Transcript

Gergely [0:27]:
I'm Gergely, author of The Pragmatic Engineer, and I'm excited to have a chat with Simon Eskildsen, founding 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?

Simon [0:50]:
Through PowerPoint. PowerPoint. I don't know if any of you know this, but in PowerPoint, 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 Turing complete real quick, right? You can sort of, you know, create very complicated convoluted games. And then at some point...

Gergely [1:14]:
You know, you make it through the Microsoft Office suite and you discover FrontPage. Do you remember FrontPage?

Simon [1:19]:
Yeah, I remember FrontPage. It was supposed to eliminate the need for any front-end developers.

Gergely [1:25]:
Exactly. And it only worked in Internet Explorer. I remember heartbreak I had one day when someone opened a website I created in Firefox and it was just all over the place. And then one day I accidentally clicked the HTML thing in FrontPage and it just showed all of this 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 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 dynamic? You learn PHP. And then for me, I exhausted the internet on Danish language programming advice and 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.

Gergely [2:20]:
So you kind of started hacking, getting 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?

Simon [2:33]:
I mean, I just started. I mean, you know, then I learned video games, then I learned English, and then, you know, this massive arsenal of the web now 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...

Gergely [2:51]:
Yeah.

Simon [2:51]:
I did. 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. Heard of this thing?

Gergely [3:08]:
Yep.

Simon [3:10]:
And 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 heard about it before. And so I found it on some 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.

Gergely [3:31]:
It's like the algorithmic-ish problems.

Simon [3:34]:
Exactly. It's sort of like this is not actually the kind of problem you'd see there, but I think it illustrates well the kind of problem that you might get, right? You can 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?

Gergely [3:53]:
Yep.

Simon [3:53]:
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. So it's these kinds of problems, right? And so I started doing that in high school while working for a startup, and then Shopify found me while I was still in high school.

Gergely [4:16]:
And the whole Shopify family, was it through your open source contributions? Was it something else?

Simon [4:22]:
It was because I had written an article where 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 drop your iPhone and you just knew it was over for the screen.

Gergely [4:36]:
Yep.

Simon [4:36]:
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 you just 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.

Gergely [4:52]:
So I wrote this article about how, oh my god, I'm like calling people and I have my sense of direction back. And I wrote an article about it, and this article went on Hacker News briefly, and the New York Times decided to feature it.

Simon [5:07]:
No way.

Gergely [5:07]:
And so a lot of traffic was driven to it, and then some astute Shopify recruiter...

Simon [5:16]:
Put it all together, and I had a call with them. And then I don't think they realized that I was still in high school, but I had a great call with them. They invited me on site to Ottawa, Canada. I had no idea what Ottawa, Canada is. I think the email says something like, what's an Ottawa? I had no idea. And so I went there, and it was just like walked into the building and it just felt right. And so I interviewed with them and then said, well, I got to finish high school first, and then I moved to Canada to work at Shopify in 2013.

Gergely [5:52]:
Yeah, I think that's a legit excuse for like not even worrying about college and university.

Simon [5:59]:
But it really crossed your mind.

Gergely [5:59]:
It did. I thought I was going. I thought I was doing a gap year. I thought I was like, okay, I'm gonna go work at Shopify for a year and then I'll probably go back and do... But I was 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 competitions. It was 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 that repeatedly. And in my first year at Shopify, every time I heard something that I didn't know what it 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 because I felt insecure that, like, well, if someone mentions TCP, surely they know exactly what's in the three-way handshake and how TLS is 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. And so I went and did that for everything that I encountered. 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 somewhere else and then come back to this because I felt like I'd already found what I wanted to do.

Gergely [7:06]:
So it sounds like it was a pretty good combination of like you just having this very natural insecurity, like you're young, you know, you don't have the education that everyone else has, and inside the company that's just doing pretty cutting-edge stuff even at the time and even today, right? Like they're leading. So you just kept self-teaching yourself, like just catching up and go do I understand that you just went deep in every concept that you understand? You didn't like just try to understand that surface level, but like go as deep as you can, search on the internet, buy books, whatever that is.

Simon [7:35]:
I think it was just that I just wanted to 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 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 Shopify. And I would just always sit next to them at lunch because I was working on the product side, but I just couldn't help myself. I just wanted to learn what it was when they were talking about a reverse proxy. I'm like, why is it reverse? I still can't answer that. I mean, okay. You know, well, what's in reverse?

