Databricks has closed a new strategic funding round at a valuation of $190 billion, according to reporting from CNBC in August 2026 – the culmination of a fundraising sprint that began with reports of $165 billion-to-$175 billion talks first published by The Information on June 9, 2026, advanced to a Coatue-led term sheet valuing the company at $188 billion in July 2026, and closed with roughly $5 billion in fresh capital. The final mark represents a jump of 41.8% from the $134 billion valuation the data-and-AI company commanded as recently as February 2026, according to a Yahoo Finance analysis of the deal, – and crowns Databricks as one of the most valuable private technology companies on the planet, trailing only a handful of frontier AI labs.
The leap in the Databricks valuation is not happening in a vacuum. It rides on a reported $7 billion annualized revenue run rate as of August 2026, up more than 80% year over year according to Yahoo Finance – an acceleration from the $5.4 billion run rate and 65% growth pace the company reported back in February 2026 – and arrives as the company signaled a potential IPO as early as 2027, noting that 2026 would be the worst year to go public. For an enterprise-software market still digesting the cost of the generative-AI buildout, Databricks has become the clearest test of whether “data infrastructure” can command the same investor premium as the model labs themselves. This analysis breaks down the numbers, the competitive stakes against Snowflake, the IPO clock, and what the round signals for the broader 2026 AI economy.
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Databricks Valuation Hits $165B–$175B: The Headline Numbers
The round, first reported as talks in the $165 billion to $175 billion range, ultimately closed higher: a Coatue-led term sheet valued Databricks at $188 billion in July 2026, per Reuters and The Wall Street Journal, before the company confirmed a strategic close at $190 billion in August 2026, per CNBC. That is a striking re-rate given how recently the prior mark was set. In December 2025, Databricks closed a Series L round that raised more than $4 billion, and Reuters reported in February 2026 that the company’s $134 billion valuation was backed by investments in excess of $7 billion, including $5 billion in equity and $2 billion in debt. Just months earlier, in September 2025, a $1 billion Series K investment had valued it above $100 billion, according to an analysis from Sacra. The trajectory – north of $100 billion to $134 billion to $188 billion to $190 billion in under a year – is one of the steepest private-market climbs in enterprise software history.
At the final $190 billion mark, the implied multiple is roughly 27 times the company’s $7 billion revenue run rate reported in August 2026 – still a premium even by 2026 AI standards, though a slight compression from the 32x multiple implied by the earlier $175 billion talk range against the $5.4 billion run rate. That is precisely the figure that has analysts split, with some questioning sustainability against hyperscaler competition. Bulls argue the multiple is justified by growth now running above 80% year over year and a widening product moat across data engineering, analytics, and AI, with AI products alone generating $1.4B annualised revenue. Skeptics counter that at 27x-plus run-rate, Databricks is being valued like a model lab rather than a software vendor, especially as rivals like Snowflake trade closer to 7x forward sales – and that public markets may not extend the same courtesy when the IPO arrives.
Crucially, Databricks is not burning cash to buy this growth. The company reported it turned free-cash-flow positive in 2025, a detail that separates it from many AI-era unicorns still posting nine-figure quarterly losses. That financial discipline is part of why a 31% valuation step-up in six months is being taken seriously by the institutional crossover funds and sovereign wealth vehicles typically involved in rounds of this size.
Funding History: From $62B to $175B in 18 Months
To understand the velocity of the current Databricks valuation, it helps to chart the recent rounds. Databricks has tapped private markets aggressively, using each raise to fund acquisitions, GPU capacity, and an expanding product surface rather than to plug operating losses. The table below traces the company’s financing ladder using the most recently reported figures.
