[{"data":1,"prerenderedAt":1522},["ShallowReactive",2],{"active-banner":3,"navbar-featured-partner-blog":24,"navbar-pricing-featured":306,"blog-\u002Fblog\u002Fintroducing-the-streamnative-mcp-server-connecting-streaming-data-to-ai-agents":1086,"blog-authors-\u002Fblog\u002Fintroducing-the-streamnative-mcp-server-connecting-streaming-data-to-ai-agents":1470,"related-\u002Fblog\u002Fintroducing-the-streamnative-mcp-server-connecting-streaming-data-to-ai-agents":1503},{"id":4,"title":5,"date":6,"dismissible":7,"extension":8,"link":9,"link2":10,"linkText":11,"linkText2":12,"meta":13,"stem":21,"variant":22,"__hash__":23},"banners\u002Fbanners\u002Flakestream-ufk-launch.md","StreamNative Introduces Lakestream Architecture and Launches Native Kafka Service","2026-04-07",true,"md","\u002Fblog\u002Ffrom-streams-to-lakestreams","https:\u002F\u002Fconsole.streamnative.cloud\u002Fsignup?from=banner_lakestream-launch","Read Announcement","Sign Up Now",{"body":14},{"type":15,"value":16,"toc":17},"minimark",[],{"title":18,"searchDepth":19,"depth":19,"links":20},"",2,[],"banners\u002Flakestream-ufk-launch","default","zRueBGutATZB0ZnFFHwaEV7F0Di4tnZUHhgOiI4cu6k",{"id":25,"title":26,"authors":27,"body":29,"canonicalUrl":289,"category":290,"createdAt":289,"date":291,"description":292,"extension":8,"featured":7,"image":293,"isDraft":294,"link":289,"meta":295,"navigation":7,"order":296,"path":297,"readingTime":298,"relatedResources":289,"seo":299,"stem":300,"tags":301,"__hash__":305},"blogs\u002Fblog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025.md","StreamNative Recognized as a Contender in The Forrester Wave™: Streaming Data Platforms, Q4 2025",[28],"David Kjerrumgaard",{"type":15,"value":30,"toc":276},[31,39,47,51,67,73,78,81,87,102,109,115,118,124,127,134,140,143,146,157,163,169,172,175,178,184,191,194,197,204,207,210,224,229,233,237,241,245,249,251,268,270],[32,33,35],"h3",{"id":34},"receives-highest-possible-scores-in-both-the-messaging-and-resource-optimization-criteria",[36,37,38],"em",{},"Receives Highest Possible Scores in BOTH the Messaging and Resource Optimization Criteria",[40,41,43],"h2",{"id":42},"introduction",[44,45,46],"strong",{},"Introduction",[48,49,50],"p",{},"Real-time data has become the backbone of modern innovation. As artificial intelligence (AI) and digital services demand instantaneous insights, organizations are realizing that streaming data is no longer optional – it's essential for delivering timely, context-rich experiences. StreamNative's data streaming platform is built precisely for this reality, ensuring data is immediate, reliable, and ready to power critical applications.",[48,52,53,54,63,64],{},"Today, we're excited to announce that Forrester Research has named StreamNative as a Contender in its evaluation, ",[55,56,58],"a",{"href":57},"\u002Freports\u002Frecognized-in-the-forrester-wave-tm-streaming-data-platforms-q4-2025",[36,59,60],{},[44,61,62],{},"The Forrester Wave™: Streaming Data Platforms, Q4 2025",". This report evaluated 15 top streaming data platform providers, and we're proud to share that ",[44,65,66],{},"StreamNative received the highest scores possible—5 out of 5—in both the Messaging and Resource Optimization criteria.",[48,68,69,70],{},"***Forrester's Take: ***",[36,71,72],{},"\"StreamNative is a good fit for enterprises that want an Apache Pulsar implementation that is also compatible with Kafka APIs.