Gergely [8:22]:
Because it's a proxy, right? I don't know. It's like an inverted index. Like what's inverted? It's like it's a terrible name anyway.

Simon [8:32]:
Yeah.

Gergely [8:32]:
I mean, it's still better when you get the NAT tables to look up some of those things. Like some of that, but yeah, I hear you. There's some weird names with this. But at Shopify, what were some of the kind of hard engineering challenges that you faced, engineering challenges, outages, like learnings that kind of defined you that were really also fun at the time or interesting to learn, but it would have been hard to get it elsewhere?

Simon [9:02]:
Yeah, so I think it was, you know, in the 2010s, there were like a bunch of SaaS companies 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 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 SaaS sometimes seem 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. 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, 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.

Gergely [9:55]:
Mm-hmm.

Simon [9:55]:
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 my dear boss Camillo used to say, you can't cache writes. So there's a fundamental point where you just have to move beyond a single shard. So I joined around the time they did the sharding, and they did it, I think they did the cutover a week before Black Friday, which is mind-blowing and very... but it worked. And then 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 because people had just been treating it as this KV store. And so we started splitting it out. We did all this stuff around making sure that if you visit a Shopify store and the thing that stores your sessions is down, the right behavior is not just for the entire of everything to be down, but that's kind of the default failure mode, right?

Gergely [11:13]:
Bye.

Simon [11:13]:
You're not going to rescue all of that unless you're in a program that really forces that decision. So we did things like build this matrix out of, okay, well, this service when this component is down should act this way. And I find 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 I came up with this idea at the time like, oh, what we're gonna do is we're just gonna shell out to GDB and then into the process and then close the file descriptor to the database to simulate deep 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 be upstream and things like that around just like handling failures at the connection layer. So then I moved on to create this proxy called Toxiproxy.

Gergely [12:01]:
That's a proxy.

Simon [12:01]:
Have you heard of this before? Yeah, Toxiproxy is just like a Layer 7 proxy that sits in between you and, well, Layer 4, but in between you and the databases. So you basically have just like this proxy, and then MySQL, whatever doesn't speak the protocol, then you can do an API call, say take the database down, make it slow, and over time it also added Layer 7 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.

Gergely [12:36]:
So basically the proxy was just like a really thin layer which like was passed through, but you built the functionality to like simulate problems of 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.

Simon [12:57]:
Exactly.

Gergely [12:57]:
Okay.

Simon [12:58]:
So you could do like MySQL, you know, proxy.mysqldown 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 Rails. Like it's just like no one in the ecosystem had been testing for this, and it was very difficult to see it as in prod, right? Because they're MySQL down, you're focused on just getting back up and not like what could the application actually have done. Yeah, it's interesting.

Gergely [13:27]:
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 problems in large 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 and if we can simulate these problems, suddenly you can... I mean, you can like predict a lot of things. The problem with state oftentimes is it's really hard to simulate problems happening ahead of time unless when they happen. So it sounds like you have pretty good success with...

Simon [14:10]:
Yeah, I think to my knowledge, it's still 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. And so we wrote all these tests against it to implement all of these different failure conditions, and it just... yeah, it worked out great.

Gergely [14:29]:
So you spent eight years in total at Shopify, so like started from like just a gap year, it just went on a year, a year, and another year. At what point did you think about leaving and why, and what was your kind of decision framework? It sounds like you were like an epic running out. Even today, Shopify is doing wonderful. It's probably doing even way better than you're 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 their rocket ship.

Simon [14:58]:
Yeah, so I spent eight years there from '13 to '21. And I think there just came a point where I wanted to see something different. Again, I've been inside Shopify since I was 18 years old, right? I've 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 dysfunction. And so I left in '21, and I'd worked on so many different parts of the infrastructure, like caching. Me and Justine, who's now my co-founder, we wrote the entire storefront for Shopify, which powered almost 100% of traffic 18 months after we embarked on it. We've worked on running Shopify in multiple data centers. We've worked on so many database scaling projects, like caching, all of these different things, right? A lot of the scalability came from the Kardashians launching lots of products on Shopify, which would force a lot of traffic. But that's eventually how I left. And so when I left, I didn't really know what I wanted to do. And so one of the projects I had while I was at Shopify was this napkin math project. Have you seen this?

