Confluent Inc (CFLT) 2024 Q4 法說會逐字稿

Welcome to the Confluent Q4 and Fiscal Year 2024 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 第四季和 2024 財年財報電話會議。我是投資者關係部的 Shane Xie，和我一起的是共同創辦人兼執行長 Jay Kreps；以及財務長 Rohan Sivaram。

During today's call, management will make forward-looking statements regarding our business, operations, sales strategy, market and product positioning, strategic partnerships, financial performance and future prospects, including statements regarding our financial guidance for the fiscal first quarter of 2025 and fiscal year 2025.

在今天的電話會議上，管理層將就我們的業務、營運、銷售策略、市場和產品定位、策略合作夥伴關係、財務業績和未來前景做出前瞻性陳述，包括有關 2025 財年第一季和 2025 財年財務指引的陳述。

These forward-looking statements are subject to risks and uncertainties which could cause actual results to differ materially from those anticipated by these statements. 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.

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。有關可能導致實際結果不同的風險因素的更多信息，包含在我們向美國證券交易委員會提交的最新 10-Q 表中。除非法律要求，我們不承擔今天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是以非 GAAP 為基礎表示的，並且所有比較都是按同比進行的。

We use these non-GAAP financial measures internally to facilitate analysis of our financial and business trends and for internal planning and forecasting purposes. 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.

我們在內部使用這些非公認會計準則財務指標來促進對財務和業務趨勢的分析以及用於內部規劃和預測目的。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。

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 investors.confluent.io. References to profitability on today's call refer to non-GAAP operating margin unless stated otherwise.

這些 GAAP 和非 GAAP 財務指標之間的對帳包含在我們的收益新聞稿和補充財務報表中，可在我們的 IR 網站 investors.confluent.io 上找到。除非另有說明，今天電話會議上提到的獲利能力均指非公認會計準則營業利潤率。

With that, I'll hand it over to Jay.

說完這些，我就把它交給傑伊。

Thanks, Shane. Good afternoon everyone and welcome to our fourth quarter earnings call. I'm happy to report we exceeded all guided metrics. Subscription revenue grew 24% to $251 million. Confluent Cloud revenue grew 38% to $138 million and non-GAAP operating margin was 5% our third consecutive positive quarter.

謝謝，肖恩。大家下午好，歡迎參加我們的第四季財報電話會議。我很高興地報告，我們超出了所有指導指標。訂閱收入成長24%至2.51億美元。Confluent Cloud 營收成長 38% 至 1.38 億美元，非 GAAP 營業利潤率為 5%，這是我們連續第三個季度實現正成長。

These results highlight our customers need for a complete data streaming platform, our world class innovation engine and our successful transformation to a consumption driven go-to-market model.

這些結果凸顯了我們的客戶對完整的資料流平台、我們世界一流的創新引擎以及我們成功轉型為消費驅動的市場進入模式的需求。

I'm also thrilled to announce a major expansion of our strategic partnership with Databricks. This collaboration brings together Confluent's complete data streaming platform and Databricks Data Intelligence platform to empower enterprises with real time data for AI driven decision making.

我也很高興地宣布我們與 Databricks 的策略夥伴關係將大大擴展。此次合作將 Confluent 的完整數據流平台和 Databricks 數據智慧平台結合在一起，為企業提供即時數據，以實現人工智慧驅動的決策。

The bidirectional integration between Confluence Tableflow with Delta Lake and Databricks Unity Catalog will provide consistent real time data across operational and analytical systems that is discoverable, secure and trustworthy.

Confluence Tableflow 與 Delta Lake 和 Databricks Unity Catalog 之間的雙向整合將在營運和分析系統中提供可發現、安全且可信賴的一致即時資料。

Built on open standards, these integrations ensure real time interoperability and flexibility across diverse ecosystems. Seamlessly working with the tools and data infrastructure that companies already use. Operational data from Confluent becomes a first class citizen in Databricks and the insights created within Databricks can be driven back into any processor in the enterprise via Tableflow and Confluent.

這些整合建立在開放標準之上，確保了跨不同生態系統的即時互通性和靈活性。與公司已經使用的工具和資料基礎設施無縫協作。Confluent 的營運資料成為 Databricks 中的一等公民，而 Databricks 中創建的洞察可以透過 Tableflow 和 Confluent 驅動回企業中的任何處理器。

This is an incredibly exciting prospect for companies worldwide. This partnership extends Tableflow's reach across the analytics ecosystem, positioning Confluent as the leading vendor for delivering data across the enterprise. This is crucial for scaling AI innovation by giving application developers, data engineers, data analysts and data scientists a single real time source of truth to power advanced analytics and Next-Gen AI driven applications.

對於全球的公司來說，這是一個令人難以置信的令人興奮的前景。此次合作擴大了 Tableflow 在分析生態系統中的影響力，使 Confluent 成為企業間資料傳輸的領先供應商。這對於擴大人工智慧創新至關重要，因為它為應用程式開發人員、資料工程師、資料分析師和資料科學家提供了單一的即時事實來源，為高級分析和下一代人工智慧驅動的應用程式提供支援。

Additionally, our partnership with Databricks includes comprehensive go-to-market efforts encompassing field and partner enablement solutions, architectures, co-marketing and co-selling initiatives. Together we will enable businesses to harness the power of real time data to build sophisticated AI driven applications for their most critical use cases. Tableflow continues to be one of the most exciting and powerful additions to our data streaming platform.

此外，我們與 Databricks 的合作包括全面的行銷工作，涵蓋現場和合作夥伴支援解決方案、架構、聯合行銷和聯合銷售計劃。我們將共同幫助企業利用即時數據的力量，為其最關鍵的用例建立複雜的人工智慧驅動的應用程式。Tableflow 持續成為我們資料流平台中最令人興奮且最強大的新增功能之一。

Tableflow exposes Confluent's data streams as continuously updating tables in cloud object storage using open standards like Apache Iceberg and now Delta Lake. This allows Confluent to act as the bridge between operational systems that run the business and analytical platforms like Athena, BigQuery Databricks and Snowflake that extract valuable insights. Historically, operational and analytical systems operated in separate silos managed by different teams and composed of different technologies and workflows.

Tableflow 使用 Apache Iceberg 和現在的 Delta Lake 等開放標準將 Confluent 的資料流公開為雲端物件儲存中持續更新的表。這使得 Confluent 可以充當運行業務的作業系統與提取有價值見解的分析平台（如 Athena、BigQuery Databricks 和 Snowflake）之間的橋樑。從歷史上看，操作和分析系統在由不同團隊管理的獨立孤島中運行，並由不同的技術和工作流程組成。

To compensate, teams built point to point batch oriented pipelines to move operational data to the analytical estate, resulting in insights based on outdated and incomplete data. In the era of real time data and AI, scale data simply isn't enough for modern businesses. Data streaming has emerged as a major data platform, putting Confluent in a unique position to replace the brittle connections between operations and analytics with something that is much more robust and much more scalable.

為了彌補這一點，團隊建立了點對點批量導向管道，將操作數據轉移到分析區域，從而根據過時和不完整的數據獲得洞察。在即時數據和人工智慧時代，規模數據對於現代企業來說根本不夠。資料流已成為一個主要的資料平台，使 Confluent 處於獨特的地位，可以用更強大、更具可擴展性的連接取代操作和分析之間的脆弱連接。

In fact, our mission and business model is built on creating continuous, trustworthy and discoverable streams of data that can be consumed anywhere in the organization. Tableflow further realizes our mission by making real time, consistent and secure data available as structured tables in cloud object storage using an open table format. With Tableflow, data is defined and stored once but accessible across multiple processors, breaking down silos and enabling faster, more accurate insights.

事實上，我們的使命和商業模式建立在創建可在組織內的任何地方使用的連續、可信任和可發現的資料流的基礎上。Tableflow 透過使用開放式表格式將即時、一致且安全的資料作為結構化表格儲存在雲端物件儲存中，進一步實現了我們的使命。使用 Tableflow，資料只需定義和儲存一次，但可在多個處理器上訪問，從而打破資料孤島並提供更快、更準確的洞察。

Tableflow was met with excitement across our customer base and is already delivering real benefits to early customers. Take a US digital native customer that simplifies bookings and operations for ground transportation companies across three continents as an example. Their customers require real time data for a seamless online booking experience with accurate pricing and precise arrival and drop off times.

Tableflow 受到了我們整個客戶群的熱烈歡迎，並且已經為早期客戶帶來了真正的利益。以美國數位原生客戶為例，為三大洲的地面運輸公司簡化了預訂和營運。他們的客戶需要即時數據，以獲得無縫的線上預訂體驗、準確的價格以及精確的到達和離開時間。

Previously, they did this by loading data into Snowflake with batch jobs for analytics. However, this caused delays and increased data processing costs. So the digital native customer implemented Tableflow as part of our early access program to bridge their operational data with their analytics systems.

此前，他們透過使用批次作業將資料載入到 Snowflake 中進行分析來實現這一點。然而，這導致了延遲並增加了數據處理成本。因此，數位原生客戶實施了 Tableflow 作為我們早期存取計畫的一部分，以將其營運數據與分析系統連接起來。

Using Apache Iceberg with Tableflow, Confluent Cloud automatically performs the required data preprocessing and ingested into Snowflake as high quality data. The customer can now derive business insights directly from the data faster and at a much lower cost, significantly speeding up analysis and decision making for its transportation customers.

透過將 Apache Iceberg 與 Tableflow 結合使用，Confluent Cloud 可自動執行所需的資料預處理並將其作為高品質資料匯入 Snowflake。客戶現在可以更快、更低成本地直接從數據中獲取商業洞察，大大加快其運輸客戶的分析和決策速度。

Tableflow is also a great example of what we discussed last quarter. Our third wave of growth will come from being a complete data streaming platform. Over the last year, we continue to see strong DSP momentum in our Cloud Connect process and govern accounted for approximately 13% of our cloud business with consumption growing substantially faster than overall cloud.

Tableflow 也是我們在上個季度討論的一個很好的例子。我們的第三次成長將來自於完整的資料流平台。在過去的一年裡，我們繼續看到 Cloud Connect 流程中 DSP 的強勁發展勢頭，並且治理占我們雲端業務的約 13%，其消費成長速度遠遠快於整體雲端業務。

No other vendor is as intensely focused as us on building and delivering a complete data streaming platform that connects, streams, processes and governs data continuously in motion. This puts Confluent in a highly advantageous position because over time, as more parts of our platform are used, a virtuous cycle of adoption drives stickiness and more growth.

沒有其他供應商像我們一樣專注於建立和交付完整的資料流平台，以連接、傳輸、處理和管理持續運動的資料。這使 Confluent 處於非常有利的地位，因為隨著時間的推移，隨著我們平台的更多部分被使用，良性的採用循環將推動黏性和更多的成長。

Above all else, we believe a complete data streaming platform solves our customer’s hardest problems, reduces complexity and provides substantial ROI. Let me walk through a few customer examples.

最重要的是，我們相信完整的資料流平台可以解決客戶最棘手的問題、降低複雜性並提供可觀的投資報酬率。讓我來看幾個客戶案例。

Zazzle is a global online marketplace and platform for creating and customizing unique designs. The company is built on a foundation of technological innovation that connects customers, creators and makers powering the creation of almost anything.

Zazzle 是一個用於創建和自訂獨特設計的全球線上市場和平台。該公司建立在技術創新的基礎上，將客戶、創造者和製造商聯繫起來，為幾乎所有事物的創造提供動力。

As a leading global marketplace, Zazzle needs to efficiently process massive amounts of clickstream data to deliver personalized experiences across its unparalleled range of products and designs. With hundreds of thousands of independent creators making a living through the Zazzle platform and customers in every country worldwide, maintaining platform performance while eliminating redundant data became critical.

