Databricks secures funding at $134b valuation

Databricks is raising over US$4 billion in a series L funding round, bringing its valuation to US$134 billion.

The round is led by Insight Partners, Fidelity, and JP Morgan Asset Management, with participation from Andreessen Horowitz, BlackRock, Blackstone, GIC, Temasek, and others.

Databricks plans to use the new capital for product development, potential acquisitions, and employee liquidity.

The San Francisco-based data and AI software company said it surpassed a US$4.8 billion revenue run-rate in Q3 2025, up more than 55% year-on-year, and has maintained positive free cash flow over the past year.

Databricks also reported that both its AI products and data warehousing business have reached a US$1 billion annual revenue run-rate.

🔗 Source: Databricks

🧠 Food for thought

Implications, context, and why it matters.

Databricks’ $134B price implies about 28x run-rate, above Snowflake’s ~12.4x NTM

At a $4.8 billion annual run-rate, a $134 billion price equals about a 28x run-rate multiple (valuation divided by annualized current revenue run-rate).
Growth runs about 55% year over year while Snowflake sits in the mid 20s. The premium likely assumes far more AI monetization than today’s roughly $1 billion AI product run-rate.
Free cash flow is positive, yet the bet on enterprise AI agent budgets (AI “agents” are autonomous software systems that perform tasks using enterprise data) remains unproven at scale.

Deals hint at openings for governance, observability, and AI tooling startups

Databricks bought Neon, a serverless PostgreSQL (an open-source relational database) startup, for about $1 billion to add cloud-managed, auto-scaling Postgres. That extends a pattern of database buys to plug gaps.
Builders working on data governance or ETL (Extract, Transform, Load)/replication or observability platforms (software that monitors data pipelines, quality, and performance) should treat Databricks as an active buyer in the modern data stack. The modern data stack is the ecosystem of cloud tools for data ingestion and storage. It also covers transformation and analytics used by enterprise data teams.
Databricks is pushing Agent Bricks, its framework for building and orchestrating AI agents on enterprise data. That opens acquisitions in AI orchestration. These tools manage multi-step workflows across models and systems. It also pulls in prompt management, meaning systems to create and version prompts that steer models with governance. Agent evaluation fits too, with testing and benchmarking of agent behavior. New capital may fund deals, giving Series A or B (early-stage venture) investors a near-term exit path.