| Round | Date | Amount Raised | Valuation | Primary Use |
|---|---|---|---|---|
| Series I | Sep 2023 | $500M | $43B | MosaicML integration, GenAI push |
| Series J | Dec 2024 | $10B | ~$62B | Sales expansion, R&D, secondary liquidity |
| Series K | Aug 2025 | $1B | >$100B | Agent Bricks, new database (Lakebase) |
| Series L | Dec 2025 | >$4B | $134B | AI infrastructure, M&A war chest |
| New round (in talks) | Jun 2026 | TBD | $165B–$175B | Pre-IPO scale, employee liquidity |
The Series J round in December 2024 was, at the time, one of the largest venture financings ever recorded, raising $10 billion at a valuation of roughly $62 billion. Notably, the company kept returning to the market not because it was running out of money – it was cash-flow positive – but to fund a relentless acquisition strategy and to give long-tenured employees and early backers paths to liquidity ahead of a public listing. Analysis from Value Add VC pegs the climb from that $62 billion Series J mark to the $188 billion Coatue-led term sheet in July 2026 as a roughly 3x jump in just 19 months, and a separate Sacra analysis published August 21, 2026 tracks the more recent leg of that run, noting the 2026 funding valuation reached $190 billion, up from $134 billion as recently as December 2025 – a pattern of “stay-private-longer, raise-more-anyway” that has defined the late-stage AI cohort alongside peers like OpenAI and Anthropic.
The $5.4B Revenue Run Rate and 65% Growth Engine
Revenue is the spine of the bull case. Databricks reached a $7 billion annualized revenue run rate by August 2026, per Yahoo Finance, growing more than 80% year over year – up from the $5.4 billion run rate and 65% growth the company reported as of February 9, 2026. The jump from $5.4 billion to $7 billion in roughly six months is an absolute dollar increment that few software companies at any stage have matched.
The growth is broad-based across three engines. The first is the legacy data-engineering and Apache Spark workload that Databricks pioneered – the foundation that built it into a lakehouse leader, a lineage explored in our deep dive on Spark vs Hadoop in 2026. The second is Databricks SQL and analytics, the segment where it competes head-on with Snowflake for the data-warehouse budget – a business that itself surpassed a $1.5 billion annualized revenue run rate by August 2026, growing more than 100% year over year, according to Databricks. The third – and the fastest-growing – is the AI stack: Mosaic AI for model training, serving, and evaluation, and the newer Genie platform for building production AI agents on top of enterprise data.
What makes the 65% figure durable, according to the company’s pitch, is consumption-based economics. Databricks bills largely on usage, so as customers move more AI and analytics workloads onto the platform, revenue compounds without proportional new-logo acquisition cost. With a reported footprint of more than 20,000 organizations worldwide – serves over 60% of the Fortune 500 – the expansion runway inside the existing base is the metric investors are underwriting.
Databricks vs Snowflake: The Data Platform Showdown
No comparison frames the Databricks valuation debate more sharply than its rivalry with Snowflake. The two companies have spent five years converging – Databricks pushing into Snowflake’s warehouse turf with Databricks SQL, Snowflake pushing into Databricks’ AI and data-science turf with its Cortex AI features and Apache Iceberg support. The fascinating twist of 2026 is that the private company is now valued at more than double the public one.
| Metric | Databricks | Snowflake |
|---|---|---|
| Status | Private (IPO targeted for 2027 rather than 2026) | Public (NYSE: SNOW) |
| Valuation / Market cap (Jun 2026) | $165B–$175B (in talks) | ~$83B |
| Revenue run rate | $5.4B (Jan 2026) | ~$4.2B annualized (Q1 FY26) |
| Latest YoY growth | 65% | 26% (Q1 FY26 product revenue) |
| $1M+ customers | Not disclosed (20,000+ orgs total) | 606 (trailing 12-mo product revenue) |
| Net revenue retention | Not disclosed | 124% |
| Implied revenue multiple | ~32x run rate (at $175B) | ~7x forward sales |
The valuation gap is the story. Snowflake carries a market capitalization of roughly $83 billion in June 2026, having recovered strongly over the prior month, on Q1 fiscal-2026 product revenue of $996.8 million – up 26% year over year – and total quarterly revenue just above $1 billion. Snowflake’s net revenue retention of 124% and its 606 customers spending more than $1 million annually point to a healthy, sticky enterprise base. Yet the market is pricing Databricks, now closed at $190 billion on a $7 billion revenue run rate, at more than 4x Snowflake’s run-rate multiple, a spread that reflects the growth differential (80%-plus vs 26%) and the perception that Databricks owns more of the AI-native workload – a claim bolstered by the Databricks SQL warehousing business itself, which crossed a $1.5 billion run rate by August 2026 while growing more than 100% year over year, well ahead of Snowflake’s pace in the segment where the two compete most directly.
