Kingsoft Cloud Holdings Limited (NASDAQ:KC) Q3 2025 Earnings Call Transcript

Kingsoft Cloud Holdings Limited (NASDAQ:KC) Q3 2025 Earnings Call Transcript November 19, 2025

Kingsoft Cloud Holdings Limited beats earnings expectations. Reported EPS is $0.02, expectations were $-0.1.

Operator: Good day, and thank you for standing by. Welcome to Kingsoft Cloud Third Quarter 2025 Earnings Conference Call. At this time, all participants are in a listen-only mode. After the speakers’ presentation, there will be a question and answer session. To ask a question during the session, you will need to press star 11 on your telephone. You will then hear an automated message advising your hand is raised. To withdraw your question, please press Please be advised that today’s conference is being recorded. I would now like to hand the conference over to your speaker today, Nicole Shan, IR Director of Kingsoft Cloud. Please go ahead.

Nicole Shan: Thank you, operator. Hello, everyone. And thank you for joining us today. Kingsoft Cloud third quarter 2025 earnings release was distributed earlier today and is available on our IR website at ir.ksyulin.com as well as on the PR Newswire services. On the call today from Kingsoft Cloud, we have our Vice Chairman, CEO, Mr. Zhou Tao, and the CFO, Ms. Li Yi. Mr. Zhou will review our business strategies, operations, and other company highlights followed by Ms. Li, who will discuss the financial performance. They will be available to answer your questions during the Q&A session that follows. There will be conductive integration. Our are for your convenience and the reference purpose only. In case of any discrepancy, management statement in original language will prevail.

Before we begin, I’d like to remind you that this conference call contains forward-looking statements within the meaning of Section 21E of the Securities Exchange Act of 1934 as amended and as defined in The U.S. Private Securities Litigation Reform Act of 1995. These forward-looking statements are based upon management’s current expectations and current market and operating conditions. And relate to events that involve known or unknown risks, uncertainties, and other factors. All of which are difficult to predict and many of which are beyond the company’s control. Which may cause the company’s actual results, performance, or achievements to differ materially from those in the forward-looking statements. Further information regarding these and other risks, uncertainties, or factors are included in the company’s filings with the U.S. SEC.

The company does not undertake any obligation to update any forward-looking statements. As a result of new information, future events, or otherwise. Except as required under applicable law. Finally, please note that unless otherwise stated, all financial figures mentioned during this conference call are denominated in RMB. It’s now my pleasure to introduce our Vice Chairman and CEO, Mr. Zhou. Please go ahead, Zhou.

Zhou Tao: Hello, everyone. Thank you, and welcome to Kingsoft Cloud third quarter 2025 earnings call. I am Zhou Tao, CEO of Kingsoft Cloud. In the era that artificial intelligence is implemented across various industry verticals, and reshaping the technological landscape, Kingsoft Cloud firmly established its strategic positioning and defined its development orientation. On the premise of steadily meeting the demands of model training, we have made adequate technical and resource reserves for the explosive growth of inference. In the face of the dual trends of rapid model iteration and increasing adoption of artificial intelligence, we have provided our clients with stable and efficient integrated training and inference intelligent cloud computing services.

And have laid out model API business to turn inference scenarios into new growth engines. The substantial high growth in revenue and the stable profit margin level validates the steady execution of our strategic measures achieving high quality and sustainable development. First, our revenue in the third quarter reached RMB 2,480,000,000.00, with year-over-year growth rate accelerating from 24% in the previous quarter to 34 to 31% this quarter. Both public cloud and enterprise cloud achieved year-over-year and sequential growth. Among which public cloud revenue increased significantly by 49% year-over-year, reaching RMB 1,750,000,000.00. Second, intelligent computing cloud business remains on a fast development track. This quarter, gross billings of intelligent computing reached RMB 782,000,000, with a year-over-year growth around 122%.

It accounted for 45% of the public cloud revenue, realizing a significant increase from 31% in the same period last year. Generative artificial intelligence and cloud are symbiotically integrated in many aspects, including technology, products, and customer cross-sales. The demand for artificial intelligence not only drives the rapid development of intelligent cloud, but also leads to the growth and technological innovation of basic public cloud and accelerates the iterative process of cloud computing technologies. From training clusters to native technologies, our computing power services, model API services, storage services, and data services have all been upgraded. Third, the Xiaomi and Kingsoft continued to offer a solid foundation. This quarter, revenue from the Xiaomi and Kingsoft ecosystem reached RMB 691,000,000, increasing by 84% year-over-year.

