Kingsoft Cloud Holdings Ltd (KC) 2025 Q3 法說會逐字稿

Good day, and thank you for standing by. Welcome to Kingsoft Cloud's third quarter 2025 earnings conference call. (Operator Instructions) 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.

Thank you, operator. Hello, everyone, and thank you for joining us today. Kingsoft Cloud's third quarter 2025 earnings release was distributed earlier today and is available on our IR website at ir.ksyun.com as well as on the PRNewswire Services.

On the call today from Kingsoft Cloud, we have our Vice Chairman and CEO, Mr. Zou Tao; and CFO, Ms. Li Yi. Mr. Zou 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 consecutive interpretation. Our interpretations are for your convenience and reference purposes 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 Security 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 on 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 US 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. Zou. Please go ahead.

(interpreted) Hello, everyone. Thank you, and welcome to Kingsoft Cloud's third quarter 2025 earnings call. I am Zou Tao, CEO of Kingsoft Cloud. In the era that artificial intelligence is implemented across various industry verticals and reshaping the technological landscape, Kingsoft Cloud has firmly established its strategic positioning and define 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 entrants. The substantial high growth in revenue and a 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 RMB2.48 billion with year over year growth rate accelerating from 24% in the previous quarter 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 RMB1.75 billion. Second, intelligent computing cloud business remains on fast development track.

This quarter, gross billings of intelligent computing reached RMB782 million, with a year over year growth of around 120%. 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-sells.

The demand for artificial intelligence not only drives the rapid development of intelligent computing cloud, but also leads to the growth in 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 ecosystem continued to offer solid foundation. This quarter, revenue from the Xiaomi and Kingsoft ecosystem reached RMB691 million, increasing by increasing by 84% year over year, and its proportion in the total revenue further rose to 28%. From January to September 2025, the total revenue from the Xiaomi and Kingsoft ecosystem reached RMB1.82 billion.

We anticipate adequately fulfilling the business cooperation under the continuing connected transactions annual quota this year and are optimistic in the further increase of the quota next year. Finally, our adjusted gross profit for this quarter reached RMB393 million, representing a year over year increase of 28%.

The adjusted operating profit turned from loss to profit, reaching RMB15.33 million -- reaching RMB15.36 million, and the adjusted operating profit margin was 0.6%. The adjusted net profit recorded a historical positive profit of RMB28.73 million for the first time. The company is aiming at both revenue growth and profitability improvement as the economies of scale is 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 the third quarter of 2025.

In terms of public cloud services, revenue reached RMB1.75 billion in this quarter, making a year over year increase of 49%. The intelligent computing cloud business has maintained strong growth. We have successfully supported the large-scale training and inference demand of various top internet customers providing high-quality, high-performance, high stable and highly efficient cloud computing services. Especially from many artificial intelligence and internet enterprises facing the simultaneous demand 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 and continue to prepare underlying resources for ecosystem customers to enhance the rapid expansion capability for intelligent computing demand.

In terms of enterprise cloud services, revenue in the quarter was RMB730 million. 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 enjoys tremendous potential for such digitalization and intelligentization.

In this $1 trillion sustainable expanding market, we have deeply explored our inherent DNA of 2D 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 in 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 demand. Taking Qingyang City in Gansu province as an example as one of the eight major nodes of the national project East Data West computing and a central area for intelligent computing business. We will be responsible for building the public services cloud platform in Qingyang to fully empower the 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 scenario, whereby not only have we achieved a deep integration of traditional Chinese medicine theory and 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 enterprise services sector, following the successful implementation of a landmark project for intelligent generation of bank credit reports, we continue 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 this proven accumulated successful experiences, market reputation and replicable core of 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 public cloud space, we continue 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 integrated capabilities for model invocation and management, laying a solid foundation for the subsequent provision of diverse model service paradigm. Second, we upgraded our online model services, integrating multiple open source foundation models and equipped with automatic scaling capabilities, offering a highly available inference platform.

Third, we launched our data annotation and data set marketplace aiming to provide customers with end-to-end support for data flow and help them efficiently advance the model training process. In enterprise cloud space, in order to meet the demand for private deployment scenarios, we have built a computing power scheduling platform, a lightweight mass platform and 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 head count 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.

Good evening and good morning, everyone, and thank you all for joining the call today. Before we walk through the details of financial results for the third quarter, I would like to highlight the following aspects. First, our revenue has consistently achieved year on year growth for six quarters, reaching RMB2,478 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 RMB1,752.3 million, a significant increase of 49% from RMB1,165.5 million in the same quarter last year.

