Confluent Inc (CFLT) 2025 Q3 法說會逐字稿

Welcome to the Confluent third quarter 2025 earnings conference call. I'm Shane Xie from Investor Relations, and I'm joined by Jay Kreps, Co-Founder and CEO; and Rohan Sivaram, CFO.

歡迎參加 Confluent 2025 年第三季財報電話會議。我是投資者關係部的 Shane Xie，與我一起參加會議的還有共同創辦人兼執行長 Jay Kreps 和財務長 Rohan Sivaram。

During today's call, management will make forward-looking statements regarding our business, operations, market and product positioning, growth strategies, financial performance and future prospects, including statements regarding our financial guidance for the fiscal fourth quarter of 2025 and fiscal year 2025. These forward-looking statements are subject to risks and uncertainties, which could cause actual results to differ materially from those anticipated by these statements.

在今天的電話會議中，管理層將就我們的業務、營運、市場和產品定位、成長策略、財務業績和未來前景做出前瞻性陳述，包括有關 2025 財年第四季和 2025 財年財務指引的陳述。這些前瞻性陳述受風險和不確定性的影響，可能導致實際結果與這些陳述預期的結果有重大差異。

Further information on risk factors that could cause actual results to differ is included in our most recent Form 10-Q filed with the SEC. We assume no obligation to update these statements after today's call, except as required by law. Unless stated otherwise, certain financial measures used on today's call are expressed on a non-GAAP basis and all comparisons are made on a year-over-year basis. We use these non-GAAP financial measures internally, the facility analysis of our financial and business trends and for internal planning and forecasting purposes.

有關可能導致實際結果不同的風險因素的更多信息，請參閱我們向美國證券交易委員會提交的最新 10-Q 表。除非法律要求，否則我們不承擔在今天的電話會議後更新這些聲明的義務。除非另有說明，今天電話會議上使用的某些財務指標均以非公認會計準則 (GAAP) 為基礎表示，並且所有比較均按同比進行。我們在內部使用這些非公認會計準則財務指標，以分析我們的財務和業務趨勢並用於內部規劃和預測目的。

These non-GAAP financial measures have limitations and should not be considered in isolation from or as a substitute for financial information prepared in accordance with GAAP. A reconciliation between these GAAP and non-GAAP financial measures is included in our earnings press release and supplemental financials, which can be found on our IR website at investor.confluent.io. References to profitability on today's call refer to non-GAAP operating margin, unless data otherwise.

這些非 GAAP 財務指標具有局限性，不應孤立地考慮或取代根據 GAAP 編制的財務資訊。這些 GAAP 和非 GAAP 財務指標的對帳表已包含在我們的收益新聞稿和補充財務報表中，您可以在我們的投資者關係網站 investor.confluent.io 上找到。除非另有數據，否則今日電話會議中提到的獲利能力均指非 GAAP 營業利潤率。

And with that, I'll hand the call over to Jay.

說完這些，我會把電話交給傑伊。

Thanks Shane. Good afternoon, everyone, and welcome to our third quarter earnings call. We're joining from New Orleans, where in 2 days, we'll host current, the data streaming event where real-time data and AI come together.

謝謝 Shane。大家下午好，歡迎參加我們第三季的財報電話會議。我們從新奧爾良趕來，兩天后，我們將在這裡舉辦當前的數據流活動，即時數據和人工智慧將在這裡融合。

Turning to the quarterly results. We delivered a strong Q3, exceeding the high end of all guided metrics. Q3 subscription revenue grew 19% to $286 million. Confluent Cloud revenue grew 24% to $161 million, and non-GAAP operating margin expanded 3 percentage points to approximately 10%. This performance underscores strong consumption growth in our cloud business, the deepening commitment of our customers and our disciplined focus on driving efficient, sustainable growth.

談到季度業績。我們第三季表現強勁，超過了所有指導指標的上限。第三季訂閱營收成長 19%，達到 2.86 億美元。Confluent Cloud 營收成長 24% 至 1.61 億美元，非 GAAP 營業利潤率擴大 3 個百分點至約 10%。這項業績凸顯了我們雲端業務的強勁消費成長、客戶日益加深的承諾以及我們對推動高效、永續成長的專注。

Last quarter, we outlined two areas of focus in our go-to-market in several areas where we were drilling down on early success, all aimed at accelerating use case expansions and supporting the long-term growth trajectory of our cloud business. I'll give a brief update on each of these.

上個季度，我們在幾個深入研究早期成功的領域中概述了進入市場的兩個重點領域，所有這些都旨在加速用例擴展並支援我們的雲端業務的長期成長軌跡。我將對每個問題進行簡要更新。

The first area of focus was tightening field alignment to drive more use cases into production. As we shared last quarter, we saw strong momentum in late-stage pipeline progression, a metric that tracks the dollar value of new use cases moving into production. That momentum continued in Q3 with more than 40% sequential growth and progressing late-stage pipeline and an accelerating pace of new use cases.

第一個重點領域是加強現場協調，以推動更多用例投入生產。正如我們上個季度所分享的，我們看到後期管道進展勢頭強勁，這一指標用於追蹤投入生產的新用例的美元價值。這一動能在第三季得以延續，季增超過 40%，後期研發管線不斷推進，新用例的步伐也正在加快。

This positions us for durable consumption growth and was a key driver of our cloud performance this quarter. In parallel, we continue to build momentum in expanding our large customer base, delivering the largest sequential net add in $100,000-plus ARR customer count in the past 2-years, along with continued acceleration in $1 million-plus ARR customer growth.

這為我們持久消費的成長奠定了基礎，也是我們本季雲端運算業績的關鍵驅動力。同時，我們繼續大力擴大龐大的客戶群，在過去兩年中實現了 10 萬美元以上 ARR 客戶數量的最大連續淨增，同時 100 萬美元以上 ARR 客戶數量的增長也持續加速。

Together, these results underscore the depth of opportunity within new workloads and the continued strength of expansion among our large customers who are increasingly standardizing on our data streaming platform and relying on Confluent to meet their business needs.

總而言之，這些結果凸顯了新工作負載中蘊藏的機會深度以及我們大客戶的持續擴張實力，這些客戶越來越多地在我們的資料流平台上實現標準化，並依靠 Confluent 來滿足他們的業務需求。

Our second focus area is centered on accelerating the build-out of our DSP specialist team to drive multiproduct selling. We previously highlighted Flink momentum in the first half of the year, and we're pleased to report another strong quarter with Q3 Flink ARR for Confluent Cloud growing more than 70% sequentially. Flink usage has continued to expand across our customer base.

我們的第二個重點領域是加速建立我們的 DSP 專家團隊，以推動多產品銷售。我們之前強調了 Flink 在上半年的強勁發展勢頭，我們很高興地報告本季又表現強勁，Confluent Cloud 第三季度的 Flink ARR 環比增長超過 70%。Flink 的使用在我們的客戶群中持續擴大。

More than 1,000 customers used Flink during the quarter. Stream processing is key as it enables companies to act on data at the moment it's created, turning information into real-time decisions and results. A great example of the power of our Flink offering is Siemens Healthineers, a global leader in medical technology with operations in more than 70 countries. The company develops imaging systems, lab diagnostics and connected medical devices used by hospitals and clinics around the world.

本季有超過 1,000 名客戶使用了 Flink。流處理是關鍵，因為它使公司能夠在資料創建時採取行動，將資訊轉化為即時決策和結果。西門子醫療公司 (Siemens Healthineers) 是我們 Flink 產品強大功能的一個很好的例子，該公司是醫療技術領域的全球領導者，業務遍及 70 多個國家。該公司開發世界各地醫院和診所使用的影像系統、實驗室診斷和連網醫療設備。

Behind these life-saving technologies is a constant stream of data that determines equipment reliability, accuracy and ultimately, patient outcomes. But Siemens Healthineers was hindered by disconnected systems that isolated critical data in sightless, lengthy file transfers, manual handling and periodic batch processing often delayed insights by weeks.

這些救生技術背後是源源不絕的數據，它決定了設備的可靠性、準確性以及最終患者的治療效果。但西門子醫療受到系統斷開連接的阻礙，這些系統將關鍵數據隔離在不可見的、冗長的文件傳輸、手動處理和定期批處理中，這常常導致洞察延遲數週。

These delays prevented timely action to improve equipment performance and product quality. So they turn to Confluent Cloud with fully managed Flink. With Confluent, Siemens Healthineers built a unified real-time data backbone that streams and processes millions of events from imaging, lab and devices daily.

這些延誤阻礙了及時採取行動來改善設備性能和產品品質。因此他們轉向具有完全託管 Flink 的 Confluent Cloud。透過 Confluent，西門子醫療建構了統一的即時數據主幹網，每天傳輸和處理來自影像、實驗室和設備的數百萬個事件。

Flink continuously filters joins and enriches these streams to deliver timely, trustworthy operational insights that help improve device reliability, manufacturing, quality and consistency of diagnostic data across its installed base. This foundation now gives Siemens Healthineers real-time visibility and the ability to move faster as it advances digital and AI initiatives that enhance care delivery and improve patient outcomes worldwide.

Flink 不斷過濾連接並豐富這些串流，以提供及時、可靠的營運見解，幫助提高設備可靠性、製造、品質以及整個安裝基礎上的診斷數據的一致性。該基礎現在為西門子醫療提供了即時可視性和更快行動的能力，因為它推進了數位和人工智慧計劃，從而增強了醫療服務並改善了全球患者的治療效果。

Next, our partner ecosystem continues to deliver strong results. Out of Q3, partners sourced well over 25% of our new business over the last 12-months. This is a clear sign of the consistency and scale we're building through our established partner relationships, which are instrumental in broadening our footprint and driving customer expansion.

接下來，我們的合作夥伴生態系統持續取得強勁成果。在第三季中，合作夥伴在過去 12 個月中為我們帶來了超過 25% 的新業務。這清楚地表明了我們透過已建立的合作夥伴關係所建立的一致性和規模，這對於擴大我們的業務範圍和推動客戶擴張至關重要。

Confident was named a MongoDB Partner of the Year and served as an AWS launch partner for the new AI agents and tools category in the AWS marketplace, further strengthening our position at the center of real-time data and AI. Lastly, we remain as competitive as ever replacing CSP streaming offerings.

Confident 被評為年度 MongoDB 合作夥伴，並成為 AWS 市場中新 AI 代理和工具類別的 AWS 發布合作夥伴，進一步鞏固了我們在即時資料和 AI 中心的地位。最後，我們在取代 CSP 串流媒體產品方面仍保持著一如既往的競爭力。

We have maintained a win rates well above 90%, with average deal size more than doubling over the past two quarters, all while continuing to increase our at bats. This is made possible with multi-tenant print clusters, enterprise clusters and workstream, which together have delivered a 4 times increase in consumption over the past three quarters.

我們的勝率一直維持在 90% 以上，過去兩季平均交易規模增加了一倍多，同時我們的交易量也不斷增加。這是透過多租戶列印集群、企業集群和工作流程實現的，它們在過去三個季度中共同實現了消費量增長了 4 倍。

Because of their multi-tenant architecture, we believe adoption of these new clusters is a tailwind to subscription gross margin over time. These differentiated offerings provide superior performance and lower TCO to our customers which also helps us soak up more of the world's Kafka workloads. This includes one of the world's largest fintech companies who signed a 7-figure deal in Q3 to move their large-scale logging and telemetry workloads from open source Kafka to conclude.

