Building Turbopuffer: Gergely Orosz (@pragmaticengineer ) × Simon Eskildsen (CEO)

Allright. It's great to be here, and today with me here I'm Gergely, author of The Pragmatic Engineer, and I'm excited to have a chat with Simon Eriksen, founder and CEO of Turbopuffer, a very technical CEO, and we're going to have a pretty technical discussion.

But before we jump into it, Simon, I wanted to ask: where did you fall in love with computers?

Um, through PowerPoint.

PowerPoint.

You know, I don't know if any of you know this, but in Power—well, you probably know this, but in PowerPoint,right, you can make the diagrams and stuff, when you click them, go to another slide. That becomes turn complete real quick,right?

You can sort of, you know, create very complicated convoluted games, and then at some point, you know, you make it through the Microsoft Office suite and you discover Front Page. Do you remember Front Page?

Yeah, I remember Front Page. It was supposed to eliminate the need for all and any front-end developers.

Exactly. And it only worked in Internet Explorer. I remember a heartbreak I had one day when someone opened a website I created in Firefox, and it just, it was all over the place. And then one day I accidentally clicked the HTML thing in Front Page, and it just showed all of the stuff that I couldn't make sense of, and I just started looking at it, and then going online and finding little snippets that you could add in to make the cursor change and all of these different, uh, different things.

And then it just sort of escalated from there. Then you upgrade to Dreamweaver, and now you're coding, and then you're like, well, how do you make the pages dynamically? You learn PHP. And then for me, I exhausted the internet on Danish language programming advice.

Mm-hmm.

Um, and I was, I was around 11 or 12. And so I just, you know, went and got addicted to World of Warcraft for four years, but that gets you really, really good at English.

So you kind of start hacking, get into deeper. Now, the logical step would have been to just, you know, go to university and learn properly about this stuff, but that's not what you did, did you?

I mean, I just, um, I, I started just, I mean, you know, then I learned video games, then I learned English, and then, you know, this, like, massive arsenal of the web. I now, it would be very interesting because the LLMs would just speak Danish to me, and you could just, you wouldn't have hit the wall like I did.

Yeah.

Um, so that would have been very interesting. Maybe I would have been better at programming. That would have been nice. And then I, yeah, then I just started picking up jobs and things like that throughout high school. And when I was in high school as well, I got exposed to this thing called the International Olympiad in Informatics.

Mm-hmm.

Heard of this thing?

Yeah.

Um, and I had a, I had an internet friend, and she lived in Australia, and she was on the Australian team. And she told me, oh, there's probably something for the Danish team as well, but I had never, I'd never heard about it before.

And so I found it on some, like, little mysterious website and then applied and then solved these programming problems that looked very different from the HTML and PHP things that I'd solved until then.

Were these, like, the algorithmical-ish programs?

Exactly.

Problems.

It's sort of like, this is not actually the kind of problem you would see there, but I think it illustrates well the kind of problem that you might get,right, is you could imagine something like, okay, here's, like, n trucks, here's m packages.

The m packages have these dimensions. Give me which trucks which packages should be in,right, and then do something optimal. Like, that's an NP-complete problem. You can't solve that, but you could compete with everyone else in the competition of doing the best thing.

Yeah.

So it's these kinds of problems,right?

Yeah.

Um, and so I started doing that. In high school, I was working, um, I was working as well, um, for a startup. Um, and then I just, Shopify found me while I was still in high school.

And, and the whole, like, Shopify found me, was it through your open source contributions? Was it, was it something else?

It was because I had written an article where I had, I had, I had dropped my iPhone, and it was, you know, the iPhones are a lot, like, there used to be a time,right, where you dropped your iPhone and you just knew it was over for the screen.

Yeah.

It doesn't really happen as much anymore. Like, the screens have gotten a lot better. But back then, it was like, yeah, one drop and it was dead, and it just, I couldn't use it anymore. And so I went back to one of these old Nokia brick phones.

And this is back in 2013, and people hadn't really realized all the pernicious effects of smartphones at the time. And so I wrote this article about how, oh my God, I'm, like, calling people, and I have my sense of direction back.

Um, and I wrote an article about it. And this article, it went on Hacker News briefly, and it, uh, New York Times decided to feature it.

No way.

Yeah. And so a lot of traffic was driven to it, and then some astute Shopify recruiter put it all together, and, um, and I had a call with them, and then I don't think they realized that I was still in high school, but, um, but I had a great call with them.

They invited me onsite to Ottawa, Canada. Um, I had no idea what Ottawa, Canada is. I think the email says something like, "What's an Ottawa?" I had no idea. Um, and so I went there, and it was just, like, walked into the building, and it was just, just the, just feltright.

Um, and so I, I, I interviewed with them and then said, well, I got to finish high school first, and then, uh, and then I moved to Canada, uh, to, to, to work at Shopify. Yeah, in 2013.

Yeah. I think that's, that's a, like, legit excuse for, like, not even worrying about college and, and university.

But I, I did.

Did it cross your mind?

It did. I thought I was going, I thought I was doing a gap year. I thought I was like, okay, I'm going to go work at Shopify for a year, and then I'll probably go back and do, but I would just, I was very insecure at the time about the fact that I hadn't studied computer science, and my only exposure had been all the IOI competition, which is a pretty good crash course in a lot of computer science.

And if nothing else, it had really taught me that you can just sit down and read a paper and just figure it out if you spend enough time on it. So I did, I did that repeatedly, and in my first year at Shopify, I just, every time I heard something that I didn't know what was, I noted it down on a piece of paper, and then I went home, and then that evening, I would just read about it.

Mm-hmm.

Because I felt insecure that, like, well, if someone mentions, mentions, like, TCP, surely they know exactly what's in the three-way handshake and how TLS is, like, layered on top, and they've looked at Wireshark and all of that. I don't think that's true, but that's what I thought.

Yeah.

So I went and did that for everything that I encountered. Um, so that was a really good crash course. And then very quickly, it became clear that, well, I just want to continue doing this. I don't want to go, go somewhere else and then come back to this, because I felt like I'd already found what I wanted to do.

So it sounds, sounds like it was a pretty good combination of, like, you just having this, like, very natural insecurity. Like, you know, you're young, you know, you don't have the education that everyone else has, and inside a company that's just doing pretty, like, cutting-edge stuff even at the time, and even, even today,right, like, they're, they're leading.