And its proportion in the total revenue further rose to 28%. From January to September 2025, total revenue from the Xiaomi and Kingsoft ecosystem reached RMB 1,820,000,000.00. We anticipate adequately fulfilling the business cooperation under the continuing connected transactions annual quarter this year and are optimistic in the further increase of the quarter next year. Finally, our adjusted gross profit for this quarter reached RMB 393 million, representing a year-over-year increase of 28%. The adjusted operating profit turned from loss to profit reaching RMB 15,360,000.00. And the adjusted operating profit margin was 0.6%. The adjusted net profit recorded a historical positive profit of RMB 28.73 million for the first time. The company is aiming at both revenue growth and profitability improvements.

As the economies of scale are becoming increasingly prominent, while accelerating the construction of intelligent computing infrastructure and technological capabilities, we are also strengthening the control of costs and expenses. Now I would like to walk you through the key business highlights for 2025. In terms of public cloud services, revenue reached RMB 1,750,000,000.00 in this quarter, making a year-over-year increase of 49%. Intelligent computing cloud business has maintained strong growth. We have successfully supported the large-scale training and inference demands of various top Internet customers providing high-quality, high-performance, high-stability, highly efficient cloud computing services. Especially for many artificial intelligence and Internet enterprises, facing the simultaneous demands for model training and inference, we have provided customers with stable and integrated intelligent computing services for different scenarios.

Meanwhile, we actively expanded customer coverage and the cross-selling of intelligent computing cloud and basic cloud. In terms of ecosystem customers, we continued to provide high-quality services to Xiaomi and Kingsoft, continue to prepare underlying resources for ecosystem customers to enhance the rapid expansion capability of intelligent computing demands. In terms of enterprise cloud services, revenue in the quarter was RMB 730,000,000. We firmly believe that in today’s rapidly evolving generative artificial intelligence landscape, intelligence will evolve from model capabilities to industry solutions empowering and reshaping diverse sectors of the economy. As the indispensable carrier for intelligent computing, cloud services enjoy tremendous potential for such digitalization and intelligentization.

In this trillion-dollar sustainably expanding market, we have deeply explored our inherent DNA of two d enterprise services, targeted advantageous selected verticals, and geographical regions, and built core competitiveness for the future. As a result, it has received widespread recognition from our customers and the broader markets. For example, in the public services sector, we aim to become the preferred cloud partner for intelligent computing in the public services agencies and enterprises for their inference demands. Taking Qingyang City in Gansu Province as an example, as one of the eight major nodes of the national project is data web computing and a central area for intelligent computing business. We will be responsible for building the public services cloud platform in Qingyang fully empower local public services affairs with intelligence and digitalization.

In the field of health care, we have achieved a milestone breakthrough in a project integrating artificial intelligence with traditional Chinese medicine clinical scenarios. Whereby not only have we achieved a deep integration of traditional Chinese medicine theory in artificial intelligence, seizing the commanding position in chronic disease management technology, but we have also verified the practical value of artificial intelligence in improving patients’ quality of life and disease control rate at the clinical level. In the enterprise services sector, following the successful implementation of a landmark project for intelligent generation of bank credit reports, we continued to advance the intelligentization transformation across the entire credit approval process.

This evolution extends from the single function of credit report initiation to a comprehensive intelligence system including customer onboarding, credit report generation, loan disbursement, monitoring and early warning, and post-loan reporting. We firmly believe that these proven accumulated successful experiences, market reputation, and replicable core solutions will enable us to seize a pioneering position in the emerging industry wave, build a solid core competitiveness, and achieve high-quality and sustainable shareholder returns. In terms of product and technology, in the public cloud space, we continued to enhance the technology of Intelligent Computing Cloud this quarter, strengthening the capability of the Starflow platform and made significant progress in the following three aspects.

First, we have launched our model API service delivering highly available and easily integrable capabilities for model invocation and management, laying a solid foundation for the subsequent provision of diverse model service paradigms. Second, we upgraded our online model services integrating multiple open-source foundation models equipped with automatic scaling capabilities, offering a highly available inference platform. Third, we launched our data annotation and dataset marketplace, aiming to provide customers with end-to-end support for data flow and help them efficiently advance the model training process. In the enterprise cloud space, in order to meet the demand for private deployment scenarios, we have built a computing power scheduling platform, a lightweight math platform, a generative artificial intelligence knowledge base.