由於其多租戶架構，我們相信採用這些新集群將隨著時間的推移對訂閱毛利率產生推動作用。這些差異化的產品為我們的客戶提供了卓越的效能和更低的 TCO，這也有助於我們承擔更多的全球 Kafka 工作負載。其中包括全球最大的金融科技公司之一，該公司在第三季度簽署了一項 7 位數的協議，將其大規模日誌記錄和遙測工作負載從開源 Kafka 轉移到最終版本。

Another great example of this is Evo Banco, a digital native bank in Spain serving hundreds of thousands of customers through its mobile-first platform. As transaction volume grew, its open-source Kafka clusters became increasingly default to scale and secure with rising operational costs and downtime during peak loads. To address this, Evo Banco migrated to Confluent Cloud as its central data backbone.

另一個很好的例子是 Evo Banco，這是一家西班牙的數位原生銀行，透過其行動優先平台為數十萬客戶提供服務。隨著交易量的成長，其開源 Kafka 叢集的可擴展性和安全性越來越受到重視，營運成本不斷上升，高峰負載期間的停機時間也不斷增加。為了解決這個問題，Evo Banco 遷移到 Confluent Cloud 作為其中央資料主幹。

The platform now streams and processes hundreds of thousands of financial events per day across payments, fraud detection and customer channels and with stream processing and fully managed connectors, Evo Banco integrated core banking systems and analytics tools in real time without managing infrastructure. Since moving to complement, the bank has improved reliability, lowered costs and accelerated delivery of new banking features.

該平台現在每天在支付、詐欺偵測和客戶管道中傳輸和處理數十萬個金融事件，並且透過串流處理和完全託管的連接器，Evo Banco 即時整合核心銀行系統和分析工具，而無需管理基礎設施。自轉向補充以來，該銀行提高了可靠性，降低了成本，並加快了新銀行功能的交付。

Q3 also marked the 1-year anniversary of our WarpStream acquisition. Over the past year, WarpStream has seen growth in consumption, and we've closed multiple 6-figure deals with marquee customers across different industries, including a Fortune five customer. We're encouraged by WarpStream's strong first year performance and remain incredibly excited about the significant opportunity at.

第三季也是我們收購 WarpStream 一週年。在過去的一年裡，WarpStream 的消費量有所增長，我們與不同行業的知名客戶達成了多筆六位數的交易，其中包括財富五強客戶。WarpStream 第一年的強勁表現令我們感到鼓舞，對其重大機會感到非常興奮。

Next, I want to spend a few minutes on a key aspect of Confluent opportunity in the AI space, providing context data for AI agents and applications. we're seeing a clear pattern across the industry. Many companies have shown they can successfully prototype AI, but fewer can get those systems into production. AI models are clearly capable, but a recent MIT study found that though enterprises are investing tens of billions of dollars in generative AI. Most of these initiatives haven't delivered the desired results.

接下來，我想花幾分鐘時間討論 Confluent 在人工智慧領域的一個關鍵機會，即為人工智慧代理和應用程式提供上下文資料。我們看到整個行業呈現出一種清晰的模式。許多公司已經證明他們可以成功製作人工智慧原型，但很少有公司能夠將這些系統投入生產。人工智慧模型顯然功能強大，但麻省理工學院最近的一項研究發現，儘管企業在生成人工智慧方面投入了數百億美元。大多數舉措都沒有取得預期的效果。

The challenge is it building a prototype. It's being able to build reliable business systems powered by AI that makes trustworthy decisions and takes appropriate actions. There are two factors that fundamentally drive the quality in AI systems, the models capabilities and the data it has access to. Both of these are significant challenges, but they fall on different people to solve. Improving the quality of large-scale AI models is a challenge largely driven by a small number of LLM producing research labs.

挑戰在於建立原型。它能夠建立由人工智慧驅動的可靠業務系統，做出可信的決策並採取適當的行動。有兩個因素從根本上決定了人工智慧系統的質量，即模型的能力和它可以存取的數據。這兩個都是重大挑戰，但需要不同的人來解決。提高大規模人工智慧模型的品質是一項挑戰，主要由少數 LLM 生產研究實驗室推動。

Enterprises can easily harness the results of this work by simply pointing their apps at a new model. But getting data into shape to act as context for AI is a problem every enterprise must solve with their own data. This is where Confluent can help. One of the reasons AI demos are often so successful is because they can be powered by a onetime manually curated data set. But to take an agent to production, it must have an up-to-date comprehensive view of all the inputs needed to do its work.

企業只需將其應用程式指向新模型即可輕鬆利用這項工作的成果。但是，將資料整理成適合人工智慧的背景是每個企業都必須利用自己的資料解決的問題。這正是 Confluent 可以提供幫助的地方。AI 演示之所以如此成功，其中一個原因在於它們可以由一次性手動策劃的資料集提供支援。但是，要讓代理商投入生產，它必須對完成工作所需的所有輸入有一個最新的全面了解。

This isn't just a matter of trying to hook the model into every source system directly. The source data is generally too messy and application specific to lead to good results. And AI Ops can't be splunking around in production databases, reading through everything and potentially leaking the wrong data to the wrong user, that would be wildly expensive, create unsustainable production workloads and be fundamentally insecure.

這不僅僅是嘗試將模型直接掛接到每個來源系統的問題。來源資料通常過於混亂且特定於應用程序，因此無法產生良好的結果。而且，AI Ops 不能在生產資料庫中閒逛，讀取所有內容並可能將錯誤的資料洩露給錯誤的用戶，那會非常昂貴，造成不可持續的生產工作負載，並且從根本上是不安全的。

Rather, the problem is about curating the right data for a given problem and creating a data set an agent can be tested with and evaluated against. Maintaining that live context is what determines how well an AI system performs.

相反，問題在於針對給定的問題整理正確的資料並建立代理可以進行測試和評估的資料集。維持即時環境決定了人工智慧系統的效能。

That's where accuracy, relevance and trust are won or lost. What businesses need is a system that can keep data in motion so it can be processed, reprocessed and served continuously as it changes. Our data streaming platform was built for exactly this problem. It works to connect data from every system application and cloud and support just these kinds of complex pipelines. With Kafka, Flink and Tableflow, teams can process in real time, combining history and live events with one unified engine.

這就是準確性、相關性和信任的得失所在。企業需要的是一個能夠維持資料流動的系統，以便資料在變更時能夠持續地被處理、重新處理和服務。我們的資料流平台正是為解決這個問題而建立的。它可以連接來自每個系統應用程式和雲端的數據，並支援這些複雜的管道。借助 Kafka、Flink 和 Tableflow，團隊可以即時處理，將歷史記錄和即時事件與統一的引擎結合。

With logic changes, you can go back and reprocess data to create the new data set. Tableflow and Flink work to combine the best aspects of real-time capabilities with the long-term historical store of data in the lake. As this goes out to production, the stream of feedback data can also be captured to measure the effectiveness of each change.

透過邏輯改變，您可以返回並重新處理資料以建立新的資料集。Tableflow 和 Flink 致力於將即時功能的最佳方面與湖中資料的長期歷史儲存相結合。隨著投入生產，還可以捕捉回饋資料流來衡量每個變化的有效性。

And in 2 days, we will host current and unveil new capabilities that are designed to make this even easier for customers and strengthen how our platform delivers real-time government context. Compliments data streaming platform is becoming the context layer for enterprise AI as businesses move from AI experimentation to production from static data to living context and from analysis to intelligent action.

兩天后，我們將推出新功能，旨在讓客戶更輕鬆地使用此功能，並加強我們的平台提供即時政府資訊的能力。隨著企業從人工智慧實驗轉向生產，從靜態數據轉向生活環境，從分析轉向智慧行動，讚美資料流平台正在成為企業人工智慧的環境層。

One customer that really illustrates this is a multibillion-dollar health and fitness chain with nearly 200 clubs in a rapidly growing digital platform. as the company expanded into AI-powered wellness, its data from wearables, class bookings and mobile apps was siloed and processed in slow batches. This made it impossible to provide real-time personalized guidance through its Gen AI companion.

真正能說明這一點的客戶是一家價值數十億美元的健康和健身連鎖店，在快速增長的數位平台上擁有近 200 傢俱樂部。隨著該公司擴展到人工智慧健康領域，其來自穿戴式裝置、課程預訂和行動應用程式的數據被孤立並以緩慢的批量處理。這使得透過其 Gen AI 伴侶提供即時個人化指導變得不可能。

With Confluent Cloud as a streaming backbone, this customer now continuously ingests and enriches this data in motion. Wearable metrics, work out history, purchase activity and engagement events are streamed and combined with contextual data, like recovery status or performance trends before being routed into AI systems to fuel personalized recommendations.

借助 Confluent Cloud 作為串流媒體主幹，該客戶現在可以持續攝取和豐富這些動態資料。穿戴式指標、鍛鍊歷史、購買活動和參與事件被串流並與上下文資料（如恢復狀態或效能趨勢）相結合，然後被傳送到人工智慧系統以提供個人化推薦。

Confidence enables them to deliver AI insights in seconds instead of ours, scaling to millions of real-time interactions while enabling security and compliance. Fully managed infrastructure frees engineers to focus on innovation, helping the company turn decades of wellness expertise into intelligent context to where experiences that deepen member engagement and fuel digital growth.

信心使他們能夠在幾秒鐘內而不是我們身上提供人工智慧洞察，擴展到數百萬個即時交互，同時實現安全性和合規性。完全託管的基礎設施使工程師能夠專注於創新，幫助公司將數十年的健康專業知識轉化為智慧環境，從而加深會員參與度並推動數位成長的體驗。

As AI evolves from innovation to utilization, context will define who wins, and we are committed to making Confluent the company enabling the ships by turning data and to continuously refresh trustworthy context for AI systems everywhere.

隨著人工智慧從創新發展到應用，環境將決定誰將獲勝，我們致力於使 Confluent 成為一家透過轉化數據賦能船舶的公司，並不斷為各地的人工智慧系統更新值得信賴的環境。

In closing, we're encouraged by the strong out consumption growth and the traction we're seeing for our complete data streaming platform, particularly with -- as AI becomes operational across every industry and geography we believe that the demand for real-time context powered by data streaming will only grow. It's an exciting time for Confluent and we're just getting started.

最後，我們對強勁的消費成長和我們完整的數據流平台的吸引力感到鼓舞，特別是隨著人工智慧在各個行業和地區的運作，我們相信對由數據流驅動的即時環境的需求只會增長。對於 Confluent 來說，這是一個激動人心的時刻，而我們才剛剛開始。

With that, I'll turn it over to Rohan.

說完這些，我就把它交給羅漢。

Thanks, Jay. Good afternoon, everyone, and thank you for joining our earnings call. Our strong third quarter performance highlights the momentum of our data streaming platform and our diversified growth strategy. We delivered strong top line growth, stabilized our net retention rate, increase the adoption of new products and drove continued margin expansion. These results demonstrate our ability to drive durable, profitable growth at scale over the long term.