\"",[48,74,75],{},[36,76,77],{},"— The Forrester Wave™: Streaming Data Platforms, Q4 2025",[48,79,80],{},"Being recognized in the Forrester Wave is a proud milestone, and for us, it highlights how far StreamNative has come in enabling enterprises to unlock the power of real-time data. In the sections below, we'll dive into what we believe sets StreamNative apart—from our modern architecture and cloud-native design to our open-source foundation and real-time use cases—and how we see these strengths aligning with Forrester's findings.",[40,82,84],{"id":83},"trusted-by-industry-leaders",[44,85,86],{},"Trusted by Industry Leaders",[48,88,89,90,93,94,97,98,101],{},"Companies across industries are already leveraging StreamNative to drive real-time outcomes. Global enterprises like ",[44,91,92],{},"Cisco"," rely on StreamNative to handle massive IoT telemetry, supporting 245 million+ connected devices. Martech leaders such as ",[44,95,96],{},"Iterable"," process billions of events per day with StreamNative for hyper-personalized customer engagement. And in financial services, ",[44,99,100],{},"FICO"," trusts StreamNative to power its real-time fraud detection and analytics pipelines with a secure, scalable streaming backbone.",[48,103,104,105,108],{},"The Forrester report notes that, “",[36,106,107],{},"Customers appreciate the lower infrastructure costs that result from StreamNative’s cost-efficient, Kafka-compatible architecture. Customers note excellent support responsiveness…","”",[40,110,112],{"id":111},"modern-cloud-native-architecture-built-for-scale",[44,113,114],{},"Modern, Cloud-Native Architecture Built for Scale",[48,116,117],{},"From day one, StreamNative was designed with a modern architecture to meet the demanding scale and flexibility requirements of real-time data. Unlike legacy streaming systems that often rely on tightly coupled storage and compute, StreamNative's platform takes a cloud-native approach: it decouples these layers to enable elastic scalability and efficient resource utilization across any environment. The core is powered by Apache Pulsar—a distributed messaging and streaming engine—enhanced with multi-protocol support (including native Apache Kafka API compatibility) to unify diverse data streams under one roof. This means organizations can consolidate siloed messaging systems and handle both high-volume event streams and traditional message queues on a single platform, without sacrificing performance or reliability.",[48,119,120,121,108],{},"Forrester's evaluation described that “",[36,122,123],{},"StreamNative aims to provide a high-performance, multi-protocol streaming data platform: It uses Apache Pulsar with Kafka API compatibility to deliver cost-efficient, real-time applications for enterprises. It appeals to organizations that want a flexible, low-cost streaming solution, due to its focus on scalability and resource optimization, while its investments in Pulsar’s open-source ecosystem and performance optimization make it the primary platform for enterprises wishing to implement Pulsar.",[48,125,126],{},"Our cloud-first, leaderless architecture (with no single broker bottlenecks) and tiered storage model were built to maximize throughput and cost-efficiency for real-time workloads. By separating compute from storage and leveraging distributed object storage, StreamNative can retain huge volumes of event data indefinitely while keeping compute costs in check—effectively providing a flexible, low-cost streaming solution.",[48,128,129,130,133],{},"This modern design not only delivers high performance, but also ensures fault tolerance and geo-distribution out of the box, so enterprises can trust their streaming data is always available and durable. As Forrester’s evaluation noted, StreamNative ",[36,131,132],{},"\"excels at messaging and resource optimization\" and “Its platform supports use cases like real-time analytics and event-driven architectures with robust scalability.","” Our architecture provides the strong foundation that today's real-time applications demand, from ultra-fast data ingestion to seamless scale-out across hybrid and multi-cloud environments.",[40,135,137],{"id":136},"open-source-foundation-and-pulsar-expertise",[44,138,139],{},"Open Source Foundation and Pulsar Expertise",[48,141,142],{},"StreamNative's DNA is rooted in open source innovation. Our founders are the original creators of Apache Pulsar, and we've built our platform with the same open principles: