Confluent (NasdaqGS:CFLT) Q3 2025 Earnings Call October 27, 2025 04:30 PM ET Company ParticipantsRaimo Lenschow - Managing DirectorJay Kreps - CEO and Co-founderRohan Sivaram - CFOMark Murphy - Executive DirectorRyan MacWilliams - Software Equity ResearchBrad Zelnick - Managing Director, Software Equity ResearchShane Xie - VP, Investor RelationsRob Owens - Head of Technology ResearchConference Call ParticipantsJason Ader - AnalystMike Cikos - VP and Senior Equity Research AnalystSandhya Singh - AnalystEric ...

Financial Data and Key Metrics Changes - Q3 subscription revenue grew 19% to $286 million, representing 96% of total revenue [24][4] - Confluent Cloud revenue increased 24% to $161 million, accounting for 56% of subscription revenue compared to 54% in the previous year [24][4] - Non-GAAP operating margin expanded three percentage points to approximately 10% [4] - Subscription gross margin was 81.8%, above the long-term target of 80% [26] - Operating margin increased 340 basis points to a record 9.7%, exceeding guidance by 270 basis points [26] - Adjusted free cash flow margin increased 450 basis points to 8.2% [26] Business Line Data and Key Metrics Changes - Confluent Platform revenue grew 14% to $125.4 million, driven by demand in financial services [24] - Flink ARR for Confluent Cloud grew more than 70% sequentially, with over 1,000 customers using Flink [31][7] - The number of customers with $100k+ ARR increased to 2,533, up 36 sequentially [27] - The number of customers with $1 million+ ARR increased to 234, representing a growth acceleration of 27% [27] Market Data and Key Metrics Changes - Revenue from the U.S. grew 13% to $172.1 million, while revenue from outside the U.S. grew 29% to $126.4 million [24] - Net retention rate stabilized at 114%, with gross retention rate close to 90% [27] Company Strategy and Development Direction - The company is focused on tightening field alignment to drive more use cases into production and expanding its DSP specialist team for multiproduct selling [6][7] - The partner ecosystem sourced over 25% of new business in the last twelve months, indicating strong growth potential [10][11] - The company aims to position itself as a leader in providing real-time data context for AI applications, emphasizing the importance of data streaming in AI deployment [20][22] Management's Comments on Operating Environment and Future Outlook - Management expressed confidence in the strong cloud consumption growth and the overall performance of the data streaming platform [22][36] - The company expects subscription revenue for 2025 to be in the range of $295.5 million to $296.5 million, representing approximately 18% growth [29] - For fiscal year 2025, subscription revenue is expected to be between $1.1135 billion and $1.1145 billion, indicating approximately 21% growth [29] Other Important Information - The company highlighted the successful integration and growth of WarpStream, which has seen eightfold growth in consumption since its acquisition [14][35] - The company has maintained a win rate above 90% in replacing CSP streaming offerings, with average deal sizes more than doubling over the past two quarters [11][12] Q&A Session Summary Question: Insights on go-to-market changes and pipeline conversion trends - Management noted that the specialization model for DSP and field execution improvements have driven strong pipeline progression, with high confidence in the late-stage pipeline [40][41] Question: RPO and CRPO as leading indicators - Management confirmed that RPO is a key leading indicator for the Confluent platform, while for Confluent Cloud, the focus is on new use cases moving into production [45][46] Question: Growth outlook and cannibalization effects - Management indicated that new offerings have proven to be a substantial tailwind, leading to larger deal sizes and increased customer engagement [54] Question: Flink adoption and future business impact - Management expressed excitement about Flink's growth and its potential to drive significant business opportunities in the future [76][78] Question: AI use cases and customer readiness - Management highlighted various AI use cases across industries, emphasizing the importance of data flow and quality in achieving successful AI deployments [86][87]