謝謝，傑伊。大家下午好，感謝大家參加我們的財報電話會議。我們強勁的第三季業績凸顯了我們的數據流平台和多元化成長策略的勢頭。我們實現了強勁的營收成長，穩定了淨留存率，增加了新產品的採用，並推動了利潤率的持續擴大。這些結果證明了我們有能力長期推動大規模、持久的獲利成長。

Turning to the results. Q3 subscription revenue grew 19% to $286.3 million and represented 96% of total revenue. Conflict platform revenue grew 14% to $125.4 million, driven by healthy demand in financial services. Cloud revenue grew 24% to $161 million, representing 56% of subscription revenue compared to 54% in the year ago quarter. We are pleased with our cloud performance this quarter, which was driven by stronger consumption across core streaming and DSP, including acceleration of new use cases moving into production.

轉向結果。第三季訂閱營收成長 19%，達到 2.863 億美元，佔總營收的 96%。受金融服務需求旺盛的推動，衝突平台營收成長 14%，達到 1.254 億美元。雲端營收成長 24% 至 1.61 億美元，佔訂閱收入的 56%，去年同期為 54%。我們對本季的雲端運算表現感到滿意，這得益於核心串流媒體和 DSP 的強勁消費，以及新用例投入生產的加速。

Turning to the geographical mix of total revenue. Revenue from the US grew 13% to $172.1 million. Revenue from outside the US grew 29% to $126.4 million.

轉向總收入的地理分佈。來自美國的營收成長 13%，達到 1.721 億美元。美國以外的營收成長 29%，達到 1.264 億美元。

Moving on to rest of the income statement. I'll be referring to non-GAAP results unless otherwise stated. While driving top line grow at scale, we continue to show significant operating leverage in our model. In Q3, subscription gross margin was 81.8%, above our long-term target threshold of 80%. Operating margin increased 340 basis points to a record of 9.7%.

繼續討論損益表的其餘部分。除非另有說明，我將參考非 GAAP 結果。在推動營業額大規模成長的同時，我們的模型繼續顯示出顯著的經營槓桿。第三季度，訂閱毛利率為 81.8%，高於我們 80% 的長期目標門檻。營業利益率增加 340 個基點，達到創紀錄的 9.7%。

The exceeding our guidance by 270 basis points. This was driven by revenue outperformance and improved sales and marketing leverage from continuing to streamline coverage to drive growth. Adjusted free cash flow margin increased 450 basis points to 8.2%. Net income per share was $0.13, using $370.6 million diluted weighted average outstanding Fully diluted share count under treasury stock method was approximately $382.4 million. We ended the third quarter with $1.99 billion in cash, cash equivalents and marketable securities, reflecting the strength of our balance sheet.

超出我們的預期 270 個基點。這是由於收入表現優異以及透過持續精簡覆蓋範圍來推動成長而提高的銷售和行銷槓桿作用所致。調整後的自由現金流利潤率增加 450 個基點至 8.2%。每股淨收益為 0.13 美元，採用 3.706 億美元的稀釋加權平均流通股數，庫存股法下的完全稀釋股數約為 3.824 億美元。截至第三季末，我們的現金、現金等價物和有價證券總額為 19.9 億美元，這反映了我們資產負債表的強勁。

Turning now to customer metrics. $20,000-plus ARR customer count increased to 2,533, up 36 customers sequentially. $100,000-plus ARR customer count was 1,487, up 48 customers quarter-over-quarter, representing the largest sequential increase in 2-years. New $100,000-plus ARR customers include many leading AI companies such as Forbes 50 AI analytics provider, an AI-powered SIM cyber security vendor, a next-gen AI automation platform company. Our $100,000-plus ARR customers continue to account for more than 90% of our ARR.

現在來看看客戶指標。 20,000 美元以上的 ARR 客戶數量增加到 2,533 人，比上一季增加了 36 人。 10 萬美元以上的 ARR 客戶數量為 1,487 人，比上一季增加了 48 人，是兩年來最大的連續增幅。新的 10 萬美元以上的 ARR 客戶包括許多領先的 AI 公司，例如福布斯 50 強 AI 分析提供商、AI 驅動的 SIM 網路安全供應商、下一代 AI 自動化平台公司。我們的 100,000 美元以上的 ARR 客戶繼續占我們 ARR 的 90% 以上。

$1 million-plus ARR customer count increased to 234, representing growth acceleration of 27%, driven by new use case expansion across cloud and platform. Additionally, more than 10 of the 15 net new $1 million-plus ARR customers increased their spend on DSP products over the previous quarter. NRR for the quarter stabilized at 114%, while GRR remained close to 90%, driven by stronger consumption growth in our cloud business.

受雲端和平台新用例擴充的推動，年平均收入 (ARR) 超過 100 萬美元的客戶數量增加到 234 家，成長率為 27%。此外，在 15 個新增淨收入超過 100 萬美元的 ARR 客戶中，有超過 10 個客戶在上一季增加了對 DSP 產品的支出。受雲端業務消費成長強勁的推動，本季 NRR 穩定在 114%，而 GRR 仍維持在接近 90%。

Turning to our outlook. For the fiscal fourth quarter of 2025, we expect subscription revenue to be in the range of $295.5 million to $296.5 million, representing growth of approximately 18%. Non-GAAP operating margin to be approximately 7% and non-GAAP net income per diluted share to be in the range of $0.09 to $0.10.

轉向我們的展望。對於 2025 財年第四季度，我們預計訂閱營收將在 2.955 億美元至 2.965 億美元之間，成長約 18%。非公認會計準則營業利潤率約為 7%，非公認會計準則每股攤薄淨收益在 0.09 美元至 0.10 美元之間。

For fiscal year 2025, we expect subscription revenue to be in the range of $1.1135 billion to $1.1145 billion, representing growth of approximately 21%. Non-GAAP operating margin to be approximately 7%, non-GAAP net income per diluted share to be in the range of $0.39 to $0.40 and adjusted free cash flow margin to be approximately 6%.

對於 2025 財年，我們預計訂閱收入將在 11.135 億美元至 11.145 億美元之間，成長約 21%。非公認會計準則營業利潤率約為 7%，非公認會計準則每股攤薄淨收益在 0.39 美元至 0.40 美元之間，調整後的自由現金流利潤率約為 6%。

For modeling purposes, we expect Q4 cloud revenue to be approximately $165 million, representing growth of approximately 20% and accounting for approximately 56% of subscription revenue based on the midpoint of our guide.

出於建模目的，我們預計第四季度雲端收入約為 1.65 億美元，成長約 20%，占我們指南中點的訂閱收入的約 56%。

Turning to the key drivers of our business. We saw strong demand in our core streaming business and good momentum across DSP, AI and our partner ecosystem. First, our continued focus on field alignment is delivering strong results. In Q3, we accelerated the pace of moving new use cases into production and sustained strong momentum in building our late-stage pipeline, which once again grew more than 40% sequentially. We're also seeing customers commit to larger and longer-term deals, reflected in RPO growth of 43%, another quarter of acceleration.

轉向我們業務的關鍵驅動因素。我們看到核心串流媒體業務的需求強勁，DSP、AI 和合作夥伴生態系統的發展勢頭良好。首先，我們持續專注於領域協調，並且取得了顯著成果。在第三季度，我們加快了將新用例投入生產的步伐，並在後期產品線建設方面保持了強勁勢頭，該產品線環比增長再次超過 40%。我們也看到客戶承諾進行更大、更長期的交易，這反映在 RPO 成長了 43%，又一個季度加速。

Together, these trends give us greater visibility into near-term consumption revenue and increase longer-term visibility with improved ARPU to revenue coverage. Second, we saw good DSP momentum across cloud and on-prem in Q3. Building on the momentum from the first half of the year, we delivered another quarter of strong performance for Flink with particular strength in cloud.

綜合起來，這些趨勢使我們對近期消費收入有了更清晰的了解，並透過提高 ARPU 與收入覆蓋率來提高長期可見度。其次，我們看到第三季雲端和本地的 DSP 勢頭良好。在上半年勢頭的基礎上，Flink 又一個季度取得了強勁表現，尤其是在雲端運算領域。

Q3 Flink ARR for Confluent Cloud grew more than 70% sequentially, and we now have more than 1,000 Flink customers, including more than a dozen customers with greater than $100,000 in Flink ARR and four customers with greater than 1 million in Flink ARR. This comprehensive breadth and depth represents the foundation for scaling into a very significant Flink market opportunity ahead.

Confluent Cloud 第三季的 Flink ARR 環比成長超過 70%，現在我們擁有超過 1,000 個 Flink 客戶，其中包括十幾個 Flink ARR 超過 100,000 美元的客戶和四個 Flink ARR 超過 100 萬美元的客戶。這種全面的廣度和深度為未來擴展到非常重要的 Flink 市場機會奠定了基礎。

Here are two customer examples to illustrate how Flink begins to drive ARR expansion in our customer base. These customers are spending currently north of $100,000-plus and $1 million-plus Flink ARR, respectively. Notably, in the last year alone, adoption of Flink has supported both customers to more than 6x total spend.

這裡有兩個客戶範例，用於說明 Flink 如何開始推動我們客戶群中的 ARR 擴展。這些客戶目前在 Flink ARR 上的支出分別超過 10 萬美元和 100 萬美元。值得注意的是，光是去年，Flink 的採用就使這兩家客戶的總支出增加了 6 倍以上。

Third, we are strongly positioned to deliver contextualized, well-governed and AI-ready data to companies. We now have more than 100 AI native customers, including 21 with $100,000-plus in ARR demonstrating confluents highly strategic role in the age of AI.

第三，我們有能力提供企業情境化、管理良好且支援人工智慧的數據。我們目前擁有 100 多家 AI 原生客戶，其中 21 家 ARR 超過 10 萬美元，展現了匯合在 AI 時代的高度策略角色。

Fourth, we are pleased with seeing continued traction in our partner ecosystem. On a trailing 12-month basis, Q3 partners sourced deals increased more than 25% of our new business, up from more than 20% last quarter. As we grow beyond the $1 billion-plus revenue scale, we expect partners to play an even bigger role in driving growth and leverage in our business in the years ahead.

第四，我們很高興看到我們的合作夥伴生態系統持續受到關注。在過去 12 個月中，第三季合作夥伴達成的交易使我們的新業務增加了 25% 以上，高於上一季的 20% 以上。隨著我們的收入規模超過 10 億美元，我們期望合作夥伴在未來幾年在推動我們業務成長和影響力方面發揮更大的作用。

Lastly, we've continued to demonstrate the effectiveness of our disciplined ROI-driven capital allocation strategy, especially in M&A. Q3 marked the 1-year anniversary of our WarpStream acquisition. And in just 1-year, WarpStream's consumption has grown nearly eightfold. Following the Immerock acquisition, we shipped our Flink product in spring of last year. And since then, we've scaled Flink into a low 8-figure ARR business.

最後，我們繼續證明了我們嚴格的投資回報率驅動的資本配置策略的有效性，特別是在併購方面。第三季是我們收購 WarpStream 一週年。而在短短一年的時間裡，WarpStream 的消費量增加了近八倍。收購 Immerock 之後，我們在去年春季推出了 Flink 產品。從那時起，我們已將 Flink 擴展為 8 位數 ARR 業務。

The strong financial performance underscores the successful path both products around and reinforces the strength of our overall capital allocation strategy. In closing, we delivered strong third quarter results demonstrating durable top line growth and margin expansion at scale.

強勁的財務表現凸顯了產品的成功之路，並增強了我們整體資本配置策略的實力。最後，我們取得了強勁的第三季業績，證明了持久的營收成長和利潤率的擴大。

We are encouraged by the strong consumption growth in our cloud business and remain focused on continuing to execute on our key growth drivers across core streaming, DSP and AI and the partner ecosystem. Looking forward, we believe we are well positioned to take advantage of the large market opportunity ahead.

