Kingsoft Cloud Holdings Ltd (KC) Q2 2026 Earnings Call Transcript

Good morning, ladies and gentlemen, and thank you for standing by for Kingsoft Cloud's second quarter 2026 earnings conference call. All participants are currently in listen-only mode. Following management's prepared remarks, we will open the call for questions. Please note that today's call is being recorded. I will now turn the call over to Mr. Jackie Zor, Senior Director of Capital markets at KingSoftCloud. Jackie, please go ahead.

Thank you, operator. Hello, everyone, and thank you for joining us today. KingSoftCloud's second quarter 2026 earnings release was issued earlier today and is available on our IR website and through PR Newswire. Joining us today are Mr. Zhou Tau, Chairman and CEO, Ms. Li Yi, CFO, Mr. Liu Tau, Senior Vice President, Mr. Tian Kai-Yan, Senior Vice President, Ms. Yu Jun, Vice President, Mr. Zhou Rilong, Associate Vice President, and Mr. Kuak Tian, Vice Secretary and Associate Vice President. Mr. Zhou will discuss our business performance and key developments, followed by Ms. Li with a review of our financial results. Management will then take your questions. Consecutive interpretation will be provided for convenience and for reference only. In the event of any discrepancy, management statements in the original language will prevail. Before we begin, I would like to remind you that today's call contains forward-looking statements made under the safe harbor provision of u.s private secretary securities litigation reform type of magnet by these statements involve risk and uncertainties and actual results may differ materially from those expressed or implied by the forward-looking statements additional information concerning factors that would cause actual results to differ materially is included in the company's following with the U.S. SBC. The company undertakes no obligation to update any overlooking payments except as required by applicable law. Unless otherwise stated, all financial figures discussed on today's call are denominated in renminbi.

With that, it is my pleasure to turn the call over to our chairman and CEO, Mr. Zhou. 面对这一深刻的行业变革,金山云继续坚持技术业与高质量可持续发展战略,全面推动AI云服务,MAS业务,FDE业务的发展,取得了可喜的成绩。 首先,AI业务继续驱动公司收入高速增长,本季度实现总收入30.7亿元,创业单季度历史新高,同比增长31%。 其中AI云账单收入达13.3亿元 同比增长82% 占公用云收入比例进一步提升 MaaS业务收入增长强劲 二季度MaaS收入较一季度增长12倍 其次 盈利水平显著提升 本季度经调整毛利率提升至15.4% 环比上升2.4% 经营利润首次转正 经调整经营利润率达4% 创历史新高 这是我们抓住AI浪潮 提升收入质量 落实降本增效等多项举措 并行 第三 客户结构持续优化 生态内外商机加速兑现 生态内本机构来自小米和金山生态的收入同比增长 占总收入比例为26% 生态外,前五大客户收入同比增长,AI云业务已覆盖互联网,前沿AI实验室,巨型智能,自动驾驶,金融科技,游戏,音视频等广泛行业,多元化的客户结构与业务布局,不仅带来收入规模的持续提升, 也是我们能够更明活的调配算益资源 提升益价能力和抗风险能力

every industry becoming a reality through a combination of model as a service agent as a service and FDE services against this backdrop think of cloud remains committed to technology leadership and high-quality sustainable growth we are accelerating the development of our AI cloud math with encouraging progress first AI continues to drive from revenue growth total revenue reached a record of RMB 3.07 billion, up 31% year-over-year. AI cloud growth billings increased 82% to RMB 1.33 billion and accounted for 56% of public cloud revenue. Mass revenue also grows strongly with Q2 revenue up more than 12 times from the Q1 level. Second, profitability improved significantly. Adjusted gross margin rose to 15.4%, up 2.4 percentage points quarter over quarter. Operating profit turned positive for the first time, with adjusted operating margin reaching a record high of 4.0%. This reflects our continued efforts to capture AI opportunities, improve revenue quality, and drive greater operating efficiency. Third, our customer mix continued to improve with stronger momentum both within and outside our ecosystem. Revenue from the Xiaoyan Kingsoft ecosystem reached R&D 810 million, up 28% year-over-year and accounting for 26% of total revenue. Revenue from our top five of non-ecosystem customers grew 51%. Our AI cloud business now serves a broad range of sectors, including internet services, Frontier AI Labs, Embodied AI, Autonomous Driving, AI for Science, FinTech, Gaming, and Online Video, to name a few. This diversified customer base supports continued growth while allowing us to allocate computing resources more effectively and strengthen our pricing power and business resilience.