Financial Data and Key Metrics Changes - Q3 subscription revenue grew 19% to $286.3 million, representing 96% of total revenue [19][20] - Confluent Cloud revenue increased 24% to $161 million, accounting for 56% of subscription revenue compared to 54% in the previous year [19][20] - Non-GAAP operating margin expanded 3 percentage points to approximately 10% [3] - Subscription gross margin was 81.8%, exceeding the long-term target of 80% [20] - Operating margin increased 340 basis points to a record of 9.7% [20] - Adjusted free cash flow margin rose 450 basis points to 8.2% [20] Business Line Data and Key Metrics Changes - Confluent Platform revenue grew 14% to $125.4 million, driven by demand in financial services [19] - Flink ARR for Confluent Cloud grew more than 70% sequentially, with over 1,000 customers using Flink [25][26] - The number of $100K+ ARR customers increased to 1,487, marking the largest sequential increase in two years [21] - The $1 million+ ARR customer count rose to 234, representing a 27% growth acceleration [21] Market Data and Key Metrics Changes - Revenue from the U.S. grew 13% to $172.1 million, while revenue from outside the U.S. grew 29% to $126.4 million [20] - RPO (Remaining Performance Obligations) grew 43%, indicating strong future revenue visibility [24] Company Strategy and Development Direction - The company is focusing on tightening field alignment to drive more use cases into production, with over 40% sequential growth in late-stage pipeline [4][24] - There is a strong emphasis on expanding the Data Streaming Platform (DSP) and multi-product selling, particularly with Flink [5][26] - The partner ecosystem is contributing significantly, with over 25% of new business sourced from partners [8][26] - The company aims to position its Data Streaming Platform as the context layer for enterprise AI, facilitating the transition from AI experimentation to production [16][27] Management's Comments on Operating Environment and Future Outlook - Management expressed confidence in the strong cloud consumption growth and the traction of the Data Streaming Platform, particularly with Flink [17][27] - The outlook for Q4 2025 includes expected subscription revenue in the range of $295.5 to $296.5 million, representing approximately 18% growth [22] - For fiscal year 2025, subscription revenue is expected to be between $1.1135 to $1.1145 billion, indicating approximately 21% growth [23] Other Important Information - The company ended Q3 with $1.99 billion in cash, cash equivalents, and marketable securities, reflecting a strong balance sheet [20] - The WarpStream acquisition has seen 8x growth in consumption since its acquisition [11][26] Q&A Session Summary Question: Insights on go-to-market changes and pipeline conversion trends - Management highlighted the effectiveness of the specialization model for DSP and the high confidence in the late-stage pipeline, which is expected to drive future consumption [29][30] Question: RPO and CRPO as leading indicators - RPO is considered a key leading indicator for Confluent Platform, while for Confluent Cloud, the focus is on the momentum of new use cases moving into production [31][32] Question: Growth stabilization and cloud business outlook - Management noted that the cloud business has shown stability, with positive trends in new use cases and product adoption [34] Question: Impact of new offerings on core streaming business - New offerings have proven to be a substantial tailwind, leading to larger deal sizes and increased customer engagement [37] Question: Flink's growth and future potential - Flink has seen significant adoption, with over 1,000 paying customers, and management is optimistic about its future growth potential [56] Question: AI use cases and customer readiness - Management indicated that AI use cases are broad, with many enterprises actively pursuing projects across various sectors [61]

vivo 移动互联网业务为全球超过 4 亿用户提供应用商店、短视频、广告等服务，其分布式消息中间件 平台承担了实时数据接入与分发的关键角色，日均处理数据量达十万亿级别。面对业务规模的持续增 长，vivo 在消息系统架构演进中通过引入 Apache Pulsar，有效解决了原有 Kafka 架构在多集群管理、 弹性扩缩容和海量分区场景下面临的诸多瓶颈。 业务痛点与挑战 随着 vivo 业务流量的数十倍增长，原有基于 Kafka 的消息系统逐渐显露出架构局限性。Topic 和分区数 量的持续增加严重影响了集群性能，大量分区导致磁盘随机读写加剧，违背了 Kafka 依赖顺序读写实现 高性能的设计初衷。集群规模扩张后，资源组隔离与集群拆分的运维成本显著上升，且 Kafka 无法实现 动态扩缩容，机器资源利用率低。在面对突发流量时，扩容速度缓慢，流量均衡耗时较长，消费端性能 过度依赖分区数量，造成元数据急剧膨胀。此外，集群基数增大导致硬件故障频发，且故障直接传导至 客户端，缺乏有效的中间容错层。 技术选型 滴滴大数据团队于 2021 年 1 月开始调研 Apache Pulsar，同年 8 月正式上线首个 Pulsar ...