我們對雲端業務的強勁消費成長感到鼓舞，並將繼續專注於繼續執行核心串流媒體、DSP 和 AI 以及合作夥伴生態系統中的關鍵成長動力。展望未來，我們相信我們已做好準備，抓住未來巨大的市場機會。

Now Jay and I will take your questions.

現在我和傑伊將回答大家的提問。

All right. Thanks, Rohan. (Operator Instructions)

好的。謝謝，羅漢。（操作員指示）

Brad Zelnick Zone, Deutsche Bank.

布拉德·澤爾尼克·區，德意志銀行。

Great. Thanks so much and good to see the good results, especially the accelerated bookings really impressive. Jay, I want to follow back on some of the go-to-market changes that you made last quarter. The field alignment changes in coverage ratios. And it's great to see the momentum in late-stage pipeline continue.

偉大的。非常感謝，很高興看到這樣的好結果，尤其是加速預訂確實令人印象深刻。傑伊，我想回顧一下你上個季度所做的一些市場變革。字段排列的覆蓋率發生變化。很高興看到後期管道的勢頭持續下去。

What are the learnings now that we're another quarter into these changes? And what conversion trends can you share on all this new pipe? And how should we think about the capacity to effectively work that much incremental pipeline?

現在我們已經經歷了一個季度的變革，我們得到了什麼教訓？您可以分享關於這些新管道的哪些轉換趨勢嗎？我們應該如何考慮有效運作如此多增量管道的能力？

Yeah, those are great questions. So yes, we put a number of things in motion heading into this year. And particularly over the last few quarters, I called out some of those the specialization model for DSP. That's really important just to be able to take these new products at scale, and it's working really well. A number of aspects of just kind of field execution around consumption.

是的，這些都是很好的問題。是的，我們在今年啟動了許多工作。特別是在過去幾個季度，我提出了一些 DSP 專業化模型。這對於大規模推出這些新產品非常重要，而且效果非常好。圍繞消費的現場執行有多種方面。

I think that's one of the biggest drivers of that kind of progression in consumption pipeline. And on that pipeline, I think we have very high confidence in it. These are ultimately customer workloads that they have people building that are reaching production that then go drive consumption in the quarters ahead. And so it's a little bit more than just an entry in sales force. And that's why we feel that it's a very promising stat and why we track it very religiously quarter-to-quarter.

我認為這是消費通路進步的最大驅動力之一。對於該管道，我認為我們對其非常有信心。這些最終都是客戶的工作量，他們讓員工建立並達到生產水平，然後推動未來幾季的消費。因此，這不僅是進入銷售團隊的第一步。這就是為什麼我們認為這是一個非常有希望的統計數據，也是為什麼我們每個季度都非常認真地追蹤它。

So I think there's a really solid improvements. I've been very impressed by the execution in the go-to-market team over the last few quarters to get this in place and do it quickly. And I think that gives us a lot more ability to help drive these consumption workloads ourselves, right, really land in the right use cases, make sure that they're using our complete product, the full DSP is the best way possible. and make sure that, that gets out to production without snags and reaches its full potential. So yeah, I think very promising in what we're seeing.

所以我認為這確實是一個實質的改進。過去幾個季度，行銷團隊的執行力給我留下了深刻的印象，他們迅速完成了這項工作。我認為這給了我們更多的能力來幫助自己推動這些消費工作負載，真正落在正確的用例上，確保他們使用我們的完整產品，完整的 DSP 是最好的方式。並確保它能夠順利投入生產並充分發揮其潛力。是的，我認為我們所看到的情況非常有希望。

Great. And maybe just a quick follow-on for Rohan. RPO and CRPO, both accelerating very nicely. Why or why shouldn't we look to that as a reliable leading indicator for Confluent specifically?

偉大的。也許這只是對 Rohan 的一個快速跟進。RPO 和 CRPO 都加速得非常好。為什麼我們不應該將其視為 Confluent 的可靠領先指標？

Thank you.

謝謝。

Yeah. Great question, Brad. Thank you. You're right. RPO, in general, what I've shared before is when you think about our business for Confluent platform, absolutely.

是的。很好的問題，布拉德。謝謝。你說得對。RPO，一般來說，我之前分享的是當您考慮我們的 Confluent 平台業務時，絕對是這樣的。

RPO is the single most important leading indicator with respect to the forward-looking organic growth of the business. For Confluent Cloud, it's a bit nuanced where over the short term, I think what we've internally focused on is the momentum of new use cases moving into production and which was a check in Q3.

RPO 是企業前瞻性有機成長最重要的領導指標。對於 Confluent Cloud 來說，情況有點微妙，在短期內，我認為我們內部關注的是新用例投入生產的勢頭，這是第三季的檢查。

So overall, we feel with the short-term drivers. But over the long term, I think coverage of RPO to revenue to cloud revenue, that has continued to increase through the year. I mean this particular quarter was the fourth consecutive quarter of accelerated RPO that we've delivered.

所以整體而言，我們感受到的是短期驅動因素。但從長遠來看，我認為 RPO 對營收和雲端收入的覆蓋率全年都在持續成長。我的意思是，這個季度是我們連續第四個季度實現加速 RPO。

So yes, like from the cloud business perspective, short term is new use cases moving into production and our ability to drive growth in the new business. newer products and long term is around the RPO. So that's going well. And for Confluent platform, absolutely, it's a leading indicator. So that's how I think about it.

是的，從雲端業務的角度來看，短期是新用例投入生產，以及我們推動新業務成長的能力。新產品和長期是圍繞 RPO。一切進展順利。對於 Confluent 平台來說，這絕對是一個領先指標。這就是我的想法。

Sanjit Singh, Morgan Stanley.

摩根士丹利的 Sanjit Singh。

Yeah, thank you for taking the questions. I guess it's a very simple one, Jay, and it's with multiple sort of vectors that you guys have in play to drive growth, including with all of the sort of rejuvenation activity within the go-to-market organization. When do you think we can see growth start to bottom is the first question.

是的，感謝您回答這些問題。傑伊，我想這是一個非常簡單的問題，你們可以利用多種載體來推動成長，包括行銷組織內的所有類型的復興活動。您認為我們何時能看到經濟成長開始觸底是第一個問題。

Yeah. I mean, look, first of all, I think we're very pleased with the results that we brought, the strength in cloud, pleased to be in a position where we're raising guidance for Q4. I think ultimately, the cloud business has been quite strong. When you look at the growth rate for Q4, there is some impact from a particular customer.

是的。我的意思是，首先，我認為我們對我們帶來的結果、雲端運算的優勢感到非常滿意，很高興我們能夠提高第四季度的業績預期。我認為最終雲端業務會非常強勁。當你查看第四季度的成長率時，你會發現來自特定客戶的一些影響。

We kind of talked about that dynamic last quarter. if you normalize for that, you are seeing kind of stability in the overall cloud growth rates. So overall, we feel pretty good about that. And then when we talk about kind of some of these tailwinds some of the GSP offerings, including Flink getting to scale and starting to contribute more sizably. The overall execution within the field team around consumption and the ability to drive use cases because there are positive trends.

我們上個季度討論過這種動態。如果將其正常化，您會看到整體雲端成長率趨於穩定。總的來說，我們對此感覺很好。然後，當我們談論一些順風時，一些 GSP 產品，包括 Flink 正在擴大規模並開始做出更大的貢獻。由於存在積極的趨勢，現場團隊圍繞消費和推動用例的能力的整體執行情況。

When it comes to the growth that you're seeing in the core streaming business, given the big ramp in like things like WarpStream and enterprise, that sort of kind of the cannibalization question. Are you seeing that kind of net accretive impact from the rise of those offerings? Or do you feel like there's any cannibalistic effect on some of the core streaming business?

當談到核心串流媒體業務的成長時，考慮到 WarpStream 和企業等業務的大幅成長，就會出現這種蠶食問題。您是否看到了這些產品的興起所帶來的淨增值影響？或者您覺得這會對某些核心串流媒體業務產生不利影響？

Yeah. It's a very fair question as we added new offerings that were particularly cost effective. Is this going to be a tailwind or a headwind. I think it's proven to be a substantial tailwind.

是的。這是一個非常公平的問題，因為我們增加了特別具有成本效益的新產品。這將會是順風還是逆風？我認為事實證明這是一個巨大的順風。

So we called out in the call that we've seen substantial improvement in overall deal size which is maybe counterintuitive, but in fact, it's not because customers are leaning in with bigger workloads, bigger migrations that might have been harder taken longer in the past.

因此，我們在電話會議中指出，我們已經看到整體交易規模有了顯著改善，這可能有悖常理，但事實上，這並不是因為客​​戶傾向於承擔更大的工作量、進行更大的遷移，而這些在過去可能更難、需要更長的時間。

And because of the architecture of these offerings, the multi-tenant clusters with enterprise and freight, WarpStream with the people I see, they're very cost effective to run. So there are a tailwind to gross margin. So it's really good on both sides. It's good deal for customers, they're leaning in and going bigger.

而且由於這些產品的架構，包括企業和貨運的多租戶集群，以及我看到的 WarpStream，它們的運作成本非常低。因此，毛利率將呈現順風態勢。所以這對雙方來說都是好事。這對客戶來說是一筆好交易，他們正在傾力投入並做大。

Mark Murphy, JPMorgan.

摩根大通的馬克墨菲。

Yeah. Great. Thank you so much, Shane. So Jay, you had mentioned, I think you said more than 40% sequential growth in progressing late-stage pipeline. And it sounds very promising. I'm not sure we have historical context on that metric.

是的。偉大的。非常感謝你，肖恩。傑伊，您提到過，我想您說的是後期研發管線的環比增長超過 40%。這聽起來很有希望。我不確定我們是否了解該指標的歷史背景。

Can you speak to what is driving such great traction there. And then what is the normal level of sequential growth you'd see in that late-stage pipeline?

您能說說是什麼推動了它如此巨大的發展嗎？那麼，您在後期管道中看到的正常連續增長水平是多少？

Yeah. So Yeah, it's a great question. We're obviously not going to turn that into some kind of external metric, but one of the things we set for ourselves as a benchmark of improvements in the field motion around consumption was, hey, get the new use cases, get into the new use cases, get them to production. And so we measure the dollar amount of those use cases. And we've seen that as these use cases hit production, they ramp up, they take traffic, they drive consumption in the quarters ahead.

是的。是的，這是一個很好的問題。我們顯然不會將其變成某種外部指標，但我們為自己設定的衡量消費領域運動改進的基準之一是，獲取新的用例，進入新的用例，並將它們投入生產。因此我們衡量這些用例的金額。我們已經看到，隨著這些用例投入生產，它們的數量會增加，它們會吸引流量，並推動未來幾季的消費。

So it's a reasonable indicator to pay attention to the forward-looking way. So yes you're asking, hey, what's the normal growth quarter-over-quarter? Well, over time, if you're bringing more dollars of use cases out to production, those are dollars that you're realizing in future quarters. It takes some quarters for different projects to wrap up. So it's not one is to one but that's roughly how I would think about it.