AI 时代所带来的云服务业务增长 潜力空前膨大 26年6月 股东大会正式批准了我们再次提升 来自小米关联收入的上限金额 2026年和2027年 来自小米关联收入的上限合计打100亿元 较上调前提升了39% 今年上半年来 自小米和金山的公有云收入同比增速 其次,新流平台MAS服务能力进一步强化,新流模型API服务持续完善,多模型服务与企业体揭露能力,目前新流平台已部署上线120个模型,主流新模型推出后做到同步上线,已揭露230多家客户。 第三,我们在新兴赛道深化与头部客户合作,面向头部聚身智能客户和头部自动驾驶客户,完成大规模算力极权交付,稳定支撑,其模型高效迭代,与AI4Science行业头部客户打成深度合作,保障其新业务快速拓展。

Let me walk you through our business progress in the second quarter of 2026. In public cloud, revenue reached RMB 2.36 billion, up 45% year over year. First, Xiaomi continues to expand AI across its human car home ecosystem, while WPS AI continues to advance. As the only strategic cloud platform for the Xiaomi and Kingsoft ecosystem, we see substantial AI driven growth opportunities. In June, our shareholders approved a further increase in the annual caps to connected transactions with Xiaomi. The combined caps for 2026 and 2027 now total RMB 10 billion, 39% higher than before the adjustment. In the first half, public cloud revenue from Xiaomi and Kingsoft grew 54% year over year. Second, we further strengthen the math capabilities of our Starflow platform. Starflow now supports 120 models with major new models launched on the platform in sync with their market release and serves more than 230 enterprise customers. Third, we defend cooperation with leading customers in emerging sectors. We delivered large-scale computing clusters to leading embodied AI and autonomous driving customers, supporting rapid model iteration, and expanded our cooperation with the leading AI for science to support the growth of this new business.

Thank you very much.

In Enterprise Cloud, revenue reached RMB 710 million. In public services, we signed an agreement with the Nanjing Communications Administration of the Yangtze River to build Jianghai Cloud, a dedicated digital infrastructure platform for Yangtze River shipping. We also formed a strategic partnership with the Wuhan Municipal Data Bureau and Wuhan Cloud across computing resource interconnection, digital governance, intelligent computing applications, and ecosystem development. In digital health, we are leading a project under the National Key R&D Program on Biology and Information Integration to develop a cloud-based virtual surgery platform, which has been deployed in more than 30 hospitals nationwide. In enterprise services, we designed our cooperation with Yunshan Gansu to jointly build and operate the Gansu Provincial Public Services Cloud under an integrated investment, construction, and operations model.

Thank you. 同时,我们将云产品周边AI化,将数据库存储等组件变成为agent和便捷调用的平台型产品。 我们优化了新流训推平台,提升资源调度灵活性,围绕训练与微调场景,新增了物理队列,资源借用与回收,以及资源算力的灵活配置能力,

In products and technology, we continued to upgrade our full-stack AI capabilities for intelligent computing and AI application deployment. This quarter, we further optimized the model deployment on Starflow Max for high concurrency inference, significantly improving throughput for several core models and enabling more granular access usage and model-level management. We also launched Agent Kit, providing secure sandbox knowledge and memory management and and evaluation and governance tools to help enterprises build production-grade AI agents. At the same time, we are making general purpose cloud products, such as database and storage, easier for agents to access and use. We enhance the Starflow training and inference platform with more flexible resource scheduling, sharing, and allocation for training and fine tuning workloads, improving utilization and reducing development and operating costs. For private deployment of domestic AI infrastructure, our Galaxy Stack Platform completed deep integration and full life cycle visual management for multiple mainstream domestic AI chips. Looking ahead, we will continue to capture opportunities both within and outside our ecosystem, improve the operating efficiency of our computing assets, and strengthen our profitability and cash generation capability amid AI industry tailwinds. We remain committed to creating long-term sustainable value for customers, shareholders, and society. With that, I will hand the call over to our CFO, VE, who will review our second quarter financial results.

Thank you, Mr. Cho and Mr. Thien, and thank you all for joining the call today. I will now discuss the second quarter financial results used as big and currency. Before we walk through details of the financial results for the second quarter, I would like to highlight the following aspects. First, our court revenue reached over $3 billion for the first time in our company history, up year over year for the last consecutive quarter. In particular, our AI Cloud Growth building increased at 82% year over year to $1.33 billion, accounting for over 43% of our total revenue errors 31% a year ago. This reflects a continued structural shift in our business mix towards AI.

Now begin the question and answer session. If you wish to ask a question, you will need to 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. We will take our first question. Your first question comes from Li Ping Zhao from CICC.

Please go ahead, your line is open. 请问对公司的mass业务的影响,基于对咱们自己平台的观察,目前用量增长的趋势是怎么样的?主要来自于哪些典型的用力? 然后第二个问题是考虑到整体新柔这个平台它的投资回收周期可能更短,不知道公司是否会向这块业务去倾斜更多的资源。 let me translate by myself so good evening mr. and miss Lee thanks for taking my questions I got two questions on your mass business first how well improvements in open source model capabilities accept the company's mass business based on your observations what's the current usage growth trend and which use cases are driving it most and second given the payback period for the mass business might be shorter? Will the company allocate more resources to it?