And we have closely collaborated with WPS AI to build a trusted intelligent product architecture for public services use cases. Meanwhile, through the organizational development of the dual R&D centers in Beijing and Wuhan, we attract talents from various regions, build a talent pipeline, and maintain sustained investment intensity in the intelligent computing field. As of the end of Q3, the number of employees in Wuhan is 2.8 times the headcount back in 2022 when we first launched our Wuhan strategy. Overall, we will firmly seize the historic opportunities presented by the Xiaomi and Kingsoft ecosystem. Continue to invest in infrastructure, focus on refining core products and solutions, and to create long-term value for our customers, shareholders, employees, and other stakeholders.

I will now pass the call over to Ms. Li Yi, our CFO, to go over our financials for the third quarter of 2025. Thank you.

Li Yi: And thank you all for joining the call today. Before we go through the details of financial results for the third quarter, I would like to highlight the following aspects. First, revenue has consistently achieved year-over-year growth for six quarters, reaching RMB 2,478 million this quarter. This represents an accelerated year-over-year growth rate of 31% up from 24% in the previous quarter. Revenue from public cloud service stood at RMB 1,752,300,000.0, a significant increase of 49% from RMB 1,165,500,000.0 in the same quarter last year. Meanwhile, robust demand from our intelligent cloud, which is also called AI cloud business, drove around 120% year-over-year billing growth, which totaled RMB 782,400,000.0. Second, profitability has seen substantial improvement.

Our adjusted gross margin rose to 16% up from 15% in the previous quarter. And adjusted EBITDA margin improved to 33% compared with 17% last quarter. Notably, we turned quarterly adjusted operational and adjusted net loss into profit simultaneously for the first time. These gains validate our strong execution in pursuing high-quality, sustainable development as well as our ability to monetize opportunities in the intelligent cloud space. Third, I would like to express our gratitude to shareholders for their support during our risk to equity financing in September. We successfully raised HKD 2,800,000,000.0. And 8% of the fund will be allocated to further investment in AI infrastructure and transfer them to general operational needs. This funding will fully underpin the growth of our intelligent cloud business and enable us to create long-term value for all stakeholders.

Now I will walk you through our financial results for 2025. And use RMB as currency. Total revenues were RMB 2,478 million. Of these, revenues from public cloud services were RMB 1,752,300,000.0, up 49% from RMB 1,175,500,000.0 in the same quarter last year. Revenues from enterprise cloud services reached RMB 725,700,000.0, compared with RMB 110,000,000 in the same quarter last year. Total cost of revenues was RMB 2,097,100,000.0, up 33% year-over-year, which was mainly due to our investment into infrastructure to support intelligent cloud business growth. Addition cost increased by 15% year-over-year, from RMB 673,800,000.0 to RMB 775,700,000.0 this quarter. The increase was mainly due to the purchase of racks which is their expanding intelligent cloud business, as well as the basic computing and storage cloud demand both by AI development.

Depreciation and amortization costs increased from RMB 297,500,000.0 in the same quarter of 2024 to RMB 649,700,000.0 this quarter. The increase was mainly due to the depreciation of newly acquired and leased servers and later work equipment, which were mainly allocated to intelligent cloud business. Solution development and services cost increased by 90% year-over-year from RMB 499,000,000 in the same quarter of 2024 to RMB 595,900,000.0 this quarter. The increase was mainly due to the solutions that no expansion. Fulfillment cost, other cost were RMB 5,200,000.0 and RMB 70,600,000.0 this quarter. Our adjusted gross margin for the quarter was RMB 392,600,000.0, increased by 28% year-over-year and 12% quarter-over-quarter. It was mainly due to the expansions of our revenue scale, the energy contribution from intelligent cloud, and the cost control of IBC racks and servers.

