Backblaze, Inc. (BLZE) Earnings Call Transcript & Summary

Good morning, everyone, and welcome to Backblaze's Investor Day for 2026. Today's session is being webcast live, and a replay will be available on our Investor Relations website following the event. My name is Mimi Kong. I'm the Head of Investor Relations. And on behalf of the team, I want to welcome everyone for joining us today here in person and those tuning in via live webcast. Today, we will have a short break around 10:45 today just to let everyone know, and we are scheduled to wrap up by noon and some house cleaning things, housekeeping. Today's presentation will include forward-looking statements, and actual results could differ materially from those statements. Please see the disclaimer on the screen for more details. Full materials, including today's slides, are available on our Investor Relations website. With that, I'd like to invite Gleb Budman, our CEO and Chairperson, to the stage.

Good morning. So my name is Gleb Budman, Co-Founder, CEO and Chairperson of Backblaze. And today is our first Investor Day in the 5 years since we've went public. And a few people actually asked me, why now? And is there something specific about today that we wanted to share at the Investor Day? And the reason that we're doing the Investor Day today is because the company and the market have shifted dramatically over the last year roughly. And what I would say is over the 20 years that we've done this, right now is the most exciting time in our journey. So we wanted to share that with you. Over the next 2.5 hours, this is the plan. I'm going to talk about how the market has shifted and what our role in that is. Dan Spraggins, our CTO, is going to talk about the platform and why it's uniquely situated for AI. Anuj Kumar, our CRO, is going to talk about the signals that we're seeing with AI and how we're building that into a repeatable and scalable engine. Marc Suidan, our CFO, is going to talk about how we're changing this opportunity into a great business. We're also going to hear from Hume AI, a leading multimodal AI company. And we're going to hear from WECA, a new partner of ours in the high-speed storage space. So listen to how those all come together and stitching together this ecosystem of AI. So we're going to cover a lot of information today. But if there's one thing that I want you to walk away with, it's that not only is AI creating a ton of data and using a ton of data, but the role of data and data storage is fundamentally changing. And that's how we're going to talk through it today. So AI is creating this seat at the table for an independent capacity tier of storage. We're going to explain why that is and what we mean by an independent capacity tier of storage. But we believe that, that seat is open, and we believe that seat is for us. And part of the way that we are ending up there is we started this company 20 years ago. We started it solving a problem that was really important at that point, which was backing up computers. We plan to do that on Amazon S3 and store the data there. We were the economics were not going to work. So we ended up building a full stack solution, servers, a cloud storage file system and the operational expertise to manage all that because we have to store massive amounts of data and do that very efficiently. So in doing that, we then spent 10 years optimizing that full storage stack. We then launched it out to developers and enterprises as B2 cloud storage. Over the last 10 years, we've been optimizing and scaling that platform. And today, it's ideal for the AI use cases that are being needed in the market. So that's the foundation from which we believe we get to have this seat at the table as an independent capacity tier. And the rules of storage are being rewritten with AI. And here are the 3 rules that are being rewritten. Number one, data was growing steadily for years and years, basically always, right? But there's now this step change that is happening where the data is just exploding, both in terms of the amount of data being created and the way it's being used. And one of the interesting things with it is that it's not just the largest companies anymore. Even small new companies have massive data sets and massive data needs. And that is a significant shift from the past. Number two is there used to be 3 hyperscalers and companies defaulted to one of them. Now there are about 200 AI infrastructure companies out there. And that is completely shifting how companies are building technology. And number three is that storage used to be just a thing companies use. But today, it's a critical performance layer of whether companies can innovate in AI and whether they can get an ROI from AI. And those 3 rules play perfectly into Backblaze's strengths. As these data sets scale massively, they need to go somewhere. And they need to be efficient in where that storage is put. Backblaze's optimization of our platform for the last 20 years makes it possible to store massive amounts of data and to do that efficiently. The explosion from 3 hyperscalers to 200 Neo clouds has 2 benefits where Backblaze put in. One is that those AI infrastructure companies themselves need cloud storage as part of their workflows, and we're ideally situated to provide that to them. And the other is that all of the AI natives and AI builders using all of these companies need an independent capacity tier to store the data to be able to use them. And the third strength is that as companies care about the performance per dollar of their storage system because it is what's critical to get both the innovation out of AI and the ROI out of AI, Backblaze's optimization of that exact thing from hard drives over 20 years is exactly what they need to be able to do that. So this all supports us taking advantage of what is a large and growing data set. The data explosion over the next few years is hard to fully wrap our heads around, right? In 2024, IDC said there was 173 zettabytes, zettabytes created. In the next 4 years, that number is going to quadruple to 700 zettabytes created in 1 year. Now not all of that data is going to be stored, not all of that data is going to be used, but an increasing amount of it is because AI is also making it possible to get value out of the data that's getting created in ways that wasn't possible before. So not only is the amount of data getting created going exponential, but the desire to keep and use it is as well. So that supports a large and fast-growing market that we get to participate in. We continue to serve the markets we've always served. The core markets where we've served disaster recovery, backup, archive, media workflows, application storage, all of those markets still exist and are growing. That hasn't gone away, and that is still an important part of the trajectory of our business. But AI layers on a big market on top of that and a faster-growing market on top of that. And so that requires a company that can scale with that. And that is what we've built. We now have over 5 exabytes of data storage under management. That makes us one of the largest cloud storage companies on the face of the planet outside of the hyperscalers. And we're planning to expand that capacity by about 30% this coming year. Now all -- obviously, that takes hard drives, networking, servers, data center space, power to do all of that. And having the ability to acquire that and the relationships to do that takes a certain level of expertise. But on top of that, it's the actual managed storage aspect of it that's really hard. It's the software platform that can scale like that. It's the operational expertise and rigor that's been built around that to be able to actually deploy and manage scale at this rate and this scalability. So we've talked about kind of the scale of the market. Now let's talk about the second thing that we mentioned, how the rules are changing, right? The dispersion of these AI infrastructure companies. And this is probably the most important slide that I'm going to show you. And so I want to spend a little bit of time on it. For the last 20 years, technology was built inside of a hyperscaler, generally one hyperscaler. Even when there were 3 hyperscalers, companies would pick one and use it and build all of their technology stack inside of that one hyperscaler. Maybe they were inside of Microsoft, maybe they Amazon, maybe they were inside of Google, but they picked one and they built everything inside of it. Today, there are 200 AI infrastructure companies, Neo clouds, sovereign clouds, inference clouds. And that's not even starting to talk about the fact that all of these clouds have different regions, including the hyperscalers. And so a common customer has their base workloads inside of one of the hyperscalers still. They're using databases, maybe they're using networking, maybe they're using one of the 200 different services or 10 of the 200 different services that the hyperscalers provide. So they're still using a hyperscaler for a bunch of stuff. But then they go and they say, now I need to do model training. And so the hyperscalers don't have access necessarily to the latest chips or they have availability of them or they don't have the price point of them. So they go to one of the Neo clouds and they say, okay, I'm going to use you for the model training piece of it. But then that one NeoCloud doesn't have everything they need, so they go to another one for some of their other model training aspects. So now they've got base workloads in the hyperscaler, data they're collecting somewhere for the model training and then using 2 Neo clouds for model training. Now once they train their model, then they want to do inference somewhere where it's optimized for that use case. So they may be using 2 or 3 different inferencing providers around the world. And beyond that, then they may also be using different regions of these. What that means is that as that AI builder, you now have 4, 5, 6, 8 places where your data needs to go. If your data is sitting inside of one of the hyperscalers, you're paying massive egress fees every single time you send your data to any of these places. And so you really need this independent capacity tier, a place to keep your data so that you can innovate with AI. It's not just about saving money. It's about the fact that you want to use the AI infrastructure that's out there in order to innovate. One of the customers that I was talking to recently said, because they switch to Backblaze, they are now enabling their AI researchers to do their model building when they feel they need to, when they have some interesting part of data and some algorithm that they want to iterate. Before this, what they did was they told their researchers, you're allowed to run one model build per quarter. So the pace of innovation that they're able to achieve has dramatically improved by switching to Backblaze because they're able to now go and actually move their data when they need to. So this is -- so this enables Backblaze to be the backbone of the AI ecosystem by providing this independent capacity tier where data can flow to wherever it needs to go. So that's for all the AI native builders. But the other part of this chart is the actual companies on this page are AI infrastructure companies. Now the hyperscalers knew how to build storage. But now you have 200 companies that are raising as fast as they can to become large AI infrastructure companies. Most of them know land, power and shell. Some of them know compute. Almost none of them know storage. And yet all of them, if they're going to be serious players long term in the AI infrastructure space, are going to need to be able to support the workflow their customers do, which means they're going to need storage. And so almost all 200 companies in that space are going to themselves need a storage platform for which we are ideally situated to provide them. So like I said, this is probably the most important chart to understand because this is all a massive replatforming that's happening in the technology industry and has only really started in the last 2 years. We went 20 years of the cloud evolution, and we are now at the very beginning of this AI infrastructure revolution. So now that we've talked about the market shift, let's talk about the third part of it, which is the role of storage itself. And so in an AI workflow, there are different parts. You collect a bunch of data first, then you process it to prepare it for monitoring, then you build your model, then you run inference on it, then you monitor and log in and output it. Every single part of that AI process creates and uses data. And every single part of that AI workflow needs a capacity tier. So that makes Backblaze a critical strategic partner to the AI builders who are building AI workflows. Now Dan is going to talk more about this in his section. But the platform that we built is what supports this. And the platform that we've built, we've optimized for scale and scalability, performance and economics. And it's taken 20 years of honing to get that platform to where it is today. That is a significant moat that's hard to replicate because even if you were able to write all that software today, you wouldn't have the 20 years of operational expertise that it took to hone that platform and all of the systems that support that. And again, Dan is going to talk more about all of that. So I've talked about the market and how the market is evolving. Now we could say, well, some of this is thesis and this is, is this going to happen? But it's not that is it going to happen. It's happening. It's happened, right? So Backblaze is already winning in this AI ecosystem. We have signed now 5 of these AI infrastructure companies. We've signed a $335 million deal with CoreWeave to provide them storage for their capacity tier, but we have also signed 4 other of these AI infrastructure companies. We also have 5 of our top 10 customers on B2 now are leading AI companies. And beyond that, we have the smallest and fastest-growing innovators through our Flamethrower program. So this is a program designed for AI start-ups, and we have hundreds of the leading AI innovators in that program now. And we've also signed on the other side of it, on the larger side of it, a leading frontier model developer to switch to us as well. So we are winning across the AI ecosystem, and we're doing that because these companies see the strategic value we provide them by offering them an independent capacity tier that allows them to innovate faster in AI and get ROI from AI. So to summarize, AI is not only creating an explosion of data, but it's changing the role of storage. It's creating an open space a seat at the table for this independent capacity tier, and Backblaze is well situated for that seat. So with that, I'm going to let Dan Spraggins, our CTO, share more about the platform and why it's uniquely built for this.