Gergely [16:07]:
Napkin math? No.

Simon [16:07]:
No. So napkin math was essentially just this table that I maintain on GitHub of how much bandwidth can you drive to DRAM? What is the 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 the collection of probably... there's probably like 50 of these numbers and then a REST script that generates them all. What all these things cost, what do you like, what does a gigabyte of memory cost? Two dollars. What does a gigabyte of S3 cost? Two cents. What does a gigabyte of this cost? Ten cents, right? What does it cost on spot? What does it cost on a three-year commit? Like I 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 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 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 drive 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. Each term has this many documents that match it. That's this many megabytes. We intersect these 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 benchmarked the wrong thing. And in some ways, some reasons, right? It's like, okay, you've done a benchmark. You 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 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 animal to go in and just be like, okay, we can just do the calculation right here. And then because I was always doing these little demos or writing little prototype scripts to demonstrate this. But it was just... I just... the argument of here's how a B-tree works, this is how many pages we have to visit, this is what a random SSD read takes, it takes one millisecond, and then present it back and say 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 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 can do equal the amount of fsyncs that you can do per second? That sort of makes sense, right? Every time we do a write, you fsync to persist a disk. So how many fsyncs can you do per second? Well, an fsync takes one millisecond, so you do a thousand writes per second. That doesn't really match up. Like, I feel like a database can do more than a thousand 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?

Gergely [19:45]:
Mm-hmm.

Simon [19:46]:
And now you...

Gergely [19:47]:
How?

Simon [19:47]:
Just batch.

Gergely [19:48]:
Is it possible?

Simon [19:48]:
Because you batch. So an fsync happens on usually a 4k write, but it's like that's not intuitive. Like it's actually... I got caught 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 then I found out that, oh, every fsync was like much larger than I would have inferred.

Gergely [20:14]:
Yeah.

Simon [20:14]:
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.

Gergely [20:43]:
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 clear, very into databases. You'd done an awesome job benchmarking like what is the theoretical limits you are very familiar with. This probably became like world expert in this niche. And then how did you want to go into databases again?

Simon [21:05]:
I think it was... there were three things that sort of came to a head. The last project that I worked on at Shopify was Search, and I didn't have a good time.

Gergely [21:12]:
What did you use back there?

Simon [21:21]:
Um, I don't... we don't need to name names of other database companies, but it was one of the traditional search companies that a lot of different companies run. And it was just very difficult to get it to do what I did. And I was just like, the projects that touch that database just... I couldn't get them to perform at the napkin math. And there's no query planner, and 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. And then the third one was that doing this, you know, leaving Shopify in '21, having spent eight years there and doing 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 just investing or something like that because I wanted to have my fingers in... I wanted to like see what else was out there. That's why I left. And this problem kept coming up again and again and again, right? Like ChatGPT came out in 2022, and I was working 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 have to reach for search very quickly, right?

Gergely [22:47]:
It was like a few kilobytes.

Simon [22:49]:
Yeah, it was eight kilobytes or four kilobytes depending on the model. It was very, very small, so you have to reach for search very quickly, right?

Gergely [22:54]:
Yeah.

Simon [22:54]:
And so I worked with them, and I created a little recommendation engine, and the recommendation engine was actually quite good. I found out that one of the co-founders' wives was pregnant through the recommendations that I was getting when I was running it on his feed. It was weird.

Gergely [23:14]:
But...

Simon [23:14]:
He was recommending... yeah, I mean, it was just like, you know, he was reading about... like I didn't get permission. I just like... yeah, I don't think anyone expected it to be good enough. And just like, okay, 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 search later. And it was going to cost 30 grand a month, and this was a company, it was a bootstrap Canadian company. They spent about 5k a month on all the other infrastructure combined, so just it didn't... these, you know, fundamentally in a company, if you're doing an investment, you have to have to run some gross margin on top of whatever you're paying, right? And it just didn't line up. And so we just didn't ship it. And I worked on... I've, you know, tuned auto-vacuum on Postgres or something like that, which is a good pastime. And then 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... and 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 write it out. And I spent the summer of '23 just hammering my head against the wall trying to find an approach where I can get the latency that I wanted. 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 256 or 512 kilobyte object on S3 is around 200 milliseconds.