Meanwhile, robust demand from our intelligent cloud, which also called as AI cloud business drove around 120% year over year billing growth which totaled RMB782.4 million. Second, profitability has seen substantial improvement. Our adjusted gross margin rose to 16%, up from 50% in the previous quarter. And adjusted EBITDA margin improved to 33% compared with 17% last quarter. Notably, returned 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, we would like to express our gratitude to shareholders for the support during our recent equity financing in September. We successfully raised HKD2.8 billion and 8% of the fund will be allocated to further investments in AI infrastructure and 12% 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 the third quarter of 2025 and use RMBas currency.

Total revenues were RMB2,478 million. Of these, revenues from public cloud services were RMB1,652.3 million up 49% from RMB1,175.5 million in the same quarter last year. Revenues from enterprise cloud services reached RMB725.7 million compared with RMB710 million in the same quarter last year. Total cost of revenues were RMB2,097.1 million, up 33% year over year, which was mainly due to our investment into infrastructure to support the intelligent cloud business growth. IDC costs increased by 15% year over year from RMB673.8 million to RMB775.7 million this quarter.

The increase was mainly due to the purchase of racks, which serve the expanding intelligent cloud business as well as the basic computing and storage cloud demand, brought by AI development. Depreciation and amortization costs increased from RMB297.5 million in the same quarter of 2024 to RMB649.7 million this quarter. The increase was mainly due to the depreciation of newly acquired and leased servers and network equipment, which were mainly allocated to intelligent cloud business.

Solution development and services cost increased by 19% year over year from RMB499 million in the same quarter of 2024 to RMB595.9 million this quarter. The increase was mainly due to the solution personnel expansion. Fulfillment costs and other costs were RMB5.2 million and RMB70.6 million this quarter. Our adjusted gross margin for the quarter was RMB392.6 million, increased by 28% year over year and 12% quarter over quarter. It was mainly due to the expansion of our revenue scale, the enlarged contribution from intelligent cloud and the cost control of IDC racks and servers.

Adjusted gross margin increased from 15% last quarter to 16% in this quarter. On the expense side, excluding share-based compensation costs, our total adjusted operating expenses were RMB420.9 million, decreased by 70% over year over year and 25% quarter over quarter. Of which, our adjusted R&D expenses were RMB188.4 million, decreased by 90% from same quarter last year. The decrease was mainly due to the decrease of personnel costs resulting in our strategic adjustments for research team as well as the expense serving from Beijing, Wuhan research and strategy. Adjusted selling and marketing expenses were RMB127.6 million increased by 15% year over year. Adjusted general and administrative expenses were RMB104.9 million, decreased by 29% year over year due to the reversal of credit loss.

The impairment of long-lived assets was new this quarter compared with RMB190.7 million in the same quarter last year. Our adjusted operating profit was RMB15.4 million. Total profit from adjusted operating loss of RMB140.2 million in the same period last year. The improvement was mainly due to the expansion of revenue scale and the gross profit, the expense control as well as the reversal 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 8 percentage points.

Our non-GAAP EBITDA profit was RMB826.6 million, increased by 3.5 times of RMB185.4 million in the same quarter last year. Our non-GAAP EBITDA margin achieved 33% compared with 10% in the same quarter last year, mainly due to our strong commitment to intelligent cloud development, strategic adjustment of business structure, stricter control of costs and expenses as well as the nonrecurring impact of subsidy in other income.

As of September 30, 2025, our cash and cash equivalents totaled RMB3,954.5 million, decreased from RMB5,464.1 million as of June 30, 2025. The decrease was mainly due to our infrastructure investment for intelligent cloud. This quarter, our capital expenditure, including those financed by third quarter and right-of-use assets obtained in exchange for finance lease liabilities was RMB2,787.8 million. Looking forward, AI technology drives the revolution of cloud computing. We do more than just fulfill the computing demand of model trailing inference.

We also empower enterprises to evoke API and apply AI probabilities to the business. Stepping into the face of rapid development in AI applications and explosive growth in demand, we will further invest into infrastructure, strengthening technology, enhanced service stability and provide customers with high value-added cloud services.

That's all for the introduction of our operational and financial results. Thank you all.

Thank you. Operator, we are now due to start the Q&A session. Please answer your question in both Mandarin and Chinese and English, if possible. Operator, please go ahead. Thank you.

