Gajan: and enterprise-specific data context across queries. So AI agents and large language models perform with the consistency and institutional memory that production environments require. Like Palantir, our engineers are trained on the Unifor platform and embedded directly inside customer environments. We are not just orchestrating infrastructure. We are orchestrating outcomes. VCF9 as the control plane, Dell for core infrastructure, Palantir and Unifor for governed AI and agent workflows, Rubrik for data resilience, AMD for enterprise-ready compute. Each partner is best in class, but the value Rackspace delivers is making them operate as one integrated system with full accountability for how the system performs and the outcomes it delivers. Looking ahead, The next phase is already emerging. As enterprise AI evolves towards agentic workflows where machines interact with machines and processes run end-to-end without human intervention, the demands of governed infrastructure become even more acute. Training will largely sit with specialized providers, but inference, particularly context-aware inference on regulated data, is where production enterprise AI lives. That is the workload we are built to operate. And as customers develop a clearer picture of their data residency requirements, more of those workloads will move into governed private cloud deployed across our global data center footprint in the jurisdictions and sovereignty zones our customers require. That is why we are doubling down on VCF9 and Broadcom this year. Our full-year private cloud growth outlook remains on track. We have signed engagements with AdventHealth, Seattle Children's, and a strategic database as a service partner onboarding through the rest of the year. We are also seeing encouraging pipeline momentum on our Palantir and Unifor partnerships, where context-aware inference and governed agent workflows are gaining traction at deal sizes that we have not historically seen. The AMD partnership announced today adds a further layer of future optionality as governed AI compute becomes more central to how regulated enterprises operate. Together, these give us confidence in the full-year private cloud growth profile we are reaffirming today. Now, for our public cloud update. First quarter public cloud revenue services revenue grew 10%, reflecting our continued shift towards higher value engagements. Our customer wins this quarter highlight the breadth of our platform capabilities and our deepening presence in the AI space. First, we are powering a large-scale enterprise-wide multi-cloud transformation for a leading healthcare technology organization. Through a governance model, we are delivering intelligent automation, and measurable cost optimization, ensuring each workload is placed on the right platform for the right reasons. Second, Rackspace is serving as the implementation and managed services delivery engine for a high-growth AI-native database as a service partner operating across both public and private cloud environments. Our execution capabilities are a direct accelerant to our partners' client acquisition and market expansion, reflecting a high-value compounding partnership driving differentiated multi-cloud database as a service outcomes. Our service portfolio is built for where enterprise AI is headed, production, not experimentation. We are embedding engineers directly into customer environments and accountability built in from day one. New partnerships expand our ability to deploy context-aware inference, governed agent workflows, and forward deployed engineers inside customer environments, giving enterprises a governed path from strategy to inference workloads in production. We are complementing this with purpose-built capabilities in AIOps identity security, and data resilience, addressing the operational and security demands that become non-negotiable once AI moves into production environments. In summary, public cloud is executing. As inference workloads move into production, we are increasingly positioned as the partner enterprises rely on to operate, secure, and optimize their cloud environments. with full accountability to match the results this quarter confirmed the thesis governed ai infrastructure as the foundation an integrated technology stack of curated partners running on top of it one accountable operator responsible for the outcomes that is what today's rack space delivers with that i will turn it over to mark for our financial results thank you gajan