Gergely [24:55]:
And you're saying p99 because like when you're talking large scale, you want to care about the p99, right?

Simon [25:00]:
Yeah, I think when you're designing a system, you want to optimize for the p99, and especially because when you're designing a system 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, it's 200 milliseconds. So in aggregate, you want to look at the p99, probably even the p999 to design the system properly because you will need to minimize the number of round trips that you have to make. So I just sat and sketched that out and tried a bunch of different approaches, and then finally in July of '23, I got something end-to-end that seemed to work and then rewrote it probably twice and then released it in October of '23 based on just that summer of working through it.

Gergely [25:56]:
And then you kind of built it all on top of S3 because I guess durability and all of it and just really good. How did you make it fast?

Simon [26:04]:
We didn't in the beginning, or I didn't in the beginning. It was just me at the time, and it was really like... it was a project. It was not a company. It was not... it was to satisfy a curiosity. It was not... I did not set out to do this like I'm going to go raise 10 million dollars and do it. I was like, I barely knew what a VC was. I was like, I just had to do this thing, and I was so focused on doing it, and 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. I didn't get buried. I barely read any of the literature on LSM. I sort of like, you know, read a bunch of it, just 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, you get the clusters, and then you put the clusters in files. The files are called cluster one, cluster two, cluster three. 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's 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.

Gergely [27:42]:
Do you know what a reverse proxy is?

Simon [27:43]:
I do know what it is. I just still don't know what the reverse is about. But anyway, the reverse proxy... reverse things. The performance in this case, maybe that's what it's about by caching, right? All of the S3 objects again.

Gergely [28:03]:
Yeah.

Simon [28:09]:
I had written more NGINX Lua than a lot of NGINX Lua. Very good software. 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 XR and just remove things 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 in a TMLUX instance. I was like, okay, let's see if anyone gives a shit.

Gergely [28:36]:
Yeah, 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 backend, their database, how they scaled. And they're telling me all these migrations, and they were telling me like, oh yeah, so we were on Postgres, but it didn't... no, they did something else in Postgres. It didn't really 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're like, oh yeah, and then we went to this thing called turbopuffer, and they worked well. Well, and I was like, what's turbopuffer? And they're like, oh yeah, turbopuffer, I think they said we were one of their first customers. And this never computed to me. Cursor was already massive at that point.

Simon [29:19]:
Yeah.

Gergely [29:19]:
How did you meet the folks, and how did they become... were they the first customer? One of the first?

Simon [29:25]:
They were the first customer.

Gergely [29:26]:
The first.

Simon [29:27]:
The first. No.

Gergely [29:30]:
They reached out after I just launched on Twitter. I was like, hey, I built this thing, and frankly, it was like... I sat... it was like, hey, I launched 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 T-MUX instance on an eight-core node somewhere in GCP. I was like, if someone goes to prod, I'll set it up properly on multiple, and like I'll just block on that. But 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. 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. But it was not a proper LSM. 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 can do a million vectors for a dollar. And before that, I think the cheapest was maybe a hundred dollars per million for something that actually worked.

Gergely [30:37]:
Yeah.

Simon [30:38]:
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. And Cursor reached out. And knowing them now, I'm sure at the time, Cursor was maybe eight people, and knowing the founders now, I am sure that they'd sat at the dinner table one day and were like, the unit economics of what we have right 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 to sit in object stores, and then we just hard load it in and out.

Gergely [31:17]:
Yeah.

Simon [31:17]:
Of the cache 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...

Gergely [31:22]:
Yeah.

Simon [31:22]:
Anything else. It made so much sense. So, I mean, at the time, they were... if you look at some of Aman, one of the co-founders' early tweets, he talks about using S3 for KV caching and things like that, which barely anyone is still doing even though the economics like it's...

Gergely [31:35]:
Sorry.

Simon [31:35]:
Yeah, it's very uncommon, and I think it will happen, right? But they were ahead of their time, and I think they were... I don't know if they were thinking of building it themselves. I think that's quite likely. 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...

Gergely [31:55]:
Oh.

Simon [31:55]:
Parviz's...

Gergely [31:56]:
How fast they had known.