Adjusted gross margin increased from 15% last quarter to 16% in this quarter. On the expense side, excluding traffic concession cost, our total adjusted operating expenses were RMB 420,900,000.0, decreased by 70% year-over-year and 25% quarter-over-quarter. Of which our adjusted R&D expenses were RMB 10,888,400,000.0, decreased by 90% from the same quarter last year. The decrease was mainly due to the decrease of personal cost resulting from our strategic adjustment for the research team, as well as the expense serving from Beijing Wuhan dual research center strategy. Adjusted selling and marketing expenses were RMB 127,600,000.0, increased by 15% year-over-year. Adjusted general and administrative expenses were RMB 104,900,000.0, decreased by 29% year-over-year due to the reverse of credit loss.

The impairment of long-lived assets was near this quarter, compared with RMB 190,700,000.0 in the same quarter last year. Our adjusted operating profit was RMB 15,400,000.0, total profit from adjusted operating loss of RMB 140,200,000.0 in the same period last year. The improvement was mainly due to the of revenue scale and gross profit, the expense control, as well as the reverse of credit loss. Adjusted operating profit margin increased from minus 7% in the same period last year to 0.6% this quarter, representing an increase of eight percentage points. Our non-GAAP EBITDA profit was RMB 826,600,000.0, increased by 3.5 times of RMB 185,400,000.0 in the same quarter last year. Our non-GAAP EBITDA margin achieved 33% compared with the 10% in the same quarter last year.

It was mainly due to our strong commitment to intelligent cloud development, strategic adjustment of business structure, strict control of costs and expenses, as well as the long recovery impact of subsidy in other income. As of 09/30/2025, our cash and cash equivalent totaled RMB 33,954,500,000.0, decreased from RMB 5,464,100,000.0 as of 06/30/2025. The decrease was mainly due to our infrastructure investment for intelligent cloud. This quarter, our capital expenditures, including those financed by third parties, and the right of use assets obtained in 24 finance lease liabilities was RMB 2,787,800,000.0. Looking forward, AI technology drives the revolution of cloud computing. We can more than just fulfill the computing demands of model training and inference.

We also empower enterprises to invoke an API and apply AI capabilities to their business. Stepping into the phase of rapid development in AI applications and explosive growth in demand, we will further invest into infrastructure, strengthen technology, enhance service stability, and provide customers with high value-added cloud service. That’s all for the introduction of our operational and financial results. Thank you all. Thank you, operator. We are now going to start the Q&A session. Please ask your question in both Mandarin Chinese and English if possible. Operator, please go ahead.

Q&A Session

Operator: Thank you. As a reminder, to ask a question, you will need to press 1 and one on your telephone and wait for your name to be announced. To withdraw your question, please press 11 again. Our first question comes from the line of Xiaodan Zhang from CICC. Please go ahead. Your line is open.

Xiaodan Zhang: So thanks management for taking my questions. And, first of all, has there been any structural change in the demand of your ecosystem and external clients for the past quarter? And secondly, how does management see the margin trend in the coming quarters? And what’s the expected mix of different computing resources acquisition models? Thank you.

Zhou Tao: So basically, the core of the reason behind the AI revenue growth in Q3 is that we have some clusters that, you know, partially delivered in the previous quarters, for example, like the 2025, and these clusters and these services have only been partially accounted for revenues from a full quarter basis. But now in Q3, they are starting to be recognized as full quarter revenues. And, also, there’s the factor of partially delayed revenue as well. Some of the revenue which we had in Q2 but was not accounted for, and then this revenue is delayed into the third quarter. Yeah. So regarding the second part of your first question, which is about the structure of internal and external customers, I think I used to say that from a large trend general trend perspective, currently in the phase of transitioning from large and top customers’ training demand to general and wider spread customers’ inference demand.

Most of at the current stage, we still see, you know, majority of our demand coming from the larger customers in their training demand. However, especially in the latest quarter, we are increasingly seeing the trend of our customers adopting artificial intelligence models into their diverse industries. So in face of this general trend, we have also, as we mentioned in the prepared remarks, we have launched our StaffLoad platform to meet the demands of such general trend. And this also goes back to the margin that you also asked about. We generally think that in the future, the inference demand will tend to exhibit a higher margin profile than the current stage of training. And therefore, we think that when that wave of demand comes, we expect to have higher margins.

Li Yi: Thank you, Xiaodan. I think because level as a proportion of the AI business continues to rise and its cost structure is mainly dominated by depreciation, we expect this EBITDA margin will still remain above 20%. But I have to mention that the significant quarter-on-quarter improvement in this quarter was mainly driven by a one-time other income, which will return to the normal level next quarter. Thank you, Xiaodan. Operator, next question, please.