因此，這是一個值得關注的合理指標。所以是的，你會問，嘿，正常的季度環比增長是多少？好吧，隨著時間的推移，如果您將更多的用例投入生產，那麼這些就是您將在未來幾季實現的收益。不同的項目需要幾個季度才能完成。所以這不是一對一的問題，但大致上這就是我的想法。

We haven't given kind of the full history of the metric, and that isn't the intention. It really is, I think, being used by us as a benchmark of execution of the field. And we felt that kind of internal metric was one of the best representations of that. We have made a number of adjustments in how folks are working these consumption projects. And I think it really has worked quite effectively.

我們還沒有給出該指標的完整歷史記錄，而這並不是我們的本意。我認為，它確實被我們用作該領域執行的基準。我們認為這種內部指標是對此最好的體現之一。我們對人們進行這些消費項目的方式做出了一些調整。我認為它確實非常有效。

Okay. And then as a quick follow-up, Jay, how is the early response to the launch of streaming agents on Confluent Cloud? Because I think we would all agree, for sure, agents need access to real-time data. frankly, they're going to look pretty unintelligent, right, and out of date if they don't have it. But then companies are -- they're so risk-averse, and they're struggling to give to get comfort giving agents free rein to all their data at it's sort of scares them.

好的。然後，Jay，作為一個快速的後續問題，Confluent Cloud 上流代理的推出的早期反應如何？因為我認為我們都同意，代理商肯定需要存取即時數據。坦白說，如果他們沒有這些數據，他們就會顯得非常不聰明，對吧，而且過時了。但是，公司非常厭惡風險，他們很難讓代理商自由地處理他們的所有數據，因為這讓他們感到害怕。

And you laid out a nice -- very nice architectural vision for that, right, in the webinar, but I'm just wondering how is the customer readiness for that product? And could you speak to -- I mean, if this takes off, can agents become pretty big in the mix a few years down the road?

您在網路研討會上為此提出了一個很好的架構願景，對吧，但我只是想知道客戶對該產品的準備程度如何？您能否談談——我的意思是，如果這項業務成功開展，那麼幾年後代理商是否會在其中佔據相當大的份額？

Yeah, I think that they absolutely can. So there's a few opportunities around AI for Confluent. One is around making the agents real time. One is about the provisioning of real-time data sets. Both of those are actually substantial, and you can do them both together or you can do them separately.

是的，我認為他們絕對可以。因此，Confluent 在人工智慧領域存在一些機會。一是讓代理變得即時。一是關於即時資料集的提供。這兩者實際上都很重要，您可以同時進行，也可以單獨進行。

And for those who follow us closely, we -- I mentioned in the prepared remarks that we're here in New Orleans for our conference current, and that's in a few days. So we'll have some announcements in this space. that I think we'll fill out the picture a bit more. But already, the streaming agents have caught on, we talked about one of the customer use cases in the call earlier. And it makes a lot of sense.

對於那些密切關注我們的人，我在準備好的發言中提到過，我們將在幾天後在新奧爾良參加我們的會議。因此，我們將在此發布一些公告。我認為我們將進一步充實這一畫面。但是串流媒體代理已經流行起來了，我們在之前的電話會議中討論了其中一個客戶用例。這很有道理。

This is a really easy way that you can run the agent on the kind of historical data on a benchmark to be able to play with it almost in a batch model, but then have it translate into production and run real time against the data that's there. It makes that kind of development much easier. And I think this is going to be a critical part of the stack. One of the things, I think, software teams are realizing is that this kind of agent development is actually a bit different from traditional software. You have to do it with the data.

這是一種非常簡單的方法，您可以在基準上對歷史資料運行代理，以便能夠在批次模型中使用它，然後將其轉換為生產並針對那裡的資料即時運行。它使這種開發變得更加容易。我認為這將是堆疊的關鍵部分。我認為軟體團隊正在意識到的一件事是，這種代理開發實際上與傳統軟體略有不同。你必須利用數據來做這件事。

Traditional software, you can kind of write some program, run some unit tests against it with fake data. If that all passes, it works, you're good to go, your program is good. But these AI systems are not that way. You can build some support agent and say, oh, this answers support questions really effectively. But if you haven't tried it with the actual customer data on actual customer questions, if you're not really developing that way, you're not doing anything.

在傳統軟體中，您可以編寫一些程序，並使用虛假資料對其執行一些單元測試。如果一切都順利，那麼程式就可以正常運行，你的程式也很好。但這些人工智慧系統並不是這樣的。您可以建立一些支援代理並說，哦，這確實有效地回答了支援問題。但是，如果您還沒有嘗試使用實際客戶資料來回答實際客戶問題，如果您沒有真正按照這種方式進行開發，那麼您就什麼也做不了。

And so the need is to be able to work iteratively with data, but then also launch something that will run in real time in production and be able to keep those two in sync as the team moves. And so I think we have really foundational capabilities. Like in many ways, that is about what streaming is, which is this ability to take some of the ideas that we had off-line with batch data processing be able to translate them into continuous processing. And so I think it's a huge opportunity for us. In many ways, it's an acceleration of what we were doing for customers anyway.

因此，需要能夠迭代地處理數據，然後啟動一些可以在生產中即時運行的東西，並能夠在團隊移動時保持兩者同步。所以我認為我們確實擁有基礎能力。就像在很多方面一樣，這就是串流媒體，它能夠將我們離線時透過大量資料處理獲得的一些想法轉化為連續處理。所以我認為這對我們來說是一個巨大的機會。從很多方面來說，這都加速了我們為客戶所做的事情。

Even if the intelligence was just smart rules in a production application that was driving personalization or customization or relevance we're already doing lots of that. And I think the AI opportunity is, in many ways, a huge generalization of that of allowing not just hard rules, but broad capabilities to access same kind of data to make data-driven decisions, take smart actions.

即使智慧只是生產應用程式中推動個人化、客製化或相關性的智慧規則，我們已經在做很多這樣的事情了。我認為，人工智慧的機會在許多方面都是一個巨大的概括，它不僅允許硬性規則，還允許廣泛的能力存取同類數據，以做出數據驅動的決策，採取明智的行動。

So hopefully, that's helpful. And stay tuned for the next couple of days, we'll have a few more announcements it's hard to always figure out the timing of these things. But since that's 2 days later, we don't get to talk about all the new products until that.

希望這會有所幫助。請繼續關注接下來的幾天，我們將會發布更多公告，很難確定這些事情的時間。但由於那是兩天後的事了，我們要到那時才能談論所有新產品。

Raimo Lenschow, Barclays.

巴克萊銀行的 Raimo Lenschow。

Perfect. Thank you. Can't wait for conference then. The two quick questions, one for Jay, one for Rohan. Jay, Flink, you gave us some extra data points. At Flink, we've been waiting for -- and while I don't want to call it an inflection point, but like the uptick here.

完美的。謝謝。已經等不及參加會議了。兩個簡單的問題，一個問傑伊，一個問羅漢。Jay，Flink，你給了我們一些額外的數據點。在 Flink，我們一直在等待——雖然我不想稱之為拐點，但我喜歡這裡的上升趨勢。

What do you see there, how customers are using it and what you're seeing in the pipeline? Does that kind of increase your optimism like talk a little bit about how that kind of translates into the business going forward? And then, Rohan, one for you. You've raised the subscription revenue guidance by more than the beating Q3. What gave you the confidence there?

您在那裡看到了什麼，客戶如何使用它以及您在管道中看到了什麼？這是否會增強您的樂觀情緒？能否談談這對未來的業務有何影響？然後，羅翰，給你一個。您已將訂閱收入預期提高至超出預期的第三季水準。是什麼給了你信心？

Thank you.

謝謝。

Yeah. I'll start with Flink that we're hugely excited. So I do think externally, this was a little bit of an unusual product development cycle because we changed our stream processing strategy and bought a Flink company but it wasn't a Flink product. It was just the team that had built the open source we were effectively starting product development with an announcement about Flink. So then we had to build the product.

是的。首先要說的是令我們非常興奮的 Flink。所以我確實認為從外部來看，這是一個不尋常的產品開發週期，因為我們改變了流處理策略並收購了一家 Flink 公司，但它不是 Flink 產品。正是建立了開源的團隊，我們才透過發布有關 Flink 的公告有效地開始了產品開發。所以我們必須製造產品。

And I think the team has done an amazing job of that to really build a modern data -- serverless data processing to do it in a way that supports high availability, real-time process. It's a big undertaking. I think the growth of that sense is kind of each and kind of got into the critical enterprise features over the last year has been spectacular.

我認為團隊在這方面做得非常出色，真正建立了現代數據——無伺服器資料處理，以支援高可用性、即時處理的方式進行。這是一項艱鉅的任務。我認為，在過去的一年裡，這種感覺的成長是驚人的，並且已經融入關鍵的企業功能中。

And that's absolutely as much as we could ask out of a kind of first year of selling for the product, and that trajectory remains very strong as we look ahead. And so yes, we are -- I think as we've communicated as we started this effort, we think that potential for that offering over time.

這絕對是我們對該產品第一年銷售業績的最大期望，展望未來，這一發展軌跡依然非常強勁。是的，我認為正如我們在開始這項工作時所溝通的那樣，我們認為隨著時間的推移，該產品具有潛力。

It's huge. The market for data processing is really big. There's all this stuff in these old batch jobs that needs to move into real time. And now I think we're starting to realize that opportunity. And it's an interesting intersection with the AI question as well because one of the things that actually aids these conversions is AI.

它很大。數據處理的市場確實很大。這些舊的批次作業中的所有內容都需要即時處理。現在我認為我們開始意識到這個機會。這與人工智慧問題也有有趣的交集，因為人工智慧實際上有助於這些轉換。

So if you're converting these batch quarries to stream inquiries, we have a set of capabilities to just help customers do this just goes through and makes the little minor adjustments. I mean, largely, it's very similar streaming queries or SQL similar language to the batch step, but of course, getting all the nuances, right? And so that's been one of the accelerants that's helped customers that are trying to go big with a lot.

因此，如果您要將這些批量採石場轉換為流查詢，我們有一套功能可以幫助客戶做到這一點，只需進行一些小的調整即可。我的意思是，很大程度上，它與批次步驟的流查詢或 SQL 類似語言非常相似，但當然，了解所有的細微差別，對嗎？因此，這是幫助那些試圖做大事的客戶的加速器之一。

And Raimo, before you answer the question, I'll just add a quick point to what Jay said. From my lens, when some of these new products are ramping I think there are two things that I'd like to focus on the breadth of adoption and the depth of adoption. For Flink, specifically, when you look at the breadth option, we have over 1,000 paying customers for Flink.

雷莫，在你回答問題之前，我只想先對傑伊所說的內容補充一點。從我的角度來看，當一些新產品正在迅速發展時，我認為有兩件事我想專注於：採用的廣度和採用的深度。具體來說，對於 Flink，當您查看廣度選項時，我們有超過 1,000 個 Flink 付費客戶。

And on the debt side, we have about 12 customers spending over $100,000 in ARR and four customers spending $1 million in ARR. So that's actually a good position to be in and on the heels of three quarters or 9-months of very solid growth that we've seen.

在債務方面，我們有大約 12 位客戶的 ARR 支出超過 10 萬美元，有 4 位客戶的 ARR 支出為 100 萬美元。因此，這實際上是一個很好的位置，並且緊隨我們已經看到的三個季度或九個月的非常穩健的增長之後。

So just to add to what Jay said, we're excited about what lies ahead on that side of the business. So coming back to your question on subscription guidance for Q4. Yeah, we are pleased to raise our Q4 subscription guide, and that's mostly coming from the confluent cloud side of the world. So if I take a step back and then analyze the Q3 performance, I'll call out three things. The first one is something that Jay called out in his prepared remarks.

