To my right, of course, is Mr. Scarpelli, the CFO. We'll start at the, at the top, which is... Well, first of all, how are you?
I'm good. Can I say something-
Yeah.
Before we do this? I have to tell you.
I can't wait.
This is actually the fifth public company I've been at. I've been dealing with analysts with callbacks for many years.
Yeah.
I have to tell you, Pat did something the other day that was the first ever I've seen...
Oh, I can't wait. I can't wait. What was it?
He was unbelievable. He actually had his son, who is a data scientist, who works at actually a customer of ours, come on the call and asked all the questions of Christian at a technical level. I've never ever seen an analyst have their son do that. That tells me two things. A, you're old.
Really old.
I've known Pat for many years. B, the value you get out of... Really, if you're a data scientist, and you appreciated that, and hopefully you got something out of that your son learned, and you could appreciate the difference between Snowflake and what others are doing out there. I really did appreciate you doing that.
Well, he...
Wasn't expecting that. I also realized Pat's cheap because his house was so cold that his son has a jacket on and he has a jacket on too. It isn't my highest PG&E bill.
I wrote it down. I'm not gonna find it in time. I wrote down Zachary's reaction. I put it as a, as a, as a positive data point. Yeah. We have four children, but, three of them are... Hopefully no one's in this. They're around here somewhere. Three of them are normal, but one of them is just really, really off the charts, right?
Yeah.
He's a data scientist, and he talks slow. Did you notice that?
Yeah. He's very methodical.
Very-
Engineers do that.
... very slow. Very slow. He would be pausing before asking the next question. I was like, "Oh my God, is there another question coming?
Yeah.
You know? It was just like, great.
He asked good questions.
It was great. It was great. He, when I emailed you guys, I said, "I'm gonna get my data science expert on." I didn't share who it was. Yeah. Snowpark is a big deal.
Yeah.
That was what I took away from the conversation with Zach, and we'll get to that. Let's start with how's business, maybe we'll go straight to Snowpark after that.
Well, we just gave an update on the business last week.
Yeah.
Nothing's changed since then.
No. Give me your characterization.
You know, Q4, from a revenue standpoint, we pretty much hit our internal guidance from the beginning. When I say internal, what we present to our board. It's where we expected it to be. What we did notice was different was Our older customers seem to have contributed to more of the growth, and the newer cohort are growing much slower. We think that is a function of newer customers today as they're ramping on Snowflake. There's a lot more knowledge out there around people who have been working on Snowflake for a period of time, have learned the pitfalls of not deploying Snowflake properly, the pitfalls of not putting in place the proper governance. We have a lot more partners who are trained on how to implement Snowflake properly.
We are doing a lot more training with customers on how to use Snowflake out of the gate. We have built a lot of capabilities within Snowflake to ensure that people are using it properly, like auto-suspend. It was always easy for people to spin up a warehouse. There's two things you wanna do. You wanna be able to select the right size warehouse, and it was easy for people to select a really large warehouse. It was easy for people to disable the auto-suspend function. A, you could be running a warehouse bigger than what you want.
Mm-hmm.
Now it's much harder to do that, we do that for you. We have all kinds of alerting when someone disables an auto-suspend function so that people know that, and there's question, do you really wanna do that? B, people are just using Snowflake more efficiently. I think too is the earlier customers were those digitally native customers that think of the Instacarts and others of the world that were just born in the cloud, that really it was growth at all costs. The cohort of customers we have now are the more established, mature companies. I don't wanna say they're the laggards or the late adopters, but they're not those necessarily the fastest moving companies. They've always had cost controls in place. We're seeing the newer cohorts just ramping more slowly.
They still all have the same end state they wanna get to. They're just gonna go at their pace and in a very controlled fashion on controlling costs.
Okay. You predict the future, you were predicting the future based on cohorts.
Mm-hmm.
The new cohort, the new customers are actually not matching the same behavior as the older cohorts.
Correct.
Is that a fair assessment?
Correct.
Yeah.
When we were going through and spending a lot of time in the second half of January and February, as we're rolling quota. You remember at the beginning of the year is when we have to roll quotas out by rep for the full year.
Mm-hmm.
We roll out consumption quota. There's a lot of discussion that happens between my finance team and the salespeople on accounts, and that's where it became evident. A lot of those newer cohort of customers are just not growing at the same pace. I don't mean customers we landed three quarters ago. I'm talking the customers we've landed over the last 1 - 3 years are just growing slower.
Over the last 1-3 years.
Yeah.
How much slower are we talking about?
