On Monday, Databricks announced it reached a $5.

4 billion revenue run rate, growing 65% year-over-year, of which more than $1.

4 billion was from its AI products.

Co-founder and CEO Ali Ghodsi wanted to share these growth numbers because there’s so much talk about how AI is going to kill the SaaS business, he told TechCrunch. “Everybody’s like, ‘Oh, it’s SaaS.

What’s going to happen to all these companies?

What’s AI going to do with all these companies?’ For us, it’s just increasing the usage,” he said.

To be sure, he also wants to distance Databricks from the SaaS label, given that private markets value it as an AI company.

Databricks on Monday also officially closed on its massive, previously announced $5 billion raise at a $134 billion valuation, and nabbed a $2 billion loan facility as well.

But the company is straddling both worlds.

Databricks is still best known as a cloud data warehouse provider.

A data warehouse is where enterprises store massive amounts of data to analyze for business insights.

Ghodsi called out, in particular, one AI product that’s driving usage of its data warehouse: its LLM user interface named Genie.

Genie is an example of how a SaaS business can replace its user interface with natural language.

For instance, he uses it to ask why warehouse usage and revenue spike on particular days.

Just a few years ago, such a request required writing queries in a specific technical language, or having a special report programmed.

Today, any product with an LLM interface can be used by anyone, Ghodsi noted.

Genie is one reason for the company’s usage growth numbers, he said.

The threat of AI to SaaS isn’t, as one AI VC jokingly tweeted, that enterprises will rip out their SaaS “systems of record” to replace them with vibe-coded homegrown versions.

Systems of record store critical business data, whether it’s on sales, customer support, or finance.

“Why would you move your system of record?

You know, it’s hard to move it,” Ghodsi said.

The model makers aren’t offering databases to store that data and become systems of record anyway.

Instead, they hope to replace the user interface with natural language for human use, or APIs or other plug-ins for AI agents.

So the threat to SaaS businesses, Ghodsi says, is that people no longer spend their careers becoming masters of a particular product: Salesforce specialists, or ServiceNow, or SAP.

Once the interface is just language, the products become invisible, like plumbing.

“Millions of people around the world got trained on those user interfaces.

And so that was the biggest moat that those businesses have,” Ghodsi warned.

SaaS companies that embrace the new LLM interface could grow, as Databricks is doing.

But it also opens up possibilities for AI-native competitors to offer alternatives that work better with AI and agents.

That’s why Databricks created its Lakebase database designed for agents.

He’s seeing early traction.

“In its eight months that we’ve had it in the market, it’s done twice as much revenue as our data warehouse had when it was eight months old.

Okay, obviously, that’s like comparing toddlers,” Ghodsi says.

“But this is a toddler that’s twice as big.

” Meanwhile, now that Databricks has closed on its massive funding round, Ghodsi tells us that the company is not immediately working on another raise, nor prepping for an IPO.

“Now is not a great time to go public,” Ghodsi said.

“I just wanted to be really well capitalized” should the markets go “south” again as they did in the 2022 downturn, when interest rates rose sharply after years of near-zero rates.

A thick bank account “protects us, gives us many, many years of runway,” he added

Highlighted sentences link to their corresponding claims. Click any highlighted sentence to jump to its detailed analysis.
Highlight Colors Indicate Claim Quality:
✓ Healthy Claim - No fallacies or contradictions detected
⚠️ Minor Issues - Has contradictions or minor fallacies
🚨 Serious Issues - Multiple contradictions or severe fallacies
Quality Criteria: Claims are evaluated for logical fallacies and contradictions with other news sources. Green highlights indicate healthy claims suitable for reference.
Source