Databricks Private Investment Profile
Valuation history, key backers, and how accredited investors access Databricks pre-IPO.
Databricks is the $134B private data and AI platform company at a $5.4B annualized revenue run rate, growing 65% YoY with positive free cash flow.
Founded
2013
San Francisco, CA
Last Private Valuation
$134B
Feb 2026 Series L
Sector
Enterprise SaaS
Data + AI Platform
Key Backers
Insight, Fidelity
J.P. Morgan, a16z, NEA
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Databricks: Valuation Trajectory
Secondary-market and institutional-round valuations over time. All figures are approximate and sourced from public reporting.
Databricks: Latest Developments
Last updated: 2026-04-15
Databricks announces $5.4B annualized revenue run rate, 65%+ YoY growth
The company also disclosed positive free cash flow over the trailing 12 months and net dollar retention above 140%. AI product revenue alone reached $1.4B annualized, representing roughly 26% of total revenue.
Series L closes at $134B post-money valuation
Led by Insight Partners, Fidelity, and J.P. Morgan. Total round was approximately $5B in equity plus an additional ~$2B in debt capacity, bringing total 2026 capital raised above $7B. Investors include a16z, BlackRock, Blackstone, Coatue, GIC, MGX, NEA, Ontario Teachers' Pension Plan, T. Rowe Price, Temasek, Thrive Capital, and others.
Databricks raises $1.8B in additional debt ahead of expected IPO
CNBC reported the debt facility was structured to give the company additional flexibility ahead of an expected H2 2026 public listing, though no S-1 has been filed publicly.
The
Private
Market
Case
A deep look at what makes Databricks one of the most studied private investments.
Databricks Valuation History: From Berkeley AMPLab Spinout to $134B
Databricks was founded in 2013 by the original creators of Apache Spark — Ali Ghodsi, Andy Konwinski, Reynold Xin, Matei Zaharia, Ion Stoica, Patrick Wendell, and Arsalan Tavakoli-Shiraji — out of UC Berkeley's AMPLab. The company spent its first several years selling Spark-as-a-service to enterprises, then expanded into a full data platform with the introduction of the Lakehouse architecture in 2020.
The valuation trajectory has been one of the steadiest in enterprise software: $1.4B in 2017, $6.2B in 2019, $28B in 2021, $43B in mid-2023, $62B in late 2024, and $134B after the December 2025 / February 2026 Series L round that brought in $5B of new equity plus an additional ~$2B of debt capacity. The round was led by Insight Partners, Fidelity, and J.P. Morgan with broad institutional participation from a16z, BlackRock, Blackstone, Coatue, Sequoia, T. Rowe Price, and others.
How Secondary Market Access Works for Databricks Shares
Databricks remains private and shares do not trade on any public exchange. Accredited investors can gain exposure through secondary market transactions — typically privately negotiated and structured as special purpose vehicles (SPVs) that pool accredited capital to meet seller minimums.
Because Databricks has been actively raising primary capital and providing employee liquidity through tender events, secondary supply has been more available than at some peer late-stage privates. Firms like WealthUnion specialize in sourcing these allocations and managing the SPV structure, due diligence, and transfer mechanics. The current expectation of an H2 2026 IPO makes Databricks one of the more time-bounded private opportunities at this scale.
Revenue Streams: Why Databricks Looks Like a Public-Market-Quality Business
Databricks generates revenue from three primary product lines. First, the core Lakehouse Platform — usage-based pricing for Spark/Delta workloads, machine learning, and SQL analytics on customer-owned cloud infrastructure (AWS, Azure, GCP). Second, the Mosaic AI suite (acquired via the $1.3B MosaicML acquisition in 2023) — pre-training, fine-tuning, and serving foundation models on customer data. Third, newer products including Lakebase (operational databases) and Genie (natural-language analytics).
By February 2026 the company hit a $5.4 billion annualized revenue run rate, growing more than 65% year-over-year. AI product revenue alone surpassed $1.4 billion (roughly 26% of total). Net dollar retention above 140% means existing customers are expanding spend faster than new logos are acquired — an extraordinarily strong indicator. The company also reported positive free cash flow over the trailing 12 months, which is rare for a late-stage software company growing this quickly and is a key reason the valuation has held up through public-market multiple compression.
Key Risks for Private Investors
Databricks faces real competition. Snowflake (publicly traded) competes head-on for the data warehouse / Lakehouse workload. Hyperscaler-native data services (BigQuery, Redshift, Synapse) provide a 'good enough' alternative for many customers and have the structural advantage of being bundled with cloud spend. Open-source alternatives (Iceberg, Hudi, Trino) put pressure on commercial pricing.
Compute and AI infrastructure costs are real risks — Databricks' margin profile depends on running AI workloads efficiently and pricing them above its variable cost. NVIDIA GPU supply constraints and AI-inference cost trajectories materially affect unit economics. Third, IPO timing — at $134B, the company is priced at the upper end of public-market enterprise software multiples, and a soft IPO market could re-price the secondary at a discount. Finally, governance: the founder-led structure with Ali Ghodsi as CEO concentrates strategic risk on a single leader.
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The
Investment
Story
Founded by the creators of Apache Spark out of UC Berkeley's AMPLab.
Valued at $1.4B after Series D — Spark-as-a-service is generating real enterprise revenue.
Introduces the 'Lakehouse' architecture, unifying data warehousing and ML on a single platform.
Series H round at $28B valuation. Company announces $1B+ ARR.
Acquires MosaicML for $1.3B, accelerating into the foundation-model era.
Valued at $62B in a Series J round; revenue run rate crosses $3B.
Closes $5B Series L plus $2B debt at a $134B valuation. Revenue run rate $5.4B (+65% YoY). AI revenue $1.4B. FCF positive. IPO targeted for H2 2026.
What Makes Databricks Special
The structural advantages that matter for private investors.
Lakehouse Architecture Standard
Databricks invented the Lakehouse pattern that has become the default architecture for AI-era data platforms. Migrations away from the platform are technically expensive and operationally risky once a customer has standardized analytics, ML, and SQL on it.
AI Revenue Acceleration
AI product revenue alone hit $1.4B annualized — about 26% of total — and is growing faster than the rest of the business. Mosaic AI lets customers train and serve models on their own data inside Databricks, locking in compute spend that competitors can't match.
FCF Positive at Hypergrowth Scale
Net dollar retention above 140% AND positive free cash flow over the trailing twelve months at >65% growth — a rare combination at this scale. Means revenue growth is funded by the business itself, not by burning down the balance sheet.
Frequently
Asked
Questions
Common questions about investing in Databricks through private markets.
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Sources & last fact-check (2026-04-15)
- valuation: https://www.cnbc.com/2026/02/09/databricks-completes-5-billion-funding-round-with-2-billion-in-debt.html (retrieved 2026-04-15)
- valuation: https://www.databricks.com/company/newsroom/press-releases/databricks-grows-65-yoy-surpasses-5-4-billion-revenue-run-rate (retrieved 2026-04-15)
- valuation: https://www.cnbc.com/2025/12/16/databricks-funding-valuation.html (retrieved 2026-04-15)
- latestDevelopments: https://www.cnbc.com/2026/01/23/databricks-obtains-1point8-billion-in-additional-debt-ahead-of-ipo.html (retrieved 2026-04-15)
- founded: https://www.databricks.com/company/about-us (retrieved 2026-04-15)