Core Insights - The article emphasizes that Chinese companies going global should not merely replicate domestic applications but engage in a "second entrepreneurship" that adapts to local markets, processes, cultures, and regulations [2] - Alibaba Cloud leverages over a decade of experience in supporting companies' international expansion, providing a comprehensive service ecosystem to facilitate seamless global business operations [3] Group 1: Global Technical Services - Companies expanding internationally face various stability challenges, necessitating a comprehensive and systematic technical service framework from cloud service providers [4] - Alibaba Cloud has established a 24/7 service system with international headquarters in Singapore and regional service centers in Portugal, Malaysia, China, and Mexico, ensuring reliable support for over 5 million global customers [4][5] - The service framework includes full lifecycle technical support, covering pre-sales consultation to post-sales operations, allowing companies to focus on their core business [6] Group 2: Technical Support and Governance - Alibaba Cloud offers a range of technical services such as emergency response, risk inspection, disaster recovery, and business continuity support to ensure stable operations for companies expanding overseas [7] - The governance services focus on achieving five key objectives: safety, stability, operational efficiency, cost-effectiveness, and high performance during the international expansion phase [7][8] Group 3: Differentiated Service Capabilities - Alibaba Cloud provides differentiated service capabilities based on public and private cloud deployment models, ensuring high-quality technical services throughout the entire cloud adoption cycle [10][11] - For growing enterprises, Alibaba Cloud offers lightweight products, ready-to-use AI tools, and dedicated technical support to facilitate efficient international expansion [12] Group 4: Collaborative Ecosystem - Alibaba Cloud recognizes the importance of a collaborative ecosystem for international expansion, partnering with various industry players to provide comprehensive solutions [13] - The ecosystem includes partnerships in payment, logistics, data analysis, and IoT, enhancing the support for companies venturing into global markets [14] Group 5: Global Service Network - Alibaba Cloud has built a global service network that integrates local service capabilities with international consulting firms, offering tailored solutions from strategic planning to operational support [16] - The company aims to provide a competitive cloud product foundation, detailed localization services, and specialized ecosystem investments to create optimal pathways for Chinese companies going global [16]

Core Insights - Brainbase Labs has launched Kafka Workforce, an enterprise platform for onboarding specialized AI employees that can be integrated into existing workflows in under an hour [1][2][9] - The platform is built on AWS infrastructure, ensuring reliability, security, and scalability for enterprises looking to deploy AI solutions [4][5] Group 1: Product Features - Kafka Workforce allows enterprises to onboard AI employees that can perform complex tasks, enhancing human productivity [1][2] - The AI employees come equipped with their own computing resources and can be accessed via email, phone, and Slack, mimicking the presence of remote human workers [1][9] - Kafka, the foundational AI agent, is capable of tasks such as data analysis and code reviews, achieving state-of-the-art performance on the GAIA Level 3 benchmark [6][8] Group 2: Market Need and Positioning - Many enterprises face challenges in adopting agentic AI due to a lack of customization and control in existing solutions, which Kafka Workforce aims to address [2][3] - The platform is designed to cater to specialized roles that are often overlooked by traditional AI solutions, providing tailored support for unique organizational needs [3][7] Group 3: Integration and Scalability - Kafka Workforce leverages AWS's cloud infrastructure, allowing for seamless integration with existing enterprise environments and ensuring data sovereignty [4][5] - The platform can scale from a single AI employee to thousands, adapting to workload demands automatically [5][4] Group 4: Future Vision - Brainbase Labs envisions a future where AI employees are indistinguishable from human workers, capable of participating in meetings and collaborating on tasks across various platforms [8][9] - The company aims to create a comprehensive workforce solution that charges a small percentage for each "worker minute," positioning itself as a supplier for the global economy [10]