freedom, flexibility, and community-driven innovation. For developers and data teams, this means adopting StreamNative comes with no proprietary lock-in—instead, you get a platform built on open standards and a thriving ecosystem. We offer broad API compatibility (Pulsar, Kafka, JMS, MQTT, and more) so that teams can work with familiar interfaces and integrate StreamNative into existing systems with ease.",[48,144,145],{},"StreamNative is the primary commercial contributor to the Apache Pulsar project and its surrounding ecosystem. We invest heavily in Pulsar's ongoing improvements our investments in Pulsar's open-source ecosystem and performance optimization bolster StreamNative's value. We also foster a vibrant community through initiatives like the Data Streaming Summit and free training resources.",[48,147,148,149,152,153,156],{},"Forrester's assessment noted that StreamNative’s “",[36,150,151],{},"events-driven agents, extensibility, and performance architecture are solid,","” and we're continuing to build on that foundation. ",[44,154,155],{},"We're actively investing in expanding our tooling for observability, governance, schema management, and developer productivity","—areas we recognize as critical for enterprise adoption and where we're committed to accelerating our roadmap.",[48,158,159,160],{},"Being open also means embracing an open ecosystem of technologies. StreamNative actively integrates with the tools and platforms that matter most to our users. We partner with industry leaders like Snowflake, Databricks, Google, and Ververica to ensure our streaming platform works seamlessly with data warehouses, lakehouse storage, and stream processing frameworks. Forrester’s evaluation observed that StreamNative’s ",[36,161,162],{},"\"investments in Pulsar’s open-source ecosystem and performance optimization make it the primary platform for enterprises wishing to implement Pulsar.\"",[40,164,166],{"id":165},"powering-real-time-use-cases-across-industries",[44,167,168],{},"Powering Real-Time Use Cases Across Industries",[48,170,171],{},"One of the greatest validations of StreamNative's approach is the success our customers are achieving with real-time data. StreamNative's platform is versatile and use-case agnostic—if an application demands high-volume, low-latency data movement, we can power it. This flexibility is why our customer base spans industries from finance and IoT to major automobile manufacturers and online gaming. The common thread is that these organizations need to process and react to data in milliseconds, and StreamNative is delivering the capabilities to make that possible.",[48,173,174],{},"Cisco uses StreamNative to underpin an IoT telemetry system of colossal scale, connecting hundreds of millions of devices and thousands of enterprise clients with real-time data streams. The platform's multi-tenant design and proven reliability allow Cisco to offer its customers a live feed of device data with unwavering confidence. In the financial sector, FICO has built streaming pipelines on StreamNative to detect fraud as transactions happen and to monitor systems in real time. With StreamNative's strong guarantees around message durability and ordering, FICO can catch anomalies or suspicious patterns within seconds. And in digital customer engagement, Iterable relies on StreamNative to process billions of events every day—clicks, views, purchases—so that marketers can trigger personalized campaigns instantly based on user behavior.",[48,176,177],{},"Our customers uniformly deal with mission-critical data streams, where downtime or delays are unacceptable. StreamNative's fault-tolerant, scalable infrastructure has proven equal to the task, handling scenarios like bursting to millions of events per second or seamlessly spanning multiple cloud regions. Forrester's report recognized StreamNative for supporting event-driven architectures with robust scalability—which