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Databricks’ Snowflake rivalry—and a possible lesson for Shopify against Amazon

In a shared interview excerpt, Ali Ghodsi says Databricks once had half Snowflake’s revenue and was growing more slowly. The listener sharing it sees a similar opening for Shopify against Amazon in shopping powered by AI agents.

4 Sources, 20d ago, first seen 20d ago

TLDR

In an excerpt from his interview with Brian Halligan, Databricks’ Ali Ghodsi pushes back on the contrast between a sales-driven Snowflake and a product-driven Databricks, praising his own team. He says Databricks had half its rival’s revenue and was growing more slowly. The listener sharing the excerpt summarizes the lesson as studying competitors and targeting their weaknesses—and argues Shopify has a similar opportunity to challenge Amazon in shopping powered by AI agents.

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4 Sources

BuccoCapital Bloke@buccocapitalI thought this was an incredibly insightful answer from @alighodsi on how he attacked Snowflake. I think @tobi and Shopify have a similar opportunity to take on Amazon in the agentic shopping era TL;DR "Study your enemy, study your competition carefully, and then go after those weaknesses." Brian Halligan: Ali, I want to switch topics on you. You had a rivalry, obviously, with Snowflake. It was an interesting rivalry over time, and my sense is you were pretty far behind, at least from a revenue standpoint. You've passed them. I just remember at HubSpot we were looking at the two and we picked Snowflake way back when. What were the chess moves that worked relative to them? And I guess I have a thesis that that was a very sales-driven company — the CEO was a sales guy, he cold-called me, actually — and you folks were product-driven. Any thoughts on that, in advising founders and CEOs about that kind of thing? Ali Ghodsi: Yeah, that was the consensus at the time, that the companies were like that. I thought that was unfair to us, because I thought with Ron and everybody — we had an amazing team. And then we had Andy come here and build up an amazing, crazy machine. It's like an army. So I thought that was unfair at the time when they were saying that. But it is true that we were half the revenue and we were growing slower. So to accelerate the revenue so much that you can overtake someone that's got double your revenue and bypass them — we knew it was going to take several years. Back to: good strategies take multiple years, you can't do them overnight. So: study your enemy carefully, understand their weaknesses, and apply your strengths to their weaknesses. This was a great company. They had built an amazing product. It was a game-changing product that disrupted all the data warehouses of all the hyperscalers. The hyperscalers were getting destroyed by Snowflake, because it was such a great innovation, such a great product. So this is not like, hey, it's a PTC-like company. No, this is hardcore. But they had a few weaknesses. One was that it was proprietary, because the folks came from Oracle. So it was a fully proprietary stack. And still today all the data gets stored inside their proprietary format. They've now added open source stuff, but that's just a small fraction of what they do. Most of the customers we bump into have this proprietary [format], and people didn't want to get locked in. So that was one weakness. Second weakness they had was that they had no support for AI. Or they would say that they did, but we knew that that was pretty weak. So that was the second thing we wanted to push really, really hard on. And those two combined with a third: it was pretty expensive. Now, it was expensive, to be fair, probably because it was a great product and they could extract a lot of margin out of it. Winners do that. They have a great product. So we went exactly for those three. We said, hey, open lakehouse means you own your own data. It's completely open. Don't lock it up over there. You can do AI on it, because our roots are AI — we've been doing machine learning and AI since 2009, that's where we came from. So apply that strength. And by the way, at the time it wasn't clear that was going to work, because AI was not a big thing in 2019, 2020. But we knew that that's a weakness, so we keep pushing on that weakness. And then cost — let's make sure that the TCO is a third. And still today, we typically win on TCO. And then we just hammered that extremely aggressively in account after account. We had a very careful playbook. And the playbook wasn't just, let's go out and say that Snowflake sucks or anything like that. It was: identify the Achilles heel and press on those. Don't just go say, hey, rip out Snowflake. It was a coexist strategy. Go in there, look at the exact workloads that could be amenable to machine learning, pull those out, move the format to open source. We had a very clear, concise playbook. And this kind of contradicts [the idea] that our sales would suck, or that they would be good at sales — because sales executed this play at Databricks. They went account by account and did that. And then it took multiple years. We also had a category that exemplified this playbook. The category was lakehouse. So we had a three-year, four-year strategy to create this category of lakehouse — which, by the way, was controversial. People didn't believe in it. But eventually the category took off. Now everybody says they have a lakehouse. Even Snowflake will say that, and that they have open formats and so on. So yeah, that's what it says: study your enemy, study your competition carefully, and then go after those weaknesses.20d
Brian Halligan@bhalligan@buccocapital @alighodsi @tobi Thanks for listening. This one was special. Links to the full episodes 👇 YouTube: https://seq.vc/oh2 Spotify: https://seq.vc/a53 Apple: https://seq.vc/ns420d
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    4 Sources