作為全球領先的市場，Zazzle 需要高效處理大量點擊流數據，以便在其無與倫比的產品和設計系列中提供個人化的體驗。由於有數十萬獨立創作者透過 Zazzle 平台謀生，且客戶遍布全球各個國家，因此維持平台效能並消除冗餘資料變得至關重要。

So Zazzle implemented Confluent's fully managed Flink offering to transform their largest data pipeline. By shifting stream processing earlier in the pipeline before writing to Google BigQuery, Zazzle reduced storage and computation costs while delivering more relevant product recommendations directly impacting revenue. But this is just the start.

因此，Zazzle 實施了 Confluent 的完全託管的 Flink 產品來轉變其最大的資料管道。透過在寫入 Google BigQuery 之前將串流處理轉移到管道早期，Zazzle 降低了儲存和運算成本，同時提供了更相關的產品推薦，從而直接影響收入。但這只是一個開始。

With this foundation in place, Zazzle plans to expand their Flink usage to additional data streams to further enhance real time personalization capabilities. A leading European grocery delivery service has also built its data architecture on Confluent's data streaming platform and leverages our Apache Flink offering to optimize real time order management. This digital first company manages the entire grocery delivery process, overseeing daily inventory updates for thousands of products across local warehouses.

有了這個基礎，Zazzle 計劃將 Flink 的使用擴展到其他資料流，以進一步增強即時個人化功能。一家領先的歐洲雜貨配送服務也在 Confluent 的資料流平台上建立了其資料架構，並利用我們的 Apache Flink 產品來優化即時訂單管理。這家數位化優先的公司管理整個雜貨配送流程，監督當地倉庫數千種產品的每日庫存更新。

To meet their ambitious promise of delivery within 20 minutes, they require a robust real time stream processing solution to monitor and manage orders end to end from warehouse to driver to customer. Previously, their reliance on REST API calls and event driven architectures using open source Kafka led to inefficiencies, delays and fragmented data sources.

為了實現 20 分鐘內送達的宏偉承諾，他們需要一個強大的即時串流處理解決方案來端到端地監控和管理從倉庫到司機再到客戶的訂單。以前，他們依賴 REST API 呼叫和使用開源 Kafka 的事件驅動架構，這導致了效率低下、延遲和資料來源碎片化。

By transitioning to Confluent Cloud with Apache Flink, they now seamlessly process key data streams, including grocery orders and warehouse locations into a unified order topic that is easy to manage and Consume.

透過使用 Apache Flink 過渡到 Confluent Cloud，他們現在可以無縫地處理關鍵資料流，包括雜貨訂單和倉庫位置，並將其處理成易於管理和使用的統一訂單主題。

Flink further enables them to aggregate orders, assign them to delivery drivers and track driver speed and location to provide accurate delivery time estimates for customers. This streamlined solution has significantly enhanced their order management capabilities, resulting in reduced development costs, minimized downtime and a faster, more seamless experience for both drivers and customers.

Flink 進一步使他們能夠匯總訂單、將其分配給送貨司機並追蹤司機的速度和位置，以便為客戶提供準確的送貨時間估計。這種簡化的解決方案顯著增強了他們的訂單管理能力，從而降低了開發成本，最大限度地減少了停機時間，並為司機和客戶提供了更快、更無縫的體驗。

A top three Fortune Global 100 telecom customer shows the value of a complete DSP as consumers make calls, send texts and access the Internet, a constant flow of data from mobile devices is sent to cell towers across the network. This data then needs to be shared with partners and subsidiaries in real time so they can optimize network coverage and analyze customer usage patterns.

《財富》全球 100 強前三大電信客戶展示了完整 DSP 的價值，因為消費者在撥打電話、發送簡訊和訪問互聯網時，來自行動裝置的恆定數據流會透過網路發送到手機訊號塔。然後，需要即時與合作夥伴和子公司共享這些數據，以便他們可以優化網路覆蓋並分析客戶使用模式。

The telco's previous legacy streaming service was unable to deliver the scale and speed required for the mission critical cell tower workloads, so they turned to Confluence Complete DSP for real time visibility and improved scalability across their wireless network. With Confluent's data streaming platform, the telecom provider streams mobile data from 70,000 US cell towers with connectors feeding data into lakes and warehouses.

該電信公司先前的傳統串流媒體服務無法提供關鍵任務蜂窩塔工作負載所需的規模和速度，因此他們轉向 Confluence Complete DSP 來實現即時可視性並提高整個無線網路的可擴展性。借助 Confluent 的數據流平台，該電信提供商可以從美國 70,000 個手機訊號塔傳輸行動數據，並透過連接器將數據傳輸到湖泊和倉庫。

Stream processing provides real time insights into network reliability and performance of mobile devices and wireless plants, which is then shared with partners and subsidiaries. This allows the telecom provider to unlock new revenue streams by making faster, smarter decisions ranging from where to invest in 5G capabilities to where to lay down fiber optic cables.

串流處理提供有關行動裝置和無線設備的網路可靠性和效能的即時洞察，然後與合作夥伴和子公司共享。這使得電信業者能夠做出更快、更明智的決策，包括在哪裡投資 5G 功能到在哪裡鋪設光纖電纜，從而開闢新的收入來源。

Citizens bank, one of America's oldest and largest financial institutions, transitioned from open source Kafka to Confluent DSP to strengthen its digital banking offerings. Today's banking customers expect real time experiences like immediate fraud alerts and instant access to deposited funds. But with an expanding data footprint, Citizens struggled self managing open source Kafka, which required 20 full time employees just to support, so they turned to Confluent's complete data streaming platform.

公民銀行是美國歷史最悠久、規模最大的金融機構之一，它從開源 Kafka 過渡到 Confluent DSP，以加強其數位銀行服務。當今的銀行客戶期望獲得即時體驗，例如即時詐欺警報和即時獲取已存入的資金。但隨著資料足跡的不斷擴大，公民難以自行管理開源 Kafka，僅支援就需要 20 名全職員工，因此他們轉向 Confluent 的完整資料流平台。

Using Confluent Cloud, Citizens Bank connects data from sources like consumer checking account, credit cards and FICO fraud scores, generating real time actionable insights. Stream governance ensures the bank's data is high quality and trustworthy by using schemas to reduce data inconsistencies and ensure compliance. With Confluent, Citizens Bank has reduced IT costs by 30% and saved $1.2 million annually by reducing fraud false positives by 15%.

利用 Confluent Cloud，公民銀行連接來自消費者支票帳戶、信用卡和 FICO 詐欺評分等來源的數據，產生即時可操作的見解。流治理透過使用模式來減少資料不一致並確保合規性，從而確保銀行資料的高品質和可信度。借助 Confluent，公民銀行將 IT 成本降低了 30%，並且透過將詐欺誤報率降低了 15%，每年節省了 120 萬美元。

Additionally, the bank has seen a 20% increase in customer engagement, a 40% faster loan processing time and a 10 point increase in net promoter score from improved customer interactions.

此外，由於客戶互動改善，該銀行的客戶參與度提高了 20%，貸款處理時間加快了 40%，淨推薦值提高了 10 個百分點。

And finally, I'd like to close with an update on the innovation and momentum in our Kafka business including wins and early WarpStream success.

最後，我想介紹 Kafka 業務的創新和發展勢頭，包括勝利和 WarpStream 早期的成功。

At Confluent our strategy starts with capturing the vast opportunity presented by Apache Kafka, which serves as the real time backbone for more than 150,000 organizations worldwide. Doing this requires having the right offering to meet the performance, TCO and reliability requirements of an incredibly wide variety of use cases.

在 Confluent，我們的策略始於抓住 Apache Kafka 提供的巨大機遇，它是全球超過 150,000 個組織的即時主幹。要做到這一點，需要有正確的產品來滿足各種用例的效能、TCO 和可靠性要求。

In 2024 we invested in offerings to better capture the entirety of this market. One area we targeted was the high volume cost sensitive workloads common in observability, analytics, security and IoT. Now generally available freight clusters are a great offering for this segment. They're designed to attract cost conscious customers with high throughput, latency tolerant workloads.

2024 年，我們將投資產品以更好地佔領整個市場。我們所針對的領域之一是可觀察性、分析、安全性和物聯網中常見的大量成本敏感型工作負載。現在普遍可用的貨運集群對這領域來說是一個很好的選擇。它們旨在透過高吞吐量、容忍延遲的工作負載來吸引註重成本的客戶。

We paired this with WarpStream, which serves a similar customer base but allows running directly in the customer's account, adding a low friction midpoint between self-managed and SaaS. We've seen great traction with both of these offerings in helping us penetrate new accounts, particularly in the large scale digital native segment with offerings that address use cases that demand zero access security and have tolerance for relaxed latency. This is playing out already.

我們將其與 WarpStream 配對，它服務於類似的客戶群，但允許直接在客戶的帳戶中運行，在自我管理和 SaaS 之間增加了一個低摩擦的中間點。我們看到，這兩種產品在幫助我們開拓新帳戶方面都具有很大的吸引力，特別是在大規模數位原生領域，這些產品可以滿足需要零存取安全性且對寬鬆延遲具有容忍度的用例。這一切已經發生了。

In fact, every WarpStream deal closed since acquisition was from our digital native cohort, including companies like Elastic, the search AI Company used by over half the Fortune 500 and Cursor, the AI code editor that's become one of the hottest names in AI and the vast majority are net new customers to Confluent. We're excited for the future of WarpStream and continue to invest in its innovation, including a whole host of new features released in January.

事實上，自收購以來，每一筆 WarpStream 交易都來自我們的數位原生群體，其中包括 Elastic（一家被超過一半的《財富》500 強企業使用的搜尋 AI 公司）和 Cursor（一家已成為 AI 領域最熱門的名字之一的 AI 代碼編輯器）等公司，絕大多數都是 Confluent 的淨新客戶。我們對 WarpStream 的未來充滿期待，並將繼續投資於其創新，包括一月份發布的一系列新功能。

One expansion with WarpStream this quarter was with a digital native customer that helps developer’s access real time data. Their large scale and high storage requirements made this a significant cost driver. Previously they used Redpanda, but as their usage increased, so did their bill. The unsustainable costs combined with performance issues with Redpanda led to their decision to adopt WarpStream.

本季 WarpStream 的擴充功能是與數位原生客戶合作，幫助開發人員存取即時資料。它們的規模和高儲存需求使其成為重要的成本驅動因素。之前他們使用 Redpanda，但隨著使用量的增加，帳單也隨之增加。難以承受的成本加上 Redpanda 的效能問題促使他們決定採用 WarpStream。

Switching from Redpanda to WarpStream delivered immediate benefits for the customer, including nearly 10x cost savings and the ability to handle much higher data volumes on their platform. This has unlocked entirely new use cases for the customer that weren't possible before.

從 Redpanda 轉換到 WarpStream 為客戶帶來了即時的好處，包括節省近 10 倍的成本，並且能夠在其平台上處理更大的資料量。這為客戶開啟了以前不可能實現的全新用例。

In closing, we're pleased with our strong performance this year. The progress we made and the innovation we delivered to customers in 2024 set us up to capture more of the significant market opportunity ahead.

最後，我們對今年的出色表現感到非常滿意。我們在 2024 年取得的進步和為客戶帶來的創新使我們能夠抓住未來更多重大的市場機會。

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

說完這些，我會把話題交給羅漢 (Rohan)。

Thanks Jay. Good afternoon everyone and thank you for joining our earnings call. We had a strong finish to fiscal year 2024, delivering durable growth, significant operating leverage and positive free cash flow.