(Operator Instructions)

Xiaodan Zhang, CICC.

(interpreted) First of all, what are the key drivers of AI revenue growth in Q3? And 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?

Please standby while the speakers reconnect. Please standby. Speakers, you are now reconnected. Please go ahead.

Sorry, Xiaodan, we didn't get your question. Could you repeat that again? Thank you.

Yes, no problem. So my first question is regarding the AI revenue. So could management break down the key drivers for AI revenue in Q3? And has there been any structural change in 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 going onwards?

(interpreted) So basically, the core of the reason behind the AI revenue growth in Q3 is that we had some quarters that partially delivered in the previous quarters for example, like the second quarter of 2025. And these clusters and these services have only been partially accounted for revenues in -- 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 are delayed into the third quarter.

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, we're currently in the phase of transitioning from large and top customers training demand to general and wider spread customers' inference demand. Most -- at the current stage, we still see a majority of our demand coming from the larger customers in their training demand. However, especially in the latest quarter, we're increasingly seeing the trend of 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 StarFlow platform to meet the demand of such general trend.

And this also goes back to the margin question that you also asked about. We generally think that the -- in the future, the inference demand will tend to exhibit 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.

As EBITDA level, as the proportion of the AI business continues to rise and its cost structure is mainly dominated by depreciation, we expect this year's EBITDA margin will still remain about 20%. But I have to mention that the significant quarter on quarter improvement this quarter was mainly driven by a onetime other income which will return to the normal level next quarter.

Operator, next question please.

Wenting Yu, CLSA.

(interpreted) The first question is, could management share the outlook and guidance on the revenue outlook for the next year and beyond the internet company's post-model training and embody intelligence scenarios that are already underway this year, which other industry 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 cloud providers in both China and US increasing the proportion of server leasing in their computing resource mix, and 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?

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 was 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 -- regarding the procurement methods, we primarily like our capital channels with actual customer needs including cluster scale, delivery time and supply inventory level.

There is no rigid total allocation target from the cost-effectiveness perspective, both approaches have their own pros and cons. The leasing model expands our supply chain channels and provides certainty with flexibility in resource allocation with the flexibility to also offer through short-term and long-term contracts. Sales procurements on the other hand give us great autonomy in control delivery timelines and managing clusters. It also reduced the profit sharing with suppliers, thereby elevating our pressure on profit margin.

Yes. As you mentioned that the robotic companies in China is a growing very fastly. So as you -- this year, we have covered most of the robot companies in China, and we can see the revenue is increasing very rapidly. So in the next year, we believe the increase of the robotic companies will also be fast. Meanwhile, as more and more Internet companies in China using token services which is the API services. We are seeing the increasement of the business very quickly. So we believe in the next few years, this will be a very important factors to drive the revenue to increase.

(interpreted) So this is (inaudible). He added that understand your question -- your second question is really about the choice between the leasing model and the CapEx model. So we talked about that before. So generally, there's a general rule of thumb. So when we're looking at the larger customers, especially the customers that have solid profile, have solid fundamentals and are trustworthy, premium customers, for example, like Xiaomi, we will tend to choose the CapEx model.

While in other growth stage companies, medium and small-sized to small- and medium-sized companies, we generally tend to adopt the leasing model, which is also a way -- a meaningful way to reduce our own risk. So Li rightly mentioned, there's no kind of a top-down target 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 to 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. And 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.

Next question please, operator.

Timothy Zhao, Goldman Sachs.

(interpreted) 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 pricing trend over the past few months or year-to-date? And in terms of the overall utilization rate of the chips of GPUs, pricing and profitability, can you share more color on the gap between training and inferences?

Look, let me answer these questions. When I'm talking about the price strategy for inference and training, there's not too much difference between two things. So the price is based on the qualities, how many servers used, which is the most important factors. And also comparing the margin rate, there are two kind of inference services. One is customers buy resource and use our platform for inference.

So that margin ratio is very similar to the training margin ratio. But compared -- another one is customers directly buy our API token services that we think that will have a better margin ratio. But this business just in the beginning. So we have -- we need time to see what is the big difference between the two things.

Due to time constraints, this concludes our question-and-answer session. So I'll hand the call back to Nicole for closing remarks.

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.

Portions of this transcript that are marked (interpreted) were spoken by an interpreter present on the live call. The interpreter was provided by the company sponsoring this event.