So we talk a lot about growth rates slowing across B2B and B2B + AI. And for many companies, they have. Snowflake is growing ~25-30%. Most public software companies can’t even crack 40%. The median growth rate on the BVP Cloud Index has been grinding lower for years.

But then there’s Databricks.

Databricks just announced it crossed $5.4 billion in revenue run-rate, growing >65% year-over-year. At $5.4B. Let that sink in.

That’s not a seed-stage company. That’s not a Series B. That’s a company doing $5.4 billion in annual revenue … accelerating.

The Numbers Are Just Stunning

Let’s walk through the trajectory here because it tells a story you almost never see in enterprise software:

• FY ending Jan 2024: $1.6B revenue, ~50% YoY growth
• June 2024: $2.4B run-rate, ~60% YoY growth
• July 2025: $3.7B run-rate, ~50% YoY growth
• September 2025 (Q2): $4.0B run-rate, >50% YoY growth
• December 2025 (Q3): $4.8B run-rate, >55% YoY growth
• February 2026 (Q4): $5.4B run-rate, >65% YoY growth

Growth is accelerating at $5B+ in revenue. From 50% to 55% to 65%. At massive scale. This basically never happens.

Nobody other public enterprise software company other than Palantir is even above 40%. Databricks is growing 65% at more than 10x the revenue of most of those companies.

$1.4 Billion in AI Revenue. Already.

Databricks’ AI products alone are now at $1.4 billion in revenue run-rate. That’s up from $1B just one quarter ago.

Think about what that means. Their AI business alone would be a top-20 public software company by revenue. And it’s growing far faster than the overall business.

Net Retention >140%. 800+ $1M Customers.

The expansion metrics are equally wild:

• Net retention >140% — that’s top-decile for any software company, public or private. For comparison, Snowflake is at ~131%.
• 800+ customers consuming at >$1M annual revenue run-rate
• 70+ customers consuming at >$10M annual revenue run-rate

This is what happens when you nail the platform play. Customers start with data engineering. Then they add the data warehouse. Then AI. Now agents with Agent Bricks. Now Lakebase for operational databases. More than 50% of customers use 6+ products.

Every new product becomes another expansion vector. And AI is the biggest expansion vector of them all.

$134 Billion Valuation. $7B+ Raised. IPO Imminent?

On the financing side, Databricks is completing investments totaling >$7 billion, including ~$5B of equity at a $134B valuation and ~$2B of additional debt capacity. JPMorganChase, Goldman Sachs, Microsoft, Morgan Stanley, and QIA all participated.

CEO Ali Ghodsi has said he “wouldn’t rule out” going public in 2026. With $5.4B in revenue, 65%+ growth, positive free cash flow, and >80% gross margins — this is the most IPO-ready company I’ve seen in years.

At $134B, Databricks is valued at roughly 25x revenue. If they IPO at these growth rates? Public markets might pay even more.

The Snowflake Comparison Is Now Almost Unfair

You can’t talk about Databricks without talking about Snowflake. These two companies have been the defining rivalry in data infrastructure for the last five years. And the gap is now enormous.

Here’s where they stand today:

Databricks has now:

• Passed Snowflake in revenue.
• And it’s growing more than 2x faster.
• Its net retention is 14 points higher.
• It has 50% more $1M+ customers.
• And its private market valuation is roughly 2.5x Snowflake’s public market cap.

Two years ago, Snowflake was the clear market leader at ~$2.8B in revenue growing 35%. Databricks was at ~$1.6B growing 50%. The revenue gap was over a billion dollars. Now Databricks has leapfrogged Snowflake entirely — and the growth differential is widening, not narrowing.

The story here isn’t that Snowflake is struggling. Growing 28% at nearly $5B in revenue is genuinely impressive by any normal standard. Snowflake still has strong product-market fit, a loyal customer base, and a healthy business.

But Databricks caught the AI wave in a way Snowflake hasn’t. $1.4B in AI revenue versus Snowflake still in the early stages of Cortex AI adoption. Databricks launched Agent Bricks, Lakebase, and Genie — each one a new product category. Snowflake’s product expansion has been more incremental.

The strategic difference is clear: Databricks bet early that the data platform would become the AI platform. They acquired MosaicML, built their own foundation models, launched Agent Bricks for production AI agents, and bought Neon for $1B to create Lakebase. Each move expanded their TAM. Snowflake has been more cautious, partnering with OpenAI and Google rather than building its own AI stack.

For founders, the lesson is stark. Two companies selling to the same buyer, competing in the same market, with similar starting positions five years ago. One leaned all the way into AI as a platform expansion. The other treated AI more as a feature add-on. The result? A 2.5x valuation gap and accelerating divergence.

That’s what happens when you get the big product bet right.

What Founders Can Learn From This

1. Platform companies can keep accelerating — if they keep launching products that work.

Databricks didn’t just ride one product to $5B. They launched Databricks SQL and it hit $1B in revenue. They launched AI products and those hit $1.4B. Now they’re launching Lakebase (Postgres for AI agents) and Genie (conversational BI). Each new product expands TAM and drives net retention. This is the playbook.

2. AI is the biggest B2B expansion opportunity we’ve ever seen.

$1.4B in AI revenue growing faster than the core business tells you everything. If you’re building in B2B and you don’t have an AI strategy, you’re leaving growth on the table. Databricks didn’t just bolt on an AI feature. They built an entire AI product line — Agent Bricks, Mosaic AI, AI-powered BI — that’s now 25%+ of total revenue.

3. The best companies grow into their valuations.

Everyone said Databricks was overvalued at $62B. Then at $100B. Now at $134B. But when you’re growing 65% at $5.4B with positive free cash flow? The valuation starts to look reasonable. The lesson: if you can keep compounding growth, valuation follows.

4. Consumption-based pricing works — when you have expanding use cases.

Databricks charges based on compute, storage, and processing usage. No fixed seats. No per-user pricing. That model only works if customers keep finding more things to do on your platform. And at 140%+ net retention, clearly they do.

One Of The Top 5 In the History Of Enterprise Software

There are maybe 5 companies in the history of enterprise software that have grown 65%+ at $5B+ in revenue. Databricks is one of them.

This is what it looks like when a company hits the AI tailwind at exactly the right time, with exactly the right platform, and exactly the right go-to-market engine.

The IPO is going to be something to watch.

And Come Learn How They Do It With CRO Ron Gabrisko LIVE at SaaStr AI 2026, May 12-14 in SF Bay!!

Grab tix here!!