    BuccoCapital Bloke@buccocapitalI thought this was an incredibly insightful answer from @alighodsi on how he attacked Snowflake. I think @tobi and Shopify have a similar opportunity to take on Amazon in the agentic shopping era TL;DR "Study your enemy, study your competition carefully, and then go after those weaknesses." Brian Halligan: Ali, I want to switch topics on you. You had a rivalry, obviously, with Snowflake. It was an interesting rivalry over time, and my sense is you were pretty far behind, at least from a revenue standpoint. You've passed them. I just remember at HubSpot we were looking at the two and we picked Snowflake way back when. What were the chess moves that worked relative to them? And I guess I have a thesis that that was a very sales-driven company — the CEO was a sales guy, he cold-called me, actually — and you folks were product-driven. Any thoughts on that, in advising founders and CEOs about that kind of thing? Ali Ghodsi: Yeah, that was the consensus at the time, that the companies were like that. I thought that was unfair to us, because I thought with Ron and everybody — we had an amazing team. And then we had Andy come here and build up an amazing, crazy machine. It's like an army. So I thought that was unfair at the time when they were saying that. But it is true that we were half the revenue and we were growing slower. So to accelerate the revenue so much that you can overtake someone that's got double your revenue and bypass them — we knew it was going to take several years. Back to: good strategies take multiple years, you can't do them overnight. So: study your enemy carefully, understand their weaknesses, and apply your strengths to their weaknesses. This was a great company. They had built an amazing product. It was a game-changing product that disrupted all the data warehouses of all the hyperscalers. The hyperscalers were getting destroyed by Snowflake, because it was such a great innovation, such a great product. So this is not like, hey, it's a PTC-like company. No, this is hardcore. But they had a few weaknesses. One was that it was proprietary, because the folks came from Oracle. So it was a fully proprietary stack. And still today all the data gets stored inside their proprietary format. They've now added open source stuff, but that's just a small fraction of what they do. Most of the customers we bump into have this proprietary [format], and people didn't want to get locked in. So that was one weakness. Second weakness they had was that they had no support for AI. Or they would say that they did, but we knew that that was pretty weak. So that was the second thing we wanted to push really, really hard on. And those two combined with a third: it was pretty expensive. Now, it was expensive, to be fair, probably because it was a great product and they could extract a lot of margin out of it. Winners do that. They have a great product. So we went exactly for those three. We said, hey, open lakehouse means you own your own data. It's completely open. Don't lock it up over there. You can do AI on it, because our roots are AI — we've been doing machine learning and AI since 2009, that's where we came from. So apply that strength. And by the way, at the time it wasn't clear that was going to work, because AI was not a big thing in 2019, 2020. But we knew that that's a weakness, so we keep pushing on that weakness. And then cost — let's make sure that the TCO is a third. And still today, we typically win on TCO. And then we just hammered that extremely aggressively in account after account. We had a very careful playbook. And the playbook wasn't just, let's go out and say that Snowflake sucks or anything like that. It was: identify the Achilles heel and press on those. Don't just go say, hey, rip out Snowflake. It was a coexist strategy. Go in there, look at the exact workloads that could be amenable to machine learning, pull those out, move the format to open source. We had a very clear, concise playbook. And this kind of contradicts [the idea] that our sales would suck, or that they would be good at sales — because sales executed this play at Databricks. They went account by account and did that. And then it took multiple years. We also had a category that exemplified this playbook. The category was lakehouse. So we had a three-year, four-year strategy to create this category of lakehouse — which, by the way, was controversial. People didn't believe in it. But eventually the category took off. Now everybody says they have a lakehouse. Even Snowflake will say that, and that they have open formats and so on. So yeah, that's what it says: study your enemy, study your competition carefully, and then go after those weaknesses.20d
    Brian Halligan@bhalligan@buccocapital @alighodsi @tobi Thanks for listening. This one was special. Links to the full episodes 👇 YouTube: https://seq.vc/oh2 Spotify: https://seq.vc/a53 Apple: https://seq.vc/ns420d
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