Simon [31:56]:
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. I didn't know anything. I was just like, I just want to help them because they were... they had some unit economics that didn't line up. So I just went to San Francisco. 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 were having some Postgres problem that they were discussing.

Gergely [32:23]:
Yeah, the AWS server problems, yes.

Simon [32:25]:
Yeah, early on. And I was like, oh, do you guys have PG Analyze? And they said, oh, no, we don't. I was like, okay, let's get that going. All right, let's look at it. And it was the same thing as it always is with Postgres, which is auto-vacuum hadn't run enough, and so they had all of these going to heap when they should be doing index scans and blah, blah, blah. So we were talking about all of that. And so it's just helping them, right? It was like my, you know, my database genius was 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 at this time, I'd also approached who I thought was the best engineer who ever worked at Shopify, my co-founder Justine, and she'd come on. And the first thing that she did was remove the reverse proxy NGINX cache with a file-based cache, just a direct cache, which again, great. Like the S3 thing worked. And so she was online. She was starting to work on it, and 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. But Cursor was a small company back then, right?

Gergely [34:04]:
Yeah, and they were just in the beginning of their massive rapid growth.

Simon [34:06]:
Exactly. And I told them that I was going to reduce their bill by 95%. And I did. Like we did. Justine and I did. They came on, and their last bill with their previous vendor and the first bill with us, it was 95% lower.

Gergely [34:20]:
Yeah, and you're nice for not saying vendors, but I can say vendors. I've talked to them, and it's in the deep dive about Cursor. It was Aurora specifically.

Simon [34:24]:
So...

Gergely [34:24]:
And...

Simon [34:25]:
This was not... this was not Postgres.

Gergely [34:26]:
No, this was a different one, but it's probably still in the write-up. We don't need to name names. But yeah, this was... and then what Swala told me is he said, like, look, there's a few things that we did. The issue never ever do, and they 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 those guys. So I guess it just comes to show that even in your case, like to me, what the story shows 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 of the 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 your stuff. Like you suddenly brought in your 10-ish or 8 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 never done that so fast. So fast forward today, turbopuffer is now a lot bigger. You're working on 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 you met Jensen, and Jensen, he really wanted to sell you on GPUs. Can you tell me how that meeting went?

Simon [35:46]:
Um, yeah, Jensen Huang, right? Yeah, I just... I never met Jensen before. We were at an event at NVIDIA, and we were just doing presentations.

Gergely [35:49]:
He and big HQ, super impressive.

Simon [35:49]:
Yeah, exactly. They invited a couple of companies to go and talk about our businesses and how we can partner with NVIDIA and so on. And I don't know. I was like, I think I was in a goofy mood that day. And so I went up on stage and I said, hey, I'm Simon from turbopuffer. 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. And this is what I said. And then he said...

Gergely [36:26]:
And...

Simon [36:26]:
Back to...

Gergely [36:27]:
Wait, who was in the room? Was it Jensen? Was it a direct report?

Simon [36:29]:
It was Jensen and then a bunch of the NVIDIA leadership, right? 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, it 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 I didn't know what to say back to that. So I said, well, Jensen, do you vape? He didn't answer the question. Someone on the team wrote to the whole company, turbopuffer company, Simon just asked Jensen if he vapes. And then, you know, this is a great start, right? And then the team had sort of talked to me beforehand. It 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, AVX-512 is so sick. Like we love SIMD, and like there's so many CPUs, they're so easy to get. Like it's just a riot in CPU land. Like, you know, I don't... I think I stopped short of saying I'm so glad I don't need GPUs, but it was just... I just couldn't stop talking about CPUs.

Gergely [37:43]:
Yeah. And so, you know, Jensen took an interest in that.

Simon [37:46]:
Yeah. So who knows? Like I'm sure you made a memorable person. Maybe he made his mission now to like at some point get you guys onto GPUs. But speaking of CPUs, can you tell me about what you're seeing inside of the hyperscale, the cloud providers? You're now on AWS, 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 AI labs, they're just getting whatever they can do. I would think getting CPUs should be easy. Is it?

Simon [38:20]:
No, it's not anymore.

Gergely [38:21]:
Why? What was happening? Can you tell us about dynamics on the why and what you've learned?