for us is a reflection of our platform's ability to meet the most demanding enterprise requirements.",[40,179,181],{"id":180},"continuing-to-innovate-ursa-orca-and-the-road-ahead",[44,182,183],{},"Continuing to Innovate: Ursa, Orca, and the Road Ahead",[48,185,186,187,190],{},"While we are thrilled to be recognized in Forrester's Streaming Data Platforms Wave, we view this as just the beginning. StreamNative's vision has always been bold: to ",[44,188,189],{},"provide a unified platform that not only handles today's streaming needs but also anticipates the emerging requirements of tomorrow",".",[48,192,193],{},"One key area of focus is the convergence of streaming data with advanced analytics and AI. As Forrester points out in the report, technology leaders should look for platforms that natively integrate messaging, stream processing, and analytics to provide AI agents with real-time, contextualized information. We couldn't agree more. Our award-winning Ursa Engine and Orca Agent Engine are aimed at extending our platform up the stack—bridging the gap between data streams and data lakes, and between event streams and intelligent processing.",[48,195,196],{},"Our new Ursa Engine introduces a lakehouse-native approach to streaming: it can write events directly to table formats like Iceberg on cloud storage, eliminating entire classes of ETL jobs and making fresh data instantly available for analytics queries. By integrating streaming and lakehouse technologies, we help customers collapse data silos and accelerate their AI\u002FML pipelines.",[48,198,199,200,203],{},"Beyond analytics integration, we are also enhancing StreamNative with more out-of-the-box processing and governance capabilities. In the coming months, we plan to introduce new features for lightweight stream processing and transformation, making it easier to build reactive applications directly on the platform. We're also expanding our ecosystem of connectors and integrations, so that whether your data lands in Snowflake, Databricks, or an AI model, StreamNative will seamlessly feed it. ",[44,201,202],{},"We're investing significantly in enterprise features including security, schema registry, governance, and monitoring tooling","—capabilities that are essential for mission-critical deployments and where we're committed to continued improvement.",[48,205,206],{},"This recognition from Forrester energizes us to keep innovating at full speed. We're sharing this honor with our amazing customers, community, and partners who drive us forward every day. Your feedback and real-world challenges have helped shape StreamNative into what it is today, and together, we will shape the future of streaming data. Thank you for joining us on this journey—we're just getting started, and we can't wait to deliver even more value as we continue to evolve our platform. Onward to real-time everything!",[208,209],"hr",{},[32,211,213],{"id":212},"streamnative-in-the-forrester-wave-evaluation-findings",[44,214,215,216,223],{},"StreamNative in ",[44,217,218],{},[55,219,220],{"href":57},[44,221,222],{},"The Forrester Wave™",": Evaluation Findings",[225,226,228],"h5",{"id":227},"recognized-as-a-contender-among-15-streaming-data-platform-providers","• Recognized as a Contender among 15 streaming data platform providers",[225,230,232],{"id":231},"received-the-highest-scores-possible-50-in-both-the-messaging-and-resource-optimization-criteria","* Received the highest scores possible (5.0) in both the Messaging and Resource Optimization criteria",[225,234,236],{"id":235},"cited-as-the-primary-platform-for-enterprises-wishing-to-implement-pulsar","• Cited as the primary platform for enterprises wishing to implement Pulsar",[225,238,240],{"id":239},"noted-for-excelling-at-messaging-and-resource-optimization","• Noted for excelling at messaging and resource optimization",[225,242,244],{"id":243},"customers-cited-lower-infrastructure-costs-and-excellent-support-responsiveness","• Customers