謝謝傑伊。大家下午好，感謝您參加我們的財報電話會議。我們在 2024 財年取得了強勁成績，實現了持久的成長、顯著的經營槓桿和正的自由現金流。

In fiscal year 2024, subscription revenue grew 26% to $922.1 million, non-GAAP operating margin improved 10 percentage points to 2.9% and free cash flow margin improved 17 percentage points to 1%, making fiscal year 2024 our first non-GAAP profitable year in the company's history.

2024財年，訂閱收入成長26%至9.221億美元，非GAAP營業利潤率提高10個百分點至2.9%，自由現金流利潤率提高17個百分點至1%，使2024財年成為公司歷史上第一個非GAAP盈利年。

These results well exceeded our initial expectations across all guided metrics entering the year, reflecting the power of our data streaming platform and our team's excellent execution against our large market opportunity.

這些結果遠遠超出了我們今年所有指導指標的初步預期，反映了我們數據流平台的強大功能以及我們團隊針對巨大市場機會的出色執行力。

Turning to the Q4 results, subscription revenue grew 24% to $250.6 million, exceeding the high end of our guidance and representing 96% of total revenue. Confluent platform revenue grew 10% to $112.7 million and accounted for 45% of subscription revenue.

回顧第四季度業績，訂閱收入成長 24% 至 2.506 億美元，超過我們預期的最高水準，佔總收入的 96%。Confluent 平台營收成長 10% 至 1.127 億美元，佔訂閱收入的 45%。

Demand for enterprise grade data streaming in regulated industries remain a key growth driver for Confluent Platform. Confluent Cloud revenue grew 38% to $137.9 million and accounted for 55% of subscription revenue compared to 49% a year ago.

受監管產業對企業級資料流的需求仍然是 Confluent Platform 的主要成長動力。Confluent Cloud 營收成長 38% 至 1.379 億美元，佔訂閱收入的 55%，而一年前則為 49%。

During the quarter, we saw stable consumption and continued use case expansion across our large customer base, driving robust growth in our core streaming business. Additionally, we saw continued adoption of new components of our data streaming platform. DSP Cloud consumption which includes connect, process and govern, grew substantially faster than overall cloud and accounted for approximately 13% of our cloud business.

在本季度，我們看到龐大的客戶群的消費量穩定且用例持續擴展，推動了我們核心串流媒體業務的強勁成長。此外，我們看到資料流平台的新元件不斷被採用。DSP 雲端消費包括連線、處理和管理，其成長速度遠快於整體雲端業務，約占我們雲端業務的 13%。

Turning to geographical mix of total revenue, revenue from the US grew 20% to $153.7 million. Revenue growth from outside the US accelerated to 26% and was $107.5 million. Earlier today we announced a multiyear strategic partnership with Jio Platforms Ltd, an Indian multinational technology company and a subsidiary of Reliance Industries Limited.

從總收入的地理結構來看，來自美國的營收成長 20%，達到 1.537 億美元。美國以外地區的營收成長率加速至 26%，達到 1.075 億美元。今天早些時候，我們宣布與印度跨國科技公司、信實工業有限公司 (Reliance Industries Limited) 的子公司 Jio Platforms Ltd 建立多年戰略合作夥伴關係。

By making Confluent Cloud available on Jio Cloud Services and Confluent Platform as a managed service the partnership is expected to accelerate India's development of Gen AI and Next Gen applications, delivering the power of real time data to more businesses in the country.

透過在 Jio 雲端服務和 Confluent 平台上提供 Confluent Cloud 作為託管服務，此次合作可望加速印度的 Gen AI 和下一代應用程式的發展，為該國更多企業提供即時資料的力量。

Moving on to rest of the income statement, I'll be referring to non-GAAP results unless stated otherwise. While continuing to drive top line growth at scale, we once again demonstrated significant operating leverage in our model.

至於損益表的其餘部分，除非另有說明，我將參考非 GAAP 結果。在繼續大規模推動營收成長的同時，我們再次在我們的模型中展示了顯著的營運槓桿。

In Q4 subscription gross margin increased 90 basis points to 82%, primarily driven by the economies of scale in Confluent Cloud. Operating margin was 5.2%, exceeding our guidance of approximately 2% and was primarily driven by revenue and gross margin outperformance.

第四季訂閱毛利率增加 90 個基點至 82%，主要得益於 Confluent Cloud 的規模經濟。營業利益率為 5.2%，超過我們約 2% 的預期，主要得益於收入和毛利率的優異表現。

Free cash flow margin expanded approximately 8 percentage points to reach a record high of 11.1%. Net income per share was $0.09 using 362.1 million diluted weighted average shares outstanding. Fully diluted share count under the treasury stock method was approximately 370.1 million.

自由現金流利潤率擴大約8個百分點，達到11.1%的歷史新高。以 3.621 億股稀釋加權平均流通股計算，每股淨利為 0.09 美元。以庫存股法計算，完全稀釋股數約為 3.701 億股。

Net income per share was $0.09 using 362.1 million diluted weighted average shares outstanding. Fully diluted share count under the treasury stock method was approximately 370.1 million and our balance sheet remains strong, ending the fourth quarter with $1.91 billion in cash, cash equivalents and marketable securities.

以 3.621 億股稀釋加權平均流通股計算，每股淨利為 0.09 美元。根據庫存股法，完全稀釋的股份數量約為 3.701 億股，我們的資產負債表依然強勁，第四季末的現金、現金等價物和有價證券為 19.1 億美元。

Turning now to other business metrics, Q4 win rate for new business once again saw a notable increase both year-over-year and sequentially. Our win rate against the CSP offerings and smaller startups remained well above 90%. This underscores the strength of our complete data streaming platform, providing outstanding performance with unparalleled reliability and flexibility and favorable TCO and ROI for our customers.

現在來看看其他業務指標，第四季新業務的成功率再次出現年比和季比顯著成長。我們對抗 CSP 產品和小型新創公司的成功率仍然遠高於 90%。這凸顯了我們完整資料流平台的實力，為我們的客戶提供卓越的效能、無與倫比的可靠性和靈活性以及有利的 TCO 和 ROI。

This coupled with our consumption transformation has driven a year of high velocity land and expand. We ended fiscal year 2024 with approximately 5,800 customers, representing an increase of 840 customers, nearly double the total increase from the previous year.

再加上我們的消費轉型，推動了一年的高速落地與擴張。截至 2024 財年，我們擁有約 5,800 名客戶，增加了 840 名客戶，幾乎是去年總成長量的兩倍。

New customers in the quarter include a top five video gaming company, one of the world's largest sports media outlets, a Fortune 100 pharmaceuticals company, a global cruise operator, a leading European airliner, and many more.

本季的新客戶包括排名前五的電玩公司、全球最大的體育媒體之一、財富 100 強制藥公司、全球郵輪營運商、歐洲領先的航空公司等等。

We also drove robust expansion in our large customer base. We grew our 100K plus ARR customer count to 1,381, an increase of 12% from a year ago. This represents approximately 24% of our total customers, a key success indicator of our expansion strategy after landing a customer. These 100K plus ARR customers represented approximately 90% of our revenue.

我們也推動了龐大客戶群的強勁擴張。我們的 10 萬以上 ARR 客戶數量成長至 1,381 人，比一年前增加了 12%。這約占我們客戶總數的 24%，這是我們獲得客戶後擴張策略成功的關鍵指標。這些超過 10 萬的 ARR 客戶約占我們收入的 90%。

Our 1 million plus ARR customers grew even faster, accelerating to 23% and ending the quarter at 194 customers. New 1 million plus ARR customers include customers from a wide variety of industries spanning financial services, healthcare, manufacturing and logistics, retail technology and more.

我們的 100 多萬 ARR 客戶成長速度更快，加速至 23%，本季末客戶數量達到 194 位。新的 100 多萬 ARR 客戶包括來自金融服務、醫療保健、製造和物流、零售技術等各行各業的客戶。

Q4 NRR was 117% while GRR remained above 90%. Our stabilized NRR in recent quarters, coupled with continued strength in GRR provides a solid foundation for delivering on our growth target this year. Before turning to our guidance, I would like to discuss Confluent’s positioning for 2025 and beyond.

Q4 NRR 為 117%，而 GRR 仍保持在 90% 以上。我們最近幾季的 NRR 趨於穩定，再加上 GRR 持續走強，為今年的成長目標奠定了堅實的基礎。在談到我們的指導之前，我想先討論一下 Confluent 在 2025 年及以後的定位。

2024 was in many ways a consequential year. First, we have optimized our pricing and packaging with the introduction of Enterprise Clusters, Freight Clusters and the acquisition of WarpStream. This has significantly increased our serviceable, addressable market as we are positioned to deliver best-in-class TCO for a broad range of use cases across self-managed, fully managed and BYOC deployment models.

從許多方面來說，2024 年都是重要的一年。首先，我們透過引入企業集群、貨運集群以及收購 WarpStream 優化了我們的定價和包裝。這大大擴大了我們的可服務、可尋址市場，因為我們致力於為自我管理、完全管理和 BYOC 部署模型中的廣泛用例提供一流的 TCO。

Second, we extended our technology lead by expanding our DSP capabilities with more than 200 features and capabilities across Stream Connect, Process and Govern. With the upcoming Tableflow GA release, we will expand our growth vector by unifying the operational and analytical estates in data management.

其次，我們透過擴展 DSP 功能，在 Stream Connect、Process 和 Govern 中添加了 200 多種特性和功能，從而擴大了我們的技術領先優勢。隨著即將發布的 Tableflow GA 版本，我們將透過統一資料管理中的營運和分析領域來擴展我們的成長向量。

Finally, we have successfully transitioned to the next generation go-to-market model focusing our team on consumption based selling. By increasing consumption of our data streaming platform, we help customers realize substantial ROI for powering their mission critical and real time AI workloads.

最後，我們成功過渡到下一代市場進入模式，我們的團隊專注於基於消費的銷售。透過增加我們資料流平台的消費，我們幫助客戶實現可觀的投資回報，以支援他們的關鍵任務和即時人工智慧工作負載。

As we drive ROI based expansions throughout our customers data streaming journey, we expect our growth and profitability profile to strengthen over time. Following a year of substantial transformation, we have established a major data platform for the enterprise, unlocking the power of data streaming for thousands of customers and operating at a $1 billion plus revenue run rate.

隨著我們在客戶資料流傳輸過程中推動基於投資回報率的擴張，我們預計我們的成長和獲利狀況將隨著時間的推移而增強。經過一年的實質轉型，我們為企業建立了一個主要的數據平台，為數千名客戶釋放了資料流的力量，營運收入運行率超過 10 億美元。

Given the strong foundation we set last year, we expect to begin reaping the benefits in 2025. Our objective is to continue soaking up the world's Kafka and to establish 2025 as the year of DSP. We will support these initiatives with the resource allocation strategy, focusing on efficient growth and prioritizing our investments in expanding our DSP capabilities, hiring and enabling our team to sell DSP, and forming strategic partnerships and alliances. We look forward to driving durable and efficient growth in 2025 as we execute against our large and growing market opportunity.

鑑於我們去年打下的堅實基礎，我們預計將在 2025 年開始收穫成果。我們的目標是繼續吸收世界上的 Kafka，並將 2025 年確立為 DSP 年。我們將透過資源配置策略支持這些舉措，專注於高效成長，優先投資擴大我們的 DSP 能力、聘用和支持我們的團隊銷售 DSP，以及建立策略合作夥伴關係和聯盟。我們期待在 2025 年抓住巨大且不斷成長的市場機遇，實現持久高效的成長。

Now let's turn to our guidance. We are providing Q1 and fiscal year 2025 subscription revenue outlook ahead of expectations. In addition to guiding fiscal year 2025 non-GAAP operating margin within our midterm target set at the time of our IPO. For the first fiscal quarter of 2025, we expect subscription revenue to be in the range of $253 million to $254 million, representing growth of approximately 22% to 23%.