Mark: In the first quarter, total company GAAP revenue was $678 million, up 2% year over year, driven by solid public cloud performance. Non-GAAP gross profit margin was 18.3% of GAAP revenue, down 160 basis points year over year, reflecting the private cloud revenue timing dynamics we discussed. Non-GAAP operating profit was $31 million, up 20% year over year, driven by continued operating expense discipline. Non-GAAP loss per share was $0.06 flat year over year. Cash flow from operations was $5 million, and free cash flow was negative $9 million. We ended the quarter with $94 million in cash and $295 million in total liquidity, inclusive of the undrawn portion of our revolving credit facility. During the quarter, we repurchased approximately $96 million of debt, reflecting our continued commitment to disciplined capital allocation and active deleveraging. This reduces our interest burden and strengthens our overall capital structure. We are making deliberate progress on leverage reduction while continuing to invest in strategic growth. Turning to our segment results, private cloud GAAP revenue for the first quarter was $235 million, down 6% year-over-year, reflecting the timing of large-deal onboarding within our healthcare vertical, consistent with the dynamics we outlined last quarter. Non-GAAP gross margin was 36%, down 110 basis points year-over-year, driven by lower fixed cost absorption on reduced revenue. Non-GAAP segment operating margin was 24.7%, an improvement at 30 basis points year-over-year, reflecting continued operating expense discipline. In our public cloud segment, GAAP revenue was $443 million, up 7% year-over-year, with services revenue growing 10% year-over-year. Non-GAAP gross margin was 8.9%, down 60 basis points year-over-year, reflecting higher infrastructure costs. Non-GAAP segment operating margin was 4.7%, up 50 basis points year-over-year, driven by improved operating expense efficiency. Now on to our guidance. We are reaffirming our full year 2026 guidance in its entirety. Revenue, EBITDA, and cash flow outlook all remain unchanged. The Q1 private cloud timing we described is fully reflected in our annual plan, and our confidence in the full-year outlook is unchanged. We continue to win larger complex engagements that carry longer deployment cycles, but deliver greater revenue visibility, higher lifetime value, and more durable recurring revenue streams. As they come online throughout the year, we expect private cloud to reflect the growth profile we committed to for 2026. With that, I'll turn it back over to Gajen.
Gajan: The market is trending in line with our expectations, and this quarter we delivered proof across every layer of that thesis. Regulated enterprises are making a deliberate decision about where their AI runs, who operates it, and who is accountable for outcomes. Healthcare is now a pillar. One of the top five epic workloads in the world runs on Rackspace-governed AI infrastructure. Epic Managed Services is proprietary Rackspace IP, decades in the making, and increasingly the foundation our healthcare customers are choosing as AI moves into production. Sovereign is validated. BT, Sovereign Cloud, runs on Rackspace-governed AI infrastructure. Sadiya in Saudi Arabia places us inside one of the world's most advanced national AI programs. These are anchor commitments, not pilots. The technology stack is complete, and this quarter we extended it further. VMware Cloud Foundation 9 as the control plane running across private, public, edge, and sovereign environments. Palantir for governed data and AI operations with our first joint deal closing and a growing pipeline. Unifor enabling agent-based workflows with context-aware inference. Rubric for Data Resilience, and AMD, where we are establishing a new category of governed enterprise AI infrastructure, delivering four integrated capabilities from silicon to outcomes. Enterprise AI Cloud, Enterprise Inference Engine, Inference as a Service, and Bare Metal AMD Instinct. One integrated system with an investment-grade counterparty co-invested in our success. and Rackspace accountable for how it performs end-to-end. We are the operator of the full enterprise AI technology stack, one accountable partner where enterprise AI goes to production. That is Rackspace. Thank you to our customers, partners, and every Racker. And with that, back to Sagar.
Sagar Hebar: Thank you, Gajan. Let us begin the question and answer session. Please go ahead.
Operator: As a reminder, to ask a question, please press star 1-1 on your telephone and wait for your name to be announced. To withdraw your question, please press star 1-1 again. Our first question comes from Kevin McVey with UBS.
Kevin McVey: Great. Thanks so much. Good morning. And let me start just congratulating you folks because obviously there's been a lot of work to be done to get you folks to this level and a lot of patients and, you know, just that needs to be recognized. And I think I just wanted to kind of highlight that because there's a lot that's going into the results that are here today. I guess, and there was an incredible amount of detail again, but maybe talk to how AMD dovetails into Palantirin. You know, what else? It sounds like the MOU is pretty far along. What else needs to be done just to, I guess, get it across the goal line? Sounds like it is, but... you know, is there anything, you know, in terms of what we should look for just as that officially gets signed or is it officially signed? It just, again, it seems like it's pretty far along, but just if you could help us with that a little bit.
