Databricks on Tuesday raised $4 billion in venture capital funding, a move that could set the stage for an initial public offering.
Insight Partners, Fidelity Management & Research Company and J.P. Morgan Asset Management led Tuesday's Series L funding round, which raised Databricks' total valuation to $134 billion, according to the vendor. In addition to the venture capital funding, Databricks has raised more than $5 billion in debt financing.
With more than $25 billion in total funding, Databricks could be positioning itself for an initial public stock offering, according to Stephen Catanzano, an analyst at Omdia, a division of Informa TechTarget.
"Databricks' ability to secure another $4 billion suggests that an IPO is likely on the horizon," he said. "Raising this additional capital now could be a strategic move to set a higher valuation ahead of going public, and their strong growth supports this approach."
Based in San Francisco, Databricks got its start in 2013 as one of the pioneers of the data lakehouse format for data management and offers traditional machine learning tools for data science.
Seizing on rising interest in AI development fueled by OpenAI's November 2022 launch of ChatGPT, Databricks has expanded its AI capabilities to include generative AI and agentic AI.
Capital infusion
The overwhelming majority of Databricks' funding arrived after it emphasized AI, including $10 billion in December 2024 and the more than $5 billion in debt financing one month later.
Databricks' ability to secure another $4 billion suggests that an IPO is likely on the horizon. Raising this additional capital now could be a strategic move to set a higher valuation ahead of going public, and their strong growth supports this approach.
Stephen CatanzanoAnalyst, Omdia, a division of Informa TechTarget
As a result, Sanjeev Mohan, founder and principal of analyst firm SanjMo, like Catanzano predicted that an IPO might be next for Databricks.
"Databricks is demonstrating the rare combination of hypergrowth and capital efficiency that defines exceptional pre-IPO companies," he said.
Databricks remains privately held and does not file a public earnings report. However, in addition to raising $4 billion in funding, the vendor reported crossing a $4.8 billion annual revenue run rate -- an annualized projection based on its third-quarter performance. That includes more than $1 billion in annualized revenue from data warehousing and more than $1 billion from AI products.
That financial performance is emblematic of a company prepared to go public, according to Mohan.
"This efficient growth profile is exactly what commands premium valuations in today's market," he said.
Of note is that Databricks hit $1 billion run rates in both data warehousing and AI products, Mohan continued.
"The data warehousing business validates their ability to compete head-to-head with [vendors such as] Snowflake, while the AI revenue shows they're capturing the next wave of enterprise spending," he said. "The market timing is ideal for an early 2026 IPO. At $134 billion valuation with this financial profile, Databricks can credibly go public and sustain or grow that valuation."
While Databricks continues to attract significant funding, venture capital interest in other data management vendors has evaporated.
Funding flowed freely across the data management and data analysis industries throughout the late 2010s and early 2020s. In 2021 alone, Aiven, Databricks, Confluent, ThoughtSpot and TigerGraph raised more than $100 million in individual rounds.
But the tech-stock sell-off of 2022 cooled venture activity. Since then, after a small increase in funding in 2024, vendors emphasizing AI, such as Anthropic, Cohere and OpenAI, have been the focus of most funding.
A recent report from Crunchbase shows that 50% of all funding in 2025 -- through Dec. 14 -- went to AI, up from 34% in 2024.
It's Databricks' emphasis on AI that makes it attractive to venture capitalists when other data management vendors, such as Informatica and Confluent, are opting to sell to hyperscale cloud vendors rather than remain independent, according to Catanzano.
In June, Databricks unveiled Lakebase, a fully managed PostgreSQL database, and Agent Bricks, a framework for developing agents.
"Databricks' focus on innovative technologies like Lakebase and Agent Bricks positions it as a key player in enabling enterprises to build data-intelligent applications, which aligns perfectly with the increasing demand for AI-driven solutions," Catanzano said.
That emphasis is wise, according to Catanzano. In addition, he suggested that Databricks use a portion of the funding to fuel further acquisitions aimed at adding capabilities that aid AI development.
"This new funding should be used to double down on innovation in their core products while also exploring strategic acquisitions to enhance their AI capabilities," Catanzano said. "Acquiring companies that specialize in generative AI, multi-agent systems or advanced data management could help Databricks expand its ecosystem and maintain its competitive edge."
Portions of the $4 billion could also support global expansion and improved customer support, he added.
"Investing in global expansion, improving customer support, and providing liquidity for employees will be critical to sustaining their momentum and preparing for a potential IPO," Catanzano said.
Mohan similarly suggested that Databricks use some of its new funding to invest in and improve its AI capabilities. In particular, he noted that Lakebase -- the result of Databricks May acquisition of Neon -- and Agent Bricks, which are two of Databricks' most recent offerings, could use improvement.
"Lakebase needs rapid maturation to compete with established operational databases," he said. "Agent Bricks requires substantial infrastructure consisting of runtime environments, orchestration, safety guardrails and evaluation frameworks."
In addition, portions of the funding could be used for acquisitions, Mohan continued. Particularly, vector database specialists, such as Pinecone and Weaviate, could be targets, as could data streaming vendors, such as, Redpanda and semantic modeling specialists, including AtScale and Cube.
Looking ahead
To serve current users and appeal to new ones, Databricks should make its tools -- particularly those related to AI adoption -- simpler to use, according to Catanzano.
"Databricks should prioritize making AI adoption easier for enterprises," he said. "Enhancing the usability and integration of their tools, expanding Lakebase to handle more complex workloads and scaling Agent Bricks for multi-agent systems are key areas to focus on."
An additional way to simplify AI adoption is to offer pre-built versions of its AI tools geared toward specific industries, Catanzano continued. The vendor already provides industry-specific versions of its lakehouse.
"Offering pre-built AI solutions tailored to specific industries and fostering a strong developer community with robust training resources would also help solidify their position as a leader in the data and AI space," he said.
Mohan, meanwhile, suggested that Databricks focus on new capabilities, such as Lakebase and Agent Bricks, and improving them so that Databricks can provide a full-featured data platform that can compete with platforms from hyperscale cloud vendors, such as Microsoft.
"Databricks needs a compelling answer for why enterprises should choose them over Microsoft Fabric," he said.
Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than 25 years of experience. He covers analytics and data management.