All right. Thank you, Gleb. I'm Dan Spraggins. I'm the CTO at Backblaze. I lead our R&D group and oversee our product road map as well. All right. So today, I'll be talking about a number of things. I'll dig into our architecture and how we're differentiating with our platform. I'll talk about the ecosystem. NVIDIA recently made some changes to the reference architecture. And so I'll speak to that and where Backblaze fits in. I'll also talk about the storage spectrum. So how SSDs, HDDs fit in the ecosystem and where Backblaze plays. Recently, I met with the Chief Strategist of WECA, which Glad mentioned previously. And so I have a conversation with him. So you'll see a short video there. I'll then get into what we're doing today with AI and how we're delivering features there and then our product road map. So I wanted to give you some insight into what's coming. Some of that isn't public yet, so that will sort of be fresh and new today. Okay. So first off, we have our architecture. And so this is something that's been in process for 20 -- roughly 20 years, and we continue to extend it. And so one of the questions sometimes I get is, okay, how are you -- you're combining commodity hardware. Why is this any different? You're just setting up racks of hard drives. And that's true, but there's a proprietary software layer on top of it that's very differentiating and hard to mimic. And so this is essentially a high-level overview of what we're doing, which is we break this into building blocks. And so these building blocks are a combination of commodity hard drives, but then we layer on different pieces and eventually, we call this a vault. And then we have a cluster. And so these clusters can get up to roughly 1.5 exabytes. And so when we sign a deal like CoreWeave, this is what we're doing behind the scenes in order to ensure that we can hit that type of scale. And outside of the hyperscalers, there's essentially no other company that can do this. And so this is very unique and again, proprietary to Backblaze. And so maybe one other piece to this is the way we're doing this is we're not relying on the individual drives for performance. Instead, we're breaking it up and in this case, into 20 separate drives and getting the performance we need by aggregating across those drives. So it's a unique architecture. It's a very valuable architecture, and it's something that we're extending even today, and I'll speak to that later. Okay. So this is probably the most important slide that I'm going to present today. It's complicated, but it's really important. So this -- to the left side here, this is a screenshot of an NVIDIA reference architecture, their data center reference architecture. And they recently updated this and added a category for object storage. So this is what Backblaze does. And specifically, they added a capacity tier. And this is how we've been talking about this for years. So it's a really major and important validation of the category that Backblaze is in and what we do. And so I mentioned WECA. WECA is in this middle category, the high-speed storage, which is great. They're a great partner, and I'll speak to that a bit. But this object storage area is something to really focus on. And we -- there's no reason we shouldn't own this category. There's no one that comes close to the scale that we can perform at. So I keep that in mind, and I'll reference it again probably a few times. Okay. So something else I wanted to talk about is I think sometimes there's a little confusion about what are the -- what is the storage spectrum? Where does Backblaze play? Who are our competitors? And so it essentially breaks down to 2 major media types. You've got SSDs and you've got HDDs. SSDs are extremely fast, very fast. And HDDs are very affordable, but still fast. And so the major difference is, if you want to get close to the GPUs, the SSDs are what you need. But if you want scale, what you need, you're going to use HDDs. SSDs cost about 6x what an HDD costs. And so this is where you get into that performance and cost, the economics. And so you can imagine a company that has a $10 million storage budget. It would cost them $60 million to store that data on SSDs versus $10 million. So this is how companies are thinking about us and how they scale. But these 2 solutions are complementary. They're not competing. So going into -- so Gleb had showed this diagram, and this is just to click in, which is essentially where SSDs lay and where HDDs are as well. And so you think about the data life cycle. And so you go all the way through the process and you want the SSDs as close to the GPUs as you can get them because you need that speed for input and output, you don't want to bottleneck the GPUs. They're extremely expensive. And so you use your SSDs, but you get that data off into the HDD layers as quickly as you can, and that's where you store it long term. And so this is where that 85% of the data goes. But the way this works is it's not like traditional compute or storage where you store it and you archive it and you don't really read it again. The way this works is you actually feed the data back in. So when you get into inference and monitoring, you're generating data that then goes to the HDDs. But when you train the next model, it comes back around. And so you feed it back into the SSDs into the model and then you go through that cycle again. So this is how this works. And again, these 2 are not competing. They're complementary, which gets into WEKA. So we announced this partnership. I think it's a great partnership and something I'm quite excited about. And so in this conversation, I'm speaking with their Chief Strategy Officer, and we'll sort of break down where each company fits and how we'll work together moving forward. [Presentation]