cited lower infrastructure costs and excellent support responsiveness",[225,246,248],{"id":247},"recognized-for-supporting-event-driven-architectures-with-robust-scalability","• Recognized for supporting event-driven architectures with robust scalability",[208,250],{},[252,253,255,256,259,260,190],"h6",{"id":254},"forrester-disclaimer-forrester-does-not-endorse-any-company-product-brand-or-service-included-in-its-research-publications-and-does-not-advise-any-person-to-select-the-products-or-services-of-any-company-or-brand-based-on-the-ratings-included-in-such-publications-information-is-based-on-the-best-available-resources-opinions-reflect-judgment-at-the-time-and-are-subject-to-change-for-more-information-read-about-forresters-objectivity-here","**Forrester Disclaimer: **",[36,257,258],{},"Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change",". *For more information, read about Forrester’s objectivity *",[55,261,265],{"href":262,"rel":263},"https:\u002F\u002Fwww.forrester.com\u002Fabout-us\u002Fobjectivity\u002F",[264],"nofollow",[36,266,267],{},"here",[208,269],{},[252,271,273],{"id":272},"apache-apache-pulsar-apache-kafka-apache-flink-and-other-names-are-trademarks-of-the-apache-software-foundation-no-endorsement-by-apache-or-other-third-parties-is-implied",[36,274,275],{},"Apache®, Apache Pulsar®, Apache Kafka®, Apache Flink® and other names are trademarks of The Apache Software Foundation. No endorsement by Apache or other third parties is implied.",{"title":18,"searchDepth":19,"depth":19,"links":277},[278,280,281,282,283,284,285],{"id":34,"depth":279,"text":38},3,{"id":42,"depth":19,"text":46},{"id":83,"depth":19,"text":86},{"id":111,"depth":19,"text":114},{"id":136,"depth":19,"text":139},{"id":165,"depth":19,"text":168},{"id":180,"depth":19,"text":183,"children":286},[287],{"id":212,"depth":279,"text":288},"StreamNative in The Forrester Wave™: Evaluation Findings",null,"Company","2025-12-16","StreamNative is recognized in The Forrester Wave™: Streaming Data Platforms, Q4 2025. Discover why Forrester highlights StreamNative's high-performance messaging, efficient resource use, and cost-effective Kafka API compatibility for real-time innovation.","\u002Fimgs\u002Fblogs\u002F693bd36cf01b217dcb67278f_Streamnative_blog_thumbnail.png",false,{},0,"\u002Fblog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025","10 mins read",{"title":26,"description":292},"blog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025",[302,303,304],"Announcements","Real-Time","Forrester","5Nr1vAcqlQ7yFQfdL0a3MLsNFerVmEOQJXD9Twz5lx8",{"id":307,"title":308,"authors":309,"body":314,"canonicalUrl":289,"category":1073,"createdAt":289,"date":1074,"description":1075,"extension":8,"featured":7,"image":1076,"isDraft":294,"link":289,"meta":1077,"navigation":7,"order":296,"path":1078,"readingTime":1079,"relatedResources":289,"seo":1080,"stem":1081,"tags":1082,"__hash__":1085},"blogs\u002Fblog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour.md","How We Run a 5 GB\u002Fs Kafka Workload for Just $50 per Hour",[310,311,312,313],"Matteo Meril","Neng Lu","Hang Chen","Penghui Li",{"type":15,"value":315,"toc":1043},[316,319,322,325,328,331,335,338,348,354,357,365,370,374,381,384,387,395,399,402,407,411,414,417,420,423,432,436,439,450,453,457,460,463,474,477,481,485,493,496,500,508,537,541,544,549,553,556,560,563,566,571,580,585,588,591,602,606,609,620,624,627,630,635,638,667,671,673,679,682,687,692,695,699,713,717,728,732,747,756,767,770,773,777,780,783,794,797,800,803,808,813,817,821,838,842,856,861,865,876,879,895,899,910,915,920,928,932,935,939,946,950,953,962,967,976,982,991,1000,1009,1018,1027,1035],[48,317,318],{},"The rise of DeepSeek has shaken the AI infrastructure market, forcing companies to confront the escalating costs of training and deploying AI models. But the real pressure point isn’t just compute—it’s data acquisition and ingestion