Gajan: Hey, Kevin. Thank you and appreciate your comments. Now, look, I think when we look at this, you know, I would sort of think about Palantir and AMD somewhat distinct from each other just so that you know, starting with the Palantir relationship, you know, that's really all about deploying and running customer workflows for the customer with forward deployed engineers, somewhat independent of what compute platform it runs on, right? Really think about compute more as what's the most efficient place to run that work, any given workload. And then having, and then the AMD piece, really fits into how, first and foremost, it gives us CPU and GPU, which I think as we move further into inference and production workloads, being able to deliver that in an efficient manner allows us to now do it across some of the CPU and GPU stack. And then in terms of the partnership itself, I think we are certainly well along the way there. I think we still need to get the financing lockdown and sort of, you know, tightened up. But we feel pretty confident that we are on our way to getting that done and hopefully get it announced here in the near future. We feel pretty good about it.
Kevin McVey: That's super helpful. And then just, again, if you could remind us, the capacity in the private cloud versus, you know, the public in, you know, as these initiatives kind of scale, particularly AMD and Palantir, Is that primarily across the private cloud as opposed to the public? Or, you know, just maybe help us understand that a little bit because obviously there's a lot to digest and just a really, really nice outcome.
Gajan: Now, great question, Kevin. You know, this is sort of this mock confusion. At least I think of it that way, right? Because customer workloads are going to run across private and public, depending on where that workload needs to land, right? And that's why sort of our VCF9 partnership, the Broadcom VMware partnership, gives us sort of, think of it as the control plane across which we could somewhat drastically drive the workload, whether it be in private or public cloud. So capacity-wise, you know, we have the partnerships on the public side, and now we have the partnership and, you know, hopefully here soon, the compute side up and running from a GPU perspective as well. which allows us then to really be somewhat agnostic with the customer, really focus on what specific outcome they want, and then how do we deliver that in the most efficient way for them across either a CPU or a GPU landscape, and that could be private or public, right? So, like you said at the beginning, Kevin, there's like a ton of work that goes into sort of figuring all of this stuff out, and, you know, part of the challenge our customers have, right, is to think all of that stuff through, right? In terms of, you know, we're building a small language model or you're running on a large language model. You know, where do you run the inference? Where do you, you know, how do you orchestrate that? How do you ensure that it's running as efficiently as possible, secure as possible, data residency is thought through, right? All of those, you know, and our ambition is, you know, how do you take that complexity off the table for them? And with our forward deployed engineers, really enable support and accelerate their journey to become, you know, more AI-enabled or operate on a fully AI stack, right? So that's the opportunity we saw, and that's what we are, you know, truly, and our customers are really guiding us through this. So pretty excited about it.
Kevin McVey: No, it's amazing. And then just one more. I don't want to be, I want to be respectful of your time, but, you know, it sounds like, you know, any sense of how this starts to kind of, fan in. It sounds like maybe the back half of 26. And then is there any way to think about kind of just what type of margin this work would be coming in? I know it's probably relatively maybe a tougher question, but just any way to think about that and then what potential capital needs you could have as you're standing some of this stuff up?
Gajan: I think, you know, we think of it this way, Kevin. We think of There are four distinct capability sets, if you will, for lack of a better way, that we are bringing to market. It's govern private cloud on AMD Silicon. Think of that as we own the entire outcome for our customer in partnership with our customer. So they don't think about anything that sits in between. So that would be, if you think of it through the lens of margin, probably our most profitable business. You know, then there's context-aware inference, which is really the next level of, you know, business where you're driving domain-specific data through inference and maintaining that domain data throughout the entire process. That's probably your next tier when you think of a margin coming down, if you will, right? Then there is the inference there, just purely we are providing the tokens or the intelligence customers are using it through an API. And then lastly, sort of, you know, a lot of what the neoclouds do, which is the, you know, bare metal, right, which is probably your lowest end on the margin, right? So, yeah, I think that as we ramp up, we will see our business sort of fluctuate across these four areas. Obviously, our intent is to end up with, you know, fully managed, governed outcomes, but there's a journey to get there, and I think that's something we need to work our way through before, you know, we can give, you know, clear guidance around how that plays out.