Well, enough that I lowered the guidance to 40% growth next year, which by the way, is still good growth at the scale we're at. I'm not gonna apologize for that type of growth.
What was it before?
In the end of November, when we were doing our preliminary planning, we were thinking 47%
Yeah.
... growth.
Okay, as an order of magnitude of what the cohorts is seeing, is that a good indicator?
Yeah.
Is there something else in there?
No, that's.
Do you layer an extra level of?
That's.
...conservativeness or something on top of it?
That's the biggest thing there.
Okay.
By the way, we're generally pretty good at forecasting consumption on an annual basis. I'll tell you, we had set our 2022 plan in February of 2022, we went to the board. We did miss that plan by 2%, giving full transparency, about $42 million or whatever it was. That was before you had Ukraine, that was before you had interest rates, that was before you had the crypto implosion, that was before FX rates-
You're talking about 4%.
2%.
2%. Yeah, yeah.
I think we do my point is.
Yeah
... I think we do pretty good job at forecasting.
Yeah. Okay. You piqued my interest when you started talking about the quotas. How do the quotas work for your salespeople?
Salespeople get two quotas. They get a growth quota, and they get a consumption quota. It could be anywhere from 50/50 to 50% of their pay. Their variable pay is based on growth, 50% on revenue. It could be as high as 90% on growth, 10% on revenue.
Mm
... if you're truly a hunter going after new logos, or it could be 10, 90. 10 on growth, 90% on consumption if you're just managing one of our top 10 accounts.
Do they complain that it's not fair to expect them as a, you know, "Hey, just Mr. salesperson, be able to influence the consumption at some major bank," for example?
Some complain, some don't.
Some other really hate it. Yeah.
They don't complain directly to me because they know what happens when-
Yeah.
... they complain to me. You know, at the end of the day, though, they can influence it.
Can they?
You're absolutely wrong.
Like, how do the good sales guys do it? Yeah.
Here's a prime example.
Yeah.
If they're out there and they're educating customers on actually how to use the product, how to use Snowpark.
Mm-hmm
We really spent our last sales kickoff on educating salespeople on how to go in and ask the right questions to customers to identify whether there's a Snowpark opportunity within that account, a Spark replacement in EMR, Cloudera, whatever. They don't actually have to sell anything. They just need to educate the customer and get a customer using, that will lead to a customer having to buy more capacity sooner. That's what we need our sales reps to do. By the way, AWS, Azure and Google Pay all the reps on revenue, not on bookings.
I missed the significance of that.
What we're doing is what is in the industry. You're saying reps can't influence a customer on consumption.
Oh, you're saying they all do that.
That's how they're paying all their reps.
Yeah. Yeah, yeah. That's how they all do it. Okay. Let's shift to Snowpark, which honestly I don't get nearly as well as I should. This will be the opportunity.
If you don't get it, I'm probably not. I'm not as technical as your son, that's for sure.
Oh, yeah. If only, if only he were here. What was the opportunity with Snowpark that you felt like you were not capturing before?
Spark workloads.
Yeah.
... within Data Engineering. What was happening is before we had Snowpark, customers would take their data out of Snowflake, move it into another system, run the Spark workloads, and then move that data back into Snowflake. That's very expensive because you're paying to move that data. By running it right within Snowflake, you know exactly.
Mm-hmm
A, you're not having to pay for moving the data, but B, you have the security and governance of Snowflake. Once you move your data outside of Snowflake, unless it's in a system that's highly secured and governed, you don't know what may happen to that data and where it could go. That's the big benefit to customers.
Yeah. What are the, to very high level, what are the kind of workloads that you would take out of-- what's the kind of analysis that you would do, in Spark that you wouldn't just be able to do in Snowflake?
You know, as I said, I'm not the technical person.
I don't know either.
I'm the accountant.
I'm gonna look at Sohail. What do you do in Spark that you wouldn't do in Snowflake?
Analytics.
Analytics.
No, you do analytics in Snowflake all the time. That's what we do. Machine learning. Machine learning.
The machine learning stuff.
It's machine learning stuff.
Yeah.
Exactly.
Okay. Were they often doing it in Databricks?
Databricks, Open Source Spark.
Yeah.
Cloudera, EMR.
Yeah. Okay. Basically, you had sort of.
It also enables you to write applications directly in Snowflake by having that. They tend to be heavily analytics applications.
All right. Keeping it really simple. Basically, we're talking about there's this machine learning opportunity, right?
Mm-hmm.
That you guys were not capturing as much of as you could. In fact, it was worse, right? Because your customers would take the data out of Snowflake-
Mm-hmm.