costs.",[48,320,321],{},"As businesses rethink their AI cost-containment strategies, real-time data streaming is emerging as a critical enabler. The growing adoption of Kafka as a standard protocol has expanded cost-efficient options, allowing companies to optimize streaming analytics while keeping expenses in check.",[48,323,324],{},"Ursa, the data streaming engine powering StreamNative’s managed Kafka service, is built for this new reality. With its leaderless architecture and native lakehouse storage integration, Ursa eliminates costly inter-zone network traffic for data replication and client-to-broker communication while ensuring high availability at minimal operational cost.",[48,326,327],{},"In this blog post, we benchmarked the infrastructure cost and total cost of ownership (TCO) for running a 5GB\u002Fs Kafka workload across different Kafka vendors, including Redpanda, Confluent WarpStream, and AWS MSK. Our benchmark results show that Ursa can sustain 5GB\u002Fs Kafka workloads at just 5% of the cost of traditional streaming engines like Redpanda—making it the ideal solution for high-performance, cost-efficient ingestion and data streaming for data lakehouses and AI workloads.",[48,329,330],{},"Note: We also evaluated vanilla Kafka in our benchmark; however, for simplicity, we have focused our cost comparison on vendor solutions rather than self-managed deployments. That said, it is important to highlight that both Redpanda and vanilla Kafka use a leader-based data replication approach. In a data-intensive, network-bound workload like 5GB\u002Fs streaming, with the same machine type and replication factor, Redpanda and vanilla Kafka produced nearly identical cost profiles.",[40,332,334],{"id":333},"key-benchmark-findings","Key Benchmark Findings",[48,336,337],{},"Ursa delivered 5 GB\u002Fs of sustained throughput at an infrastructure cost of just $54 per hour. For comparison:",[339,340,341,345],"ul",{},[342,343,344],"li",{},"MSK: $303 per hour → 5.6x more expensive compared to Ursa",[342,346,347],{},"Redpanda: $988 per hour → 18x more expensive compared to Ursa",[48,349,350],{},[351,352],"img",{"alt":18,"src":353},"\u002Fimgs\u002Fblogs\u002F679c71b67d9046f26edc7977_AD_4nXfvTqyBNUBu2lObdkKAx-5UNkpNP8UYULLZyOcixE6z99VMZUUEsUqWjzexI7vjyNGRNSAUoM9smYvdTP55ctAhIbrs5lmQgcSVMWdaoigbWouCl95DVSQsxooY-qqfGcYqS4g4zA.png",[48,355,356],{},"Beyond infrastructure costs, when factoring in both storage pricing, vendor pricing and operational expenses, Ursa’s total cost of ownership (TCO) for a 5GB\u002Fs workload with a 7-day retention period is:",[339,358,359,362],{},[342,360,361],{},"50% cheaper than Confluent WarpStream",[342,363,364],{},"85% cheaper than MSK and Redpanda",[48,366,367],{},[351,368],{"alt":18,"src":369},"\u002Fimgs\u002Fblogs\u002F679c602d77e9c706de5343b8_AD_4nXeDv8rrv_C1CTCCiqYo1zpvlGYbdBk1r0VEqovAPu22iFMQZgh54Hfw9PBMLzM7jDFxKwAFDxbdG0np4XVk_tGsWhEKMloLRcmmea7lvueCx-0cFsyaE3Mya4Mxc1Dox95A6JEc.png",[40,371,373],{"id":372},"ursa-highly-cost-efficient-data-streaming-at-scale","Ursa: Highly Cost-Efficient Data Streaming at Scale",[48,375,376,380],{},[55,377,379],{"href":378},"\u002Fblog\u002Fursa-reimagine-apache-kafka-for-the-cost-conscious-data-streaming","Ursa"," is a next-generation data streaming engine designed to deliver high performance at a fraction of the cost of traditional disk-based solutions. It is fully compatible with Apache Kafka and Apache Pulsar APIs, while leveraging a leaderless, lakehouse-native architecture to maximize scalability, efficiency, and cost savings.",[48,382,383],{},"Ursa’s key innovation is separating storage from compute and decoupling metadata\u002Findex operations from data operations by utilizing cloud object storage (e.g., AWS S3) instead of costly inter-zone disk-based replication. It also employs open lakehouse formats (Iceberg and Delta Lake), enabling columnar compression to significantly reduce storage costs while maintaining durability