Nilesh, thank you for joining us today and looking forward to the conference doing some really interesting work. So I appreciate the time. And I guess to start, if maybe you could tell us about what you're doing, what's your role at WEKA and what the company is doing and what problems you're solving.

Thanks for having me, Dan. Really appreciate the opportunity and looking forward to working with you. So this is Nilesh Patel, I'm the Chief Strategy Officer at WEKA. I lead alliances and partnership strategy globally. I've been with WA for almost 5 years now. WA is building a software-defined storage that delivers memory-like latencies on various workloads and addressing the needs of data layer from GPU clusters to even some of the edge deployments as well. We, as a company, we serve AI-native companies, enterprises, new cloud, sovereign clouds and providers who are really driving the GPU infrastructure business today. And anybody who needs to move data at the speed of the compute is what we are able to do. And that is actually the problem we solve. The traditional storage cannot keep up with the AI workloads and their needs, and they cannot keep the GPUs fed efficiently and WEKA is addressing that.

Excellent. So what are some of the changes that you've seen? So I know we're seeing a lot of change on the Backblaze side with new AI workloads. I'm curious what WEKA is seeing.

So what we are seeing is from a data layer perspective, there are a couple of interesting dimensions that the AI workloads are pushing storage. There are roughly around 2 axes we see both on the performance and capacity. And both are being stretched harder than anything we have seen before. GPUs need microsecond axles to keep the -- sitting through the data Otherwise, they are sitting idle. At the same time, the large amount of data sets from the actual enterprise data to vectorized data, we hear there is a 3 to almost 10x expansion of data in the enterprises. So that the checkpointing and then all the generated data are growing in an exabyte range, which is the hard part isn't like picking one axis. It's like customer needs both simultaneously, and they need to scale fast. physical storage has lead time challenges and so on and AI demand doesn't wait for hardware to show up. So some of those challenges are really stretching the need today, and it's putting a lot of pressure in the folks are building either enterprise AI factories or building the new cloud infrastructure at scale. And that's where we see the storage requirements are growing in both dimensions.

Yes. Makes a lot of sense. We're seeing the same thing, throughput, API requests per second, just overall storage needs greater than they've ever been. So you get on a term that's especially important for us at Backblaze on capacity. And so can you speak to how you and the team at Wecker are thinking about performance versus capacity with storage and AI workloads?

Yes, that's a great question. In fact, what we see is like 2 walls are hitting customers at once. Inferencing and agentic workloads are hitting the memory wall. Long context, multi-ur, multistep reasoning, all needing to sit right next to the GPU, faster than memory itself in many cases. So WEKA solves that. We deliver better than memory latency at scale. And then there is the capacity wall. Customers need more storage now. But hardware lead times are -- they don't move fast enough and they don't move at AI speed. And I believe Backblaze solves that. Instant capacity, no supply chain constraint, no location lock-in. Put together, WEKA extends its global need space across that capacity, so you can get massive distributed scale without ever slowing down the workload sitting next to the GPU. So in a way, speed where it matters and scale wherever you need it is what I think together, we are able to deliver.

Yes, I couldn't agree more. And I think you're hitting on where the partnership makes a lot of sense between the 2 companies. I think they're very complementary. So maybe can you elaborate a bit on how you see our 2 companies working together with these performance, capacity, et cetera?

Yes. So I guess the partnership, in my mind, lets customers scale capacity instantly and cost efficiently without supply chain delays. I think that's a big part of it. And without all that without sacrificing the performance and the workloads, right? But it's not really about placing -- finding a place to park your data. I think the object storage becomes almost like, in many cases, a secondary distributed data repository. And because it's part of WEKA name space, the way we integrate the solution that any capacity you add on Backblaze infrastructure could then very well be part of WEKA managed name space. Customers can now not only expand the capacity wherever they want, but they can also spin up compute and WEKA clusters that mount that secondary repository. And now they have an instant scale, not only on storage, but also on the compute and be able to, on the fly, get access to the data and so on. So this is where combining the 2 companies together, the solution can not only address some of the capacity scale needs, but also be able to deliver data to the compute where it exists and the fact that Backblaze has distributed data access at a very cost-efficient and scale-efficient way, I think that really benefits the customers who are scaling their environment and the AI workloads.

Yes, I definitely agree. And I think it's just such complementary solutions. So I have one more question, and I know you're over strategy at WEKA. I'm curious about where you think things are going over the next several years. We've seen a lot of change. Where do you see this heading as you look over things at WEKA?