Where Each Platform Wins in 2026
Snowflake’s strengths remain ease of use, governance maturity, and a frictionless SQL experience that data teams adopt with little training. Databricks counters with a unified lakehouse that collapses the wall between data warehousing and machine learning, plus deeper roots in unstructured data and model training. For organizations standardizing on open table formats, Databricks’ 2024 acquisition of Tabular – the company founded by the creators of Apache Iceberg – was a strategic move to neutralize Snowflake’s Iceberg advantage and own both major open formats (Delta Lake and Iceberg), and the model-serving side of that pitch deepened further in September 2026, when Databricks Model Serving added support for OpenAI’s GPT-6.1 Sol, per Databricks documentation, giving enterprises another frontier model to call natively without leaving the platform. The practical upshot for buyers is that the choice in 2026 is less about features and more about which platform’s pricing and AI roadmap fit a given workload.
The Acquisition Machine: MosaicML, Tabular, and Neon
A large share of Databricks’ cash has gone toward acquisitions that bolted on capabilities the company could not build fast enough organically. The strategy reshaped its product surface and, by extension, its growth narrative.
The pivotal deal was MosaicML, acquired in 2023 for approximately $1.4 billion. MosaicML gave Databricks the model-training infrastructure that became Mosaic AI, the foundation of its generative-AI offering and the reason enterprises can fine-tune and serve custom models on the platform. In 2024, Databricks acquired Tabular, the Apache Iceberg startup, in a deal reported to be worth more than $1 billion – a defensive and offensive play to control open table-format standards.
Then in 2025 came the roughly $1 billion acquisition of Neon, the serverless Postgres company, which became the backbone of Databricks’ new operational database, Lakebase. Neon’s scale-to-zero, branch-on-demand architecture gave Databricks a transactional database to sit alongside its analytical lakehouse – a direct shot at the operational-database market. Readers weighing that technology can see how Neon stacks up in our Neon vs Supabase comparison. Together, these three deals – more than $3.4 billion in disclosed and reported value – transformed Databricks from a Spark-centric analytics vendor into a full-stack data-and-AI platform spanning training, serving, warehousing, and transactional workloads.
Inside the Product Stack: Mosaic AI, Unity Catalog, and Lakebase
The product portfolio underpinning the Databricks valuation is now one of the broadest in enterprise data. At the storage layer sits Delta Lake, the open table format that made the lakehouse architecture viable. Governance and lineage run through Unity Catalog, the unified catalog that controls access, audit, and discovery across all data and AI assets – increasingly the lock-in layer that keeps customers on the platform.
On top of that foundation, Databricks SQL delivers warehouse-grade analytics, while Lakeflow handles data ingestion and pipelines. The AI tier is where the company is spending most aggressively: Mosaic AI covers model training, fine-tuning, and serving – a lineup that, as of September 2026, expanded to include OpenAI’s GPT-6.1 Sol inside Databricks Model Serving, per Databricks documentation – and Agent Bricks – introduced in 2024 as Agent Bricks Custom Agents – provides a workspace for creating production-ready, accurate, and cost-efficient AI agents that reason on governed data. Lakebase, announced as a transactional database engine at Data + AI Summit 2026, stores data in low-cost lakes for easy access to AI applications and operational workloads.
The strategic logic is consolidation. Every product that lives inside Unity Catalog and bills through the same consumption meter increases switching costs and net expansion. That is the mechanism behind net expansion growth – and the reason investors are willing to underwrite elevated multiples, though specific growth rates and multiples require current financial disclosures. The risk, of course, is that a sprawling portfolio invites competition on every front simultaneously: from Snowflake in analytics, from the hyperscalers in storage and compute, and from the model labs in AI serving.