現在讓我們來談談我們的指導。我們提供了超出預期的第一季和 2025 財年的訂閱收入預測。除了指導 2025 財年非 GAAP 營業利潤率達到我們首次公開募股時設定的中期目標。對於 2025 財年第一季度，我們預計訂閱營收將在 2.53 億美元至 2.54 億美元之間，成長約 22% 至 23%。

Non-GAAP operating margin to be approximately 3% and non-GAAP net income per diluted share to be in the range of $0.06 to $0.07. For fiscal year 2025, we expect subscription revenue to be in the range of $1.117 billion to 1.121 billion, representing growth of approximately 21% to 22%.

非公認會計準則營業利潤率約為 3%，非公認會計準則每股攤薄淨利潤在 0.06 美元至 0.07 美元之間。對於 2025 財年，我們預計訂閱收入將在 11.17 億美元至 11.21 億美元之間，成長約 21% 至 22%。

Non-GAAP operating margin to be approximately 6% and non-GAAP net income per diluted share to be approximately $0.35. I'd also like to provide a few modeling points. For subscription revenue seasonality, at the midpoint of our guidance, we expect the first half of fiscal year 2025 will be approximately 46.5% in line with the average of the first half seasonality in the last two years.

非公認會計準則營業利潤率約 6%，非公認會計準則每股攤薄淨利潤約為 0.35 美元。我還想提供一些建模要點。對於訂閱收入季節性，在我們指導的中點，我們預計 2025 財年上半年將與過去兩年上半年的平均季節性一致，約為 46.5%。

For Cloud revenue, we are comfortable with the current consensus dollar estimate for fiscal year 2025 and we expect to see approximately one point of increase in cloud subscription revenue mix each quarter with the Q4 25 exit of approximately 59% to 60%.

對於雲端收入，我們對 2025 財年的當前一緻美元估計感到滿意，並且我們預計每個季度雲端訂閱收入組合將增加約一個點，2025 年第四季度的增幅約為 59% 至 60%。

For free cash flow margin, we expect a one-time negative impact of approximately 15 points to Q1, 2025 or approximately 3 points to 4 points to fiscal year 2025 resulting from a change to timing of cash compensation payments for most of our non-go-to-market employees.

對於自由現金流利潤率，由於大多數非上市員工的現金補償支付時間發生變化，我們預計對 2025 年第一季將產生約 15 個點的一次性負面影響，或對 2025 財年產生約 3 個點至 4 個點的一次性負面影響。

Excluding this onetime impact, we expect adjusted free cash flow margin for fiscal year 2025 to be approximately 6%. In closing, our strong finish to 2024 is a testament to our large TAM, the market leadership of our technology platform and our world class team.

除此一次性影響外，我們預計 2025 財年的調整後自由現金流利潤率約為 6%。最後，我們在 2024 年的強勁收官證明了我們龐大的 TAM、我們技術平台的市場領導地位以及我們世界一流的團隊。

Powered by the secular tailwinds of cloud, data and AI, we are incredibly excited to take advantage of the market opportunity ahead. Before turning to Q&A, we will host Investor Day 2025 on March 6th in San Francisco. If you are interested in attending in person, please contact the IR team at investors@onfluent io.

在雲端、數據和人工智慧的長期推動下，我們非常高興能夠利用未來的市場機會。在進入問答環節之前，我們將於 3 月 6 日在舊金山舉辦 2025 年投資者日。如果您有興趣親自參加，請聯絡 IR 團隊 investor@onfluent io。

Now Jay and I will take your questions.

現在我和傑伊將回答大家的問題。

(Operator Instructions)

（操作員指令）

Matt Hedberg with RBC followed by Wells Fargo.

加拿大皇家銀行 (RBC) 的 Matt Hedberg 和富國銀行 (Wells Fargo) 的總裁兼執行長。

All right, thanks a lot, Shane. Congrats team on the results. Really strong end to the year, I guess. Jay, I want to start with you. There was news a couple weeks ago about Snowflake potentially looking at acquiring Redpanda. And today you announced really an excited, expanded relationship with Databricks.

好的，非常感謝，肖恩。恭喜團隊取得的成績。我想，這真是強勁的一年收官。傑伊，我想從你開始。幾週前有消息指出 Snowflake 可能考慮收購 Redpanda。今天，您宣布與 Databricks 建立更令人興奮且更廣泛的合作關係。

I guess I'm wondering, could you talk more at a high level of how customers think about using Confluent for streaming and processing outside of analytic engines and then maybe where that processing makes sense within the data lake or data warehousing layer?

我想知道，您能否從更高層次更多地談論客戶如何看待使用 Confluent 在分析引擎之外進行串流和處理，以及在資料湖或資料倉儲層中進行這種處理是否有意義？

Yes, yes, it's a great question. So there's a really two kind of key points that are driving this. One is the rise of these AI applications, which increasingly it's not just like a report that has yesterday's data or kind of a warehouse where people can do ad hoc analysis periodically.

是的，是的，這是一個很好的問題。所以其實有兩種關鍵點推動這現象。一是人工智慧應用的興起，它越來越不再只是一份包含昨天數據的報告，或是一個人們可以定期進行臨時分析的倉庫。

This is actually operating as part of the business and so it needs data that's kind of up to speed with what's happening in the business and that's driving the need for real time data in the analytics world. The second is the rise of these open data formats. So Iceberg or Delta, these are a new way of laying out all the data in the analytics realm in kind of open object storage.

這實際上是業務運作的一部分，因此它需要與業務發生情況保持同步的數據，這推動了分析領域對即時數據的需求。第二是這些開放資料格式的興起。因此，Iceberg 或 Delta 是一種在開放物件儲存中佈局分析領域中所有資料的新方法。

It may not be obvious, but this table flow offering that we talked about is kind of we think, the best way to get this. So it's a real time feed of all the data that's available in the streaming world that's then projected out as Iceberg or Delta tables. And so we talk about the partnership with Databricks. It's both integration into Delta their format and unity their catalog, which opens up anything in Confluent for use in the Databricks ecosystem, including those types of applications.

這可能並不明顯，但我們認為，我們所談論的這種桌面流程服務是實現這一目標的最佳方式。因此，它是串流媒體世界中所有可用的即時數據，然後以冰山表或增量表的形式投射出來。因此我們談論與 Databricks 的合作關係。它既整合了 Delta 格式，又統一了他們的目錄，從而可以打開 Confluent 中的任何內容以供 Databricks 生態系統使用，包括這些類型的應用程式。

And then going along with that is go-to-market activities to take that out to customers. And back to the question of hey, where is this useful to customers? There's a very clear ecosystem of analytics use cases applications. There's a very clear ecosystem of operational kind of run the business applications.

然後隨之而來的是進入市場的活動，將其推向客戶。回到這個問題：這對客戶有什麼用？分析用例應用的生態系統非常清晰。有一個非常清晰的營運業務應用程式生態系統。

And then there's a whole murky middle of all the AI stuff and things that are happening all the way in between. Our goal is to unify that around streaming data and make that available through all these different form factors to the different use cases that we need. So yes, we see it as incredibly complementary and we're excited to be working with the Databricks folks.

然後，在所有人工智慧事物和其間發生的事情之間，出現了一個模糊的中間地帶。我們的目標是統一流數據，並透過所有這些不同的形式因素將其提供給我們需要的不同用例。所以是的，我們認為這是非常互補的，我們很高興能與 Databricks 的員工合作。

Got it. That's great, great answer. And then maybe just one quick follow up. Ron mentioned that 2025 is the year of DSP, which is I think great to see, especially given it increasing in the mix of cloud revenue, I guess. How do you feel like what gives you the confidence that those trends continue? Are you starting to see, some anecdotal evidence of, of just like increased pipeline? What are, what are some of the tea leaves that you're reading that makes you suggest that to be the case?

知道了。太棒了，答案太棒了。然後可能只需快速跟進一次。羅恩提到 2025 年是 DSP 之年，我認為這是件好事，尤其是考慮到它在雲端收入結構中的成長。您覺得是什麼讓您有信心這些趨勢會持續下去？您是否開始看到一些關於管道增加的軼事證據？您讀到哪些證據讓您認為情況確實如此？

Yes, yes, it's always a mixture of qualitative and quantitative. So qualitative is, look, some of these things, the connector swing, they have massive open source adoption.

是的，是的，它總是定性和定量的混合。所以定性的是，看，其中一些東西，連接器擺動，它們都有大量開源採用。

So we just know that it's out there. We know that customers are excited about this or talking about it, want it as part of a managed cloud offering. And that comes out just in more or less every conversation we would have with our customers.

所以我們只知道它就在那裡。我們知道客戶對此感到興奮或正在談論它，希望它成為託管雲端產品的一部分。這幾乎在我們與客戶的每次對話中都會出現。

There's really been an evolution of what's expected in the streaming world from just kind of the pure stream of data to a whole set of real time data capabilities. And we think we're kind of at the forefront of delivering that. The second bit is a little more quantitative which is, yes, we just, we look at the actual ramp and growth of consumption of these areas and the kind of forward looking pipeline we think we have for customers.

串流媒體世界確實已經從單純的資料流演變為一整套即時數據功能。我們認為我們在實現這一目標方面處於領先地位。第二點更定量一點，是的，我們只是看這些地區的實際消費成長，以及我們認為我們為客戶提供的前瞻性管道。

A lot of the changes we've made in terms of our consumption transformation make us much better at tracking these workloads, driving, kind of product area by product area, the growth of these specific items.

我們在消費轉型方面所做的許多改變，使我們能夠更好地追蹤這些工作量，推動各個產品領域的特定項目的成長。

Thanks, Jay, Shane, congrats.

謝謝，傑伊、肖恩，恭喜。

Michael Turrin with Wells Fargo followed by Deutsche.

富國銀行的邁克爾圖林 (Michael Turrin) 和德意志銀行的邁克爾圖林 (Michael Turrin) 緊隨其後。

Hey, thanks very much. There's a lot of useful commentary throughout the prepared remarks, so appreciate that.

嘿，非常感謝。準備好的發言中有很多有用的評論，因此我很感激。

Jay, I wanted to spend some time with you just speaking through what you're seeing in terms of Gen AI related use cases for streaming and or DSP technology. Is there commonality in terms of customers use cases you're seeing and maybe how you'd expect that to progress over the coming year?

傑伊，我想花點時間和你談談你所看到的與串流媒體和/或 DSP 技術的 Gen AI 相關用例。就您所看到的客戶使用案例而言，是否存在共同點？

Yes, yes, I'm happy to do that. So, if we think about the first wave of use cases, it was a lot of chatbots and the usage pattern there was hey, get the data for the AI. And so to kind of boil it down, it was basically real time ETL, like capture the data, be able to transform it into the right format, be able to store it in a way that's accessible and usable for a RAG application which is combining the stored data with LLM or language model. And we've seen a number of those kind of progress through to, things that are either internally facing in customers or externally facing. And so that was the first usage pattern was, get the data.