And so you just kept self-teaching yourself, like, just catching up and go. And then do I understand that you just went deep in every concept that you understood? You didn't, like, just, like, try to understand at a surface level, but, like, go as deep as you can, search on the internet, buy books, whatever that is?

I think it was just that I just wanted to know, keep learning how computers work, and I think that this is something that I now look for when we interview engineers, is that you just, you can't help yourself but trying to peel back the layers.

And for me, that ended up with the infrastructure layer. That was, you know, the people closest to the metal at, at Shopify. And I would just always sit next to them at lunch because I was working on the, on the product side, but I just, I couldn't help myself.

I was so, I just wanted to learn what it was. When they were talking about a reverse proxy, I'm like, why is it reverse? I, I still can't answer that.

I, I, I mean, okay.

You know.

Well, what's in reverse?

Because it's a proxy,right?

I don't, I don't know. I don't know. It's like an inverted index. Like, what's inverted? It's like, it's a terrible name. Anyway.

Yeah. I, I, I mean, it's still better when you get the NAT, NAT tables, the lookups, some of those things, like some of that. But yeah, I, I hear you. There, there's some, like, weird names with this. But at, at Shopify, what were some of the kind of, like, hard engineering challenges that you engineering challenges, outages, like, like, learnings that kind of defined you that were really also fun at the time or interesting to learn, but it, it would have been hard to get it elsewhere?

Yeah. So I think it was, you know, in the 2010s, there's, like, a bunch of SaaS companies that, that scaled really quickly, and I felt so fortunate to have a front-row seat to that. And so I ended up on the infrastructure team, and this was back in, you know, '13, '14, and, uh, Docker was coming out, and so we were containerizing everything, and we were just, every single year, we had to, you know, the growth rates of, of, of SaaS sometimes seems quaint in comparison to the growth rates of companies today, but it was a company that was growing at, you know, 120, 140 percent year over year.

Um, and so every year, we were just preparing for a Black Friday that was going to be a lot worse than the last. And this is back in the day of we're buying physical hardware,right? We have to, like, place an order at a particular point in time and do some interpolation based on that.

Um, and the software also had to scale. And when you're scaling most software, a lot of the application layer problems end up back at the database layer.

Mm-hmm.

And so I just naturally found myself at this layer between rails and the databases. Shopify didn't, at the time, at least, contribute many patches to the databases themselves, but mostly just spent time orchestrating. So we were doing sharding because, as, um, my, my dear boss Camilo used to say, you can't cache writes.

So there's a fundamental point where you, you just, you have to move beyond a single shard. Um, so I wasn't, I joined around the time when they did the sharding, and they did it, I think they did the cutover a week before Black Friday, which is mind-blowing, uh, and very, but it worked.

And then the, the subsequent years, we worked on things like going into multiple data centers. We also had this big mysterious Redis server that was like, you know, 128 gigabytes of RAM, which was a lot at the time.

Today, it's not that much. And no one really knew what was in it. And then it went down one day, and people were like, well, that's super terrifying, um, because people had just been treating it as this KV store.

Um, and so we started splitting it out. We did all this stuff around making sure that if you, if you, if you go visit a Shopify store and the thing that stores your sessions is down, theright behavior is not just for the entire, the, of everything to be down.

But that's kind of the default failure mode,right? You're not going to rescue all of that, um, unless you're in a programming language that really forces that decision. So we did things like, um, build this matrix out of, okay, well, this service, when this component is down, should act this way.

Um, and I found myself writing the test suite for a bunch of that. And then I was like, okay, well, we can't just mock all of this. And so, um, I came up with this idea at the time of like, oh, what we're going to do is we're just going to, um, shell out to GDB and then into the process and then close the file descriptor to the database to simulate through the entire layer that the database fails.

That was a little crazy, and we never shipped that on CI, but it did uncover a massive amount of issues in rails that'd be upstream and things like that of around just, like, handling failures at the connection layer.

So then I moved on to create this proxy called ToxyProxy, and.

ToxyProxy.

Have you heard of this before?

No, no.

Yeah. ToxyProxy is, it's just like a layer seven proxy that sits in between, um, you and, well, layer four, but in between you and the databases. So you basically have just, like, this proxy, and then MySQL, whatever, doesn't speak the protocol, but then you can do an API call, say, take, take, uh, take the database down, make it slow.

Um, and over time, it also added layer seven things of, like, do a bunch of failures. This way, you're not mocking the low-level drivers, but you're testing the drivers and their failure handling as well. So then this entire matrix could be implemented in CI.

So the, basically, the proxy was just, like, a really thin layer which, like, was passed through, but you built the functionality to, like, simulate problems with database or things like data corruption or whatever you wanted to do, so you could just do it in there, and then you can anything that built on top of it.

But, but then, uh, oh, yeah, and then everyone had to, like, call this proxy or it needs to be on, in, in a layer.

Exactly. So you could do, like, do, like, MySQL, you know, ToxyProxy.MySQL.down and then pass it a lambda of what you wanted to do, like, get this page, do a checkout, whatever, with the sessions table down. And this just uncovered tens of issues,right, in the MySQL driver, in the rails.

Like, it's just, like, no one in the ecosystem had been testing for this, and it was very difficult to see this in prod,right, because in MySQL down, you're focused on just getting it back up and not, like, what could the application actually have done.

Yeah. It's, it's interesting. Of course, we're going to talk a bit more about databases, obviously, but just thinking about how a lot of the problems or some of the most gnarly pro-problems in our systems are always to do with state.

And I never connected until now that, I mean, state is usually there's a database. If there's no database, if you have stateless services, you know, I mean, you still have problems. You have nodes going down. You have, I don't know, corruption, whatever, but it's usually, like, more isolated.

But basically, like, if we have state, we typically have databases. If we have databases, then if we can simulate these problems, suddenly you can, I mean, you, you can, like, predict a lot of things. So the problem with state oftentimes is it's really hard to simulate problems happening ahead of time unless when they happen.

So did you, it sounds like you had pretty good success with, with.

Yeah. I think, to my knowledge, it's still, um, running in, like, the CI system of Shopify today. I don't know if anyone in the crowd is from Shopify, but I'm pretty sure that it still does. Um, and so we wrote all these tests against it to implement all of these different, uh, different failure conditions, and it just, yeah, it was, it was, it worked out great.