Market Impact: What a $175B Private Valuation Signals
A closed $190 billion mark for a data-infrastructure company carries signal well beyond Databricks itself. It is the clearest evidence yet that investors are pricing the “picks and shovels” of the AI era – the platforms that store, govern, and operationalize enterprise data – at premiums historically reserved for the application or model layer. In a year when AI memory chips face shortages and frontier-model economics remain unproven, infrastructure generating a $7 billion revenue run rate and growing more than 80% year over year is exactly what the market is rewarding.
The round also tightens the late-stage AI valuation hierarchy. Databricks at $190 billion still sits far below the frontier labs – OpenAI reached an implied $1 trillion pre-IPO valuation in recent reports, detailed in our coverage of the OpenAI IPO at an $850B tag, while Anthropic closed a round at a $965 billion valuation, though IPO paperwork remains filed but unlaunched, examined in our report on Anthropic’s $65B Series H. But Databricks now comfortably outranks most application-layer AI startups, cementing a three-tier structure: frontier labs on top, infrastructure platforms in the middle, applications below.
For the public markets, the read-through is competitive pressure on incumbents. The same momentum lifting Databricks is visible in hyperscaler cloud results – Google Cloud posted a 63% surge in our analysis of Alphabet’s Q1 2026 earnings – and it raises the bar for what a data platform must deliver to justify its multiple when it finally faces public-market scrutiny.
Expert Reaction: Analysts Weigh the 32x Multiple
Reaction across the industry has split along the predictable bull-bear lines. CEO Ali Ghodsi has consistently framed an eventual public listing as a matter of timing rather than intent, telling investors the company would go public when the business – not the market window – was ready. In recent interviews he has emphasized that Databricks intends to remain “the data and AI platform of record” for the enterprise, positioning durability over short-term valuation optics.
Databricks’ own leadership has been unusually direct about ambition. The company’s chief revenue officer told Inc. in June 2026 that Databricks is “building a trillion-dollar company,” a statement that captures the internal confidence driving back-to-back mega-rounds – confidence borne out weeks later when a Coatue-led term sheet penciled in a $3 billion investment at a $188 billion valuation in July 2026, per Reuters and The Wall Street Journal, ahead of the round’s final close at $190 billion in August. Whether that framing reads as vision or hubris depends largely on how the IPO is received.
The skeptical case was crisply articulated by market commentators reacting to the news. As one analysis circulating after The Information’s report put it, at $175 billion Databricks would trade at roughly 32 times its run rate – “a number that assumes the growth never decelerates and the AI tailwind never fades.” The skepticism sharpened further on September 14, 2026, when a Yahoo Finance analyst estimate pegged Databricks’ underlying operating value at just $68.7 billion – some 64% below the $190 billion headline valuation – arguing that strategic premiums and defensive capital have pushed the round’s sticker price well past what standalone fundamentals would support. That tension – extraordinary growth against a demanding multiple – is the single most important variable for anyone modeling the company’s eventual public debut.
Independent enterprise-technology analysts have also flagged the broader memory and compute shortage as a wildcard. With HBM, NAND, and RAM in tight supply through 2026, the cost of scaling AI workloads – the very workloads driving Databricks’ growth – could compress margins industry-wide, a dynamic that would test the consumption model’s resilience.
The IPO Clock: Will Databricks Go Public in Late 2026?
The most consequential question attached to this round is what it means for the long-anticipated Databricks IPO. Reporting around the new financing indicates the company is “in IPO preparation mode,” with an expected listing as soon as late 2026. The pre-IPO round serves classic purposes: it sets a reference valuation, delivers liquidity to employees and early investors before lockups, and pulls in crossover funds who want exposure ahead of the public float.
The timing is shaped by a crowded 2026 IPO pipeline. OpenAI’s filing has dominated headlines, and a wave of AI-adjacent companies are testing public-market appetite. CEO Ali Ghodsi has already signaled Databricks won’t rush the window, telling Bloomberg Television that 2026 would be “the worst year to go public” and pointing instead to a possible 2027 debut. A Databricks listing at or above the $190 billion mark it closed at in August 2026 would be among the largest technology IPOs ever – and would force a public revaluation of the entire data-platform category. If Databricks debuts and holds a 25x-plus multiple, it validates the private mark; if it slips toward Snowflake-like 7x territory, it would compress late-stage valuations across the sector.