... put it somewhere else, and then there'd be all these inefficiencies in doing that.
Mm-hmm.
When did Snowpark come out?
For GA, for Python just came out, last quarter.
You had...
We really just pushed with the sales force at our sales kickoff in February.
Yeah. I mean, we're right there.
Yeah.
I guess that's what I'm-
We, we've had it for Java and Scala earlier, but Python is the most common programming language that the data engineers wanna use today.
Yeah. By the way, a little aside, that kid that you're talking about, I'm like, "Zachy, what's everyone using ChatGPT for?" He goes, "Well, Dad, I wrote a program in Scala, and I needed it to be, I think, in Python.
Mm-hmm.
I asked ChatGPT to rewrite it for me in Python.
Mm-hmm.
I'm like, "Did that work?" He goes, "Oh yeah, I submitted the code yesterday, it got accepted.
Yeah.
Crazy.
It is. That's actually what we see as one of the biggest use cases for AI, is really helping develop code. I'm still trying to figure out how ChatGPT itself is gonna make money.
Yeah. Did you listen to this week's All-In Podcast?
No.
No. I can't believe that I recommend it. It's so freaking good, right? It's so good because that one guy is David Sacks, is the head of venture firms, so there's however, you know, whatever 40, 50 portfolio companies, and he just kinda tells you what he's seeing, right? Which is very rare to get a VC to do that, right?
Yeah.
They generally hold things closer to the vest. Anyways, yeah, this week's, this week's session is all about Dave. They're not sure either is the bottom line. They see where there's a lot of benefits coming to consumers-
Yeah.
Right? They're not so sure that that's not just gonna get absorbed by the big tech players. They're not so sure either.
It was interesting. It was well-publicized. There was a Princeton grad student over a weekend, he wrote an application in Streamlit using ChatGPT to tell whether a paper was machine-generated or written by humans.
Yeah.
I think that had over 8 million views.
Yeah. We had this conversation and our 14-year-old was like, "Yeah, Dad, why are all the teachers so freaked out at Redwood High School about this ChatGPT thing? What is it anyways?
Right.
Right? We have the data scientist and the 14-year-old at the table, right?
Yeah.
Zachary goes, "GG, ask ChatGPT..." She's in ninth grade. He says, he goes, "What are you studying in school right now?" She goes, "global warming." He goes, "Ask ChatGPT to write a five-paragraph..." "ChatGPT, write a five-paragraph effort, essay about global warming that's appropriate for a 10th-grader.
Yeah.
Right. That's the beauty.
Yeah.
Right? That's appropriate for a 10th-grader. The next example is even crazier. My wife is an author, and she's got a bunch of articles on the web, and she was about to interview someone for a class that she's doing, who is the CEO and female founder of a video game company. Write the interview questions that she should ask the CEO founder of the video game company in the voice of Samantha Parent, my wife. She gets a little bossy when she's on this topic. There's a lot of musts.
Yeah.
You know? Literally the questions come out in her voice. How is that all gonna benefit Snowflake? I don't know.
What we see happening, and I'm just gonna, I'll call them large language models.
Yeah.
We're never gonna be the one developing these large language models. Why? To develop something like that is like, it's, could be $100 million in compute.
Yeah.
... to develop these models. These models need to be fine-tuned on real data. We have the data. The big thing that we think is enabling those models to run directly on the data in Snowflake is how we think we will benefit. Companies will take those large language models they license and run it against their Snowflake data for their business to fine-tune the models for them.
Right.
That's how we believe. What's interesting to us, though, is people to be able to write their own queries in Snowflake using ChatGPT to develop the queries. We're already seeing people do that. There's all kinds of YouTube videos around that out there.
Give us an example of that.
You wanna query a certain data, and you wanna write the code in Python.
Right.
Similar to what your son was saying.
Yeah.
... but not convert it. Actually write the queries themselves.
Yeah.
You can do.
Wow. Wow.
By the way, they're not always 100% accurate.
No.
That's way too.
No. Okay, cool. Let's go back to what you're seeing in the macro. Lengthening sales cycles.
Sales, sales cycles.
More approvals, all that stuff that everyone else is talking about.
Sales cycles themselves, I've been saying since day one, these large Global 2000 accounts are 1 to 2 to 3-year sales cycles. It really depends. They all start small. We generally, when we land a Global 2000, the average size is like $100,000. It's not like they're big. It's their follow-on deals typically are bigger. Don't see anything changing there. What I do see is customers wanting. It's more with our existing customers, where they've consumed faster than their contract rate.