So you spent eight years in total at, at Shopify. So like, starting from, like, allright, just a gap year, it just went on a year, a year, and another year. Um, at what point did you think about leaving and why?

And what was your kind of decision framework? It sounds like you were, you were, like, on EPICright now. Even today, Shopify, it's doing wonderful. It's probably doing even way better than your, like, like, you know, that growth kind of kept on.

So I'm sure there would have been an argument to stay and, you know, stay on the rocket ship.

Yeah. So I, I spent, I spent eight years there from '13 to, to '21. Um, and I, I think there just came a point where I wanted to see something different. Again, I've been inside of Shopify since I was 18 years old,right?

I'd been seeing one other startup in high school. I was like, if I want to learn more about computers and learn faster, it might be time to inject some novelty into this function. Um, and so I, I left in, in, in '21, and I'd worked on so many different parts of the infrastructure, like caching.

Um, me and Justine, who's now my co-founder, rewrote the entire storefront, um, storefront for Shopify, um, which powered almost 100% of traffic 18 months after we embarked on it. Um, we worked on running Shopify in multiple data centers.

We worked on so many database scaling projects, like caching, all of these different things,right? Um, a lot of the, a lot of the scalability came from the Kardashians launching lots of products on, on Shopify, which would force a lot of traffic.

Um, but that's, that's eventually how I left. And so when I left, I didn't really know what I wanted to do. And so I, one of the projects I had while I was at Shopify was this napkin math project.

Have you seen this?

Napkin math? No.

No. Um, so napkin math was essentially just this table that I maintain on GitHub of how much bandwidth can you drive to DRAM, what does a round trip to S3 cost, and how long does it take, how much bandwidth can you drive to an NVMe SSD, how much bandwidth can you drive to an EBS volume, just a collection of probably, there's probably like 50 of these numbers, and then a Rust script that generates them all.

Um, what all these things cost, what do you, like, what does a gigabyte of memory cost? $2. What does a gigabyte of S3 cost? $0.02. What does a gigabyte of, um, disk cost? $0.10,right? What does it cost on Spot?

What does it cost on a three-year commit? Like, I had just have a massive table and then create flashcards for almost every single cell, so I know all these numbers. And this was a project I started taking on at Shopify because I found myself in, um, this role a lot where I would go in and review a project,right?

So some product team would be like, okay, we got to do, we got to build this thing, so we got to build this infrastructure to support the feature. And a lot of the times, they would say, okay, well, we've gone and benchmarked it on database A, but the benchmarks are not very good, so we're going to go with database B.

And I hate benchmarks so much because that's not a satisfying answer to me. To me, it's like, this does not jive with my intuition. Database A that you're saying takes 10 seconds to do this

should take 10 milliseconds if you do the napkin math,right? If it's a search query,right, it's like, okay, you're searching for three terms. They're, each term has this many documents that match it. That's this many megabytes. We inter-intersect these many, this many lists.

You have DRAM bandwidth on multiple cores of 100 gigabytes per second. This should take 10 milliseconds. You tell me the benchmark takes 10 seconds. One of us is wrong. Either there's a gap in my understanding, which is very likely, or you have benchmarked the wrong thing.

And in some ways, some reasons,right, it's like, okay, you've done a benchmark. You don't, didn't realize that your benchmark is doing a distributed query across 100 different nodes. And so of course the p99 is going to be really, really high,right, unless you've cut that off or, or made some different set of trade-offs.

So I just found myself in these discussions repeatedly where people were making infrastructure decisions based on poor benchmarks. And so I needed some, I, I needed some ammo to go in and just be like, okay, we can just do the calculationright here and then, um, because I was always doing these, like, little demos or, like, writing little prototype scripts to, to demonstrate this.

But it was just, I just, the argument of here's how a beachy works. This is how many pages we have to visit. This is what a random SSD read takes. It takes one millisecond. You have to visit 1,000, blah, blah, blah, blah, blah, and then present it back and see, this is the difference to your query.

Well, like, is the query plan correct? Like, is there a bug in MySQL? Do we have bad disks? Like, what's the discrepancy here? And I just got caught with that bug. And so after I left Shopify, I was just writing a lot of articles about this.

I was just like, well, how long should this query take? And then I, one hypothesis that I had at some point is like, okay, well, how many writes per second can MySQL do? Well, shouldn't the amount of writes per second that MySQL do equal the amount of FSyncs that you can do per second?

That sort of makes sense,right? Every time you do a write, you FSync to persist a disk. So how many FSyncs can you do per second? Well, an FSync takes one millisecond. Should you do 1,000 writes per second? That, well, that doesn't really match up.

Like, I feel like a database can do more than 1,000 writes per second. Why can it do that? So that was one of those things where I tested and it's like, okay, well, MySQL on a little dinky box could do 10,000 writes per second.

Well, how is that possible?

Mm-hmm.

And now you would just ask.

How, how is it possible?

Because you batch. So an FSync happens on usually a 4K.

Yeah.

Right? But it's like, that's not intuitive. Like, it's actually, I, I, I got caught. It was like, just like, you know, probably some, like, 24-hour period where I just got obsessed with this question where it's like, you're writing, like, the BPF traces and all of that to do all of this.

This is, like, pre-LLM, so it took forever. And you, and then I found out that, oh, every FSync was, like, much larger than I would have inferred.

Yeah.

Like, oh, it's batching. You go into the code and you read it, and then you found some obscure article by, it's always someone in, like, a central German town that's, like, written some article about, like, how some intricacy of MySQL works and a patch that they did to, it's like, the entire internet runs on small towns in Bavaria, I'm convinced.

Yeah.

And then you decided to start Turbopuffer.

Yeah.

How did you decide? Did you know what you wanted to build, or was it more like, I want to build something, something databases? Because you were clearly very into databases. You, you'd done an awesome job benchmarking, like, what is the theoretical, like, limits.

You were very familiar with this, probably became, you know, like, world expert in, in this niche. And then how.

I.

Did, did you want to go into databases again?

I think it was, there's three things that sort of came to a head. Um, the last project that I worked on at Shopify was search, and I didn't have a good time.

What, what, what did you use back there?

Um, I don't, we don't need to name names.

Okay.

Of, of other database companies, but it was a, it was one of the, one of the, like, traditional search companies that a lot of different, um, companies run. And it was just very difficult to get it to do what I did.