Summary of Confluent's Conference Call Company Overview - **Company**: Confluent - **Mission**: To set data in motion and serve as the central nervous system for organizations' data [3][6] - **Revenue**: Over $1 billion in annual recurring revenue (ARR) as of the last reported quarter [7] Industry Context - **Data Architecture**: Organizations typically have two estates: operational (for running the business) and analytical (for analyzing the business). Confluent aims to bridge the gap between these two estates using real-time data infrastructure [4][5][6] - **Market Size**: The data streaming market is projected to exceed $100 billion, up from $50 billion at the time of Confluent's IPO, driven by increasing data production, cloud migration, and AI integration [16][17] Core Use Cases - **Industries Served**: Confluent has mission-critical use cases across various industries, including: - **Financial Services**: Fraud detection and high-frequency trading [9][10] - **Retail**: Point of sale inventory and real-time marketing [10] - **Manufacturing**: Inventory management [11] - **Customer Base**: Over 40% of Fortune 500 companies utilize Confluent's services, with no single customer contributing more than 2% of ARR, indicating low customer concentration [8][13] Competitive Landscape - **Main Competitors**: - **Open Source Kafka**: Over 150,000 organizations use it, presenting both competition and opportunity for Confluent [24][25] - **Hyperscalers**: Compete and partner with major cloud providers [27] - **Application Integration Players**: Includes ETL players and venture-funded startups [29] Product Strategy - **Transition to Platform**: Confluent is evolving from a single product company to a multi-product platform, enhancing its value proposition by offering enriched data and various product options [6][17][32] - **Managed Service**: Confluent's managed cloud product offers scalability and lower total cost of ownership (TCO) compared to open-source solutions [30][31] Go-to-Market Strategy - **Focus Areas**: - **Product-Led Growth**: Engaging developers and technologists to drive initial adoption [34] - **Enterprise Sales**: Targeting tech executives to drive large-scale consumption [35] - **Partner Ecosystem**: Building strategic partnerships to amplify reach and scale [37] Financial Performance and Outlook - **Cost Optimization**: Some large customers are focusing on cloud cost optimization, but no significant macroeconomic impact has been observed [39][40] - **Growth Drivers**: Key growth areas include streaming opportunities, the data streaming platform, AI integration, and partnerships [43][45] Underappreciated Aspects - **Complex Data Problem**: The increasing complexity of data challenges presents a significant opportunity for Confluent, which offers the industry's only complete data streaming platform [46][47] Conclusion - Confluent is well-positioned to capitalize on the growing data streaming market, driven by its innovative product offerings, diverse customer base, and strategic partnerships, while addressing the complexities of modern data architecture and AI integration [48][49]

Group 1 - The backend project is built on a microservices architecture using SpringCloud and SpringBoot, with the frontend on a WeChat mini-program mall [1] - Key components include service gateway Zuul, service registration and discovery using Eureka and Ribbon, and service fault tolerance with Hystrix [1] - The architecture supports distributed locking with Redis, service calls via Feign, and message queuing with Kafka [1] Group 2 - The characteristics of the flash sale scenario include a significant surge in website traffic as many users attempt to purchase simultaneously, with the request volume far exceeding available inventory [1] - The design philosophy for the flash sale architecture emphasizes traffic limiting, peak shaving, asynchronous processing, and memory caching to enhance system performance [1] - Scalability is crucial for supporting more users and higher concurrency, allowing for elastic expansion of the system as traffic increases [1]

Core Points - The article discusses the architecture and technology choices for social applications, emphasizing the importance of selecting the right frameworks and services for development [5][8][9]. Group 1: Frontend Development - The frontend of a social app consists of mobile (iOS/Android) and web applications, utilizing frameworks like React.js, Vue.js, and Angular for single-page applications [3][5]. - Mobile app development can be native (using Swift for iOS and Kotlin for Android) or cross-platform (using React Native, Flutter, uni-app, or Taro), each with its own advantages and disadvantages [6][8]. Group 2: Backend Development - The backend handles business logic, data storage, user authentication, and API interfaces, with popular frameworks including Spring Boot for Java, Django for Python, and Express.js for Node.js [9]. - Java is noted for its high performance and stability, making it suitable for large-scale applications, while Python offers rapid development capabilities for smaller projects [9]. Group 3: Database and Storage Solutions - Relational databases like MySQL and PostgreSQL are commonly used for structured data, while NoSQL databases like MongoDB and Redis are preferred for unstructured data and high-speed access [9]. - Object storage services from providers like Alibaba Cloud and Tencent Cloud are essential for managing user-generated content such as images and videos [9]. Group 4: Cloud Services and Compliance - For the Chinese market, compliance with local regulations, including ICP filing and app registration, is crucial, along with the selection of domestic cloud service providers like Alibaba Cloud and Tencent Cloud [8]. - The article highlights the importance of integrating third-party SDKs for functionalities like instant messaging and content moderation, with a focus on local providers [8][9]. Group 5: Development Tools and Technologies - The use of message queues (e.g., Kafka, RabbitMQ) and search engines (e.g., Elasticsearch) is recommended for system decoupling and enhancing user experience through personalized content [9]. - Containerization technologies like Docker and Kubernetes are suggested for efficient application deployment and management [9].