Yeah.
They want additional discounts. They want an economic benefit to do a new deal. If not, they're just gonna continue to buy under their existing contract, we're just saying, "Fine, buy under your existing contract." It's just buying capacity, that's what we saw. Some of our largest customer just bought enough capacity to bridge them through to March rather than do an annual contract. Why? Because they can under their contract, they can continue to do that until July. In July, they have to do something.
Mm.
... of equal to or greater than their old deal, or they lose the discount they have. I'm hearing more and more customers wanting different payment terms. If you went back over a year ago, interest rates were virtually zero, and holding cash wasn't a big deal. Today, you can earn 4.9% in overnight money.
Okay. Good. We have five minutes, so, let's open it up. I have a couple more I wanna ask you at the end, but let's open it up to any questions from our audience.
Who's your most, I guess, aggressively growing competitor, and how is your strategy aggressive?
There is no change in the competitive landscape. It continues to be Google BigQuery number one. It has been since the time we went public. I don't even want to say number two because Microsoft, I would say, woud be number two. Databricks is the other one. Our whole Snowpark really goes directly at them. The reality is they coexist in many of our accounts. Why? We brought them into many of those accounts early on when we partnered with them. It's definitely Google, I would say, is the most competitive. Google and Microsoft have the best. They can do a lot of bundling, which they do. Databricks doesn't have that pricing pressure that a Google or a Microsoft can put on us.
By the way, we've been dealing with that for years with, those guys offering stuff for free, and free isn't free. You gotta look at the total cost of ownership of it.
How do you think about the public sector opportunity, and when do you expect to achieve that ramp high cert?
So-
We have to repeat the question.
The question was is, what do I think about the public sector opportunity, when do we expect to have FedRAMP High certification. I expect to have FedRAMP High certification very soon. We've submitted everything, and it's just waiting on the getting awarded that. As far as I know, it could be a week or it could be two or three months waiting on them. The public sector opportunity is only upside because they're pretty small. It's less than 1% of our business today. I do think public sector in general, I'm not just talking the U.S., is a big opportunity for us. I just came back from Korea, I was actually meeting with a consortium of companies that are advising the Korean government on their digital strategy and what they want to do.
This is going on around the world. There are data sovereignty issues that we're working through, U.K. is another good one there. Overall, public sector could be 10% of our business, but it's gonna take some time to get there. We're doing very well within state and local section of government that doesn't require that FedRAMP High, but it almost seems like the goalpost had moved where we thought FedRAMP Moderate was gonna be enough, and now they're saying, no, they need FedRAMP High. Hopefully it's very soon. IL5 will be next.
What's IL5?
It's another,
Another level?
Higher certification.
Is it really?
Yes.
Geez.
Yeah. IL6 is, I don't think we'll ever get there, is probably the most rigorous.
I've never heard. Yeah, I gotta learn more. Okay. I am curious, though, so when you say we're waiting back to hear from them, who's them? Is it like a particular department, or is it? How does? Is there?
So-
Yeah.
What happens is, you have to do all this stuff and demonstrate that you have all the controls and everything and procedures in place to operate in a FedRAMP High. You literally hand all your files over to a third party to do an audit. That third- party. You need a sponsor within the government, and we have a sponsor within the government that has to pay for that third party to do the audit of everything for them to sign off on and give you.
Oh, is that how it works?
Yes.
Oh. They have to pay for it.
It's a lot more than that.
Yeah. They.
I'm saying.
They have to pay for it.
The sponsor has to pay for it.
Yeah. Yes. Interesting. Okay, one minute and a half. What, what do you think investors don't get as well as they should about this story?
You know, I get asked this question all the time.
Really? I thought I had the most original question. It's not that original?
You know, I think, in general, investors get it. I'm not gonna say they don't get anything. I think a lot of people don't appreciate that how long some of these migrations are going to take with customers, and that we have some customers that it's gonna take them 10 years before they have all their data moved to Snowflake because their on-prem data estate is so big and they move so slow. I think if you look at... A lot of people think that we've been just an on-prem data warehouse migration. Less than 20% of our business has actually been these big on-prem data warehouses. We have a lot of new-
Mm-hmm.
Digitally native companies that were never a on-prem migration. If you look at, we've signed up over, well over, I think it's over 1,500 on-prem data warehouse migrations of principally Teradata, and less than 100 of those customers have actually shut Teradata down.
Yeah.
It's a long tail.
Yeah. It is a durable opportunity.
Yeah.
All right, Mike. It's always great to have you here. We appreciate it.
Thank you.
Thanks for coming.