And I was just like. The projects that touched that database just, I couldn't get them to perform at the napkin math. And, like, there's, there's no query planner, and, like, I couldn't figure out why it wasn't there. And sometimes it tracked, and then sometimes it really didn't track at all.

And so I tried to learn as much as I could to figure out and, like, start reading the source code of it. And I was just, I couldn't get it to track very often. It was very difficult to operate.

And so I just, that was sort of, like, in the back of my head. I never thought I would touch that again. Then the second ingredient was the napkin math project because it sort of just gave me a lot of facility with all of these napkin math numbers of what might be achievable with the machine if you utilized it perfectly.

Properly. Yeah.

And then the third one was that doing this, you know, leaving Shopify in '21, having spent eight years there, and during that time, I did this, I called it angel engineering. So I was, like, joined my friend's companies, and then I just vested equity instead of, um, instead of just investing or something like that.

And because I wanted to have my fingers in it. I wanted to, like, see what else was out there. That's why I left. And this problem kept coming up again, again, and again, and again,right? Like, ChatGPT came out in 2022, and I was working with, with a company then, and they wanted to connect a bunch of documents to AI, and that's when the context windows were really small, so you had to reach for search very quickly.

So it's like a few kilobytes.

It was 8 kilobytes or 4 kilobytes, depending on the model. It was very, very small. So you had to reach for search very quickly,right?

Yeah.

And so I, I, I worked with them, and I was, I was, I created a little recommendation engine, and the recommendation engine was actually quite good. Um, like I saw, I found out that one of the co-founders' wife was pregnant through the recommendations that I was getting when I was running it on his feed.

Um, like it was, it was, it, it.

Weird, but yeah.

It was recommending. Yeah. I mean, it was just like, you know, he was reading about, like, and I did get permission. I just, like, I don't think anyone expected it to be good enough. And it's just like, okay, it's, this, this thing is working.

And then I ran the back of the envelope math on what it would cost to do this for everyone, like all the users. This is a company called Readwise. So it's like articles that you save and then, and then search later.

And it was going to cost 30 grand a month. And this was a company, it's a bootstrap Canadian company. They spend about 5, they, at the time, they were spending about 5K a month on all the other infrastructure combined.

So it just, it didn't, the, you know, fundamentally in a company, if you're doing an investment, you have to, have to earn some gross margin on top of whatever you're paying,right? And it just didn't line up. Um, and so we just didn't ship it.

And I worked on, I, you know, tuned to autovacuum on Postgres or something like that, which is a good pastime. And then you, I just couldn't stop thinking about why it was so expensive to store all of these vectors that we were using for the recommendations.

And I just sat and did the napkin math one day of, like, can we just use it all in S3 and do some clustering and then organize the files and just away? And it's like, maybe you could build that.

And then one day I just kind of said, fuck it, and did it. And, like, sat down and started to, like, to write it out. Um, and I spent the summer of, of '23 just hammering my head against the wall, trying to find an approach where I can get the latency that I wanted.

Um.

Because the problem with S3 is it has really good durability, but latency, we're talking hundreds of milliseconds,right?

Yes. The p99 on a, uh, 256 or 512 kilobyte object on S3, um, is around 200 milliseconds. Um.

And, and you're, you're saying p99 because, like, when you're talking large scale, you want to care about the p99,right?

Yeah. I think when you're.

That's why we're not talking about p50.

When you're designing a system, you want to optimize for the p99. And especially because when you're designing a system on, on S3, generally in every round trip, you're not doing one request. You're often doing lots of requests,right?

You're going to hit the p99 real quick.

Exactly. So it's like if you're navigating a tree on S3,right, it's like, okay, you get the upper layer of the tree, 200 milliseconds. You get, like, another layer of the tree, 200 milliseconds. You get a bunch of leaves of the tree, and it's 200 milliseconds.

So in aggregate, you have, like, you want to look at the p99, probably even the p99 to design the system properly because you will need to minimize the number of round trips that you had to make. So I just sat and sketched that out, um, and tried a bunch of different approaches.

And then, and then finally in, in, in July of '23, I, I, I got something end to end that seemed to work. I then rewrote it probably twice and then released it in, in October of, of '23 based on, um, based on just that, that summer of, of, of working through it.

And then you kind of, you built it on, on top of S3 because, I guess, durability and, and all of, and just really good. You, how did you make it fast?

We didn't in the beginning, or I didn't in the beginning. Um, it was just me at the time.

Like a team leader. Yeah.

And it was really, like, it was, it was, it was a project. It was not a company. It was not, it, it was, it was, it was to satisfy a curiosity. It was not, I did not set out to do this like, I'm going to go, like, raise $10 million and do, like, I was, like, I barely knew what a VC was.

Like, I was like, I just had to do this thing, and I was so focused on doing it. It was so clear to me that if I wasn't going to do it, someone else was going to do it.

And I just became fully obsessed that summer with it. And so the first version was the simplest possible thing. I think I'm a very pragmatic person. Like, I, I didn't get buried. I barely read any, like, of the literature on LSM.

I sort of, like, you know, read a bunch of it. It was like, got the basic idea, barely implemented that because that would have taken too much time. It was the simplest possible version of what it could be.

Like, really what you have to imagine is that the simplest way you could do this is you run some clustering algorithm on the vectors.

Mm-hmm.

You get the clusters, and then you put the clusters in files. The cl, the files are called cluster one, cluster two, cluster three.

Yep.

And then you have another file called centroids of the clusters. And then you do the search by downloading centroids, looking at the centroids, and then downloading the n-closest clusters. There was a few optimizations around merging some clusters that were adjacent in files and so on just to, like, control some costs and some performance.

But that was basically it. And then getting that to scale. That was the first version. And then how do we make it fast? Well, I didn't even implement a caching layer. I just put the reverse proxy in front of S3 with Nginx and then had it cached the file.

Do you, do you know what a reverse proxy is?

I, like, I do know what it is. I just still don't know what, what the reverse is about. But anyway, um, the reverse, the reverse proxy reversed things. Um, the, the performance in this case, um, maybe that's what it's about by caching,right, all of the, all of the S3 objects.

It was, again, it was the simplest. Like, it's like, I'm just going to put that in front. I knew how to configure Nginx. Like, I've written more Nginx Lua than, uh, than a lot of Nginx Lua. Very good software.