是的，是的，我很樂意這麼做。因此，如果我們考慮第一波用例，我們會發現有很多聊天機器人，並且使用模式是，獲取人工智慧的數據。所以，簡而言之，它基本上是實時 ETL，例如捕獲數據，能夠將其轉換為正確的格式，能夠以可訪問和可用於 RAG 應用程序的方式存儲它，該應用程序將存儲的數據與 LLM 或語言模型相結合。我們已經看到許多此類進展，這些進展要么是面向內部客戶，要么是面向外部。所以第一個使用模式就是取得數據。

The second usage pattern we're seeing is this ability to apply, language models kind of on the stream of data directly. Right. Carry out some actual use cases, something like say in insurance, customer may have a claim filed.

我們看到的第二種使用模式是將語言模型直接應用於資料流的能力。正確的。執行一些實際用例，例如在保險中，客戶可能提出索賠。

There was a set of digital work that would happen, but there was also a set of manual work that would happen to actually prep, analyze, look at different documents. Being able to orchestrate that and take it further in the digital realm is a huge cost savings. It's a big quality improvement.

會發生一系列數位化工作，但也會發生一系列手動工作，以實際準備、分析和查看不同的文件。能夠協調這一點並在數位領域進一步推進可以節省大量的成本。這是一個很大的品質進步。

And I think that's very much the goal that people have in the adoption of AI is not just more chat bots, but actually being able to plug this into parts of the business, have it take over some of the work that would otherwise be done by humans or augment that work in some way. And so I think that's the second pattern that we're starting to see rise up in customers.

我認為人們採用人工智慧的目標不僅僅是擁有更多的聊天機器人，而是能夠將其融入業務的各個部分，讓其接管一些原本需要由人類完成的工作，或以某種方式增強這些工作。所以我認為這是我們開始看到客戶出現的第二種模式。

Great. And then just as a follow up, if I may, Rohan, you mentioned some differences between 2024 and 2025. You're calling for pretty consistent growth rates. We look at the Q1 subscription revenue guide and the rest of the year maybe just frame for us what you're assuming with that initial guide in terms of some of the changes that you're laying out and just more context there, I think would be helpful. Thank you.

偉大的。然後作為後續問題，如果可以的話，羅漢，您提到了 2024 年和 2025 年之間的一些差異。您要求的是相當穩定的成長率。我們來看看第一季的訂閱收入指南，而今年剩餘時間的指南可能只是為我們提供了對初步指南的假設，就您所列出的一些變化以及更多背景信息而言，我認為這會有所幫助。謝謝。

Thanks for your question Michael, will absolutely do. First of all, we're very pleased with our guide for Q1 and fiscal year 2025 for subscription. It is above the consensus estimates that were out there. And when you really look at the underlying drivers, I'll call out maybe three of them. Right.

謝謝你的提問，邁克爾，我一定會做的。首先，我們對第一季和 2025 財年的訂閱指南非常滿意。該數字高於現有的普遍預期。當你真正審視潛在的驅動因素時，我可能會指出其中的三個。正確的。

First is for the cloud business, we called out that we are comfortable with the estimates that are out there, which is roughly greater than 30% growth rate, give or take at a very high scale. And that's kind of underpinned by just the stability in the NRR that we spoke about in the second half of the year.

首先是雲端業務，我們表示，我們對目前的估計感到滿意，即成長率大約超過 30％，這是一個非常高的水平。這在某種程度上得到了我們在下半年談到的 NRR 穩定性的支持。

The second aspect I'll call out is some of the drivers. The first driver is DSP that we spoke about. The DSP products are in their earlier stages of their growth curve. So obviously we have a lot of runway out there. So that's an opportunity. The second piece around the growth areas are some of the newer products that we spoke about. We just (inaudible) freight cluster.

我要指出的第二個方面是一些驅動因素。第一個驅動程式是我們談到的 DSP。DSP 產品正處於成長曲線的早期階段。顯然，我們還有很多跑道。所以這是一個機會。關於成長領域的第二部分是我們談到的一些新產品。我們只是（聽不清楚）貨運集群。

We're going to be gain Tableflow, Wall Street momentum. So that's a category number two and category number three is the partnerships that we spoke about, Jio and Databricks. And these partnerships are not only on the technology side but also on the go-to-market side.

我們將獲得 Tableflow 和華爾街的動力。這是第二類，第三類是我們談到的合作關係，Jio 和 Databricks。這些合作不僅涉及技術面，也涉及行銷方面。

So the revenue contribution from the partnerships is probably not as material but these are all kind of drivers of growth as we look ahead into next year. What I'll tell you is when I take a step back, I think it's helpful to have multiple growth drivers and more importantly multiple parts to get to your objective. So that's how I categorize it.

因此，合作夥伴關係帶來的收入貢獻可能不那麼重要，但展望明年，這些都是成長的動力。我要告訴你的是，當我退一步思考時，我認為擁有多個成長動力、更重要的是多個部分對於實現目標是有幫助的。這就是我對其的分類。

Great. Thanks very much.

偉大的。非常感謝。

Brad Zelnick with Deutsche Bank, followed by Morgan Stanley.

德意志銀行的布拉德‧澤爾尼克 (Brad Zelnick)，其次是摩根士丹利。

Thanks very much. Great to see everybody and congrats. Jay, maybe just for starters, this past year you made transition in your go-to-market, changed up the incentives as we look to 2025, what's the big message coming out of sales kickoff? What adjustments, if any, are you making to the model that we should keep in mind that'll perhaps change incentives and behavior? Thanks.

非常感謝。很高興見到大家，恭喜。傑伊，也許只是開始，去年你在行銷方面進行了轉型，改變了激勵措施，展望 2025 年，銷售啟動時傳遞的重大訊息是什麼？您對模型做了哪些調整（如果有的話），我們應該牢記這些調整可能會改變激勵和行為？謝謝。

Yes, yes, it's a great question. So obviously last year was a bigger transformation. We were changing a lot of the fundamental incentives, but that trickles down to even how we would track workloads with customers, our definition of pipeline, number of organizational changes to support it.

是的，是的，這是一個很好的問題。顯然去年的轉變更大。我們正在改變許多根本的激勵措施，但這甚至影響到我們如何追蹤客戶的工作量、我們對管道的定義以及支持它的組織變革數量。

I think one of the really positive things heading into this year is we feel like broadly those changes have been effective. It was a big adjustment. But heading into this year it's a much more minor set of tune ups which are kind of equivalent of what you would do heading into any new year where you kind of tune this aspect or that aspect of the comp plan.

我認為今年真正積極的事情之一是我們感覺到這些變化總體上是有效的。這是一個很大的調整。但進入今年，這將是一系列較小的調整，這類似於你進入新的一年時所做的調整，即對補償計劃的這個方面或那個方面進行調整。

One of the big advantages in what we've done is we can now track and drive the specific workloads and the amount of each DSP component that's part of that is not just some generic commit to know, some raw number of dollars. It really is a set of use cases which are going to drive consumption of these individual new product areas and that allows us to drive it.

我們所做工作的一大優勢是，我們現在可以追蹤和推動具體的工作負載，而每個 DSP 組件的數量不僅僅是需要了解的一些通用承諾，一些原始的美元數字。這實際上是一組用例，它們將推動這些單獨的新產品領域的消費，並使我們能夠推動它。

So the -- we actually just had our sales kickoff this last week. A lot of fun, really positive energy, I think from the team. I came back pretty excited and with a little bit of a sore throat from talking so much. But yes, I think it resonated really well and people are excited to go take this out to all their customers.

所以—我們實際上上週剛開始了銷售。我認為這支團隊非常有趣，充滿了正能量。我回來時非常興奮，而且由於說了太多話，喉嚨有點痛。但是的，我認為它確實引起了很好的共鳴，人們很高興把它帶給所有的顧客。

Awesome. And maybe just a quick follow up. Rohan, you talked about packaging changes that you made that expand the serviceable, addressable market. And we just hope that you can expand on that because that sounded like a really important point and I want to make sure that we understand it. Thank you.

驚人的。或許只是快速跟進一下。Rohan，您談到了為擴大可服務和可目標市場而進行的包裝變更。我們只是希望您能詳細說明這一點，因為這聽起來是一個非常重要的觀點，我想確保我們理解這一點。謝謝。

That's right, Brad. The best way to think about it is when I look at, say, 18 months back, our -- when you look at our pricing and packaging options that were out there, it was primarily our dedicated clusters and the standard and basic cluster, which were probably the 2 bookends.

沒錯，布拉德。最好的思考方式是，當我回顧 18 個月前時，當您查看當時的定價和包裝選項時，主要是我們的專用集群以及標準和基本集群，它們可能是兩個書擋。

In the last 18-odd months, we've added enterprise SKU, which is essentially a multi-tenant, and it provides private networking, provides the right amount of security. Things like you can instead fly in private, you can fly commercial right now with Enterprise. And then we also came up with our freight clusters, which is essentially for high throughput, latency and sensitive workloads, you can do it at a very high TCO ROI.

在過去的 18 個多月裡，我們增加了企業 SKU，它本質上是一個多租戶，它提供私人網絡，提供適當程度的安全性。你可以選擇搭乘私人飛機，也可以選擇搭乘 Enterprise 的商業航班。然後我們還提出了貨運集群，它本質上是為了高吞吐量、高延遲和敏感工作負載，您可以以非常高的 TCO ROI 來實現它。

And then we acquired WarpStream, which is pretty similar to freight clusters, but doing it in your own infrastructure. When you kind of look at all of this, it is all around Confluent providing our customers with the right amount of ROI and TCO for all your workloads, not selective workloads. So that's the commentary around us being able to expand our serviceable addressable market and essentially take a step forward to our mission around looking up the World Kafka. I hope that helps.

然後我們收購了 WarpStream，它與貨運集群非常相似，但在自己的基礎設施中進行。當您審視這一切時，您會發現 Confluent 致力於為我們的客戶提供針對所有工作負載（而非選擇性工作負載）的適當 ROI 和 TCO。因此，這就是我們能夠擴大可服務目標市場並朝著尋找世界卡夫卡的使命邁出的一步的評論。我希望這能有所幫助。

It does. Thank you so much.

是的。太感謝了。

Sanjit Singh with Morgan Stanley, followed by Barclays.

摩根士丹利的 Sanjit Singh 緊隨其後，巴克萊銀行也位列其後。

Hey thank you for taking the questions. Congrats on Q4. Jay, you mentioned that 2024 was a pretty transformational year across go-to-market changes, broadening out the product portfolio. I mean, you made some changes on pricing over the last 12 months to 15 months as well. I guess the last piece of the question is or the piece of the puzzle is kind of the broader spending environment. And so how would you sort of characterize Q4 across like sort of executing its pipeline, consumptions going into the holidays and then consumption trends coming out of into January into early February?

嘿，謝謝您回答這些問題。恭喜第四季。傑伊，您提到 2024 年是市場變革和產品組合拓寬的變革年。我的意思是，在過去的 12 個月到 15 個月裡，你們也對定價做了一些調整。我想這個問題的最後一個部分或說難題的一部分是更廣泛的支出環境。那麼您如何描述第四季度的執行情況、假期前的消費情況以及 1 月到 2 月初的消費趨勢？

Yeah, So I would say overall, certainly, we've seen kind of stability in the market in our different customer segments. We feel like that's continued into what we've seen so far in Q1. As reflected in the results, right? We felt very strong results, not some step change from Q3 or what came before, but overall strength in demand, a little bit more certainty in a kind of budgets and spending. And so yes, we're expecting that continue through to this year.

是的，所以我想說總的來說，我們在不同的客戶群中看到了市場的穩定性。我們感覺這種情況一直延續到第一季目前為止。正如結果所反映的那樣，對嗎？我們感受到了非常強勁的結果，這不是與第三季或之前相比的一些重大變化，而是整體需求強勁，預算和支出更加確定。是的，我們預計這種情況將持續到今年。

Understood. And then on the sort of go-to-market side, I imagine partnerships will be a little bit more in focus. Last year was more sort of consequential change on the go-to-market side. But what if any changes sort of left going into 2025?