Mark: Hey, Kevin, this is Mark. I would agree with that. I also think that, you know, it's going to be largely on par, if not accretive to existing gross market rates across our private cloud business. And just in terms of timing, you know, this is not something that we've got materially factored into our 26 guidance, right, just in terms of supply chain and delivery timing.
Kevin McVey: No, listen, it sounds like you're well on your way. And again, congratulations. Thank you.
Operator: Our next question comes from David page with RBC capital markets.
David Page: Hi, good morning. Thank you for taking my question and, uh, congrats on the great results here. Um, I guess at that, a higher level, it seems like a rat space is moving in the right direction. Um, you move not only internally as a company, but where the industry is going in terms of, you know, CPU, GPO, running SLMs, LLMs, et cetera. So I'm just curious, you know, you seem like you're the first, you know, you're the leader, but I guess how's the competitive environment looking? And I guess as a follow-up, you mentioned the pipeline is strong, so should we expect more deals in the future or maybe just flush that out a little bit? Thank you.
Gajan: Sure. Good to meet you, David, and thank you for your comments as well. So, yeah, I think... Now, when I think about where we are, the orientation of the business right now is very much along the lines of helping customers really understand how they want to run AI workloads. Because if you think about where we sit today in our private cloud business especially, a lot of the customer workloads that are regulated run on our environment. And so, you know, the ability for us to sort of guide them from there on to running AI-based workloads is sort of where we are seeing the most opportunity. And when you look at the partnerships, either on the application stack, the Palantir Uniforce, or on the compute stack, they just give us a much more integrated view of trying to tie all of this together, or not trying, but tying all of this together and delivering it. So when you think of kind of your first question in terms of competitive environment, I haven't seen anyone yet that is able to put all of this together in one place and then own the outcome. I think that sort of makes a distinct difference, especially in a regulated or sovereign environment, because I think that it becomes significantly unique To give you an example, when I say governed in healthcare, it means HIPAA compliance, PHI security, clinical SLAs. All of that has to be put onto the same platform and integrated. and then deliver, right? So I don't, I'm not, you know, I'm sure there will be competitors that show up, but having the consulting, the forward-deprived engineers, the infrastructure, the compute, and the partnership all stitched together, I hope it gives us a little bit of a lead and an edge in terms of where we sit. Sorry for the long answer, but hope that makes sense, David.
David Page: No, that was very helpful. Thank you. And I would agree. It does seem like you have that leadership position, which is great. So, I guess, yeah, no, thank you. That's helpful.
Gajan: Thank you.
David Page: Yeah, maybe one more. There were some comments about the capital structure. It looks like it's getting into a better place. Just how should we think about the capital structure over the next, like, 12 to 24 months just evolving? Thank you.
Mark: Yeah, hey, David. This is Mark.
David Page: Hey, Mark.
Mark: Look, our – our motivation or our intent is deleveraging, right? That's our top priority, right? So as we think about some of the deals we've announced, some of our capital requirements for this year, ultimately, you know, we've got our eye on 2028, the maturity, the debt stack that's going to be due in the middle of 2028 and getting deleveraged through, you know, an increase in operating leverage EBITDA as well as additional cash flow. So, as we structure some of these deals, right, the intent isn't to go, you know, take on more, you know, expenses to kind of add to our existing debt maturities, but to, you know, structure things in a way that, you know, you know, don't create further leverage, right? We have decreased our operating leverage by, I think, from 8.6 to 8.3 quarter over quarter, right? And we continue to stay focused on, you know, the out-quarters and finding ways to delever, right? You'll notice in the quarter, we actually repurchased some of our debt, roughly 96 million notional We're looking for ways to deploy capital such that, you know, we can reduce that, you know, get ourselves the refinanceability over the next 12, 18 months.
David Page: Great. Thank you. That's very helpful. Congrats on the momentum and looking forward to working together. All right, boys.
Gajan: Thank you.
Operator: That concludes today's question and answer session. I'd like to turn the call back to Sagar Hebar for closing remarks.
Sagar Hebar: Thank you, everyone, for joining us. If you have any questions, please email us at ir.trackspace.com. Have a great rest of your day. Thanks, Liz.
Operator: Thank you. This concludes today's conference call. Thank you for participating. You may now disconnect.