and availability.",[48,385,386],{},"In contrast, traditional streaming systems—like Kafka and Redpanda—depend on leader-based architectures, which drive up inter-zone traffic costs due to replication and client communication. Ursa mitigates these costs by:",[339,388,389,392],{},[342,390,391],{},"Eliminating inter-zone traffic costs via a leaderless architecture.",[342,393,394],{},"Replacing costly inter-zone replication with direct writes to cloud storage using open lakehouse formats.",[40,396,398],{"id":397},"how-ursa-eliminates-inter-zone-traffic","How Ursa Eliminates Inter-Zone Traffic",[48,400,401],{},"Ursa minimizes inter-zone traffic by leveraging a leaderless architecture, which eliminates inter-zone communication between clients and brokers, and lakehouse-native storage, which removes the need for inter-zone data replication. This approach ensures high availability and scalability while avoiding unnecessary cross-zone data movement.",[48,403,404],{},[351,405],{"alt":18,"src":406},"\u002Fimgs\u002Fblogs\u002F679c602e21b3571bb7117dca_AD_4nXd7Oahc77NjRLNvA9clLt0tsyU6MrIqVibFYv5pW5giTIcCHPr3EA_yTGzfVEUIVO3VXK56qWK8zmBCp5lY0E_4nmlWIPFrHjtHylA5NhwELjn-UB0fLG2h_kbrxrc7Cs_edvveNA.png",[32,408,410],{"id":409},"leaderless-architecture","Leaderless architecture",[48,412,413],{},"Traditional streaming engines such as Kafka, Pulsar, or RedPanda rely on a leader-based model, where each partition is assigned to a single leader broker that handles all writes and reads.",[48,415,416],{},"Pros of Leader-Based Architectures:\n✔ Maintains message ordering via local sequence IDs\n✔ Delivers low latency and high performance through message caching",[48,418,419],{},"Cons of Leader-Based Architectures:\n✖ Throughput bottlenecked by a single broker per partition\n✖ Inter-zone traffic required for high availability in multi-AZ deployments",[48,421,422],{},"While Kafka and Pulsar offer partial solutions (e.g., reading from followers, shadow topics) to reduce read-related inter-zone traffic, producers still send data to a single leader.",[48,424,425,426,431],{},"Ursa removes the concept of topic ownership, allowing any broker in the cluster to handle reads or writes for any partition. The primary challenge—ensuring message ordering—is solved with ",[55,427,430],{"href":428,"rel":429},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Foxia",[264],"Oxia",", a scalable metadata and index service created by StreamNative in 2022.",[32,433,435],{"id":434},"oxia-the-metadata-layer-enabling-leaderless-architecture","Oxia: The Metadata Layer Enabling Leaderless Architecture",[48,437,438],{},"Ensuring message ordering in a leaderless architecture is complex, but Ursa solves this with Oxia:",[339,440,441,444,447],{},[342,442,443],{},"Handles millions of metadata\u002Findex operations per second",[342,445,446],{},"Generates sequential IDs to maintain strict message ordering",[342,448,449],{},"Optimized for Kubernetes with horizontal scalability",[48,451,452],{},"Producers and consumers can connect to any broker within their local AZ, eliminating inter-zone traffic costs while maintaining performance through localized caching.",[32,454,456],{"id":455},"zero-interzone-data-replication","Zero interzone data replication",[48,458,459],{},"In most distributed systems, data replication from a leader (primary) to followers (replicas) is crucial for fault tolerance and availability. However, replication across zones can inflate infrastructure expenses substantially.",[48,461,462],{},"Ursa avoids these costs by writing data directly to cloud storage (e.g., AWS S3, Google GCS):",[339,464,465,468,471],{},[342,466,467],{},"Built-In Resilience: Cloud storage inherently offers high availability and fault tolerance without inter-zone traffic fees.",[342,469,470],{},"Tradeoff: Slightly higher latency (sub-second, with p99 at 500 milliseconds) compared to local disk\u002FEBS (single-digit to sub-100 milliseconds), in