明白了。然後，在市場進入方面，我想合作關係將會更加受到關注。去年在市場進入方面發生了更重大的變化。但如果到 2025 年還留下任何變化怎麼辦？

Yeah, Certainly, in terms of the consumption-related changes, it's more of a kind of tuning here and there, how you incentivize the GSP multiplier and other smaller aspects of the comp plan, it's less revolutionary. We are putting more effort into the support for selling use cases that really consume this whole DSP being able to land that directly, being able to support some of these use cases to integrate into the analytics realm. A little bit of we talked about with the partnership with Databricks. So those are definitely some areas of particular fits for us.

是的，當然，就消費相關的變化而言，這更多的是一種這裡或那裡的調整，如何激勵普惠制乘數和補償計劃的其他較小方面，它的革命性較小。我們正在投入更多的精力來支援銷售真正消耗整個 DSP 的用例，能夠直接實現這些用例，能夠支援將其中一些用例整合到分析領域。我們談了一些與 Databricks 的合作關係。所以這些絕對是特別適合我們的領域。

Appreciate the thought, thank you.

感謝您的想法，謝謝。

Raimo Lenschow with Barclays, followed by JPMorgan.

巴克萊銀行的 Raimo Lenschow 緊跟在後，摩根大通也名列其後。

Hey thank you. Jay, the last few quarters, we talked about -- this time last year we talked a lot more about Flink. And at this time, because you added so many more products, it's kind of like -- it kind of came a little bit out of the limelight. Can you talk a little bit what you're seeing here and what you can maybe do to kind of drive momentum there in the coming year if you have any plans there? And I have one follow-up.

嘿，謝謝。傑伊，過去幾個季度，我們談論過——去年這個時候我們談論了很多關於 Flink 的事情。而此時，由於你添加了太多產品，它就有點不再那麼引人注目了。您能否談談您在這裡看到了什麼，以及如果您有什麼計劃的話，在來年您可以做些什麼來推動那裡的發展勢頭？我還有一個後續問題。

Yes. We've been pleased with the progress. So the -- it's a fairly large effort to get a new cloud data processing layer up and operational. We've seen a lot of maturity in the product offering, including opening up across a lot of the private networking types and the different clouds, which then allows us to start to begin to service more production workloads.

是的。我們對這項進展感到非常滿意。所以，建立並運行新的雲端資料處理層需要付出相當大的努力。我們已經看到產品供應已經非常成熟，包括跨多種私有網路類型和不同雲端的開放，這使我們能夠開始服務更多的生產工作負載。

We've seen good broad-based adoption in the customer base. It takes time for them to kind of rebuild applications that they have. But overall, good strength in the build there, and we're kind of pleased with the progress and basically feel equally bullish as we would have Q1 of last year.

我們看到客戶群廣泛地採用了該技術。他們需要時間來重建現有的應用程式。但總體而言，那裡的建設實力良好，我們對進展感到滿意，並且基本上像去年第一季一樣感到樂觀。

And then on the Databricks relationship, like obviously, there's a big discussion. Do you meet kind of see you guys in that journey or not? Can Databricks do it themselves. This is obviously a very, very strong confirmation about your role in that ecosystem. And what are you seeing there in terms of like how you would fit in other vendors? How exclusive is Databricks? What's the opportunity to broaden this out? Thank you. Congrats from me as well.

然後關於 Databricks 的關係，顯然，有一個很大的討論。你們在旅途中是否遇見你們？Databricks 自己能做到嗎？這顯然是對你在該生態系統中所扮演的角色的非常非常有力的確認。那麼您認為您如何適應其他供應商呢？Databricks 的獨特性如何？擴大這範圍的機會是什麼？謝謝。我本人也對此表示祝賀。

Yes. Look, our role is as this broker of streaming data these different platforms within the analytics realm, that's increasingly very relevant as they look at trying to be able to participate in some of the AI workloads that are more real time that are more about running the business.

是的。你看，我們的角色是作為分析領域內不同平台的串流數據經紀人，這變得越來越重要，因為他們試圖參與一些更即時、更與業務運營有關的人工智慧工作負載。

We think we're in a very good position in that ecosystem and with some of the product functionality we're bringing to market. And we're really pleased to be working with them. We certainly view them as a leader in that space, and I think it's going to be a very productive partnership.

我們認為，我們在該生態系統中處於非常有利的地位，並且我們已經將一些產品功能推向市場。我們非常高興能與他們合作。我們確實將他們視為該領域的領導者，而且我認為這將是一次非常有成效的合作。

Okay, perfect. Thank you.

好的，完美。謝謝。

Pinjalim Bora with JPMorgan, followed by Piper Sandler.

摩根大通 (JPMorgan) 的 Pinjalim Bora 緊隨其後，Piper Sandler 緊隨其後。

Great. Thank you guys. Congrats on the quarter. Jay, I just want to ask you one thing. In our conversations with partners and customers, kind of notion has emerged that the agentic architecture, it's not only real-time data flows, but real-time data processing is kind of becoming important for agents to take decisions. Is that broadly true? Are you seeing kind of link patents attach rates higher for Agentic work flows? And then one for Rohan. Anyway to understand like how -- what is assumed within the guidance for 2025 around DSP mix and Flink mix?

偉大的。謝謝你們。恭喜本季取得佳績。傑伊，我只想問你一件事。在我們與合作夥伴和客戶的對話中，出現了這樣一種觀點：代理架構不僅是即時資料流，而且即時資料處理對於代理程式做出決策也變得越來越重要。大體上是這樣嗎？您是否看到 Agentic 工作流程的連結專利附加率更高？然後再為羅翰獻上一曲。無論如何，要理解如何——2025 年指導中圍繞 DSP 組合和 Flink 組合的假設是什麼？

Yeah, it's a really insightful question. This is actually the topic of my keynote for the last current conference that we held in the US for our customer base, and I talked about some of these use cases. Exactly as you say, I talked in the previous question about these agents that are actually reacting to what's happening in the business, right.

是的，這是一個非常有見地的問題。這實際上是我在美國為客戶群舉辦的上一次會議上的主題，我談到了其中一些用例。正如您所說，我在上一個問題中談到了這些代理商實際上正在對業務中發生的事情做出反應，對吧。

So, you could see the streams of data as being a little bit like the sensory system that's saying, hey, what's happening, being able to take bits of work as they come in, act on them, produce whatever output to the rest of the business. That is fundamentally a stream processing problem.

因此，你可以將資料流視為有點像感測系統，它會說，嘿，發生了什麼事，它能夠接收傳入的工作片段，對其採取行動，並為其他業務產生任何輸出。這從根本上來說是一個流處理問題。

However, you tackle it. And so yes, we feel like the technology we have in that space is particularly relevant. The advantage of using this kind of streaming technology is very much that you can kind of take the input data run it against it, look at the output, run it again, tweak your model, try a different model, add new context information, bring together other information you have. So yes, I think it's very applicable.

然而，你解決了它。所以是的，我們覺得我們在該領域擁有的技術尤其重要。使用這種串流技術的優勢在於，您可以獲得輸入資料並對其進行運行，查看輸出，再次運行它，調整模型，嘗試不同的模型，添加新的上下文信息，匯總您擁有的其他信息。是的，我認為它非常適用。

And if anything, one of the interesting things that's happening in the AI world, with relationship to the older machine learning is, in some sense, the action is kind of moving into real time, prior to Confluent I worked at LinkedIn, one of the teams we had was a big data science team that would do model building, right.

如果有什麼不同的話，那就是人工智慧領域正在發生的一件有趣的事情，與舊的機器學習相比，在某種意義上，行動正在轉向實時，在加入 Confluent 之前，我在 LinkedIn 工作，我們團隊之一是大數據科學團隊，負責模型構建，對吧。

And so the role for string relative to that team would speed a bunch of stuff into some lake, and they would go offline and kind of build some models that would eventually be kind of shipped out. But each model is very specific to the problem being soft.

因此，相對於該團隊而言，string 的作用是將一堆東西快速運送到某個湖中，然後它們會離線並建立一些模型，最終將其運出。但是每個模型都是針對特定軟問題。

If you think about what's happening in the new world, companies aren't building as many bespoke models. We're mostly getting your model prebuilt off of a very large general purpose data set mostly what you're doing is applying your specific contact data with the model at inference time at run time. That's kind of the RAG architecture.

如果你思考一下新世界正在發生的事情，你會發現公司並沒有打造那麼多客製化車型。我們主要是從一個非常大的通用資料集中預先建立你的模型，你主要做的是在運行時推理時將特定的聯絡人資料應用到模型中。這是一種 RAG 架構。

So if you think about, well, what's the resulting data infrastructure from that? It's going from this kind of off-line processing to something that's very much online, that's in sync with the business, and that's obviously advantageous, I think, for folks in the streaming world.

那如果你想想，那麼，由此產生的資料基礎設施是什麼？它正在從這種離線處理轉變為線上處理，與業務同步，我認為這對串流媒體領域的人來說顯然是有利的。

Yeah, and Pinjalim, to answer your second part of the question. When I think about DSP consumption, it grew substantially faster than the overall cloud business in Q4. And as I mentioned, it accounted for roughly 13% of our cloud business, which we're obviously very pleased with.

是的，Pinjalim，回答你問題的第二部分。當我想到 DSP 消耗時，它的成長速度遠遠快於第四季的整體雲端業務。正如我所提到的，它約占我們雲端業務的 13%，我們對此感到非常滿意。

And as I think ahead, all the key components of the DSP are in the earlier stages of their growth curve. And we expect them to be material drivers of growth for the next couple of years. And specifically for 2025 well, I'm not going to get into exact numbers. We expect that to increase as we go through the year.

我認為，DSP 的所有關鍵組件都處於其成長曲線的早期階段。我們預計它們將成為未來幾年成長的重要動力。具體到 2025 年，我不會給出確切的數字。我們預計，隨著時間的推移，這一數字將會增加。

Thank you. All right.

謝謝。好的。

Rob Owens with Piper Sandler, followed by Goldman.

羅伯歐文斯 (Rob Owens) 和派珀桑德勒 (Piper Sandler) 一起，接著

Thanks, Shane and good afternoon, everyone. One question, but multiple parts, so you guys can both answer. I guess I'd like to focus a little bit on your $1 million customer or success upmarket. What's driving that are customers getting to that $1 million bogey quicker, especially as you move to the DSP platform. And for Ro, long-term deferred revenue up $20 million sequentially kind of sticks out, especially given the move to consumption. So maybe you can address that. Thank you.

謝謝，Shane，大家下午好。一個問題，但有多個部分，因此你們都可以回答。我想我將重點放在您的 100 萬美元客戶或高端市場的成功。推動這一趨勢的因素是客戶能夠更快地獲得 100 萬美元的收入，特別是當您轉向 DSP 平台時。對 Ro 來說，長期遞延收入比上一季增加了 2,000 萬美元，這相當突出，尤其是考慮到消費的轉變。所以也許你可以解決這個問題。謝謝。

Yeah, I'll take the first part of that. Yes, obviously, a large part of what we're trying to do is build the journey from that first use case to a large cross-company platform that has a significant portion of the data and application workloads in the company. And that's a big project in each one of these customers. And everything we do is about how can we accelerate it.