Simon [38:25]:
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 refuse to speculate too much about the macro, but I think as RL is becoming a very, very large amount of the workloads, that needs a lot of GPUs. So the labs are sucking up a lot of GPUs because you need GPUs to be like, okay, we need to teach this model how to search. We need to teach it how to use grep. We need to teach it how to boot up bash. It needs to run real things and learn from that. It takes a lot of GPU. And so I think as RL is consuming a lot of GPU, and then also... so 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 the demand curve is sort of shifting to the right and it's becoming more and more applied, and that feeds back into RL, by the way, right? Because as things become more applied, like, oh, the models are not that good at CAD or chip building, I don't know. And then, you know, you have to spin up even more RL environments to do that. 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. And a lot of this right now is tied up in DRAM, right? Of where like...

Gergely [39:38]:
Yeah.

Simon [39:38]:
You need a lot of that also for the GPU servers, but I would assume that it gets a lot worse before it gets a lot better on the CPU side. 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 really difficult. And so you write things to try to make sure you get these CPUs as fast as possible.

Gergely [40:05]:
Yeah, and then 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 labs or AI startups, but a lot of them, one of them, Reflection, had a huge massive amount of footprint, and they were telling me that they're in a situation where they cannot buy more. Like 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 go beyond a small fish to like a medium size or even a large fish that now like...

Simon [40:38]:
It's interesting.

Gergely [40:39]:
So now you have this, and even you're having this kind of fight behind the scenes that is maybe not as visible.

Simon [40:43]:
Exactly. And I mean, you work with the clouds, right? You work with them to talk about which regions have CPU, which regions are getting... 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? And so we have to work with some of our biggest customers on that. So these are real constraints, right, 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 an S3, and then we're in a good place. But there's lots of changes that we can make even to the architecture to try to protect from a lot of this. Now, I'd rather spend that engineering effort on other things, but...

Gergely [41:30]:
Yep.

Simon [41:30]:
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 on...

Gergely [41:42]:
And then...

Simon [41:42]:
Under CPU, meaning that's a fancy name for like the different machine types.

Gergely [41:44]:
Yes, exactly, right? Like, you know, C4D or IAG or whatever they're called under...

Simon [41:48]:
What's your favorite one?

Gergely [41:49]:
We really like right now the C4s on GCP.

Simon [41:51]:
GCP, yeah. The Z4Ds are also performing really well now that we've done a bunch of optimizations to them. Those are really, really great machine types. We really like those. And then the ARM C4As as well on GCP. We like those. 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 Shopify, I was also part of, you know, deciding if I had a 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. And one way, of course, to get infrastructure and also just credibility is venture capital. If you raise $100 million, $1 billion, some of your customers just raised $2 billion. Actually, I talked with them yesterday. 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 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 I feel you have a very fresh and different perspective than what is typical inside of Silicon Valley.

Simon [43:05]:
Yeah, so I think to understand how I think about capital, you have to go back to 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 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 Cursor. 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. You and I have talked before about how software that ages well and some of the advantages of seeing... 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. 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 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, it's like it is like a whole stack, right, of people that need to... So at the time, I was like, I don't know if this could be a billion-dollar company. I didn't know that in the very, very beginning. It wasn't completely clear to me. It felt like a very niche kind of product, right, to build this particular search engine. And that was completely fine with me. So I, you know, it was fine. And so then I just looked at the Cursor bill and I looked at my GCP bill, which is what we started on. And, you know, it's 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 spent enough time in San Francisco because I think the money over here works a little bit differently. That's just... that's all I knew. You were doing business 101 as long as you're making a profit, you're good, right?

Gergely [45:05]:
Yeah.

Simon [45:05]:
That was like... I'm not kidding in this exaggeration. That 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 we could get some other workloads, we could start paying ourselves. But that was like very much the philosophy at the time because I didn't know if I could go raise a bunch of money. I didn't know anyone who had the money. I didn't have any relationships. I was an absolute outsider to the... I was an outsider. I was like an outsider squared, right? I grew up in Aarhus, Denmark, and 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 principles, 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. In January then, there was a person that I was at IOI with in 2012 and 2013, and his name is Boyan, and he was on the North Macedonian team at IOI. And he was really good. He was so good that the North Macedonian team called him God. I don't know why, but that was what he went by. And he was, yeah, he was very good. He grew up, and I really wanted to work with Boyan, but I couldn't afford to work with Boyan. And he was very much like, this is what I could live off. Like, you know, I just...