Um, just had that cache in front. And then the way that I would do things like deleting in the cache was just like, shell out to XRX and just remove, like, things in the, in the cache and reverse engineer the directory structure on Nginx.

And that's what we shipped. And it was just running on a single server and a TMux instance. I was like, okay, let's see if anyone gives a shit.

Yeah. So so far, I mean, this is kind of like cool engineering and, like, a cool side project and, like, a bunch of novel ideas. And I, you know, like, I think just some hardcore engineering. How did Cursor come into play?

Because, like, when I learned about Turbopuffer, I was talking with Cursor about, like, how they built their, their backend, their database, how they scaled it. And they were telling me all these migrations, and they were telling me, like, oh, yeah, so we, we were on Postgres, but it didn't, no, they did something else in Postgres.

It didn't even work that well. They went to AWS Aurora, which is AWS Managed Service for Postgres, and it didn't work well, which is very surprising. And they were like, oh, yeah. And then we went to this thing called Turbopuffer, and they worked well.

And I was like, what's Turbopuffer? And they were like, oh, yeah, Turbopuffer. I think, I think they said, like, we were one of their first customers, and this never computed to me. Cursor was already massive at that point.

Yeah.

How did you meet the folks? And how did they become, were they the first customer? One of the first?

They were the first customer.

The first.

The first.

No.

Um, they, they, they reached out, um, after I just launched on, on Twitter. I was like, hey, I built this thing. And frankly, it was like, in, it, I exac, it was like, hey, launch this thing. And to me, it was like, I am so sick of working on this.

Like, I was like, I've been working on this all summer. I don't know if anyone cares. I only want to work on this if anyone cares. Let's put it on Twitter. Again, single TMux instance on a eight-core node somewhere in GCP.

I was like, if someone goes to prod, I'll, I'll set it up properly on multiple and, like, I'll just block on that, but let, let's see if anyone cares. It was like the MVP of MVP. Anyone who's actually worked in the internal on databases would never have had, like, would have had too much pride to ship anything like that.

Um, and I've just, you know, I've worked on, I was just releasing it like a SaaS project. Why can't you work on a database like it's SaaS? I don't, you know, it's like, if anyone uses it, we'll do it properly.

I know how to run software with a lot of nines. Um, but it was not a proper LSM. Like, it was very, very, it was the simplest version of what it could be. And then I released it on Twitter.

I was like, yeah, you could do a million vectors for a dollar. And before that, I think the, the cheapest was maybe $100 per million for something that actually worked.

Yeah, yeah.

Um, and I knew it was reliable,right? I knew, like, I had these invariants, like, if you shut down all the VMs, like, no data is lost, like, all the writes are committed directly to objects. Like, it has all the same invariants it had today.

Um, and Cursor reached out. And knowing them now, I'm sure at the time the Cursor was maybe eight people. And knowing the founders now, I am sure that they had sat at the dinner table one day and were like, the unit economics of what we haveright now, where all the vectors are in DRAM, are not working.

Why hasn't anyone built it where we can put it in S3 and the actual code bases that are actively being used, we can put in memory and everything else just sit in object storage, and then we just hot load it in and out of the cache.

Yeah.

Makes so much sense,right? You open the code base a few seconds and it's in RAM, and then the queries are as fast as on anything else.

Yeah.

It made so much sense. So, I mean, at the time, they were, if you look at some of Amon, one of the co-founders' early tweets, he talks about, uh, using S3 for KV caching and things like that.

Yeah.

Which barely anyone is still doing, even though, um, the economics.

Alamokul, yeah. Price-wise.

Yeah. It's, and it's, it's very, it's very uncommon, and I think it will happen,right? But they were ahead of their time.

Mm-hmm.

And they, I think they were, I don't know if they were thinking of building it themselves. I think that's quite likely. Um, and they found Turbopuffer, and it just perfectly pattern matched into that. Again, I don't know if this dinner conversation happened or if this was just inside Arvid's head.

Well, happens now.

Um, but it pattern matched something. And so we exchanged a bunch of emails, and then something compelled. I didn't know anything about B2B sales. Now I love B2B sales. Um, I didn't know anything. I was just like, I just want to help them because they, they were, they had some unit economics that didn't line up.

So I just went to San Francisco,right? I live in Canada. I went to San Francisco, and I showed up at the office. And when I showed up at the office, they, um, they were having some Postgres problem that they were discussing.

Yeah, the AWS Aurora problems. Yes.

Yeah, early on. And I was like, oh, do you guys have pganalyze? And they said, oh, no, we don't. I said, okay, let's, let's, let's get that going,right? Let's look at it. And it was the same thing as it always is with Postgres, which is autovacuum hadn't run enough.

And so they had all of these, like, going to heat when they should be doing index scans and blah, blah, blah. So we were talking about all of that. And so I was just helping them,right? It was like my, you know, my database gene just, like, kicked in.

And I think this built enough trust with them that, okay, well, maybe if he knows how to help us with the database, maybe he also would know how to build one. And, um, at this time, I'd also approached who I thought was the best engineer who ever worked at Shopify, my co-founder Justine.

Um, and she'd come on, and the first thing that she did was, um, remove the reverse proxy Nginx cache with a, uh, file-based cache,right? Just a direct cache, which, again, great. Like, the S3 thing worked. Um, and so she was online.

She was starting to work on it. And, um, and Cursor, Cursor, Cursor then that night was like, okay, well, we're going to migrate. And so they migrated everything over the course of, like, a week or two after that.

Um, but Cursor was a small company back then,right?

Yeah. And they, they were, they were just in the beginning of their massive rapid growth.

Exactly. And I, I told them that I was going to reduce their bill by 95%. And I did. Like, we did. Justine and I did. We, like, they came on and their last bill with their previous vendor and the first bill with us, it was 95% lower.

Yeah. And you're, you're nice for not seeing vendors, but I, I can see vendors. I talked to them, and, and it's in the deep dive about Cursor. It was, it was AWS Aurora specifically. Uh, so.

This was, this was not, this was not Postgres, no. This was a.

Oh, it, it was a different one, but it's probably still in the write-up. We don't, we don't need to name names, but, uh, yeah, but they were, the reason they went there is reliability was their main, main, main pain point.

I'm sure the unit economics would have been there. But yeah, this was, and then what Swale told me is he said, like, look, like, there's a few things that we did that you should never, ever do. And he said, one of them, you should never, ever bet your business on a tiny startup where you are their only or biggest customer except for Turbopuffer.

