- Databricks has discussed raising fresh funding at a valuation of $165B–$175B, with a new round potentially starting as soon as next month, according to The Information.
- The company surpassed a $5.4B revenue run rate in February 2026, up 65% year-over-year, with AI products alone generating $1.4B in annualised revenue.
- The round would follow Databricks’ $7B+ raise at a $134B valuation in February 2026 and come as CEO Ali Ghodsi signals a potential IPO as early as 2027.
In August 2025, Databricks was valued at $100 billion. In December 2025, it was valued at $134 billion. Four months later, it closed a $7B+ round at that same valuation.
Now, according to The Information, it is in discussions for a new round that could value it at up to $175 billion. That pace tells you less about Databricks’ underlying business and more about how much capital is chasing the infrastructure layer of the AI economy.
The reported fundraising follows Databricks’ $5 billion round in February 2026 at a $134 billion valuation, split between $3 billion in equity and $2 billion in debt, led by JPMorgan, with Goldman Sachs, Morgan Stanley, Neuberger Berman, Glade Brook Capital, and the Qatar Investment Authority among equity participants.
Databricks also confirmed it had surpassed a $5.4 billion revenue run rate in February 2026, up 65% year-over-year, with AI products generating $1.4B in annualised revenue. CEO Ali Ghodsi has told investors the company is IPO-bound, potentially as soon as 2027, noting that 2026 — with major listings including SpaceX on the calendar — would be the “worst year” to go public.
The challenge it tackles
While much of the AI industry’s attention has focused on foundation models and chatbots, enterprises face a more fundamental problem: managing and making sense of vast volumes of fragmented data spread across disconnected systems.
Founded in 2013 by Ali Ghodsi, Matei Zaharia, Reynold Xin, Patrick Wendell, and Ion Stoica, all emerging from UC Berkeley’s AMPLab, where they developed Apache Spark, Databricks was created to solve that challenge. The founding team’s open-source roots became the foundation for one of enterprise software’s most valuable companies.
The company now employs approximately 9,000 people.
What Databricks does
Databricks provides a unified platform helping organisations ingest, organise, analyse, govern, and use data to build AI applications.
Its Lakehouse architecture combines the structured querying of traditional data warehouses with the flexible storage of data lakes, allowing companies to run both analytics and AI workloads from a single platform rather than maintaining separate systems.
Companies use it to train AI models, build analytics platforms, create enterprise AI assistants, and manage large-scale machine learning workloads.
Recent product additions include Lakebase, a serverless Postgres database built specifically for AI agents, and Genie, a conversational AI tool that lets employees query company data using natural language, rather than writing code. Both were announced alongside the February round and signal Databricks’ push to become the central platform for enterprise AI development.
Competition
Databricks operates in one of the most competitive segments of enterprise software, where control of enterprise data increasingly determines who wins the AI race.
Its biggest rival is Snowflake, the cloud data platform that reported Q1 FY2027 revenue of $1.39 billion, up 34% year-over-year, driven largely by Cortex AI, which the company credited as the catalyst for its strongest sequential growth in history.
Cloud hyperscalers are also intensifying competition: Microsoft Fabric, Google BigQuery, and Amazon’s data and AI services increasingly overlap with Databricks’ offerings as enterprises consolidate their technology stacks.
Databricks has maintained its position by combining open-source roots, deep AI capabilities, and a single unified platform, avoiding the fragmentation that characterises most enterprise data architectures.
What’s next?
The latest reported fundraising discussions are another sign that investors view data infrastructure as one of the most durable layers of the AI economy. While companies such as OpenAI and Anthropic capture headlines with foundation models, those systems still require vast amounts of enterprise data to deliver business value.
The harder question Databricks must answer at a $175 billion valuation is whether its 65% growth rate can sustain that multiple, or whether the AI infrastructure wave eventually gets absorbed by the hyperscalers it competes against.