So I think over the last 2 to 3 years, we saw training and model training and so on really driving the demand for data. And I think that really pushed the burden on the performance vector, but mostly on the throughput and some of the capacity challenges. As we are seeing the inferencing use cases playing out, inferencing can happen anywhere. And also inferencing also pushes, as I mentioned earlier, memory limits of what can be stored in the cluster itself on the DRAM. So having the ultra-low latency petabyte scale storage will behave like a memory cache is becoming a critical need, and we are able to address that. And that particular requirement is going to continue to go harder and stronger because the whole context windows are growing, the visiting models are getting richer and richer and the complexity and the amount of context scale that everybody has to operate at is going to go high as well. And along with that, the need on the capacity and the bounds of where the data resides is also going to be challenged. So the AI workload needs to be inferred upon wherever data is. And so the data locality is not going to be something that AI workload will respect and they need to be sell across the board. So what we see from our perspective is a need for further expanding the inferencing at scale, which is driving the demand on memory latency and performance. need for data distributed everywhere, so a global name space and being able to leverage infrastructure like Backblaze, where there is a tremendous amount of capacity available to operate off of. And then finally, the -- all the data that is being stored in -- across the enterprise, old and new are going to be all current and required and processed. And so being able to access data wherever they decide is going to be another major trend that we'll see for the data layers to come up more and more.

That makes a lot of sense. Well, thank you so much for your time. You all are doing such good work at WEKA. It's a great company, really excited about the partnership. Again, I think the 2 technologies really complement each other. I have nothing but respect for all the great work you're doing. And thank you again for the time.

Thanks for the opportunity, Dan. I'm really looking forward to working with you and the team and see what we can do together.

I think that's an excellent partnership and again, quite excited about it. So Gleb touched on this earlier, which is we're working with a lot of great companies today, and we have an existing solution with some of the fastest-growing AI companies in the industry. And so probably one of the themes that you've heard is just this complexity demands around performance, scale, economics. So we have a solution today that's working well for these companies. And one of the key points is throughput. So 1 terabit per second, this is one of the fastest in the industry. Retrieval time is about as fast as it gets for the HDD layer. We continue to have 11.9s of durability, and we scale at a level that no other company in the industry is scaling at, which gets into why did CoreWeave choose us. So they had a choice. They could have built and they seriously considered this or they could buy. They looked at the complexity and decided they wanted to buy. And they looked at the competition, and they told us there were essentially 2 things that stuck out to them. So one is our ability to scale. So we've been doing this for 20 years, and I've mentioned scale a lot. Another is operational transparency. And so quarterly, we released something called Drive stats. And so they explicitly said this was one of the criteria that they looked at because it showed what we're doing. So essentially, what we have is we report on reliability statistics every quarter, and we've been doing this since 2013. for reliability stats for all of the drives. This is very unusual. Like no other cloud does this. And so we're showing everything we have, failure rates and how we're adjusting. And they explicitly said this was a major factor in their decision because it gave them insight into we knew what we were doing. Another important piece of this is a managed storage offering. And so Marc will speak to this later. Anuj will speak to it as well. This is really important for Backblaze and a major strategic change for us and a new product offering. So there's a lot of talk around CapEx for obvious reasons. This plays to our strength, which is we don't have to have CapEx here. So in this case, CoreWeave is handling the CapEx and our software is running inside of their data centers. So our software is portable. We're not making custom changes for CoreWeave, and we're using the same software for the long tail all the way up to the exabyte level customers. And so there's a lot of work going into this, but I'm quite excited, and I think it's really going to be a good business for us. We'll sort of get then to what are we seeing now and what's the future. So one of the things that we're seeing is AI agents. So everyone is talking about AI agents. If you drive through San Francisco, every billboard you see is around AI agents, right? We're seeing the same thing. So there's this large uptick in the traffic we're getting. The most recent data I saw is that nonhuman traffic is actually growing faster than any other traffic we have on our site. And so we're adjusting for AI agents. And we have a whole team and their sole job is to focus on this. So they've been focusing on things like discoverability because in the past, things like marketing, brand, et cetera, still super important. That's not something that AI agents think about. It needs to be discoverable. So it needs to be machine readable docs. We need to have integrations with the best open source tools. We need to integrate with AI platforms. We need to have what's called an MCP server, which is essentially a protocol for how these agents talk. So we're doing all of this work, and we're seeing a large uptick in the traffic we're getting. I think we're making some good decisions here. And we have an engineering team whose focus is just on this to ensure that we're getting this right. And so last up, I wanted to speak briefly to what we're working on in addition to the things that I've already spoken about. And so this isn't public yet, and so now it is. These are internal targets that we have. So again, performance, scale, economics. And this is what we're seeing from our customers. And so we're committing to internally and now externally, 2x increase on throughput, 5x increase on API requests and a 20% increase on drive capacity. So we're actively working on this, high confidence for new deployments moving forward, this will be -- these are the metrics that we're aiming for in addition to a lot of other interesting work we're doing around managed storage, AI agents, enterprise features, a number of things. But this is hard. And so maybe that's the thing that I would leave you with. This isn't something that other companies can come in and just copy. We're extending our existing architecture. We're building on top of something we've been doing for 20 years with tens of thousands of optimizations, reducing bottlenecks. And so this is -- I get the -- what's the moat? This is the moat. And so we're working on really hard things and extending our lead in an area that we're already leading in. So next up, I have my friend, our CRO, Anuj, and he'll cover where we're at with revenue.