There is also a strategic reason to move sooner rather than later. Free-cash-flow positivity and 65% growth give Databricks a rare combination – profitability narrative plus hypergrowth – that may not coexist forever. Going public while both are intact maximizes the story bankers can tell. The countervailing pressure is macro: rising rates or an AI-sentiment correction could shut the window, which is exactly why the company is raising private capital now as a buffer.
- As of April 6, 2026, Databricks has not publicly filed or announced a confidential S-1 registration statement with the SEC, a step typically required 6–12 months before a public IPO .
- CEO Ali Ghodsi stated on Bloomberg Television on June 4, 2026 that Databricks will not go public this year because it is a “terrible year to go public” amid a surge of anticipated tech IPOs .
- Following its December 2025 Series L funding round of $4 billion, Databricks holds a pre-money valuation of $134 billion, driven by revenue growth that has surpassed a $5.4 billion annualized run rate .
- Recent financial milestones indicate the company has crossed $4.1 billion in Annual Recurring Revenue (ARR), with cash flow positivity achieved as of early 2026 .
- Analyst commentary and the lack of an S-1 filing suggest an IPO in the second half of 2026 is increasingly likely, with early 2026 targets now considered fluid pending regulatory approvals and market conditions .
- In January 2026, Databricks secured $1.8 billion in debt financing led by JPMorgan, bringing its total debt and investments to over $7 billion to accelerate IPO preparation .
Historical Context: How Databricks Outran the Lakehouse Skeptics
It is worth remembering how contested Databricks’ core thesis once was. When the company popularized the “lakehouse” – a single architecture combining the cheap, flexible storage of a data lake with the performance and governance of a warehouse – many analysts dismissed it as marketing. Snowflake and the traditional warehouse vendors argued the two worlds could not merge without compromise. A decade later, the open table formats Databricks championed (Delta Lake, and via Tabular, Iceberg (now widely adopted across major data platforms)) are the industry default, and even Snowflake has embraced Iceberg as of its recent open table format integration announcements.
That history matters for the valuation debate because it demonstrates a pattern: Databricks has repeatedly bet on architectural shifts ahead of consensus and been vindicated. The lakehouse bet, the open-format bet, the early MosaicML bet on generative AI in 2023 – each looked aggressive at the time and looks prescient in hindsight. The $165 billion to $175 billion funding round in talks is, in effect, the market pricing in the expectation that the company’s AI-platform bet will follow the same arc. The open question is whether past pattern guarantees future repetition, or whether the law of large numbers finally catches a company that added $2 billion of run-rate revenue in a single year, reaching $5.4 billion total.
Five Predictions for Databricks Through 2027
Based on the current financing trajectory, competitive dynamics, and reported guidance, here are five concrete predictions for where the Databricks valuation story heads next:
- The round closes near $170 billion. Given how The Information’s range clustered, expect the final mark to land between $165 billion and $175 billion, most likely around $170 billion, with crossover funds and sovereign capital leading.
- Databricks files for an IPO by Q4 2026. The pre-IPO round, free-cash-flow positivity, and a crowded but receptive pipeline make a late-2026 confidential filing the base case, with a first-half-2027 debut.
- Revenue run rate crosses $8 billion within 12 months. Sustaining even a decelerating 50% growth rate would push the run rate past $8 billion by mid-2027, anchoring the public-market pitch.
- The valuation gap with Snowflake narrows at IPO. Public markets will likely apply a more conservative multiple than 32x, compressing the premium over Snowflake even if Databricks debuts above $175 billion.
- Agent Bricks becomes the headline growth story. Expect the AI-agent platform, not legacy analytics, to be the metric Databricks leads with in its prospectus, mirroring how the market now prices AI-native revenue.
What It Means for Enterprise Buyers and the AI Economy
For the CIOs and data leaders who actually deploy these platforms, the valuation drama has practical consequences. A larger, better-capitalized Databricks (now valued at $175B in a potential 2027 IPO round) means accelerated R&D and a faster product cadence — but also a vendor with more pricing power and a stronger incentive to deepen lock-in through Unity Catalog and consumption billing. Buyers negotiating renewals in 2026 should expect less discounting and should scrutinize multi-year consumption commitments carefully, especially as Databricks eyes a $175B valuation ahead of a potential 2027 IPO.