所以，補充一下傑伊所說的，我們對該業務的未來感到興奮。所以回到你關於第四季訂閱指南的問題。是的，我們很高興提高我們的第四季度訂閱指南，這主要來自世界融合的雲端。因此，如果我退一步分析第三季的表現，我會指出三件事。第一個是周傑倫在準備好的發言中提到的。

Just momentum of new use cases moving to production. And we saw two consecutive quarters of acceleration over there, so which is good. The second area is around -- we are seeing more normalized levels of optimization. I would actually put it in the category of healthy levels of optimization. So that's number two.

這只是新用例轉向生產的動力。我們看到那裡連續兩個季度加速成長，這是好事。第二個領域是──我們看到了更規範化的最佳化水準。我實際上將其歸類為健康優化水平類別。這是第二點。

And the third is continued strength in Flink and the cloud side of Flink. So these are some of the drivers and the momentum builders in Q3, and that's giving us confidence with respect to our Q4 cloud guidance. And I'll leave you with one more big picture thought that I touched on my first response that is these are short-term visibility drivers for the cloud business. When I take a step back and look at the long term, the RPO to cloud revenue coverage through the year has continued to increase and improve. And that's less of a Q4 visibility, but more of a slight long-term visibility, we feel good with that increasing coverage as well.

第三是 Flink 和 Flink 雲端的持續實力。這些是第三季的一些驅動因素和勢頭構建因素，這讓我們對第四季度的雲端運算指引充滿信心。我還會給你留下一個更大的想法，我在第一個反應中提到了這些是雲端業務的短期可見性驅動因素。當我退一步看長遠時，我發現全年的 RPO 對雲端收入的覆蓋率一直在持續增加和改善。這不太像是第四季度的可見性，而是更像長期可見性，我們對覆蓋範圍的擴大也感到滿意。

Ryan MacWilliams, Wells Fargo.

瑞安‧麥克威廉斯，富國銀行。

Hey, thanks, guys. Jay, as enterprises continue to move from testing to production with AI use cases, are there any AI use cases that come to mind that involve Confluent that could be more likely in production in the near term, like a customer service use case or an IoT use case?

嘿，謝謝大家。Jay，隨著企業繼續利用 AI 用例從測試轉向生產，您是否想到了涉及 Confluent 的 AI 用例，這些用例更有可能在短期內投入生產，例如客戶服務用例或物聯網用例？

Yeah. We're seeing -- these tend to be quite broad, right? So there's similar patterns around customer support. There's patterns around anomalies and investigations. Many businesses have some operational side kind of looking for the bad thing and then diving into the bad thing that cuts across businesses that might be doing IoT, manufacturing, different production processes but also things like retail.

是的。我們看到——這些往往相當廣泛，對吧？因此，客戶支援方面也存在類似的模式。異常和調查存在一定的模式。許多企業在營運方面都會尋找不好的事情，然後深入研究那些可能涉及物聯網、製造、不同生產流程以及零售等業務的壞事。

But even businesses, financial services, insurance, companies you might think of this being more risk averse. I think have very active projects in this area. And so I think for all of these, it's about whether they can really complete that connectivity and make it into production with these systems. We think that a big part of that is about data flow, data quality, the ability to actually iterate and test and get from something that kind of 99% works to something that 99.99% works.

但即使是企業、金融服務、保險等公司，你可能會認為這更厭惡風險。我認為這個領域的項目非常活躍。因此，我認為對於所有這些，關鍵在於他們是否能夠真正完成連接並利用這些系統投入生產。我們認為其中很大一部分涉及資料流、資料品質、實際迭代和測試的能力，以及從 99% 有效到 99.99% 有效的能力。

So it sounds like a small difference, but we operate already in a business where, operationally, the difference between 99% and 99.99% is actually a really big deal for our customers. And so you can totally see why on the quality side for any of these things. It's hard to get that last bit done. And I think why we think we're well positioned for it.

因此，這聽起來似乎是一個很小的差異，但從營運角度來看，在我們經營的業務中，99% 和 99.99% 之間的差異實際上對我們的客戶來說是一件大事。因此，您完全可以理解為什麼這些東西的品質如此之高。完成最後這一點很困難。我想這就是為什麼我們認為我們已經準備好了。

I understand it as well. I got 99 things right and one thing wrong, you remember just which one. And then for Rohan, you mentioned last quarter that a large AI native company was moving to self-hosted after signing a self-managed deal in the third quarter. Any commentary on how much that large customer contributed in the third quarter? And as that large customer spend drops off from the cloud next quarter, could the self-managed portion step-up contribute further?

我也明白。我答對了 99 件事，錯了一件事，你只記得是哪一件事。然後對於 Rohan，您在上個季度提到，一家大型 AI 原生公司在第三季度簽署了一項自我管理協議後，正在轉向自我託管。您能評論一下該大客戶在第三季的貢獻有多大嗎？隨著下個季度大客戶在雲​​端運算方面的支出減少，自我管理部分的提升能否進一步做出貢獻？

Just any commentary on the mechanics of that large customer deal could help?

任何有關該大客戶交易機制的評論都有幫助嗎？

Yes, yes. Ryan, a few data points that I'll share. First, and reiterate what I said in the Q3 call, and what we said in the Q3 call was this large customer basically made this move from Confluent cloud to on-prem. And as a result of this dynamic, their spend towards confluent would be significantly reduced. So that's the data point.

是的，是的。瑞安，我將分享一些數據點。首先，重申我在第三季電話會議上所說的話，我們在第三季電話會議上所說的是，這個大客戶基本上從 Confluent 雲端轉移到了本地。由於這種動態，他們在匯合方面的支出將大大減少。這就是數據點。

And what that would do is it would have a low single-digit impact to our Q4 cloud revenue. And Jay called out earlier, when you normalize that impact of the low single digit and you compare our Q4 guidance versus Q3 actual cloud performance, you'll see somewhat flattish year-over-year growth rate.

這將對我們第四季的雲端收入產生較低的個位數影響。傑伊早些時候指出，當你將低個位數的影響正常化，並將我們的第四季度指引與第三季度的實際雲端效能進行比較時，你會看到同比成長率略顯平穩。

So that kind of signs of stabilization. And specifically, that large customer, obviously contributed in Q3 from a revenue perspective and the real impact, the low single-digit impact we are going to see from our cloud business as in Q4 and that's incorporated in our guidance for Q4.

這就是穩定的跡象。具體來說，從收入角度來看，這位大客戶顯然在第三季度做出了貢獻，而實際影響是，我們將在第四季度看到雲端業務產生的低個位數影響，這已納入我們對第四季度的指導中。

Rob Owens, Piper Sandler.

羅伯歐文斯、派珀桑德勒。

Thanks, Shane. Good afternoon. Thanks for taking my question. Jay, maybe you could elaborate a little bit more on the CSP replacement opportunity? Just how big you think it is? And why do you think this is inflecting over the last couple of quarters?

謝謝，肖恩。午安.感謝您回答我的問題。傑伊，也許您可以更詳細地闡述 CSP 替代機會？您認為它到底有多大？您認為為什麼這種情況在過去幾季會發生變化？

Yeah. It's quite sizable. We also, of course, are continuing to do very large open source takeouts, and there's quite a lot of the open source. But both for the outsource and the CSP offerings I think one of the -- there's really two things that I think are making this something customers really want to take action on right away. The first is the TCO of making the change.

是的。相當大。當然，我們也持續進行大規模的開源外賣，而且開源的數量相當多。但對於外包和 CSP 產品，我認為其中之一——我認為有兩件事讓客戶真正想要立即採取行動。首先是進行變革的總體擁有成本 (TCO)。

And that comes out of the Fundamentally, the improvements we've made in Kora that enable things like enterprise clusters, freight clusters. It's just something that's kind of better, faster, cheaper. And I think that's very compelling. Secondly, I think these DSP capabilities have become just a bigger and bigger part of what customers think about when they think about streaming and what they need to do to be set up to use this technology in their organization. And I think that's really quite appealing to customers making the move.

從根本上來說，我們在 Kora 所做的改進使得企業集群、貨運集群等成為可能。它只是更好、更快、更便宜的東西。我認為這非常引人注目。其次，我認為這些 DSP 功能已經成為客戶在考慮串流媒體時所考慮的越來越重要的一部分，也是他們在組織中使用這項技術時需要做的事情。我認為這對採取行動的客戶來說確實很有吸引力。

So I think those two things are the two biggest needle movers. The biggest enabler, I would say, on our side, is really working on tools around migration, making it easy. I think once you have a bunch of customers that want to do it, well, this is a big live data system migration, we want to make it as easy as pushing a button, that's ongoing work to really make that easier and easier. And as I think we continue that, I think we'll see an even faster transition of these systems, which is great.

所以我認為這兩件事是兩個最大的推動因素。我想說，就我們而言，最大的推動因素是真正致力於開發遷移工具，使遷移變得簡單。我認為，一旦你有一群客戶想要這樣做，那麼，這是一個大型的即時數據系統遷移，我們希望讓它變得像按下按鈕一樣簡單，這是一項持續的工作，使它變得越來越容易。我認為，如果我們繼續這樣做，我們將看到這些系統的更快轉變，這很好。

Great. And then as a follow-up, Rohan, in your contemplating guidance for the fourth quarter, you mentioned healthy levels of optimization. And I know this has been an issue in the first half of the year. When you -- I'd actually just to parse the question a little bit more on the comments a little bit more, is this healthy levels from prior optimizers or these net new optimizers that aren't to the same extent that you saw before. And so I guess within that question, maybe an update on optimization, is it still relevant as a headwind from the first half.

偉大的。然後作為後續，羅漢，在您對第四季度的考慮指導中，您提到了健康的優化水平。我知道這是今年上半年的問題。當你 - 我實際上只是想在評論中對這個問題進行更多的分析，這是來自以前優化器的健康水平還是這些淨新優化器與你以前看到的程度不同。因此，我想在這個問題中，也許是關於優化的更新，它是否仍然與上半年的逆風有關。

And is this more balancing active net new or kind of the whole thing in aggregate?

這是更平衡的主動網路新事物還是整體而言的整體事物？

Thank you.

謝謝。

Yeah, Rob, when I think about the cloud business or rather how we manage and run the cloud business, they are typically like three things that are important to focus on, right? The first is, as you're entering up water, you're entering up water with a book of business and like for the existing customers, what is the growth that they are showing. And that's where optimizations generally come up. And as we've said, optimization is kind of part and parcel of every cloud business. And we want our customers to fine-tune and kind of use Confluent in a more efficient manner.

是的，羅布，當我考慮雲端業務或更確切地說我們如何管理和經營雲端業務時，通常有三件重要的事情需要關注，對嗎？首先，當您進入水中時​​，您會帶著一本商業書籍進入水中，對於現有客戶來說，他們表現出了什麼樣的成長。這就是優化通常出現的地方。正如我們所說，優化是每個雲端業務不可或缺的一部分。我們希望我們的客戶能夠以更有效率的方式微調和使用 Confluent。

That's part and parcel that's something, that's why I called it healthy levels of optimization. If it compares to prior historical optimizations that we've seen and which is not an outlier. So that was my comment. The second data point around how we kind of look at the business momentum is net new use cases moving into production. And the third is around adoption of new products.

