double down on Lakebase so developers can create operational databases built for AI agents. At the same time, we're investing in Genie to let every employee chat with their data, driving accurate and actionable insights."
- Ali Ghodsi
Co-Founder & CEO, Databricks
Table of Contents
- Funding Momentum Builds
- Where Databricks Fits in the AI Stack
- How Databricks Plans to Spend $7B
- Key Financial Milestones
- About Databricks
Funding Momentum Builds
Databricks achieved massive growth in 2025, raising $4 billion in Series L funding in December 2025. This followed a September 2025 Series K round of $1 billion at a valuation exceeding $100 billion.
The company deployed capital to advance three core products:
- Agent Bricks for building multi-agent systems
- Lakebase
- Databricks Apps
Lakebase gained thousands of customers in its first six months, growing revenue at twice the pace of its data warehousing product, and was launched into general availability in February 2026. The company agreed to acquire serverless Postgres provider Neon for roughly $1 billion in May 2025.
Where Databricks Fits in the AI Stack
Enterprise AI deployment is converging toward unified platforms that embed governance, data lineage and semantic capabilities natively, moving beyond experimentation into production-ready systems.
The global AI in data analytics market is expected to grow from roughly $18.5 billion in 2023 to $236.1 billion by 2033. However, recent MIT research found that despite spending close to $40 billion on generative AI over two years, only 5% of enterprises could point to real business returns.
How Databricks Plans to Spend $7B
Databricks says the new capital will directly support its push into AI-native databases, agents and enterprise analytics.
Here's a breakdown of the company's stated investment priorities:
| Investment Area | Strategic Focus |
|---|---|
| Lakebase | Serverless Postgres database designed for AI agent workloads |
| Genie | Conversational AI assistant for natural-language data queries |
| AI Research | Funding allocated to advance AI research initiatives |
| Strategic Acquisitions | Capital reserved for potential M&A activity |
| Employee Liquidity | Portion of funds designated for employee stock programs |
Key Financial Milestones
Databricks reported several Q4 milestones:
- $5.4 billion revenue run-rate with more than 65% YoY growth
- $1.4 billion revenue run-rate for AI products
- Net retention rate exceeding 140%
- 800 customers consuming more than $1 million annually
- 70 customers consuming more than $10 million annually
- Positive free cash flow over the past 12 months
About Databricks
Databricks provides a unified data intelligence platform for platform and data leaders, data engineers and analytics teams. Founded in 2013 in San Francisco, the company focuses on integrating data lakes and data warehouses to support analytics and AI workloads at scale.
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The platform offers lakehouse architectures, governance through Unity Catalog and analytics via Databricks SQL. These capabilities support enterprises building large language models and other AI applications on unified data foundations.