And he said, I love, love those guys. So I guess it just comes to show that even in your case, like, to me, what the story is, shows is, is you can do things when you build high-quality things and you're pushing for things, good things can happen.

And on the other side, Cursor, when you're a startup, it's okay to take sometimes irrational risks when you have conviction. And it sounds to me that you gave them conviction by showing up in person, by helping them, by showing that, you know, you know your stuff.

Like, you suddenly brought in your, your 10-ish or eight years of Shopify experience and your curiosity, and they probably took a risk because of that, not because you were some, you know, random vendor. They probably would have never done that.

So fast, fast forward today, uh, Turbopuffer is now a lot bigger. You're, you're working on some, some, some cool things, but you have this very interesting business where for you, CPUs are important,right? You run on mostly CPUs. And you told me a story over dinner yesterday that, uh, you met Jensen, uh, and Jensen, he really wanted to sell you on GPUs.

Can you tell me how that meeting went?

Um, yeah.

Jensen Huang,right?

Yeah. I just, I, I never met, uh, I'd never met, uh, Jensen before. We were, we were at an event at, uh, at, at Nvidia, and we were just doing, um, presentations.

This is in a big, big HQ. Super impressive.

Yeah, exactly. They'd invited a couple of companies to go and, and, um, and, and, and talk about, um, uh, talk about our businesses and how we can partner with Nvidia and so on. And

I, I don't, I, I don't know. I was like, I think I was in a goofy mood that day. And so I went up on, on stage and I said, um, hey, I'm Simon from, from, from Turbopuffer. And, uh, and yeah, if you're wondering about the name, it's like, if everything goes south, we can always pivot into vapes.

I was kind of nervous. I said, this is what I, this is what I said. And then, and then he said back to me.

And what was, who was in the room? Was it Jensen? Was it a direct report?

It was, it was Jensen. And then I don't, he has like, I don't know if it's just 50 direct reports or it was like, you know, it was, there was, it was Jensen.

No question.

And then a bunch of the, um, like, Nvidia, Nvidia leadership,right? Um, because you go there and then you talk about that, you find opportunities to partner and work together,right? And so I said, yeah, you know, so plan B could be that we could pivot into vapes.

And then he said, I was already nervous. He said, judging by your slide, maybe you should.

No, he did not.

And, and then

I didn't know what to say back to that. So I said, well, Jensen, do you vape?

And he didn't, he didn't answer the question.

And then someone, um, someone on the, um, someone on the, on the team, um, wrote to the whole company, Turbopuffer company. Simon just asked Jensen if he vapes. Um, and then, you know, this is, this is a great start,right?

And, um, and then the team had, team, team had sort of talked to me beforehand and was like, Simon, we got to make sure we don't say the C word. We can't say CPUs.

And so I just couldn't stop talking about CPUs. I was like, AVX512 is so sick. Like, we love SIMD and, um, like, we, we, we, like, there's so many CPUs. They're so easy to get. Like, um, it's just a riot in CPU land.

Like, you know, I, I don't, I think I stopped short of saying I'm so glad I don't need GPUs, but, but, but it was just, I just couldn't stop talking about CPUs. Yeah. And so, you know, Jensen took an interest in that.

Yeah.

So it'd be, who, who knows? Like, I'm, I'm sure you made, you made a memorable impression. Maybe he made it his mission now to, like, at some point get you guys onto GPUs. But speaking of CPUs, can you tell me a bit what you're seeing inside of the hyperscales, the cloud providers?

You're now in AWS, you're, you're in GCP, you're on Azure. What I would think naively is there's a GPU shortage, and when I talk with inference companies, they are, and, and, and AI labs, they're just getting whatever they can do.

I would think getting CPUs should be easy. Is it?

No. It's not anymore.

Why? What's happening? Can you tell us about the dynamics on, on, on the why and what you've learned?

Yeah. So I think that

GPUs will probably continue to be scarce. Like, I don't know, maybe there's going to be some surplus. I, I refuse to speculate too much about the macro, but I think as, as RL is becoming a very, very large amount of the workloads, that needs a lot of CPUs.

So the labs are sucking up a lot of CPUs because you need CPUs to be like, okay, we need to, like, teach this model how to, how to search. We need to teach it how to use grep. We need to teach it how to boot up bash.

We need, it needs to run real things and learn from that. It takes a lot of CPU.

Mm-hmm.

Um, and so I think as we RL is consuming a lot of CPU, and then also just all of the agents are running on CPUs,right? They need to do all kinds of very general purpose things on a CPU.

And so as, as, as the demand curve is sort of shifting to theright and it's becoming more and more applied, and that feeds back into RL, by the way,right? Because as things become more applied, it's like, oh, the models are not that good at CAD or ship building.

I don't know. And then, you know, you have to spin up even more RL environments to do that. So I think that's what we're seeing. And so we're on the other end of that, needing these CPUs. We need a lot of NVMe SSDs as well.

Um, and a lot of thisright now is tied up in DRAM,right? Of, of where, like, you need a lot of that also for the GPU servers. Um, but I would assume that it gets a lot worse before it gets a lot better on the, on the CPU side.

Um, and I think even the big companies are fighting amongst each other,right, to get the allocations. And even we, you know, we're selling to companies that we also fight for CPU with and against,right? It's, uh, it's, it's, it's really difficult.

And so you write things to try to make sure you get these CPUs as fast as possible.

Yeah. And yesterday I was at a dinner that you hosted with your team where you actually have a bunch of Turbopuffer customers. A bunch of them are AI, AI labs or, or AI startups, but a lot of them, one of them, uh, Reflection had, have huge, massive amount of footprint.

And they were telling me that they're in a situation where they cannot buy more. Like they, when it comes to GPUs or CPUs, they max out. They have the longest contracts that possible. And I didn't realize how competitive it is in the cloud when you, you go beyond a small fish to, like, a medium size or even a, a large fish that now, like, it's, it's interesting.

So, so now you have this, and even you're having this, this, uh, kind of fight behind the scenes that is maybe not as visible.

Exactly. And I mean, you, you work with the clouds,right? You work with them to talk about which regions have, um, have CPU, which regions are getting, it, it comes down to power,right? Of like, okay, well, where is the power, which is generally where they're going to ship the new CPUs.