這是不可或缺的一部分，這就是為什麼我稱之為健康的最佳化水準。如果它與我們所見過的先前的歷史最佳化相比，並且不是一個異常值。這就是我的評論。關於我們如何看待業務發展動能的第二個數據點是投入生產的淨新用例。第三是關於新產品的採用。

So when I talk about our guidance or just the momentum in Cloud business, these three kind of all go hand in hand. And the optimization levels to specifically answer your question, are in the ranges that we've seen historically, that is kind of more normalized and again, healthy and good optimization.

因此，當我談論我們的指導或雲端業務的發展勢頭時，這三者都是齊頭並進的。具體回答您的問題的最佳化程度處於我們歷史上看到的範圍內，這是一種更規範的、健康和良好的最佳化。

Jason Ader, William Blair.

傑森·阿德、威廉·布萊爾。

Yeah, thanks, Shane. Good afternoon, guys. I know we've seen better cloud consumption trends across the vendor landscape, really over the last quarter or so how much of the better performance that you guys saw in Q3, do you think is due to better sales execution versus overall macro tailwinds, including AI?

是的，謝謝，肖恩。大家下午好。我知道我們已經看到整個供應商領域的雲端消費趨勢有所改善，實際上在過去一個季度左右，你們在第三季度看到的更好表現有多少，您認為是由於更好的銷售執行力還是包括人工智慧在內的整體宏觀順風？

Yeah, it's a great question. It's -- obviously, it's always hard for us to pull ourselves out of the environment in which we operate in because we only get to run each quarter once, there's no counterfactual where it was a different environment. That said, I do think some of these improvements are kind of very mechanically obviously helping things.

是的，這是一個很好的問題。顯然，我們總是很難擺脫我們所處的營運環境，因為我們每季只能運行一次，而且不存在不同環境的反事實。話雖如此，我確實認為其中一些改進在機械上顯然對事情有幫助。

And so I do think we've made a set of structural improvements that are paying off. The new products are obviously new products, which are kind of bringing in Flink revenue or connect revenue or covenants revenue that we would not otherwise have had in those customers.

因此我確實認為我們已經做出了一系列結構性改進，並且正在取得成效。新產品顯然是新產品，它們為這些客戶帶來了 Flink 收入、連結收入或契約收入，而這些收入是我們原本無法獲得的。

So yes, I can't ascribe it between the two. I am aware that there was kind of good results in some other providers. But we do feel like we've made some pretty important structural improvements from what we're doing.

所以是的，我不能將其歸因於這兩者。我知道其他一些供應商也取得了不錯的成績。但我們確實覺得，從我們正在做的事情來看，我們已經取得了一些非常重要的結構性改進。

Okay. And then Rohan, for you. You didn't talk about U.S. federal at all, but the shutdown here is going into week 4 or something. Did you bake that in? Did you bake in some conservatism to your Q4 outlook, especially on the Confluent platform side from potential weakness in US Federal?

好的。然後是羅漢，給你。你根本沒有談論美國聯邦政府，但這裡的政府停擺已經進入第四週左右了。你把它烤進去了嗎？您是否對第四季的展望抱持著一些保守態度，尤其是在 Confluent 平台方面，因為美國聯邦儲備委員會可能會表現疲軟？

Yeah. For -- Jason, that's a great question. I mean, before I go into Q4, our Q3 federal performance, which is generally a big federal quarter, was in line with our expectations. So pretty much in line, no surprises there. And when you look at federal as a percentage of total revenue.

是的。對於——傑森，這是一個很好的問題。我的意思是，在進入第四季之前，我們的第三季聯邦表現（通常是一個重要的聯邦季度）符合我們的預期。所以基本上是一致的，沒有什麼意外。當您將聯邦收入視為總收入的百分比時。

I've shared this before. It is in the low single digits for us, which is good and bad, good as it's a big opportunity for us as we look ahead. And so that's great. And from a Q4 perspective, we have a couple of deals that are appropriately baked into our guidance.

我之前分享過這個。對我們來說，這是一個較低的個位數，這有好有壞，好的一面是因為這對我們來說是一個巨大的機會，讓我們放眼未來。這太棒了。從第四季的角度來看，我們有幾筆交易已適當地納入我們的預期。

Mike Cikos, Needham.

麥克·西科斯，尼德姆。

Alex Zukin, Wolfe Research.

沃爾夫研究公司的亞歷克斯祖金 (Alex Zukin)。

Hey guys, can you hear me okay?

嘿夥計們，你們聽得到我說話嗎？

Yes.

是的。

Perfect. Maybe just first one for Jay, of the 21 AI native customers that you guys signed over $100,000 or that are using the product for over $100,000, is there a common pattern and how they're using Confluent? Are the AI products built around Kafka or Flink? Or are there use cases similar to what you're seeing with other companies?

完美的。也許首先要問 Jay 的問題是，在你們簽約的價值超過 10 萬美元或使用產品價值超過 10 萬美元的 21 個 AI 原生客戶中，是否存在一個共同的模式以及他們是如何使用 Confluent 的？AI產品是圍繞著Kafka還是Flink建構的？或者是否存在與您在其他公司看到的類似的用例？

Because that's a really, really powerful stat. I wanted to see if you could unpack it a little bit.

因為這是一個非常非常強大的統計數據。我想看看你是否可以稍微解釋一下。

Yeah. So first of all, AI companies or tech companies, so they have a set of usage patterns, they're exactly like every other tech company, which is they use it for a bunch of different stuff, right? But there is a set of use cases that are common in these companies, which are very specific to AI.

是的。首先，人工智慧公司或科技公司有一套使用模式，它們與其他科技公司完全一樣，它們將其用於許多不同的東西，對嗎？但這些公司中有一組常見的用例，這些用例非常特定於人工智慧。

And that's about the flow of data about the suggestions, recommendations, actions are being taken. So I kind of touched on this briefly in the script, but the big difference in these AI systems is it is not just upfront testing. You need to do this kind of ongoing evaluation, which is really looking at what are the actions it's taken, are they good?

這就是有關建議、推薦和正在採取的行動的資料流。所以我在腳本中簡要地提到了這一點，但這些人工智慧系統的最大區別在於它不僅僅是前期測試。您需要進行這種持續的評估，真正了解所採取的行動是否有效？

How are we going to evaluate that. You have a bunch of different ways of doing that, including just asking humans to judge asking the model to judge it. But the flow of that data is really kind of right at the heart of a lot of these systems, and it's a very natural kind of streaming problem. You're going to collect that in real time, it's going to flow out maybe through table flow or other mechanisms into kind of long-term storage, you're going to be able to iterate on that.

我們將如何評價這一點？有很多不同的方法可以做到這一點，包括直接讓人類來判斷或讓模型來判斷。但資料流實際上是許多此類系統的核心，這是一個非常自然的流問題。您將即時收集這些數據，這些數據可能會透過表流或其他機制流出到長期儲存中，您將能夠對此進行迭代。

It's also a very important kind of real-time analytic in terms of how well you're doing for your customers. minute to minute as you're out there, if you release if you take in a new model or you make changes to your system, ultimately, are you better or worse with your customers? That's kind of the fundamental question. So in many of these systems, that's one of the use cases. And this is not surprising.

就您為客戶提供的服務品質而言，這也是一種非常重要的即時分析。當您每分鐘都在外面時，如果您發布新模型或對系統進行更改，最終，您對客戶的滿意度是提高了還是降低了？這是一個基本問題。因此，在許多此類系統中，這是用例之一。這並不奇怪。

This is a similar use case we had with more traditional machine learning use cases. I think it's just now translated into the AIR.

這與我們在更傳統的機器學習用例中遇到的用例類似。我認為它現在才被翻譯成 AIR。

Perfect. And then maybe just a quick one for you, Rohan. You gave us a lot of stats that are really encouraging. RPO accelerating and the coverage ratio is improving. You talked about I think being past kind of a peak negative optimization headwind where it's kind of stabilizing and you're talking about more visibility longer term.

完美的。然後也許對你來說只是一個快速的問題，羅漢。您給了我們很多令人鼓舞的數據。RPO加速，覆蓋率不斷提升。您談到，我認為負面優化逆風已經過去，現在處於穩定狀態，您談論的是長期更高的可見性。

And you gave guidance for cloud platform revenue -- sorry, for cloud revenue for Q4, but not guidance, sorry. You gave a modeling point of being around 20%. And as I look at a year ago when -- at least for my model versus the out year, there was about a 2-point delta in that.

您給了雲端平台收入的指導 - 抱歉，是第四季度的雲端收入，但不是指導，抱歉。您給出的建模點約為 20%。當我回顧一年前的情況時——至少對於我的模型與去年相比，其中大約有 2 個點的差異。

And so I guess I know we're not guiding to or maybe even in modeling points yet for next year. But as we look at our models and that 20% exit rate, do we -- what kind of step down given some of those dynamics that are maybe headwinds for Q4 that reverse?

所以我想我知道我們還沒有為明年制定指導甚至建模點。但是，當我們查看我們的模型和 20% 的退出率時，我們會——考慮到某些可能對第四季度造成不利影響的動態，採取什麼樣的降幅呢？

Should we think about as we look at next year cloud revenue?

當我們展望明年的雲端收入時，我們是否應該考慮這一點？

Yeah, Alex. As we speak, we're kind of dotting the eyes and crossing the teas on our fiscal year '26 plan. So I'm not going to be providing guidance either for total revenue or cloud revenue in this call. Having said that, I think it's important to reiterate some of the data points that I shared around like, I think you said it late-stage pipeline moving into production, the optimization levels being stabilized, normalized and which I like to call healthy, right? And the link driver of business, Flink has been really good.

是的，亞歷克斯。正如我們所說的那樣，我們正在為 26 財年的計劃進行仔細的討論。因此，我不會在本次電話會議中提供有關總收入或雲端收入的指導。話雖如此，我認為有必要重申我分享的一些數據點，例如，我想您說過後期管道正在進入生產階段，優化水平已經穩定、正常化，我喜歡稱之為健康，對嗎？作為業務的連結驅動，Flink 表現非常出色。

So we expect and coupled with the long-term visibility. So when you think about these and then you couple that with the low single-digit impact that we saw in Q4, which will obviously have an impact over the first half of next year, right? So those are some of the puts and takes. If I were you, I would look at as I think about fiscal year '26. But in our Q4 call, I'll be sharing a lot more color and details around our cloud revenue guidance.

因此，我們期待並結合長期可見性。因此，當您考慮這些，然後將其與我們在第四季度看到的低個位數影響結合起來時，這顯然會對明年上半年產生影響，對嗎？這就是一些需要解決的問題。如果我是你，我會考慮 26 財年。但在我們的第四季電話會議上，我將分享更多有關我們的雲端收入預測的細節。

Mike Cikos, Needham

麥克·西科斯，尼德姆

Can you guys hear me okay?

你們聽得到我說話嗎？

Loud and clear.

聲音響亮、清晰。

Sorry about that. And thanks for the second shot, Xie Shane. I just wanted to come back to Rohan first. On the consumption trends, can you just give us maybe a little bit more granularity on how those month-over-month trends played out in Q3? You obviously outperformed the guide here.

很抱歉。感謝謝珊的第二槍。我只是想先回到洛汗。關於消費趨勢，您能否更詳細地介紹一下第三季的月度趨勢如何？顯然，您的表現超出了這裡的指南。

But I don't know that we necessarily broke it down to the month-over-month trends the way that we were getting that detail in Q1 and Q2 of this year.

