MotherDuck

“On top of compute-heavy warehouses, agent-driven queries would create a serious cost problem. MotherDuck’s architecture is an excellent fit for the kind of exploration and unpredictable queries that agents need to be useful.”

“Every meeting now is shifted from trying to find answers to what to do about them. Users are able to make a Dive or chat with MotherDuck through Claude, find new design issues, and ask: what should we do about it?”

“Jev's not going to be the piece that fills in that gap,”

“Here it's simple. We get a probability, we can rank order by the probability that we're right or wrong, and then dig into it.”

“Dragging 100,000 of these through an LLM means there's going to be a lot that gets missed,”

“As a lender, we want to avoid lending to small businesses that have multiple other open loans with competitors," says Mike Failor. "If we miss somebody and miscount how many competitor products they have, this is somebody we might not want to lend to, because they're a lot riskier.”

“Jev helps us iterate on processes today and find these missing areas quickly and efficiently.”

“With AI running right inside MotherDuck, we're catching competitor loans in bank statements we would have otherwise missed. Closing those gaps gives us a more accurate assessment of every business we lend to.”

“The MotherDuck-Hex combo is fantastic," Pablo says. "It's been transformative for the company.”

“I didn't have to worry about accidentally costing the company a lot of money with my side data projects. And I could feel pretty comfortable that it would be able to serve my team for their needs.”

“"You'd be surprised — the create table or replace is one of the best tools DuckDB and MotherDuck has provided. It allows us to just shift data around so seamlessly.”

“"The biggest win from using DuckDB locally has been speed of iteration on our dbt stack. Even our CEO Clint, he's a data guy by trade, so he's in there as well. It's really a velocity thing.”

“"We couldn't make it work in a production cloud environment," Patrik recalls. "Just being able to handle concurrent writers was important.”

“MotherDuck gives us the compute power of DuckDB, scaled up into a production-grade platform. It makes it simple to iterate while solving for the concurrent writes limitation of local DuckDB.”

“Our customers are getting the answers they need instantly through the agents we've built on MotherDuck.”

“Separating customers with hypertenancy makes MotherDuck secure by design for analytics agents. I love that,”

“You can query an individual Duckling as much as you want: humans or agents. It doesn't impact other customers at all, which is amazing. I'm so glad MotherDuck came along.”

“MotherDuck’s integration with Tableau Cloud unlocks familiar Business Intelligence at the speed of DuckDB, supercharged by MotherDuck’s powerful cloud technology.”

“We found that DuckDB and MotherDuck are amazing tools for small data teams like ours.”

“We're converging on a pattern of fast, agent-driven, software-style iteration loops," said Alon Mahl, Vice President of Design Verification. "That's been a big shift: bringing that level of velocity and self-serve, data-driven culture into hardware, which historically just hasn't operated that way.”

“A single license for a power analytics tool for six months could be $50,000. There are basically only three companies that create this vendor tooling. You're operating in a space with a virtual monopoly.”

“They got it Claude-enabled and that changed everything,”

“Three, four, five extra steps in Snowflake for anything you wanted to do. We were shipping new products every week. We needed the simplest, fastest solution.”

“People aren't used to working with databases. They don't even know what they can do with the data yet—why would they spend all their time and effort setting that up?”

“For every single thing I wanted to do, I had to grant permission. Even with the managed setup, it's super exhaustive.”