Gergely [46:43]:
Yep.

Simon [46:43]:
Like I want to build this thing. 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 spent like tens of thousands of dollars on like GCP bills and all of that. And I was like, I don't think we can do it. And so I had met one individual in Silicon Valley. His name is Lachy, 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, just you 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 taken down. We'll return everything to you. I think that was the first time you heard anyone say it like that. And 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. And to me, it was just like, 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 we were... it was very clear to us that we wanted to do this, 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 that it was actually going to become really, really big. And so we did that and hired Boyan and then became profitable later that year and then just continued to hire. And then it's like to raise more money, you need sort of... there are six reasons to raise capital. The first reason to raise capital is to fund R&D. 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. 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 built something and you want to tell the world about it, and you want to spend more capital to do that. The third reason to raise capital is for the founder's ego. It's a very popular... it's...

Gergely [49:05]:
I appreciate the honesty.

Simon [49:06]:
...very popular, very, very popular, right? Big numbers, lots of press. 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 diluting all of your employees when you do it. You are setting a certain price for future employees and their upside. 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. But I do think that it happens. So the fourth reason to raise capital is to reward your employees, right? 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. That was the reason that we took more capital in December, was to allow the employees to liquidate some of their equity instead of waiting for some event like an IPO or something like further out. The fifth reason to raise is for a strategic partnership. There are strategic partnerships that have been made in this city that have made companies. The sixth reason to raise would be doing M&A or something like that. But it's like you have to be very honest about what reason you are raising in those six. The first reason to raise was one, and the second reason we raised was for...

Gergely [51:05]:
Which was?

Simon [51:05]:
The first reason to raise was R&D.

Gergely [51:06]:
R&D.

Simon [51:06]:
And the second reason was to provide liquidity to the employees.

Gergely [51:08]:
Employees. Yep. I think it's a nice and healthy way. And I think, yeah, the ego part, we don't talk about it. The identity, and especially the closer you are to ecosystems where a lot of people are raising, it will be part of it. As closing, I want 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 just AI products. A lot of them prefer in-person, having an HQ, oftentimes in SF or wherever your headquarters may be, London or somewhere else, because you often... these companies often find that they have faster iteration. It's just fewer layers cut in between, and of course, speed is very, very important. You have started for remote, and you're still for remote. How is it working, and what kind of quirks or like turbopuffer ways have you found to make this work better?

Simon [52:05]:
Yeah, I think the company started in '23, sort of like on the cusp of COVID, where a lot of companies were just remote. The Shopify infrastructure was remote since the very, very beginning because it's very difficult to get them all to move to Ottawa. So it was natural to me. It's like, okay, I think there's kind of maybe two cities where you can build a database company fast, and that's San Francisco and maybe New York. There are maybe other cities, right? But that's like kind of where it's been done. And so if you don't want to do that, I think you have to go all in 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 some location. Earlier this year, we were in Banff, right? And then we were in Mexico City and so on. So it's like that's not that uncommon. But one of the things that 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 spend time together. And we encourage everyone to come. We've also gone to the extent now of we won't encourage that, but not everyone needs to go to the campfire all the time. Some people just want to, you know, lock in and hack in the tent, and that's great. We have people that just make it to the off-site twice a year, and otherwise they're home, they're with their families, and they don't spend time on an airplane. 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. 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 New York to hang out with the team, right? And I think that's fantastic. And we've also introduced these things where if you do a conference talk or a blog post or something like that, a turbopuffer, something a bit extracurricular, we give you a turbocredit. And a turbocredit allows you to upgrade your next flight to business class, which again encourages spending time together with the team. And now, I mean, turbocredits 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 turbocredits and doing a betting market on the turbocredits. And so like this might take on its life on its own. And you know, if you're at a conference like this, there's some of our engineers here who just want to interact with customers and be on like... and standing on an expo floor all day is quite taxing. And so if you do that for two days because you want to do it well, you get a turbocredit, right? And so it's just like these fun little things that we try to do to encourage people to meet if they want to meet.

Gergely [55:05]:
Thank you. Well, in this session, 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 and the human connection, how important it is for people to work together to trust each other. So just thank you very much for this. So let's give a big round of applause for Simon. Thank you so much.

Simon [55:30]:
That's great. Thank you.