Thanks, Dan. Good morning, everybody. Tough act to follow. The CEO set the stage for the market opportunity. Product guys telling you we have awesome things on the truck already and even better things to come. But I've been here about 4 months. What do I know? But I'll share that I saw this opportunity as a really market inflection point. And what I truly see is Backblaze is one of the very, very few companies that have really the scale, the performance and the capability to take advantage of this inflection that we have. Before I go into it, I think my main section is about, as Gleb said, from signal to revenue. And I think it's important I define what the signal is. And for a sales guy at heart, I will say, I want to break it down into 4 simple things. What is the signal that we are going to talk about because what we need to do is where is the demand really coming from? Why is the signal really working and why you should believe that the signal is really, really strong. From there, I will take you to who are these people that are coming for the signal and what we can do to convert them finally into how this engine comes together to convert all this demand into something that we can repeat and scale. So you're the room full of hopefully, math guys. I was a math major as I was growing up, and I think you'll appreciate this. On one of my long-time mentors said, Anuj, life is really simple. It's just a math equation, right? And if you break it down. So let's just start with the numbers since I'm sure you'll appreciate this more than anything else. What we look at in a go-to-market engine in a signal is what is it doing over a period of time? And what I'm sharing with you here is our own data over the past 12 months. This is data from our AI customers. And when you see the conversion rates, we are winning at least 10 points higher than conversion rates of a non-AI customer. The deals are landing. This is really important for us because we want to make sure we are going after the most addressable opportunity. The deals are landing 7x. This is not an error in the slide. It's 7x the deal size of the rest of the customer base. But what makes this signal even stronger is once they land within a short period of time, they actually compound. This is the beautiful formula that I would love to have with every piece of the business. This compounding happens within very, very few months. This is the total data in aggregate over the 12 months. Why don't I give you an example behind the scenes. And I'm truly fortunate, Olea is going to join us from hum as well. She's going to share her story. This is another story because this is not just a unit of one. This is from hundreds and thousands of prospects that we see in our base. And what you see here is what we are coming back to the table of what we shared with you in the first quarter. If you remember, we had said in our Q1 earnings that we had a customer that came in with AI and that deal converted in about 11 days. And what you see this is this actual customer came to us from another customer reference. And we were able to do from a trial to the first land within 11 days. Great. It's almost 1 million, life is good. But you fast forward less than 12 weeks later, the same customer compounded by doubling their capacity. And now we're in the third quarter, this customer has already grown to about 2.5, 2.7 discounting, almost 3x. But if you aggregate the total, we're looking at about a 4x compounding from this place that we started from the provider that we had. Now why is this customer putting so much data on it? I think Dan alluded to, Gleb alluded to, AI really has a lot of ingestion, a lot of data collection, and this is a continuous stream as they are running inference models and want to have a capacity tier to take care of it as long as they can have this available with high throughput for the base that we have. Now we've talked about Backblaze's history in terms of why it really, really works. This is the second big piece of what I'd like to share with you. The why that I would say has worked for Backblaze has always resonated. You know from the beginning of time, are we have really what we call disruptive economics. Our value proposition has been tried and tested. It's true against the hyperscalers. And we also offer a very, very predictive model. You get no egress, but -- and that gives you a way to make sure that you can put your data where it is, but also you want to make sure that you have something affordable that you can run the models from. But in the AI space, it's slightly different. What has given us is it's not something new, but it's really given us an opportunity to expand the use case and the value that we have for our customer base. In AI, there's 3 specific things these customers are looking for. They're not just looking for low cost, but what they're looking for is sustained high throughput. That's what we call performance. At the end of the day, you're trying to do price economics, but you want to make sure that the performance doesn't suffer when your models are calling it for inference. And so high performance, high throughput is really, really important. The number two thing is rapid scale. I think Dan talked about it in the WEKA conversation. It's not just the tiering of the data, but it's also instant availability of that capacity because you know GPUs are very, very expensive when they're sitting idle. I think there's a whole term token economics that's come around. At the end of the day, we don't want to keep these GPUs idle. Yes, the flash has its role to play, but hard drive and our managed storage service has a role to play to make sure that the capacity is constantly available at high throughput for the GPUs to be continuously working. At the same time, what you do want is you don't want your data to be locked into any one particular location. I think Dan shared this -- the one slide that Dan you shared in terms of if you take away one thing, which is all these Neo clouds that really didn't exist until maybe 5, 6, 10 years ago, and you have so many choices. At the end of the day, these AI builders really want to keep the data where they choose to, and they shouldn't be locked in, in any one place. So making sure that you have architectural freedom to keep the data where you have, make it available at scale and make sure that it's available at high throughput for this AI customer base is really, really important. And so what has helped us do is now we see an expanded use case in terms of our value proposition. That's probably the biggest why. And now I'll probably get into the key pieces. When I think about a go-to-market team and a go-to-market engine, what do we do wake up in the morning and try to do from what it is? Because you clearly know there is a value. You clearly know that there is a strong signal. But then how do you make sure that you start converting that signal into something that you can build an engine from? The first step is really to make sure you're segmenting it right. A lot of sales organizations will show you triangles where there's strategic, enterprise, commercial at the bottom. The way we look at the world is really, really simple. What's -- if you see on the left, what really is, is the core workloads, backup, disaster recovery, security, they have always existed. They will always exist, and there is a constant need for backup and data recovery and disaster recovery. And that stays true. We continue to grow in that market. We continue to grow above market rates, fantastic. The other 2 adjacent is what is giving us the addressability of the use cases I just talked about, the high throughput, the performance the scale, the instant capability, the ability to make sure that we can move the data, keep the data where it is. And it gives us 2 clear segments in the market. One is what we call AI builders. These are GenAI media companies. One of them all is going to be here from you. We talked about a few others in the GenAI media space or the physical AI space that are really training insane in large amounts of data to kind of get to where they are. And I would say the other big part is the Neo cloud, where we have about 200 of these Neo clouds that didn't really exist about 4, 5 years ago. But what they need is instant capacity, but also the ability to offer managed storage as a service. That is the service that we can now deliver for them, either coming to our data center, which you can today, but also as part of a managed service, which is how CoreWeave leverages us. So both of these are now giving us new addressable markets to go after in this AI space. And that's a clear segmentation of the who we really address when we go to market. From the who, the next logical question you will say is, well, how do you convert this who that you're trying to get to, to the how that we want to make sure that all the activities we are doing from the signals that we're seeing to convert into a repeatable, I would say, process and as well as a deliberate process of connecting with the buyers in all of those 3 segments. So to think about it, I'll keep it really, really simple. There's 3 main parts. We have to make sure that we are educating the community. We want to make sure that we're educating the community. We are engaging them where they're going. And we're also making sure that they experience the product as they choose to. In education, you've seen us, we've talked about it. It's for the last, I would say, 13, 14 years, we've been producing quarterly drive stacks. This is incredible information from a cloud storage company that doesn't no other cloud provider actually provides. And this helps us build a community of engagement where they actually see us being really, really transparent, not only in our pricing model, but also in our development model and scale model in terms of what we do. The engagement then really gets to we want to meet where the buyer is. And today's buyer is not sitting necessarily in a physical, let's say, events. Of course, we do those, but they're really engaging more in social and specifically new social channels like Reddit, Stackable, and we want to meet them where they are. And that's the Flamethrower program working with hundreds and hundreds of start-ups on a daily basis to make sure that we can engage these start-ups and get them to meet them where they are and help them understand who we are and what we stand for. And finally, I'm the last guy that somebody like Dan wants to talk to when I call and say, "Dan, I'm Anuj from Backblaze. I'd love to talk to you about this cloud storage thing. CTOs, CPOs, CIOs, they rarely talk to the sales guy in the first call." And so we want to make sure that they can get their hands dirty in the platform on their own with no sales assist. And that's really important because we want to make sure that we are available in all these channels, whether it's Hugging Face, whether it's open source SDK tools, we want to meet them wherever they go and however they want to engage, making sure that they can touch and feel the product and the service before we ever call out and say, "Hey, we see that there's a signal you might want to engage with us." This is a concerted continuous process. So think about this education, the enablement, the experience, it's something that we do on a constant basis. So we want to make sure the signal is not random, but it is something that is repeatable, something that is measurable, something that is instrumented and we can see the results and we can keep tweaking in terms of what it is. We're not trying to convert humans to robots, but we're trying to make sure that we can actually see what's happening and make sure that we are available in all of these channels. Finally, as part of this, how do you make sure that all this wonderful signal we have and all the engagements that we are doing converts to something that, as we often say in sales, there is something on the truck, and I got to go and then convert it. Like the big thing here is I've got to convert from all this demand that I see into something that is a repeatable revenue engine. And this is the part where I would just say, in sales life, it's just 3, 2 and 1. I showed the 3 segments that we have. The demand comes through any of the 3 segments, and we have 2 motions to address this demand. The 2 motions are self-service and then, call it, sales assisted or direct sales. The self-service, you choose as you come, you can just pay as you go, you get on to the platform and you can scale as much as you want. We've offered the service, and we continue to offer it as a viable option. On the direct sales, specifically in the AI builder and neocloud, this is where we see the difference where the customer does engage a little bit on the PayGo, but very quickly, they want to have really high-value workloads that they want to do. And that's what we want to make sure that when they test, they get the experience to convert in days and weeks and not months and years. And so the direct sales team is a dedicated coverage model to make sure that when we see the demand signal for high value, we attach the sales team. Both of these motions are powered by an ecosystem that's in 2 pillars, sell with and sell-through. I'll come to it in a quick second. But the goal is that these 2 motions are working in tandem to make sure that we are addressing the demand that's coming in. To make sure that this actually converts from these 2 motions, what we have is a very strict, I would call it, sales process, and this is layers. It's -- the layers is just an acronym. It sounds simple, but the goal is we want to make sure when you come in, the trial converts to something of an opportunity, that's landing. We want to make sure that what you committed for and what you want to consume as part of the data is what the adoption team takes care of. And each of these are dedicated teams to make sure that we can then take you through the journey of expansion. Once you've got a good workload on. I'm sure there are additional use cases, all about cross-sell and upsell and then making sure that the revenue line, the retention line stays strong. And the glue that kind of pulls this all together is our support team because at the end of the day, you came in for a managed service. You didn't come in just for software that you were running on your own. So storage support blue really pulls it all together to make sure that we have this process, which is quite rigid, something that we measure, something we want to make sure that we are seeing the conversion rates and making sure that we can address them. But this process kind of keeps all those segments, the motions true in terms of where we want to go. I'm sure the next logical question you'll have from me is great. You've got -- Dan said he's going to grow like 20% on the capacity, 5x the throughput, et cetera. Similarly, as we are going to grow in the go-to-market team, I would say it doesn't really come from just infinite headcount. I think that's a bad day. I started like Marc, I just want to go and hire 50 more people because I need to double the people that we have. So how do you think we're going to get there? And this is, I would say, the final piece of that engine, which is to make sure that we are really building a deliberate process and a deliberate motion with a partner ecosystem. It's really, really simple. There's 2 pillars. There's a cell with. You saw an example with WEKA. The goal is we are doing validated reference designs, validated architecture. So it's actually tested and integrated at source. The goal is we don't want the customer to be spending the time to try to integrate 2 parties. And we know flash and SSDs -- sorry, SSDs and HDDs work in tandem. And the cloud capacity is something that adds on to help the capacity in flash to make sure that you can run your models. And so we want to make sure that this is tested at source, so customers can have a validated design to go with. WEKA is just a prime example of that. And once we have those designs, what we do is we convert them into a downstream ecosystem of partnerships. These are resellers, distributors, cloud marketplaces, basically allowing the customer to have a choice in terms of whichever partner they choose, they can engage with us on. The goal is these 2 work together. The more validated designs we have, the more options we have for the customers and partners to engage. And this is the deliberate motion that we are putting together. This is a relatively new muscle, I would say, for Backblaze. However, we've got a good part of this engine flowing already. And this to us is really the big motion for us to scale beyond, let's say, infinite headcount that we would have. To wrap it up, as I said, I'll try to talk to you about these signals that we believe are really, really strong. And I hope I've given you some proof that the signals have some depth in them in terms of we really measure and instrument every part in terms of what's coming in and how fast it's converting. The second piece of that is really in terms of making sure that these signals don't sit in random, and we have a process to convert the signal to a demand that we can then finally convert with the process that we can then scale with the ecosystem. So I just want to thank you for your time, and welcome Mimi on stage.