是的，我將選擇第一部分。是的，顯然，我們正在嘗試做的很大一部分是建立從第一個用例到大型跨公司平台的旅程，該平台擁有公司中很大一部分資料和應用程式工作負載。對於每個客戶來說，這都是一個大專案。我們所做的一切都是為瞭如何加速這個進程。

So when we think about services offerings, partnerships, features, it's all about how can we build the journey and make that easier for customers to achieve in particular, a lot of the DSP functionality is very much that, right? So the connectors make it really easy instead of having to come in and build a bunch of custom integration.

因此，當我們考慮服務產品、合作夥伴關係和功能時，我們考慮的都是如何建立旅程，讓客戶更輕鬆地實現這一目標，特別是許多 DSP 功能都是這樣的，對嗎？因此，連接器讓一切變得非常簡單，而不必介入並建立一堆自訂整合。

You can just plug this into the things that you have and have streams of data flowing. Flink makes it much easier to build real-time applications that are fall tolerant and scalable and correct and work with the language, as you know, like SQL, which is kind of the lingua franca of the data world.

您只需將其插入您已有的東西即可獲得流動的資料流。Flink 讓建立即時應用程式變得更加容易，這些應用程式具有容錯性、可擴展性和正確性，並且可以與 SQL 等語言協同工作，而 SQL 是資料世界的通用語言。

And similar with the kind of governance functionality that makes this actually usable across the organization, and we do see that flywheel, right? Indeed, as customers adopt these things, we see greater usage of the core streams of data.

類似於使其在整個組織內真正可用的治理功能，我們確實看到了飛輪，對嗎？事實上，隨著客戶採用這些東西，我們看到核心資料流的使用率越來越高。

And so we think very much that as these things spin up, they all kind of feed off of each other, and that should very much take customers more quickly to larger scale within the organization. And I think that we're starting to see realization of that.

因此，我們認為，隨著這些事物的發展，它們會相互影響，這將使客戶在組織內更快地擴大規模。我認為我們開始意識到這一點了。

So for CIOs, CTOs, engineering leaders, as they're thinking about an overall data strategy, as they're thinking about what's needed for AI, I do think this is a very important piece that they're thinking about and starting to contemplate the role of the data streaming place and that kind of broad understanding of our space has not always been there. In the early days of the company, it was not -- but I do think that, that's a key accelerant when you think about getting customers to buy in, to build around something and to take it to a large scale quickly.

因此，對於資訊長、技術長和工程領導者來說，當他們思考整體資料策略、思考人工智慧需要什麼時，我確實認為這是他們正在思考的一個非常重要的部分，他們開始思考資料流的作用，而對我們這個領域的這種廣泛的了解並不總是存在的。在公司成立初期，情況並非如此——但我確實認為，當你考慮讓客戶購買、圍繞某樣東西進行構建並迅速將其推向大規模時，這是一個關鍵的加速器。

Rob, I'll add a couple of quick points to what Jay just said, then I will go to the deferred revenue base. On the total customer -- large customer ecosystem, I also called out that when you look at our 100K plus ARR customers, that cohort now basically contributes approximately 90% of our subscription revenue.

羅布，我想對傑伊剛才說的內容補充幾點，然後我將談論遞延收入基礎。關於整體客戶——大型客戶生態系統，我還指出，當你看看我們的 10 萬以上的 ARR 客戶時，這個群體現在基本上貢獻了我們訂閱收入的約 90%。

And when I -- and then when you look at our $1 million-plus customer and you look at the last 12 months, the momentum has been great. So I'll give you a different lens as in when I think about consumption, there are probably 3 drivers of consumption, right? You have an existing use case and there is more data flowing through that. That's number one.

然後當我——當你看到我們價值超過 100 萬美元的客戶並回顧過去 12 個月時，你會發現發展勢頭非常好。因此，當我考慮消費時，我會給你一個不同的視角，消費可能有 3 個驅動因素，對嗎？您有一個現有的用例，並且有更多資料流經該用例。這是第一點。

The second area is our ability to unlock net new use cases. And with AI, that's obviously a lever of growth. And the third, albeit in the early days is us selling DSP into our existing customer base. So the opportunity with our existing customers is still pretty large, just to add to what you said.

第二個領域是我們解鎖全新用例的能力。而有了人工智慧，顯然是一個成長槓桿。第三，儘管還處於早期階段，我們仍在向現有客戶群銷售 DSP。因此，我們現有客戶的機會仍然很大，只是補充您所說的。

Now going back to your second question around deferred revenue. Honestly, not a whole lot to read into it. Like I said, when you look at the revenue or RPO, that's probably not the only indicator for the organic momentum of the business purely because how we think about cloud, we are consumption first and we're truly not leading with the largest commit possible.

現在回到有關遞延收入的第二個問題。老實說，沒有太多值得深入研究的內容。就像我說的，當你查看收入或 RPO 時，這可能不是業務有機發展勢頭的唯一指標，純粹是因為我們對雲端的看法是，我們是消費優先，而且我們並沒有真正以最大的承諾為先。

It's all around unlocking the concession and working with the customer around it. So changes in deferred revenue is primarily driven by timing of large content platform, multiyear deals. So not a whole lot of read to do it.

這一切都是為了解鎖特許權並與客戶合作。因此，遞延收入的變動主要受大型內容平台、多年期交易時機的影響。因此，不需要讀太多的書就可以做到這一點。

Kash Rangan with Goldman Sachs, followed by Guggenheim.

卡什·蘭根 (Kash Rangan) 來自高盛，其次是古根漢。

Hey thank you very much. Jay, Rohan and Shane, good to see you guys. Jay, I was wondering since we've had the benefit of a year of the rollout of the new model and we brought in Flink in the fold.

嘿，非常感謝。傑伊、羅漢和肖恩，很高興見到你們。傑伊，我很好奇，因為我們已經受益於新模型推出的一年，並且我們將 Flink 引入其中。

How satisfied are you with tilt towards compensating salespeople through consumption. Because I think the year back or the hope was that we come out of this with the ability to grow even faster and allow customers to consume even more freely, more faster.

您對透過消費來補償銷售人員的傾向有多滿意？因為我認為，去年的希望是我們能夠走出困境，實現更快的成長，讓顧客更自由、更快消費。

And what are the things we should be watching for to ensure that, that is happening, maybe net expansion rates are not there yet. The cloud growth rate could be hitting an inflection point maybe that is still ahead.

我們應該關注哪些事情來確保這種情況正在發生，也許淨擴張率尚未達到。雲端運算的成長率可能即將到達一個拐點。

So how satisfied are you that we've seen or are we begin to see the effect of the fruits of all the actions that were put in the last year. And also second finally, if you have the time, if AI were to be a tailwind for the company, where would it show up in the way that people like us can read in the financial statements. Thank you so much once again.

那麼，對於我們所看到的或我們是否開始看到去年採取的所有行動的成果，您是否感到滿意？第二，最後，如果你有時間，如果人工智慧成為公司的順風，它會以我們這樣的人可以在財務報表中讀到的方式展現出來。再次感謝您。

Yeah, So to the first question. Yes, there's really two things we're trying to achieve. One is really match the way our customers were buying and thinking about the type of product that we offer, make it easier for them to land, make it easier for them to expand and add workloads. The second was to be in a position to really drive adoption of DSP around our cloud. And I think we exit this last year, entered this year -- have a position to do both of those.

是的，所以回答第一個問題。是的，我們確實想要實現兩件事。一是真正匹配我們的客戶購買和思考我們提供的產品類型的方式，讓他們更容易獲得，讓他們更容易擴展和增加工作量。第二個目標是真正推動雲端 DSP 的採用。我認為，我們在去年退出了這個市場，在今年進入了這個市場，我們有能力同時做到這兩點。

And that was very much the objective. I think that's a much better position to be in relative to what we're doing with customers. I think it means we're tracking the kind of individual workloads that they're bringing out to production. And so, yes, I do view that as a growth driver for us over time. And as a necessary step to be in sync with the peer companies and in sync with the new offer we want to bring to market.

這正是我們的目標。我認為，相對於我們為客戶所做的事情而言，這是一個更有利的地位。我認為這意味著我們正在追蹤他們投入生產的個體工作負載類型。是的，我確實認為這將成為我們長期成長的動力。作為與同行公司保持同步以及與我們想要推向市場的新產品保持同步的必要步驟。

To the latter question on AI use cases, yes, I think it's really about the broader set of these coming into production usage that's we're going to see this happen. I think you would see it both in the kind of customer references, we've shared some of the ones that we've seen so far as well as in the kind of overall growth numbers for the company.

對於後一個關於人工智慧用例的問題，是的，我認為這實際上與更廣泛的人工智慧投入生產使用有關，我們將會看到這種情況發生。我想您會在客戶推薦中看到這一點，我們已經分享了一些我們迄今為止看到的客戶推薦，以及公司的整體成長數據。

But there's obviously not a broken out category. I don't think it's necessarily disproportionate cloud or CP, we see use cases across both. So yes, I don't think you would see it a single customer stat that we produce.

但顯然沒有細分的類別。我認為這不一定是不成比例的雲或 CP，我們看到了兩者的用例。所以是的，我認為您不會看到我們提供的單一客戶統計數據。

Howard Ma with Guggenheim, followed by Mizuho.

霍華德·馬 (Howard Ma) 帶領古根漢公司，其次是瑞穗公司。

Great. Thanks. It's great to see everyone and congrats on a strong finish of the year. Jay, in your prepared remarks, you mentioned a couple of customer examples where I think -- I believe they're replacing traditional data integration vendors with Confluent.

偉大的。謝謝。很高興見到大家，並祝賀你們今年取得了圓滿的結局。傑伊，在你準備好的演講中，你提到了幾個客戶案例，我認為——我相信他們正在用 Confluent 取代傳統的數據整合供應商。

I believe to date, though, a Confluent -- most of Confluent's new business has come from replacing open source Kafka for real-time use cases. But it seems like now you're going maybe more direct rip and replace of commercial data integration vendors for batch processing.

不過，我相信到目前為止，Confluent 的大部分新業務都來自於替換開源 Kafka 以滿足即時用例的需求。但現在似乎你可能會更直接取代批次的商業資料整合供應商。

And I'm not sure if this is always the case on what the historical mix has been, but can you confirm if that is true that you're winning more of these larger commercial batch workloads. And if that is the case, is that changing your go-to-market at all, the types of prospects you're targeting and how you're targeting them?

我不確定歷史組合是否總是如此，但您能否確認您是否確實贏得了更多此類大型商業批量工作負載？如果確實如此，這是否會改變您的市場進入方式、您所針對的潛在客戶類型以及您定位他們的方式？

Yeah, It's a really good question. So I would say we've -- it's always been the case that we've been replacing call it, other legacy integration technologies that's not solely new. However, often our first land was a conversion of OpenSource Kafka.

是的，這是一個非常好的問題。所以我想說，我們一直在取代其他並非完全新技術的傳統整合技術。然而，通常我們的第一站是 OpenSource Kafka 的轉換。

Now of course, the OpenSource Kafka may have replaced some legacy data integration. I think it may have been a net new project. But one of the things we've always found is whatever that initial land is as customers get to scale, they start to think about the portfolio of technology and what is it that they want to bet on going forward.

當然，現在開源 Kafka 可能已經取代了一些傳統的資料整合。我認為這可能是一個全新項目。但我們始終發現，無論最初的土地是什麼，隨著客戶規模的擴大，他們開始考慮技術組合，以及他們想要在未來押注什麼。

And they start to have a plan of what they're divesting and what they're investing in. And I do think it's very much the case in our large customers so that they see data streaming as the future of how data flows across the organization.

他們開始製定計劃，確定要剝離哪些資產以及要投資哪些資產。我確實認為我們的大客戶確實如此，他們將資料流視為資料在組織內流動的未來。

And I think that, that does just place a number of the existing vendors in that space. And I think they are looking at replacing that. Now can we land directly against those use cases. I think the answer is increasingly often, yes, right? One of the things that's happened is there's more awareness of the data streaming space overall.