For the broader AI economy, the recent $4B Series L round (Dec 2025) is a vote of confidence in durable infrastructure over speculative application bets, though a new round targeting $175B may follow in 2026. It suggests that the most defensible position in the AI stack may not be the model itself — where capabilities commoditize quickly — but the governed data layer that every model and agent must touch, a thesis reinforced by Databricks’ $134B valuation and potential $175B re-rating. That thesis, if it holds through a successful IPO, will reshape where the next wave of capital flows. For now, Databricks sits at the center of that bet, a cash-positive, fast-growing platform (with $5.4B ARR and >65% YoY growth) asking public and private markets alike to believe the data layer is worth as much as the intelligence built on top of it, now targeting a $175B valuation.
Frequently Asked Questions
What is the new Databricks valuation in 2026?
Databricks closed its new funding round at a valuation of $190 billion in August 2026, according to a Databricks press release and CNBC – the culmination of talks that started in the $165 billion-to-$175 billion range and passed through a Coatue-led $188 billion term sheet on July 16, 2026. The final figure represents an increase of roughly 42% over the company’s prior $134 billion valuation set in December 2025.
How much revenue does Databricks make?
Databricks reported a $7 billion annualized revenue run rate as of August 2026, per Yahoo Finance, up more than 80% year over year and well ahead of the $5.4 billion run rate and 65% growth pace it disclosed in February 2026. The company also said it became free-cash-flow positive in 2025, distinguishing it from many AI-era startups that remain deeply unprofitable.
How does Databricks compare to Snowflake?
As of June 2026, Snowflake was a public company with a market capitalization of approximately $83 billion, though public market fluctuations may have altered this figure since, and roughly $4.2 billion in annualized revenue growing 26% year over year. Databricks, now private at a closed valuation of $190 billion as of August 2026, is running at a $7 billion revenue rate growing more than 80% year over year. Databricks is valued at more than double Snowflake ($190B vs. roughly $83B) on a larger, faster-growing revenue base ($7B vs. $4.2B), reflecting its steeper growth curve and AI-native positioning.
When will Databricks IPO?
Reporting around the new round indicates Databricks is in IPO preparation mode, with a public listing expected as soon as late 2026. CEO Ali Ghodsi has long framed an IPO as a question of timing rather than intent. No formal IPO filing had been confirmed as of June 20, 2026, with Databricks still in IPO preparation mode and public listing expected as soon as late 2026.
What companies has Databricks acquired?
Databricks acquired MosaicML in 2023 for approximately $1.3 billion (now Mosaic AI), per updated reporting from The Information in June 2026, Tabular in 2024 in a deal reported above $1 billion (Apache Iceberg expertise), and Neon in 2025 for around $1 billion (the basis of its Lakebase database). These deals expanded the company from a Spark-centric analytics vendor into a full-stack data-and-AI platform.
Why is Databricks valued so highly?
The valuation reflects revenue growth of more than 80% year over year (to a $7 billion run rate as of August 2026), free-cash-flow positivity, a footprint across more than 20,000 organizations including over 60% of the Fortune 500, and a broad product stack spanning data warehousing, machine learning, and AI agents. Investors are pricing Databricks as core infrastructure for the AI era – though at roughly 27x its run rate, the multiple still assumes growth remains robust through and beyond an eventual IPO.
Is Databricks profitable?
Databricks reported that it turned free-cash-flow positive in 2025. That does not necessarily mean it is net-income profitable under standard accounting, but free-cash-flow positivity is a meaningful signal of financial discipline that sets it apart from many high-growth AI companies still burning significant capital.
External Sources
- Databricks Newsroom – Series K Investment
- The Next Web – Databricks $165B–$175B Round
- Snowflake – Q1 Fiscal 2026 Financial Results
- Databricks – Unity Catalog
- Wikipedia – Databricks Company Overview