So we're going to take about a 10-minute break right now, and then we're going to welcome Hume to the stage to a fireside chat. So please take your time and get some refreshments. [Break]

I'm going to bring on stage our guests for a fireside chat, and I'm going to let Anuj take it away, and you'll get to learn more about Hume.

Okay. Well, welcome back. I hope you guys had a good break. Don't worry, this is not a sales call. I'm not going to ask you, [ Olia ], which I'm still learning how to pronounce her name properly. But really, really, first, thank you for coming in here. Great to have you. Thanks for obviously being a customer or a partner. I'm sure we'll talk a little bit about Hume, but I just wanted to formally introduce Olia. I met her for the first time yesterday in person. We talked a few times before, but she's got an incredible journey in basically being and come up to the Chief Product Officer at Hume, and she is responsible for all of the strategy, all of the product. I think your portfolio has everything from strategy, products, cybersecurity, the current the future, it's a lot. So thank you so much for spending a little bit of time and being with us here today and talk with our analysts. So why don't we begin a little bit about, tell me -- I show this audience. I talked about GenAI media, but that's really, really, really high level. You can talk a little bit about you, Hume, a little bit of the journey, I think it would be great, great.

Yes, absolutely. And thank you for having me. We were practicing names back and forth. So it's a -- we're getting there. So I have a background in machine learning, initially for health care and then transition through highly regulated sectors and then made my way more to machine learning product and then now more voice AI. So Hume as a company is a really interesting organization because we have -- half of our team is AI research. And so a lot of what we do is we train models to improve other models. And we also have data solutions, which means we need to process a lot of data. And every time that we process data, we create new data. So Backblaze has been quite helpful in our needs there. Additionally, what we've been doing in our goal to improve voice AI because we initially started out with 10 years in semantics Phase III research, really focused on assets, which is city. It is understanding of how you communicate. And so that would be emotion and how you can derive that from acoustic signals. And so we built models around that. We built the infrastructure around that, everything to process audio as well as the systems to evaluate and improve other voice AI models.