我認為，這確實會將一些現有的供應商置於該領域。我認為他們正在考慮替代它。現在我們可以直接針對這些用例嗎？我認為答案通常是“是的”，對嗎？發生的一件事是人們對整體資料流領域的認識有所提高。

And also, as our product is more complete, having out of the box set of connectors that plug in, having transformation capabilities, it is more of a apples-to-apples replacement of some of these things was something that's net better. It's more scalable, it's real time. That's an open application platform that has a lot of benefits, but actually can just kind of just place a lot of woods there. And so yes, I do think we're seeing more of that.

而且，由於我們的產品更加完善，擁有開箱即用的插入式連接器，具有轉換功能，因此可以對其中一些東西進行同類替代，這是一種更好的選擇。它更具可擴展性，並且是即時的。這是一個開放的應用平台，有很多好處，但實際上只能在那裡放置很多木材。是的，我確實認為我們會看到更多這樣的情況。

Great, thanks so much.

太好了，非常感謝。

Gregg Moskowitz with Mizuho, followed by D.A. Davidson.

瑞穗 (Mizuho) 的 Gregg Moskowitz，其次是 D.A.戴維森。

Thanks and congrats on the results on the partnership announcements. Jay, it sounds like WarpStream got off to a very good start. And so you have three types of deployment mechanisms, each of them valuable for different reasons in addition to things like freight clusters. That being said, could this potentially lead to a little more customer deliberation a slight lengthening of sales cycles as part of that decision-making process? Or is that really not a concern from your perspective?

感謝並祝賀合作公告的結果。傑伊，聽起來 WarpStream 已經有了一個很好的開始。因此，您有三種類型的部署機制，除了貨運集群之類的因素之外，每種機制都有不同的價值。話雖如此，這是否可能導致客戶在決策過程中進行更多的思考，從而稍微延長銷售週期？或者從您的角度來看這確實不是一個值得擔心的問題？

Yeah, It's not a huge concern. Obviously, any time you're adding more packages or options, you want to be cognizant of that. But we thought that actually when you look at cost workloads, there's a fair amount of diversity.

是的，這不是什麼大問題。顯然，當您添加更多套件或選項時，您都需要意識到這一點。但我們認為，實際上當你查看成本工作量時，會發現存在相當大的多樣性。

And so the first order thing to do is make sure that you have a really excellent TCO story deployment story set of capabilities for each of those. And you can kind of walk into a customer and say, hey, tell me what you're doing. We've got the right solution for all of these things kind of out of the box.

因此，首先要做的事情就是確保你擁有一套真正出色的 TCO 故事部署故事功能集。你可以走進顧客並說，嘿，告訴我你在做什麼。我們已經為所有這些問題找到了正確的解決方案。

And one of the things I mentioned we had our sales kick off. One of the things that I talk our team about and that a lot of our product leaders talked about was the fact that entering this year, we really felt like we were in this position where across every use case, we were dramatically superior to whatever the customer is doing in the streaming space.

我提到的一件事是我們的銷售已經開始了。我和我們的團隊以及許多產品領導人討論過的一件事是，進入今年，我們真的覺得我們處於這樣的位置：在每一個用例中，我們都遠遠優於客戶在串流媒體領域所做的一切。

And then it could be kind of a no-brainer both on functionality, but also on price. From a very large scale to the kind of early starter stuff to the very high SLA, but maybe not is, really across the board, we had a very strong story. And that was a deliberate path over the course of last year.

那麼從功能和價格上來說，這都是不二之選。從非常大的規模到早期的啟動項目，再到非常高的 SLA，但也許不是，而是真正的全面，我們有一個非常強大的故事。這是我們去年經過深思熟慮後制定的路線。

Now it does mean we've got more cluster types. But I think in any of these areas that succeed, like you look at the AWS product offerings.

現在這確實意味著我們擁有更多的叢集類型。但我認為在任何領域都可以取得成功，就像你看到的 AWS 產品一樣。

Sure, they start with three EC2 instances. As this gets more use, they do kind of fill out the matrix a little bit more to be able to really cover all the different compute needs that a customer may have. And I think that's actually kind of a very important part of maturity in this kind of cloud infrastructure. So net-net, we feel very good about the kind of full portfolio.

當然，他們從三個 EC2 執行個體開始。隨著其得到越來越多的使用，他們確實會進一步填充矩陣，以便能夠真正滿足客戶可能擁有的所有不同的計算需求。我認為這實際上是這種雲端基礎設施成熟度的一個非常重要的部分。因此，總體而言，我們對這種完整的投資組合感到非常滿意。

Terrific. Thank you.

了不起。謝謝。

Rudy Kessinger with D.A. Davison, followed by Canaccord.

凱辛格 (Rudy Kessinger) 和 D.A.戴維森 (Davison)，其次是 Canaccord。

Hey thanks guys. I know you talked about the year two just go-to-market change, really, how that's gone pretty smoothly and just kind of some tune-ups at this point. I am curious though, Rohan, like within the guide, are you assuming higher sales productivity levels this year just given you're going into year two? Or just what's implied from that standpoint?

嘿，謝謝大家。我知道您談到了第二年的上市變革，實際上，一切進展得相當順利，目前只是進行了一些調整。不過，我很好奇，羅漢，就像指南中所說的那樣，考慮到您已經進入第二年，您是否認為今年的銷售生產力水平會更高？還是只是從這個角度暗示了什麼？

Yeah, that's a great question. Again, I'll start with 2024, Rudy. When I look at 2024, and I look at just a broad efficiency on the go-to-market side of the world. In fact, we improved our sales and marketing as a percentage of revenue in 2024 by roughly 6 percentage points. That essentially tells you that if we kind of gone through the year, we've gotten more and more efficient. So that's one aspect of it.

是的，這是一個很好的問題。再次，我將從 2024 年開始，魯迪。當我展望2024年時，我只關注全球市場進入方面的整體效率。事實上，到 2024 年，我們的銷售和行銷佔收入的百分比提高了約 6 個百分點。這實際上告訴你，如果我們度過這一年，我們會變得越來越有效率。這是其中的一個面向。

And given our resource allocation philosophy, we're not going to lose sight of that. That's going to be a continued focus for us, us getting more and more efficient as we deliver try to deliver and deliver durable growth.

考慮到我們的資源分配理念，我們不會忽視這一點。這將成為我們持續關注的重點，我們在努力實現持久成長的同時，也將變得越來越有效率。

When you have another thing to -- where we are from a capacity standpoint at the beginning of the year, we feel good with the capacity we have. We've had a good hiring year towards the latter half of 2024. So we have the capacity on board to deliver on the plans that we have for 2025.

當您有另一件事時—從年初的產能角度來看，我們對現有的產能感到滿意。2024 年下半年是我們的招募旺季。因此，我們有能力實現 2025 年的計劃。

Kingsley Crane with Canaccord.

金斯利·克蘭 (Kingsley Crane) 與 Canaccord 合作。

Thanks. So data gravity, truly important when you look at all the strategic moves and acquisitions made by these large data platforms, a lot of them tied back into data gravity. Data mobility has also never been higher Tableflow fits perfectly into that narrative. Just taking a step back, does all of this give you more confidence that stream processing will be a much bigger market over time? And then what inning do you think we're in, in that market?

謝謝。因此，當你查看這些大型數據平台的所有策略性舉措和收購時，數據引力確實非常重要，其中許多都與數據引力息息相關。資料移動性也從未達到如此高水平，Tableflow 完全符合這項描述。退一步來說，所有這些是否讓您更有信心，隨著時間的推移，流處理將成為更大的市場？那麼您認為我們處於那個市場中的什麼局面？

Yeah, Yes. So if you look at the kind of broad trends that we're seeing the rise of AI and push of a lot of the analytics world more into real-time that continued investment in a set of different data platforms across the rise of the public cloud but the endurance of some of the on premise stuff, net-net, the connectivity between all of this is really important and more of the workloads are moving into this kind of operational, continuous processing world. So yes, it's a phenomenal setup for streaming.

是的，是的。因此，如果您看一下我們所看到的廣泛趨勢，即人工智慧的興起以及大量分析世界更多地轉向實時，在公有雲的興起過程中對一系列不同數據平台的持續投資，但一些內部部署內容的持久性，網絡，所有這些之間的連接確實非常重要，並且越來越多的工作負載正在進入這種操作性、持續處理的世界。是的，這對於串流媒體來說是一個非凡的設定。

What inning are we inning in, we are a very early inning. So if you add up the dollar spend on batch processing, is large file dollars, right? If you say, what percentage of those dollars have moved to streaming, well, a good percent we're excited to talk about it every quarter, but there's a lot more dollars to go and that -- I think that's an exciting thing for us.

我們處於哪一局？因此，如果把批次花費的錢加起來，就是大文件的花費，對嗎？如果你問，這些資金中有多少比例轉移到了串流媒體，嗯，很大一部分比例，我們很高興每個季度都談論這個問題，但還有很多資金需要投入——我認為這對我們來說是一件令人興奮的事情。

And I think as we talked about before, what this area encompasses is both a set of data integration, kind of bespoke half worked out data integration verticals, but also a set of application workloads that were maybe running off-line at the end of the day and are moving into the operation of the business.

我認為，正如我們之前談到的，這個領域既包含一組數據集成，一種定制的半成品數據集成垂直領域，也包含一組可能在一天結束時離線運行並進入業務運營的應用程序工作負載。

Both of those are very exciting opportunities. Both of those are being done by our customers today and the scope of those is expanding both with the kind of DSP capabilities we're bringing as well as the continued expansion in these customers as this becomes a critical data platform for that.

這兩個都是非常令人興奮的機會。目前，我們的客戶正在做這兩項工作，而且隨著我們引入 DSP 功能以及這些客戶的持續擴展，這兩項工作的範圍正在不斷擴大，因為這已成為一個關鍵的數據平台。

Great. And a quick follow-up. So great to see different streaming adoption, video gaming, sports media, pharma airlines. Are you seeing any particular vertical strength with respect to newer AI-related projects? Or is that similarly diverse?

偉大的。並快速跟進。很高興看到不同的串流媒體、電玩遊戲、體育媒體、製藥航空公司的採用。您是否看到與較新的 AI 相關項目相關的任何特定的垂直優勢？或者說同樣多樣化？

Yeah, We've done really well in the kind of AI company -- dedicated AI companies. And then, of course, inside of the existing industry kind of AI projects. The AI companies are obviously wonderful customers to have. We've talked about OpenAI in the past.

是的，我們在人工智慧公司——專門的人工智慧公司方面做得非常好。當然，還有現有產業內部的人工智慧專案。人工智慧公司顯然是值得擁有的優秀客戶。我們過去曾談論過 OpenAI。

We talked about cursor in the call today, which is a really cool tool that helps software engineers write code faster. So we're very excited about what's happening there. Those are very fast-growing set of customers that we're pleased to be working with.

我們在今天的電話會議中討論了遊標，這是一個非常酷的工具，可以幫助軟體工程師更快地編寫程式碼。所以我們對那裡發生的事情感到非常興奮。這些都是成長非常迅速的客戶群，我們很高興能與他們合作。

All right. So this concludes our earnings call today. Thanks again for joining us. We look forward to seeing many of you at our March Investor Day. Have a nice evening. Take care.

好的。今天的財報電話會議到此結束。再次感謝您的加入。我們期待在三月的投資者日見到你們。祝你今晚愉快。小心。

Thanks, everyone.

謝謝大家。

Thank. You.

感謝。你。