Operator: Thank you. Our next question comes from the line of Wenting Yu from CLSA. Please go ahead. Your line is open.

Wenting Yu: The first question is, could management share the outlook and guidance on the revenue outlook for next year? And beyond the Internet companies’ post-model training and in-body intelligence scenarios that are already underway this year, which other industry and application scenarios are expected to have strong computing power demand that could drive the revenue growth next year? And the second question is with multiple providers in both China and the US increasing the proportion of server leasing in their computing resource mix, how does management view the current market dynamics for procurement versus leasing? And from a cost-effectiveness and profit margin perspective, how would the company allocate the resources between these two approaches?

Li Yi: Wenting, thank you for your question. The company’s budget process is currently underway and expected to be completed around the beginning of the next year. We will share the specific details with you once it is finalized. However, regarding the demand for our AI business, we are fully confident in the subsequent demand growth. And for your second question about the procurement method, we primarily align our capital channels with actual customer needs, including cluster scale, delivery time, and supply inventory level. There’s no rigid total allocation target from the cost-effectiveness perspective. Both approaches have their own pros and cons. The leasing model is to find our supply chain channels and provide a certain degree of flexibility in resource allocation, with the flexibility also offered through short-term and long-term contracts.

Self-procurement, on the other hand, gives us great autonomy in control delivery time rates and managing plus. It also reduces the profit sharing with suppliers, thereby, elevating our pressure on profit margin.

Zhou Tao: Yeah. You know, as you mentioned that the robotics companies in China are a growth environment partly. So, you know, as you this year, we have covered most of the robot companies in China, and we can see the revenue is increasing very rapidly. In the next year, we believe the increase of the robotic companies will also be fast. Meanwhile, you know, as more and more Internet companies in China using talking token services, which is the API services, we are seeing the increase of the business is very quickly. So we believe in the next year, this will be a very important factor to driving the revenue to increase. Thank you.

Li Yi: So this is the CEO. He added that yes, that is your question. Your second question is really about the choice between the leasing model and the CapEx model. So we’ve talked about that before. So, generally, there’s a general rule of thumb. When you’re looking at the larger customers, especially the customers that have solid profiles, have solid fundamentals, and are trustworthy. Premium customers, for example, like Xiaomi. We would tend to choose the CapEx model. While in other growth stage companies, medium and small-sized companies, we generally tend to adopt the leasing model. Which is also a way a meaningful way to reduce our own risk. So as we rightly mentioned, there’s no kind of a top-down target for the split between these two different methods.

And we also talked about in the last quarter as well that the impact of these two different methods have different impacts on our gross margins. However, we have seen the financial results for the past three quarters. Which we have adopted various combinations of these two different models. You know, especially when you compare the gross margin for the third quarter versus the second quarter, it actually also improved sequentially. So I would say that at the current stage, we do not expect material changes to the current status. But generally speaking, in the future, we do expect the margin to improve. Thanks, Anthony. Next question, please.

Operator: Thank you. Our next question comes from the line of Timothy Zhao from Goldman Sachs. Please go ahead. Your line is open.

Timothy Zhao: Thank you, management, for taking my question. My question is regarding the differences between AI training versus inferences. Could management share what is the pricing methodology between these two kinds of demand and what has been the part pricing trend over the past few months or year to date? And, in terms of the utilization rate of the chips of GPUs, pricing, and profitability, can you share more color on the gap between training and inferences? Thank you.

Zhou Tao: Okay. Let me answer these questions. You know, we’re not talking about the price strategy for inference and training. You know, there’s not too much difference between two things. So the price is based on the qualities. How many resources to use, which is the most important factor. And also comparing, you know, the margin rate, you know, there are two kinds of inference services, which one is, you know, customer by resource and use our platform to influence. So that margin ratio is very similar to the training margin ratios, but another one is, you know, customers do directly by our API talking services. That we think that will have a better margin ratio. But, you know, this business is just in the beginning, so we have we need time to see what is the big difference between the two things. Thank you.

Operator: Sounds good. Thank you. Due to time constraints, this concludes our question and answer session. So I’ll hand the call back to Nicole for closing remarks.

Nicole Shan: Thank you. Thank you all once again for joining us today. If you have any questions, feel free to contact us. Look forward to speaking with you again next quarter. Have a nice day. Bye-bye.