Yes. And Yes, when we were talking, I think one of the things that absolutely fascinated me with a couple of like key things that you mentioned. One, just the layers of complexity that a simple thing like voice has. I think when you think about it, it's not just the amount of data collecting transcripts and trying to put something in just to understand some of the tonality or how we are actually feeling when we are talking. We can hear it on the phone, but you guys are actually trying to pull all of this together. Can you tell us a little bit about that complexity and what it takes to actually put that model together?

So on the surface, voice AI seems like one dimension, right? Same as text. It's predominantly one dimension. But when you actually peel back the layers, it is so multidimensional. So think about us speaking right here. I have certain contacts that I came in to this conversation with as do you. I respond to your inflection changes. I account for the background noise, of which we have none in this room. And there are all these other components within that, right? There's the nth of the conversation. And all of these things are actually very challenging for AI to understand. So there are 2 core things that we solve for, which is, number one, does AI truly understand? And number two, does the user feel understood. And so right now, what we've been seeing is voice AI is at an inflection point. where previously you had predominantly text interactions, right? We type on our computers. And now the new modality that we're seeing for people to interact is voice. And so you have to solve for all of that complexity and you need to ground it in real-world use cases, which is quite challenging, too.

True. And I mean, that itself is one dimension. But the other dimension you also mentioned was just normal models like why wouldn't just open the eye and tropic all of these guys, they also have a model. But you said something really, really interesting that caught me yesterday, which is those companies are only interested in the research, I think, is what you said. They're really just doing it for raw research. But as you guys are building it in layers and layers of modality that really gets down to?

I think there's 2 parts to that. I think OpenAI and Anthropic and all of the large labs are fantastic. They're also large labs are customers, so we love them. But what's interesting there is they have a bit of a different focus, right? They want to push towards AGI. They want to make sure that they have the smartest, most capable models. Our goal is to improve all of the voice models, the whole ecosystem. And so you need models that can evaluate those models. And to do that, you need to be really, really good. I think there is the second half of it, which is as enterprises are coming up and sovereign environments are coming up, I think what we're noticing is that a lot of people recognize that they have a lot of data, and they want to build their own models as well. And so we actually get a lot of outreach to us about, hey, like we want to do XY Z as this enterprise to build an AI model that optimizes for speech. And we want to make sure that it's performing well, and we want to make sure that it has all the emotional understanding within it, which is really interesting and perhaps relevant for you guys as well because as folks are now -- as we're thinking about this model landscape, we're not only thinking about OpenAI or Anthropic. -- we're thinking about sovereign clouds, enterprises who are now trying to leverage all the data that they have to build their own AI models.

It's like models, platforms, like they're doing both things at the same time, and it's constant growth. I think I called it compounding in my section. is basically just defines it in a way that's really, really, really, really contextual, fantastic. No, I appreciate that. Well, let's bring it a little bit into how you see what you're building and like some of the reasons you came to us, any of the things we talked about, which were the things that really resonated for you in helping you make the decision to Backblaze? And how do you see this relationship in the context of everything that you're building?

Yes. So I think Gleb was actually walking through the journey, and I was like, oh, that's our journey. That's right. So essentially, we go through and we collect a lot of data. Data is obviously very important for AI. It's a hungry, hungry Hippo. And so we collect that data, we process it and enrich it. And so we actually had a lot of disparate environments where we would store our data. So not only did we want to consolidate it, but it was really important for us to not get taxed for using our own data, which ties to Backblaze's egress, I guess, lack of cost. So every time that we go through and process our data and we have petabytes of data and every time that we go and process audio data, what we create is transcript data, we have 600-plus emotional tags, expression tags that we put on top of that. We segment it out. And so every time we process data, we create new data. And we have to do that not just on a continuous basis, but we have client deliveries as well. And so our clients come in and they want to process a lot of data, too. So when we have to do that, we need to be able to transfer all of that very quickly. And what was really important to us as well was not just our ability to transfer it or our ability to store it all in one place. But the third thing was how do you make sure that we don't have to have our own continuous compute that's up and running. And so we have serverless compute. And so what we do is we have this data store. And then when we need it, we spin up the GPU clusters and then we transfer it over to process the data accordingly, which gets used for our clients or for our models as well.

So the high throughput we talk about, the instant availability, the capacity, those are all good things for you to have that really available.

Yes. And honestly, the Backblaze team has been quite helpful. When we were processing a lot of data in one go. They really made sure that we had really great through there. So it was quite helpful.

Excellent. Excellent. Well, that's great. So how do you see this partnership? I also -- I don't want to leak the stuff that you just shared with me, but if you're comfortable, Great. I didn't realize that there was some commonality here, too, but how do you see this extended partnership and this relationship kind of continue to foster?

Yes. So I was just telling Anuj, that we actually also use WEKA. And so typically, what we do is we have our core data store with Backblaze and then when we need to use it as part of our models and processing, we have it in our GPU cluster, and we have the WEKA storage there as well. And so I think, as you can imagine, as the tailwinds of folks switching over to voice as a modality pick up, and they already are, we obviously anticipate processing a lot more data. And so it's really helpful to us that both Backblaze and WEKA work seamlessly together, and we are able to continue kind of growing and supporting this hungry, hungry hippo.

Well, Anna, we got to make sure that the integration really work. She's on our -- she's our Head of Partnerships and channels. So we just want to make sure we get all that validated design really cooking, so you don't have to do all the hard work. Great. Well, excellent. I think you gave us a lot of really fantastic valuable insights. Really appreciate you being here. Anything else you'd like to share before we wrap?

No. I mean it's been fantastic. Thank you so much to the Backblaze team, and we are excited for continued collaboration together.

Awesome. Well, thank you very much.

Thank you.