Thank you. 存在这种国产模型对海外模型的替代效应啊,所以这是一个对国产模型的需求的增长,另一方面呢,agentic的这个渗透性在提高啊,越来越多的场景在使用agentic了,包括我们很多客户的agent开始上生产了,所以他引出了我们的agentic的产品啊,另一方面呢,在agentic的使用上我们可以看到两种分化的需求,一方面是在做复杂问题解析的时候,可能会倾向于用k3包括-5.3这样的新型模型,但是呢,当他需要去完成日常任务的时候,

In relation to the first question, the development mainly three impacts. Number one is that we're seeing very big demand coming from Vibeco. And traditionally, and K3, this kind of high-performance models, agentic scenarios also brought change to our business. As mentioned in the prepared remarks, the agent kit product to satisfy such a need. And certainly, it is worth mentioning that in terms of day-to-day routine task, the choice usually is essentially the Chinese models. So that is why this development, open-source large-run model, is actually beneficial for our business. And regarding the balance between mass business and the computing power, so we basically have different business models for this two business. For computing power business, essentially, once we sell the computing power, the utilization is by nature 100%. And we usually come with long-term contracts to secureization throughout a prolonged period of time. And therefore, it's relatively the mass business. It is subject to quite a few factors, including the fluctuation of the token price, the launching of new models, which the customers might prefer to use, and also the offer. We generally balance these two business models and hope to have each one of them complement the other one. So we generally dynamically evaluate these two businesses.

Thank you, Tauzong. That's very helpful.

We will take our next question. The next question comes from Wendong Vu from CISA. Please go ahead. Your line is open.

Tauzong,李总,各位管理员,晚上好. Thank you for the opportunity to give me this question. And thank you for the time for the time of the year. I have two questions. The first question is from 6月 to the current year. The first question is, That's since June. How has the chip procurement progressed in recent months? And the second question is about the enterprise cloud. This segment of revenue has decelerated. How should we think about the full-year enterprise AI transformation and medium-term positioning?

The first question I will answer. The supply chain problem is that we are very concerned about every day. I believe that you will be able to see that in three years ago, TIJC this time.

Let me quickly translate. So the answer comes from our SAP Mr. Tiantai Yan. So three points. Number one, actually since 2023, it's been three years, and the market has always been hearing voices about the limited supply. So I would say this is actually a new norm. The such supply difficulty is actually a lot of computing or AI industry development has not been restricted or actually restricted by that. Such situation is that we try to increase the number of business partners that we work with. We try to increase the number of suppliers we work with. And we also work with increasing the compatibility of made-in-China chips. you are all very well, very much aware of the, you know, recently many of the made in China ships are becoming public and they are particularly good in use cases such as model inference. Now, number three, I would like to say that when you look at the CapEx number, from a month to month basis, it is usually quite volatile. And I have to say that the purchasing number, because it's usually a large chunk of money in a relatively small number of purchases. Such as a number, if you look at it by a monthly basis, it's actually not a linear number. So I would say that for our whole year CapEx estimate, it should still be in line with what we have been expecting, and our CFO, the EU, should be able to give you more details.

Thank you.

We will take our next question.

Oh, sorry, we have, we need to continue for another question. Apologies.

So this answer comes from our meeting with Yu Jun. So generally, I don't think, although we're seeing relatively slow growth in the enterprise cloud from the linear, we're speaking of is that the upstream supply of the pricing high so quite significantly in recent fact is actually the SOE companies and also the government agencies to, they have to frequently adjust their budgeting, which delayed their decision-making process so that's number one and number two um you're all quite aware that the technology in enterprise cloud in the delivery and the revenue recognition are concentrated in the second half of every year so we have actually a quite a strong pipeline to deliver in second actually a result of a proactive adjustment of our business um structure uh namely uh proactively from the operating space business model from a financial reporting perspective it's automatically classified into a public cloud. So this is not, you know, simply as it would cease.

Thank you. We will take our next question. Your question comes from Timothy Zell from Goldman Sachs. Please go ahead. Your line is open.