Um, and so we have to work with some of our biggest customers on that. So these are real constraints,right, that are, that are making our way to us. We're just very fortunate that it's very easy for us to run lots of Turbopuffer clusters because all we need are like a few CPUs and NVMe SSDs and then S3, and then we're in a good place.

But there's lots of changes that we can make even to the architecture, um, to try to protect from, from a lot of this. Now, I'd rather spend that engineering effort on other things, but we are very, very good at using a lot of very different SKUs,right?

So we don't need everything to be a particular CPU or instance type. We can run with many, many different types of machine types. Um, on.

And SKU meaning that's the, it's a fancy name for like the different machine types.

Yes, exactly. Right. Like, you know, C40 or IAG or whatever they're called.

What's your favorite one?

Um, we really likeright now the, um, C4s on, on GCP.

GCP, yeah.

Um, the Z4Ds are also performing really well. Um, now that we've done some, done a bunch of, of, um, of optimizations to them, um, those are really, really great machine types. Uh, we really like those. Um, and then the ARM C4As as well, um, on, on GCP.

Um, we like those. But I think that in general, like when you're, yeah, when you're small, it's very easy to suck up a bunch of, but at, at Shopify, I was also part of, you know, deciding ahead of BFCM,right?

A few months out, you have to tell the cloud providers how much you're intending to use. Do commits on all of that,right? The clouds are not infinite as they seem when you're small.

Now, one way, of course, to, like, get, like, infrastructure and, and also just, like, credibility is venture capital. If you raise a hundred million dollars, a billion dollars, some of your customers just raised two billion dollars. Actually, I talked with them yesterday.

You know, it gives you credibility, it gives you cash. You can pay for this thing. Your specific Turbopuffer's relationship to venture capital seems very interesting. I never heard you announce a raise until maybe just very recently. Can you tell me how you, and, and you told me that when you started this thing, you didn't think too much outside of just building some cool stuff.

How did you think about venture capital and how do you think about raising? Because again, I feel you have a very fresh and different perspective than what you, which is typical inside of Silicon Valley.

Yeah. So I think to, to understand my, how I think about capital, you have to go back to the, the beginning of Turbopuffer,right? Where I promised Cursor that Justine and I could get their bill to 4K a month.

And this was based on some very rough napkin math on, okay, if, if Turbopuffer was a better implementation than it currently is, then it should cost this much. And that's the pricing we ship with. And that's what we guaranteed, um, guaranteed Cursor.

Um, but the software was not that good. Like it was very reliable, but it was very simple,right? And that's like a core engineering principle of me is simplicity above everything. Um, you and I have talked before about how software that ages well and some of the advantages of seeing be having long tenures inside of companies.

You had a long tenure at Uber. I had a long tenure at Shopify. So you see simplicity just almost always wins. Um, and at the time, I was not convinced whether this was a venture scale opportunity because I understood that if you take venture capital, no matter how many smiles there are in the room, everyone's sort of expecting that you have to earn a big return on that on some timeline that makes sense to everyone involved.

And everyone involved are, you know, pension funds in Canada. Like that, it's like it, there's like a whole stack,right, of, of, of people that, that need to. So at the time, I was like, I don't, you know, I don't know if this could be a billion-dollar company.

I didn't know that in the very, very beginning. Um, it wasn't completely clear to me. It felt like a very niche kind of product,right, to build this particular search engine. Um, and that was completely fine with me. So I, you know, it's, it's, it was fine.

And so then I just, I just looked at the Cursor bill and I looked at my GCP bill, which is what we started on. And, you know, I was like a, you know, dumb Danish person who's just like, okay, like this number should just be lower than the other number.

Yeah.

That's sort of like, you know, and it's just, I don't think I'd spend enough time in San Francisco because I think the money over here, it works a little bit differently. Um, that's just, that's all I knew.

You, you were doing business 101. As, as long as you're making a profit, you're good,right?

Yeah. It's like, I'm, I'm, I'm not kidding in this exaggeration that it was just like, that just made sense to me that Justine and I were just going to go optimize this until these numbers were roughly equal. And maybe if, if, if we could get some other workloads, we could start paying ourselves.

But that was like very much the philosophy at the time. Um, because I didn't know if I could go raise a bunch of, of, of money. I didn't know anyone who had the money. I, I didn't have any relationships.

Um.

You were an absolute outsider to the, the.

I was, I was an outsider. I was like an outsider squared,right? I grew up in Aarhus, Denmark, and I, um, I then moved to Ottawa, Canada. So it's like I'm an outsider to Canada, and in Canada, I'm an outsider to San Francisco.

So I was just thinking about this from first principle. I was like, oh, you're a venture capital. You need this return. You need it on this timeline. I don't know if I can deliver that yet. I would need more data to decide that because I want to, like, I kind of want to keep working on this.

And now I have to get to this point for it to not be a failure. Um, in, in, in January, then I, uh, there was a person that I was at IOI with in, in, uh, in 2012 and 2013, and his name is Boyan, and he was on the Northern Macedonian team, um, at IOI.

Um, and he was, he was, he was really good. He was so good that the North Macedonian team called him God. Um, I don't know why, but that was what he went by. And he was, yeah, he was very good.

Grew up, and, and I really wanted to work with Boyan, but I couldn't afford to work with Boyan. Um, and he was very much like, this is what I can live off. Like, you know, I just, like, I want to build this data.

Like that would be like, this is what it can do. But at this point, Justine and I hadn't taken a salary for like six months, and we'd already, we'd already spent like tens of thousands of dollars on, like, on GCP bills and all of that.

And I was like, I don't think we can, I don't think we can, we, we can do it. And so I had met one, one individual in, in, in Silicon Valley. Uh, his name is Locke. And it just, I ended up just calling him and saying, hey, I kind of want to learn a little bit faster here.

Can I, can we raise like 700K? That's like what I wanted to raise. So it's just like, I want to have like two engineers for the rest of the year. Justine and I still don't need to be paid.

And then a little bit of buffer room. It's like, this is what I need. And if this doesn't have PMF and is a big opportunity by the end of the year, I don't think we're going to bother and we'll just shut the whole thing down and we won't have it taking the time.

We'll return everything to you. Um, I think there was the first time you heard anyone say it like that. Um, and, um, I told some other VCs that at the time, and that was terrifying to them. I think to someone on the West Coast, this sounds like you have low ambition or something like that.