All right. Hello, everybody. Good morning, and thank you for being here. I'm Marc Suidan, the CFO, Chief Financial Officer. Glad told us you got to show up looking your best. So I figured, listen, either got to do some biohacking, get more hair on the head or pay $40 to get the AI to do it for me. I think it went a bit too far, so I'll have to do a bit of refinement there in that picture. Okay. Let's get rolling. I think it's a great day. We really wanted to get out adding new faces to the discussion. Generally, Gleb and I and Mimi have been in extensive discussions with a lot of you. So we really wanted to get a lot more of the extended team, so you could all meet them. But from a financial standpoint, what this all translates to is the 2 things we've always focused on, right? More growth, and operating leverage, right? So I'm going to walk through how we've delivered on what we said we're going to deliver and how we're going to do more of it. So when you step back and you look at everything we promised 2 years ago, we set out a few goalposts, and we've delivered on all those goalposts. The first one we said we're going to do is strengthen the balance sheet. And back in Q4 of '24, we did a secondary offering that was oversubscribed, and we did a restructuring driven by zero-based budgeting exercise. And in that exercise, we reduced the OpEx, and we reallocated some of those savings to invest to accelerate growth. And then we said we're going to reaccelerate B2 growth. So in Q1 of 2025, we started to reaccelerate B2 revenue growth. then we said we're going to get B2 revenue growth to over 30%, which we did in Q2 of '26, it was 34%. And we said for the rest of this year and next year, it will be 40% or higher. So we're delivering on all the things we said we're going to deliver, and we said we're going to do it in a profitable way. So under the capital lease model where our CapEx is financed by capital leases, we turned free cash flow positive exactly when we said we would. And we're well in that motion there. So we always said, let's use B2 revenue growth and our free cash flow margin as -- to judge our Rule of 40 scoring. And in Q2, that was 34 plus 8, so 42. So we hit 42% in Q2 of '26, up from 18 a year before that. So delivering on the Rule of 40 score that we promised we'd deliver on. What's driving that? The accelerating revenue growth is translating to a lot of operating leverage. You get a healthy gross margin of 63%, which is really good for an Infrastructure as a Service company, combined with really disciplined OpEx management has translated to tremendous EBITDA margin improvement. So the operating leverage is well in motion and in action. Underlying that is the mix shift. A few years ago, computer backup made up the majority of the business. Right now, B2 forms 62% of the business, and we will probably be around 75% somewhere around middle of next year. So the mix shift is well in action and the underlying fundamentals of the B2 platform is really attractive. ARR growing 39% year-over-year in Q2. really strong net revenue retention at 113%. And the gross customer retention at 89%, you're talking customers that stay with us with an average of 9 years. So phenomenal fundamental metrics. The business for V2 has historically been consumptive, very pay-as-you-go. But as we're accelerating this growth, we felt it was prudent to start getting into longer-term contracts. So our revenue performance obligations, which are committed contracts are gone up more than 5x year-over-year. And in the marketplace, just given the supply constraints, a lot of customers actually prefer to be on the committed contracts because they know that we'll give them and guarantee them the capacity. So that's really helping in giving us a lot more visibility in the growth and where to put our investment to fuel that growth. Unit economics. Over the lifetime value of a customer, we deliver 60% to 70%. It's currently 70%, right? But just to be conservative with the changes in hardware prices because it takes time to adjust the pricing models, we're saying it could be 60%. But that's the value we get out of all incremental dollars from our customers. They generally stay with us for 9 years. Every cohort since B2 launched in 2016 has grown their data consistently year-over-year. You have to obviously build the CapEx upfront. It takes less than 2 years to pay back the CapEx. And then you've got the direct cost. Those are roughly -- I mean, they're step function variable costs, but it's effectively data center rent and power, telecom, data center technician, customer support, sales incentive comp. So that's pretty much our variable cost, right? That carries throughout, but the CapEx is upfront and then you recover heavily over the following 9 years. So you look at our EBITDA performance, we'll continue to improve our EBITDA margin and also operating margin, GAAP gross margin, all of those will continue to improve as operating leverage continues to feed the bottom line. Looking at the debt. So we raised our convertible debt a few weeks ago. We raised $201 million, also oversubscribed, 0% coupon rate. So that strengthens our balance sheet for the next 5 years and helps us to invest. We're putting it all to CapEx. We have to put the CapEx as we have the committed contracts and we have the revenue coming in. So we got to invest in the CapEx. The difference is instead of paying 12.9% interest expense on leases, we'd be paying 0%. So if you think about the cost avoidance over 5 years, that basically pays off half the principal of the debt right there. So we felt that, that was a prudent move to have a stronger balance sheet for the next 5 years to build up this cash position and deploy it on CapEx, and then we would spring load and come out of that with a much stronger free cash flow margin. So the metrics to watch for us over the next 2 years will continue to be EBITDA and operating margin. That's where you're going to see the operating leverage continue to improve. For free cash flows, we did turn positive in Q2, but now we're going to deploy this capital. So operating cash flows will improve. But when you pay for the CapEx and cash, you got to deduct it on those operating cash flows. So for the next 4 to 5 quarters, the free cash flows will turn negative, and then we're spring loading the free cash flow margin to come out much stronger coming after that. So when you think about all the P&L key levers you got to look at and what the outlook is for each one of those, I'll start off with revenue. We said revenue will grow 40% or more for the rest of this year and 2027. By the way, that 40% assumes the same existing guidance philosophy, which is no large deals, and we're defining large deals is greater than $0.5 million. We did $3 million in Q1. We did 4 in Q2. So once again, this number assumes none of these large deals and assumes no customers are committing over their minimum. So we're just putting in the minimum committed contracts there. So it's a pretty prudent 40% is what I'm saying for the next 6 quarters. Gross margin, as we're deploying the CapEx, and of course, it is more expensive, there's going to be a bit of a lag between when you deploy the CapEx or depreciating and when the revenue comes right afterwards. That lag could reduce gross margin by 300 to 600 basis points through the middle of next year, and then it starts recovering from there. And then as you get into 2028, Dan and Gleb spoke about the managed storage. The managed storage resembles a lot more a SaaS kind of gross margin. So that will start to be accretive into 2028 on the gross margin as that comes to feed in. On the OpEx side, we'll continue to manage OpEx in a very disciplined way. We will make targeted investments in R&D and sales and marketing, but OpEx as a percent of revenue on the current trajectory we're on, should continue to be where it is or improve as a percent of revenue. So a lot of operating leverage there. And as it relates to adjusted free cash flows, like I said, for the next 4 to 5 quarters, you will see the adjusted free cash flow turn negative as we deploy the CapEx and then we spring load it so that it meaningfully improves coming after that in a very healthy way under the current construct if we move back to the capital lease model. So key takeaways. We have better growth visibility supported by committed contracts and proven long-term earnings power, and we're going to keep powering that up. So those are the key takeaways for the financial section. So with that, Mimi, we're going to turn it over to the Q&A session.

Execs up on to the stage.

Ittai Kidron from Oppenheimer. I appreciate the presentation today and great to see the acceleration in the business. Maybe a couple for me. First, on the CoreWeave transaction, clearly a landmark deal. Can you talk about the milestones that you have to go through in order to ramp the managed service in fiscal '28? I mean, clearly, it has huge potential from a margin standpoint, as you mentioned, Marc. But clearly, there's also a lot of work that needs to be done behind the scenes to get that going. So we greatly appreciate if you could provide some color on that. And then on the go-to-market side, thanks for clarifying the 3 different kind of cohorts. It was very helpful to understand where the efforts are focused. Maybe a little bit more of a philosophical question here now where in those 3 segments, do you feel you're best aligned and where you have more work to do, number one. And number two, if you had an incremental dollar to put since Marc now has a lot of money in his pocket, if you had another dollar to put in the go-to-market organization, where is the next dollar going to?