好的. 周总,李总,晚上好. 感谢接受我的提问. 那我这边有两条问题想要请教一下. 第一个可能还是回到我们的这个星流Math的业务. 那想请教一下我们金山云平台相对于市场上面的其他 其他的一些行政对手,我们的定位或者是场景或者竞争优势,具体有哪些,那关于我们的MAS服务的这个收入确认和利润率的情况,可不可以再更仔细的分享一些,这是第一条问题,那第二条问题是关于我们的这个AI云服务的一些价格的变化,那可不可以去关于分享一下我们过去几个月看到的这个AI云服务的这个价格变化的趋势以及行业的价格情况是怎样的。 We also announced some additional additional additional additional additional action. We can see how the customer's feedback is. We can help us to measure the cost-review of the AI-yuen revenue. I'll quickly翻译 it. Thank you, Majin, for taking my question. My first question is regarding the mass. Benesh just wondering, compared to the peers in the market, How do you think about the Kinsale Cloud competitive advantage in the mass services in terms of the application scenarios, et cetera? And could you share more about the revenue recognition and the profitability profile of the mass service business? A second question is regarding the overall pricing trend in the AI cloud business. Just wondering if you can share what is the latest trend over the past couple months and what have you heard from the customers after you announced certain price hikes or discount reduction over the past few months and whether you are able to quantify the impact from the price hike to your overall AI Cloud revenue growth.

Thank you. 第一我们并没有自研的大模型 第二就是我们就是一家云场商 我想咱们在市场上看到的做这个MAS的场景有几种客户 一种是这个做大模型的云场商 比如说像阿里等等包括百度 这种呢是最近可能大家看到像什么规矩流动之类的这个所谓的纯的云端掏根工厂啊 我想我们可能和他们都互相有些区别 首先我们没有自研的大模型 so, at the marketing company, we are usually guiding our sales space to sell the best and to buy the business. And we also need to buy the most fantastic market, like Zdance and almost Minimax H3. In this way, we will look at our customers to buy the best and player, and we will not have to collect a solution in the business. The market is now a re 것은 from the other ways of thinking. This is a question about the percentage.

we have we do have a unique positioning in the mass business. We have to sell the model that our affiliated companies and as a result we're able to actually sell and we actually encourage our sales team to sell the models that our customers so that's so that's number one and you have your proprietary or your own computing power which is the only way that you can actually secure significant profitability. So there are basically two products or solutions that we have our computer computing demands being overall ticket size and therefore in the vast majority of the customers that we negotiated with they are relatively easily accepted actually able to not only in some cases not only pass through the number two in terms of computing power because of our specific capabilities including past capabilities as well as key operating maintenance and network capabilities again we are able to pass through that cost hike into our customers in some of the cases we also increased our profitability and in this quarter we have also some projects which we are doing manage the services thank you we will take the next question your next question comes from

Wei Zhang from UBS, please go ahead, your line is open.

Let's define each other. Good evening, management. Congrats on a solid quarter, and thank you for taking my question. Considering the proprietary models and user ecosystem of other cloud providers, how should we think about our long-term positioning in the cloud market and the sustainable margin level down the road?

I'll answer this question. Actually, we just mentioned that MaaS is a part of the top-down model, and a part of the model is a good model. 啊那么作为一个中立厂商呢我们始终会和各家原厂保持良好的关系并且呢在第一时间推出这些模型的线上服务并且呢啊我们自有的资源和原厂的资源组合在一起呢能够给客户提供一个高可用高可靠的这个云上的曼徒啊那我想这一点来说就是我们在这方面是没有任何就是所谓的心理负担的啊 On the other hand, we can see that from our users' resources and resources, in the case of SRV, we can provide more and more information on SRV. This is a great support for a large scale of the market. From the next perspective, we will be able to collaborate with the design of SRV, So we believe that's enough for our crowd air

cloud service provider is important to be able to offer the top models which the customers like and also stable services to our customers so as mentioned as a neutral cloud player we are able to be in good relations with all of the top model providers large language model labs and be able to provide the best model of you based on our technology capabilities we're able to provide a highly available and highly refined. Thirdly, in relation to the profitability question, for example, to optimize of the models to increase our model inference, which is we're able to get to a very close level or even reach the same level of the inference from the LLM companies.

Thank you. We will take our final question. And your final question comes from Ying Liu from Morgan Stanley. Please go ahead. Your line is open.

Let me translate my question. I would like to ask under the two business models, Computing Power Leasing and Model as a Service, what is ROIC for these two business models, and what is the marginal change for the ROIC? Thank you.

Thank you, Lu Yang. At this stage, we don't separate RIC on mass and AI computing power services. Because RIC varies across projects, driven by feedback sectors, force margin, fixed access, and depreciation policies. Overall, mass be worth much better probability than AI computing power services at this stage. We have seen continued improvement in operating leverage as our AI business scales up, fixed costs are steadily diluted, and our trillion-trend-months adjusted operating profit has turned positive to be a gradual recovery in our overall RIC. We adhere to a demand-driven and disciplined AI investment strategy with a strong focus on capital efficiency. With the continuous business structure optimization and the material AI optimization, I think our overall LSE will keep improving the status.

Okay.

Thank you. There are no further questions. Apologies to conclude the question and answer session. I will hand back for closing remarks.

Thank you all for joining us today. if you have any further questions please contact our IR team so have a good evening you may now disconnect thank you this concludes today's conference call thank you for participating you may now disconnect