Mm-hmm.

Um, and to me, it was just like, I, I don't know, it just came from a, when I don't know how to play a game, I just play with open cards. Like this is how I see it. And so I, we were, it was very clear to us that we wanted to do this.

And, but also it became clear to us that we didn't want to just like keep working on this unless it could become big. And we were starting to develop conviction, conviction that this actually become really, really big. And so we, we, we did that and hired Boyan and then became profitable later that year.

Um, and then just continue to hire. And then it's like to raise more money, you need sort of, there needs, there's six reasons to raise capital. The first reason to raise capital is to fund R&D.

Mm-hmm.

That was the reason that we raised capital in January because we funded R&D with a lot of our own, you know, opportunity costs and not taking a salary and then paying the bills ourselves. Um, but we wanted to learn a little bit faster.

And so we hired Boyan and Morgan as the first engineers. And then the second reason to raise capital is to fund growth. You've, you've, you've built something and you want to tell the world about it and you want to spend more capital to do that.

Um, the third reason to, to, to raise capital is for the founder's ego. Um, it's a very.

I appreciate the honesty.

It's very popular. Very, very popular,right? Big numbers, lots of press, like, um, and I think this is a very, very dangerous reason to raise money. And I wish that it was more talked about because you're deluding all of your employees when you do it.

You are, um, setting a certain price for future employees and their upside. It's, it's, it, for some people, it can become a status game. And that's not what it's about. We're here to build a big business together. And this is not a reason to raise money.

Um, but I, I do think that it happens. Um, the fourth reason to, to, to raise capital is to reward your employees,right? It's a, you're on a very long journey and you want to work with the best people in the world.

And by definition, there's not that many best people in the world. So you want to reward them. Um, that was the reason that we took more capital in December, um, was to allow the employees to liquidate some of their equity, um, instead of waiting for some like event, like an IPO or something like further out.

Um, the fifth reason to raise is for a strategic partnership. There are strategic partnerships that have been made in this, in this city that have made companies. Um, and, um, the sixth reason to raise would be do, doing M&A or, or something like that.

But it's like you have to be very honest about what reason you are raising in those six. First reason we raised was one, and second reason we raised was four. Um.

So which, which one's the first reason to raise was?

R&D.

R&D. And the second reason was?

Um, to provide liquidity to the employees.

The employees.

Yep.

I, I, I think it's a, it's a nice and healthy way. And I think, yeah, the, the ego part, we don't talk about it. And the identity and especially the, the closer you are to, to tech ecosystems where a lot of people are raising, it, it will be part of it.

As closing, I, I wanted to ask you about the way you have a remote culture. These days, I'm seeing it, especially for companies that do anything with AI, may that be building AI infra or, or, or just AI products.

A lot of them prefer in-person, having an HQ, oftentimes in SF or wherever your headquarters is, may that be London or somewhere else, because you often, these companies often find that they have faster iteration. Uh, it's just fewer layers cut in between.

And of course, speed is, is very, very important. You have started for remote and you're still for remote. How is it working? Uh, and what kind of quirks or, like, or turbopuffer ways have you found to, to make this work better?

Yeah, I think to the, the company started in, in 2023. So sort of like on the, on the, on the cusp of COVID where a lot of companies were just remote. Um, the Shopify infra chain was remote since the very, um, very beginning because it was very difficult to get them all to move to Ottawa.

Um, and

so it was natural to me. It was like, okay, I think there is kind of maybe two cities where you can build a database company fast, and that's San Francisco and, and, and maybe New York. There are maybe other cities,right?

But that's like kind of where it's been done.

Yeah.

And so if you don't want to do that, I think you have to go all in on, on, on some distributed model. And so we've tried to figure out what does that distributed model mean for turbopuffer. It doesn't mean the absence of in-person.

We get everyone together twice a year in, in some, in, in some location. Uh, earlier this year, we were in, in Banff,right? And then we were in Mexico City and so on. So it's like that's, that's not that uncommon.

Um, but one of the things that we, we, we've been trying to do is we have this concept called campfires. And the concept of the campfire is that when a couple of people just sort of randomly congregate in a place, you call it a campfire and you encourage as many people as you want to come and join.

So for example, this week is a turbopuffer campfire in San Francisco because I'm here for this conference and a bunch of other things. And so everyone is invited to come. Like we're going to go meet customers,right? We're going to put on dinners for our customers and things like that.

And we just make a thing out of it and, and spend time together. And, uh, we encourage everyone to come. We've also gone to the extent now of, um, we want to encourage that, but not everyone, not everyone needs to go to the campfire all the time.

Some people just want to, you know, lock in and hacks into tent. And that's great. We have people that just make it to the offsites twice a year, and otherwise they're home, they're with their families, and they don't, they don't spend time on an airplane.

Um, fantastic. Like that is completely compatible with this model. And there are other people at the company who are on a plane probably every two weeks. Um, we had someone the other day where they saw a campfire happening in New York and everyone was dialing in from a meeting room in New York.

And she had so much FOMO that she took an Uber straight to the airport in Ottawa and flew to, flew to New York to hang out with the team,right? And I think that's fantastic. Um, and we've also introduced these things where, um, if you, if you, uh, if you do a conference talk or a blog post or something like that, a turbopuffer, something a bit extracurricular, we give you a turbo credit.

And a turbo credit allows you to upgrade your next flight to business class, which again, encourages spending time together with the team. Um, and now, I mean, turbo credits are probably going to take on a life of their own.

Someone was talking about doing a central bank and doing interest rates on the turbo credits, um, and doing a betting market on the turbo credits. And so like this might take on its life on its own. Um, and, uh, you, you know, if you, um, if you're at a conference like this, there's some of the, our engineers here who are just want to interact with customers and be on, like, and standing on a, like expo floor all day is quite taxing.

And so if you do that for two days because you want to do it, oh, you get a turbo credit,right? And so it's just like these fun little things that we try to do to, to, to encourage people to meet if they want to meet.

Thank you. Well, in this session, uh, what I found very interesting is turbopuffer is so many AI companies are using you as an infrastructure layer, but in this conversation, we managed to talk very little about AI and a lot more about engineering principles, pushing, being curious, and the human connection, how important it is for people to work together, to trust each other.

So just thank you very much for that. So let's give a big round of applause for Simon.

Thank you so much. This is great. Thank you.