Databricks Grows >55% YoY, Surpasses $4.8B Revenue Run-Rate, and is Raising >$4B Series L at $134B Valuation

Databricks Inc., a data and AI company, announced it is raising a >$4 billion Series L investment, valuing the company at $134 billion.Additionally, the company crossed a $4.8 billion revenue run-rate during its Q3, growing >55% year over year, including >$1 billion revenue run-rate from its Data Warehousing business and >$1 billion revenue run-rate from its AI products – all while delivering positive free cash flow over the last 12 months.

The parallel rise of vibe coding and generative AI is accelerating the development of data intelligent applications in the enterprise. Databricks will use this new capital to help customers build AI apps and agents on their proprietary data, leveraging Lakebase as the system of record, Databricks Apps as the user experience layer, and Agent Bricks to power multi-agent systems.

The round was led by Insight Partners, Fidelity Management & Research Company, and J.P. Morgan Asset Management with additional participation from Andreessen Horowitz, funds and accounts managed by BlackRock, funds managed by Blackstone (“Blackstone”), Coatue, GIC, MGX, NEA, Ontario Teachers Pension Plan, Robinhood Ventures, accounts advised by T. Rowe Price Associates, Inc., Temasek, Thrive Capital and Winslow Capital. This new investment builds on Databricks’ accelerating financial results and underscores the company’s vision to make data and AI accessible to all organizations.

Databricks’ Financial Momentum
This funding follows continued strong momentum across Databricks’ business, including:

Surpassing $4.8 billion revenue run-rate, growing >55% YoY
Continuing to achieve positive free cash flow over the last 12 months
Achieving >$1 billion revenue run-rate for its AI products
In its first six months, Lakebase already has thousands of customers and is growing revenue at twice the pace of its Data Warehousing product
Achieving >$1 billion revenue run-rate for its Data Warehousing product in less than four years from general availability
Net retention rate sustaining >140%
>700 customers consuming at over $1 million annual revenue run-rate

The Rise of Data Intelligent Applications
Databricks’ Series L funding will advance product development across three strategic products, helping customers build data intelligent applications

Lakebase is the first serverless Postgres database purpose-built for the age of AI
Databricks Apps offers world-class speed and security to build and deploy data and AI applications
Agent Bricks makes it easy for organizations to build and scale high-quality agents on their data

In addition to fueling its growth, this capital is expected to be used to provide liquidity for employees. The investment is also expected to support future AI acquisitions and deepen AI research.

“Enterprises are rapidly reimagining how they build intelligent applications, and the convergence of generative AI with new coding paradigms is opening the door to entirely new workloads. With this investment, we’re deepening our commitment to help every organization innovate with AI on their own data,” said Ali Ghodsi, co-founder and CEO, Databricks. “By anchoring transactional data in Lakebase, delivering intuitive experiences through Databricks Apps, and enabling advanced multi-agent systems with Agent Bricks, we’re giving customers a unified foundation to build trusted, high-performance data intelligent applications at scale.”

“Our continued investment in Databricks reflects our deep conviction in their extraordinary momentum today and their ambitious vision for the future,” said John Wolff, MD, Insight Partners. “Databricks leads the way in turning AI innovation into enterprise impact. We’re thrilled to deepen our investment in a team that continues to pair strong financial performance with real customer results, setting the standard for how AI creates value for businesses. Databricks is just getting started.”

Comments

The scale of Databricks’ latest - and final - funding round is nothing short of extraordinary. A $4 billion Series L investment values the company at $134 billion, bringing total funding to $25.3 billion, according to Crunchbase. By comparison, the previous $1 billion round closed in September valued the company at $100 billion post-money. These figures naturally raise the question: why does Databricks need to raise capital on this scale, far beyond the typical $500 million or even $1 billion rounds?

The answer lies in the current AI wave. Databricks epitomizes a new generation of companies rooted in big data and analytics, now supercharged by generative and agentic AI to deliver data intelligence platforms for the enterprise. This funding round underscores both the intensity of competition and the massive opportunity emerging at the intersection of data, AI, and cloud infrastructure.

This trajectory also highlights the profound influence of open source in this market, and the central role of the Apache Software Foundation and its global community. Databricks’ founders and extended teams have been deeply involved in major open-source projects such as Apache Spark, Delta Lake, Apache Iceberg, Unity Catalog, MLflow, Delta Sharing, and Redash - many of which they helped initiate, build, and maintain. The company also supports widely adopted technologies such as TensorFlow, PyTorch, RStudio, and Terraform.

Together, these elements paint the picture of a truly global, highly specialized team - and explain why investors are willing to back Databricks at an unprecedented scale.

Databricks clearly leads the field, setting the pace for competitors while attracting ideas, talent, and momentum through a series of high-profile acquisitions. The company has become a powerful magnet within the data and AI ecosystem. Founded in 2013, it has grown into an industry heavyweight in just over a decade - a trajectory that is both remarkable and unusually rapid in the IT and software landscape.

At the same time, enterprise applications are undergoing profound change. Organizations are rethinking how teams collect, process, and analyze data at scale, integrating AI algorithms and techniques directly into their operational and analytical workflows.

Today, Databricks is championing the evolution from a modern data platform to a full AI platform, unifying data warehousing, analytics, machine learning, and AI pipelines within its broader lakehouse vision. This strategy is materializing across three main portfolio pillars: Lakebase, Databricks Applications, and Agent Bricks. With these initiatives, the company has fully embraced AI and is clearly aiming to capture a significant share of the enterprise market.

Yet a key question remains unanswered: why does Databricks need to raise capital at such scale and such speed? And what form of exit could realistically satisfy investors at these valuation levels? At this magnitude, the dynamics differ substantially from more traditional growth and exit models.