但我不知道我們是否有必要將其分解為月度趨勢，就像我們在今年第一季和第二季獲得的細節一樣。

Yes. For our month-over-month trends, obviously, we spoke around the performance drivers for Q3, which were three I just laid out. And given these drivers are month-over-month consumption growth rates improved sequentially. And in general, going forward, I will try to avoid providing that level of detail. But specifically, we brought it up last quarter.

是的。對於我們的月度趨勢，顯然，我們圍繞第三季度的業績驅動因素進行了討論，我剛才列出了其中三個。鑑於這些驅動因素，月度消費成長率環比提高。總的來說，今後我會盡量避免提供這種程度的細節。但具體來說，我們在上個季度就提出了這個問題。

So our month-on-month growth improved sequentially, and we were pleased with it.

因此，我們的環比成長連續改善，我們對此感到滿意。

That's great to hear. And if I could just tack on one more, I know that you guys had the double down initiatives and some of the near-term focuses that we went through last quarter. Jay, maybe for you. But on the DSP specialization team, again, encouraging to hear some of these data points. Has the team been built out at this point?

聽到這個消息真是太好了。如果我可以再補充一點的話，我知道你們已經採取了雙倍投入的舉措，並且我們上個季度已經討論過一些近期重點。傑伊，也許對你來說是這樣。但對於 DSP 專業團隊來說，再次聽到這些數據點令人鼓舞。目前團隊已經組完畢了嗎？

I know last quarter, we were talking about accelerating that buildout. Are the bodies in the seats? And where are we in maturing the playbooks and that team at this point?

我知道上個季度我們正在談論加速這一建設。屍體在座位上嗎？那麼，目前我們的劇本和團隊的成熟度如何呢？

Yeah, that team has built out in full execution mode.

是的，該團隊已經完全進入執行模式。

Howard Ma, Guggenheim.

霍華德·馬，古根漢。

Hey, thanks for taking the question. I appreciate all the commentary on the optimization trends. And I get that the Q3 outperformance sets the bar higher head into Q4. But for -- I guess, one for Rohan. Does the Q4 cloud guide specifically still assume optimizations or consumption trends well below historical trends? And then when you take into consideration the large AI native customer, does it imply that NRR will be -- will decelerate versus the 114%?

嘿，謝謝你回答這個問題。我感謝所有關於優化趨勢的評論。我知道第三季的優異表現為第四季設定了更高的標準。但對於──我想，一個是給羅翰的。第四季雲端指南是否仍假設最佳化或消費趨勢遠低於歷史趨勢？然後，當您考慮到大型 AI 原生客戶時，是否意味著 NRR 將會——相對於 114% 會減速？

Yeah. So I'd say a couple of questions, so I'll break it down, Howard, to start off like we always have optimization. And that's all quarters, there is optimization. And that's why I kind of made sure that I commented around like normalized level of optimization that we saw in Q3. So that is hopefully answering the first part of your question.

是的。所以我想問幾個問題，所以我會把它分解開來，霍華德，就像我們總是進行優化一樣。這就是所有方面，都有優化。這就是為什麼我要確保我對我們在第三季度看到的標準化優化水平進行評論。希望這能回答您問題的第一部分。

And when you kind of normalize the impact of the one large customer that we called out last quarter, our Q4 guidance is when you look -- compare the year-over-year growth rates, it's roughly flattish to what we saw in Q3.

當你將我們上個季度提到的一個大客戶的影響正常化時，我們的第四季度指引是——比較一下同比增長率，它與我們在第三季度看到的大致持平。

And from a net retention rate perspective, obviously, we are pleased with stabilization of our net retention rates. And when you think about what are the drivers, it's primarily around our stronger consumption growth that we saw, both in cold streaming and DSP, both are drivers of stabilization. And from a net retention, again, I'm not going to guide for Q4 or fiscal year '26 for net retention, but I'll leave you with two data points. In the short term, net retention can generally fluctuate. But over the long term, some of the opportunities that we are focused on, beat core streaming, DSP, AI partner ecosystem, these are going to be the drivers of net retention rate. And all of these drivers have had positive results in Q3.

從淨留存率的角度來看，顯然，我們對淨留存率的穩定性感到滿意。當您思考驅動因素是什麼時，主要圍繞著我們所看到的更強勁的消費成長，無論是在冷流還是 DSP 中，都是穩定的驅動因素。從淨留存率來看，我不會對第四季或 26 財年的淨留存率做出預測，但我會給你留下兩個數據點。短期內，淨留存率一般會發生波動。但從長遠來看，我們關注的一些機會，例如核心串流媒體、DSP、AI 合作夥伴生態系統，將成為淨留存率的驅動力。所有這些驅動因素在第三季都取得了積極成果。

Got it. And Rohan, given how important Flink is as a driver now, you gave the disclosure Flink Low 8-figure ARR, Flink on cloud up 70% sequentially. I think if you triangulate it, you can get to maybe low single digit, call it, $2 million to $3 million of sequential increase in the cloud side. So is that fair? And should we expect that sort of sequential -- assuming that number is right, increase on the cloud type going forward, maybe as a baseline.

知道了。Rohan，考慮到 Flink 現在作為驅動因素的重要性，您揭露了 Flink 低 8 位數 ARR，雲端 Flink 環比增長 70%。我認為，如果進行三角測量，雲端的連續成長可能會達到個位數的低位，也就是 200 萬到 300 萬美元。那麼這公平嗎？我們是否應該預期這種連續性——假設這個數字是正確的，那麼雲類型將會繼續增加，也許可以作為基準。

Thank you.

謝謝。

Yeah. Again, I'm going to stay away from providing guidance, but we are very pleased with the Flink performance. And from a Flink performance, again, I'll say because it's important to note both the breadth and the depth of our link performance is something that we should note.

是的。再次強調，我不會提供指導，但我們對 Flink 的表現非常滿意。從 Flink 的效能來看，我再次強調，因為重要的是要注意連結效能的廣度和深度。

We have a lot of customers, over 1,000 customers using Flink and then we have 12 customers spending over $100,000, four customers spending over $1 million. And in Q3, we just reported greater than 70% quarter-over-quarter growth for that business. So we're very pleased with how the Flink business is progressing, and it will be a material contributor to Confluent Cloud in fiscal year '26.

我們有很多客戶，超過 1,000 個客戶使用 Flink，然後我們有 12 個客戶花費超過 100,000 美元，4 個客戶花費超過 100 萬美元。在第三季度，我們剛剛報告該業務的環比增長超過 70%。因此，我們對 Flink 業務的進展感到非常滿意，它將在 26 財年為 Confluent Cloud 做出重要貢獻。

Eric Heath, KeyBanc. Eric?

埃里克·希思，KeyBanc。艾瑞克？

Great, thanks. Maybe a lot of good questions have been asked. Maybe if I could just come back to Flink for a minute here, Jay. Just curious to hear more maybe about some of the easy wins you're seeing with Flink customers. Some of the learnings are applying to scale that Flink adoption across the customer base.

太好了，謝謝。也許已經提出了很多好問題。也許我可以在這裡回到 Flink 一會兒，Jay。只是想好奇地聽聽您看到的 Flink 客戶取得的一些輕鬆勝利。一些經驗教訓正在應用於擴大 Flink 在客戶群中的應用。

I know we talked a lot about go-to-market and the DSP team, but any color there? And Jay, maybe just lastly, any thoughts or the feedback on how we should think about competition with data bricks structured streaming product that was announced this quarter?

我知道我們談論了很多有關進入市場和 DSP 團隊的事情，但有什麼具體細節嗎？傑伊，最後，關於我們應該如何看待與本季發布的數據磚結構化串流產品的競爭，您有什麼想法或回饋嗎？

Yeah, happy to talk about both. So yes, there's actually a very broad set of use cases for Flink. If you were trying to bucket them, there's a bucket that's kind of these real-time data pipelines, getting data to some AI application or at getting data into the analytics ecosystem, Databricks flake provider things. And then there's a set of use cases, which are acting on the data, right?

是的，很高興談論這兩個話​​題。是的，Flink 實際上有非常廣泛的用例。如果您嘗試對它們進行分類，那麼有一個儲存桶就是這些即時資料管道，將資料傳送到某些 AI 應用程式或將資料傳送到分析生態系統、Databricks 薄片提供者的東西中。然後有一組用例，它們對數據起作用，對嗎？

Trying to predict fraud personalize things for customers, do something smart in reaction to an event in the business. Those are the two kind of buckets that we see customers using. Both are actually doing quite well. And both are represented in kind of the numbers that we would overall describe. I would say some of the larger customers are customers that are kind of taking existing batch processes and converting them over.

嘗試預測詐欺行為，為客戶個人化服務，針對業務中的事件採取明智的措施。這是我們看到客戶使用的兩種桶子。事實上，兩者都做得很好。兩者都以我們總體描述的數字來表示。我想說的是，一些較大的客戶是採用現有批次流程並將其轉換過來的客戶。

So I talked about a number of customers just doing these kind of migrations. That's obviously the most challenging to orchestrate for a new product is to really take something that's been built up over many years and kind of move it over. But we're finding that we're now at a point of maturity where we can start to do that and do it successfully with customers. So that's I think that's an exciting thing.

所以我談到了許多正在進行此類遷移的客戶。顯然，對於一款新產品來說，最具挑戰性的是真正地將多年來累積的東西轉移到新產品上。但我們發現，我們現在已經處於成熟階段，可以開始這樣做，並成功地與客戶合作。所以我認為這是一件令人興奮的事情。

Relative to Databricks, we remain actually very close partners working together on applications for hundreds of joint customers. We're providing a set of real-time data that often flows as their environment. There are some overlaps and capabilities in both what we're doing and what they're doing. I think in practice, we tend to serve different constituencies. We tend to have more kind of real-time operational application systems, software engineers, they would tend to have more data engineers, analytics, data scientists, type user base.

相對於 Databricks，我們實際上仍然是非常密切的合作夥伴，共同為數百個共同客戶開發應用程式。我們提供一組隨其環境而流動的即時數據。我們所做的事情和他們所做的事情有一些重疊和能力。我認為在實踐中，我們傾向於為不同的選民服務。我們傾向於擁有更多類型的即時操作應用系統、軟體工程師，他們傾向於擁有更多資料工程師、分析師、資料科學家和用戶群。

But for sure, there's some things that you could do in either product. On the whole though, I think we've been pretty complementary in going to market together. And even though that kind of overlapping feature set may increase, I think that will remain the case. Ultimately, customers have chosen us for that real-time hub of integration for data and many customers have chosen Databricks as the kind of late destination where all the data goes for historical analysis. And so ultimately, customers want those things to work together, we're happy to start with together.

但可以肯定的是，這兩種產品都可以做些什麼。總的來說，我認為我們在共同進軍市場方面具有很強的互補性。儘管這種重疊的功能集可能會增加，但我認為情況仍然會如此。最終，客戶選擇我們作為資料即時整合中心，許多客戶選擇 Databricks 作為所有資料進行歷史分析的後期目的地。因此，最終，客戶希望這些東西能夠協同工作，我們很高興能一起開始。

Great. This concludes our earnings call today. Thanks again for joining us. Have a nice evening, everyone. Take care.

偉大的。今天的財報電話會議到此結束。再次感謝您的參與。祝大家晚上愉快。小心。

Thanks all.

謝謝大家。