exchange for significantly lower costs (up to 10x lower).",[342,472,473],{},"Flexible Modes: Ursa is an addition to the classic BookKeeper-based engine, providing users with the flexibility to optimize for either cost or low latency based on their workload requirements.",[48,475,476],{},"By foregoing conventional replication, Ursa slashes inter-zone traffic costs and associated complexities—making it a compelling option for organizations seeking to balance high-performance data streaming with strict budget constraints.",[40,478,480],{"id":479},"how-we-ran-a-5-gbs-test-with-ursa","How We Ran a 5 GB\u002Fs Test with Ursa",[32,482,484],{"id":483},"ursa-cluster-deployment","Ursa Cluster Deployment",[339,486,487,490],{},[342,488,489],{},"9 brokers across 3 availability zones, each on m6i.8xlarge (Fixed 12.5 Gbps bandwidth, 32 vCPU cores, 128 GB memory).",[342,491,492],{},"Oxia cluster (metadata store) with 3 nodes of m6i.8xlarge, distributed across three availability zones (AZs).",[48,494,495],{},"During peak throughput (5 GB\u002Fs), each broker’s network usage was about 10 Gbps.",[32,497,499],{"id":498},"openmessaging-benchmark-workers-configuration","OpenMessaging Benchmark Workers & Configuration",[48,501,502,503,507],{},"The OpenMessaging Benchmark(OMB) Framework is a suite of tools that make it easy to benchmark distributed messaging systems in the cloud. Please check ",[55,504,505],{"href":505,"rel":506},"https:\u002F\u002Fopenmessaging.cloud\u002Fdocs\u002Fbenchmarks\u002F",[264]," for details.",[339,509,510,525,534],{},[342,511,512,513,518,519,524],{},"12 OMB workers: 6 for ",[55,514,517],{"href":515,"rel":516},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002Fd1094122270775e4f1580947f80c5055",[264],"producers",", 6 for ",[55,520,523],{"href":521,"rel":522},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F06bada89381fb77a7862e1b4c1d8963d",[264],"consumers"," across 3 availability zones, on m6i.8xlarge instances. Each worker is configured with 12 CPU cores and 48 GB memory.",[342,526,527,528,533],{},"Sample YAML ",[55,529,532],{"href":530,"rel":531},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F204c1f26c4d44a218ae235bf2de99904",[264],"scripts"," provided for Kafka-compatible configuration and rate limits.",[342,535,536],{},"Achieved consistent 5 GB\u002Fs publish\u002Fsubscribe throughput.",[40,538,540],{"id":539},"ursa-benchmark-tests-results","Ursa Benchmark Tests & Results",[48,542,543],{},"The following diagram demonstrates that Ursa can consistently handle 5 GB\u002Fs of traffic, fully saturating the network across all broker nodes.",[48,545,546],{},[351,547],{"alt":18,"src":548},"\u002Fimgs\u002Fblogs\u002F679c602d7b261bac1113f7d6_AD_4nXdDPsRc3koXICiFF0bqSmGWbJt_RlUy4FE3ruuWOfbCfpcqZ1dejjqGbkaCJv2hQFL1nirRouBVRW2l5uMWBvY9naMqGB_wHcLI14dBM0f85TXhmdm3UxEv1yGX9Y4hf5FttSkZew.png",[40,550,552],{"id":551},"comparing-infrastructure-cost","Comparing Infrastructure Cost",[48,554,555],{},"This benchmark first evaluates infrastructure costs of running a 5 GB\u002Fs streaming workload (1:1 producer-to-consumer ratio) across different data streaming engines, including Ursa, Redpanda, and AWS MSK, with a focus on multi-AZ deployments to ensure a fair comparison.",[32,557,559],{"id":558},"test-setup-key-assumptions","Test Setup & Key Assumptions",[48,561,562],{},"All tests use multi-AZ configurations, with clusters and clients distributed across three AWS availability zones (AZs). Cluster size scales proportionally to the number of AZs, and rack-awareness is enabled for all engines to evenly distribute topic partitions and leaders.",[48,564,565],{},"To ensure a fair comparison, we selected the same machine type capable of fully utilizing both network and storage bandwidth for Ursa and Redpanda in this 5GB\u002Fs test:",[339,567,568],{},[342,569,570],{},"9 × m6i.8xlarge instances",[48,572,573,574,579],{},"However, MSK's storage bandwidth limits vary depending on the selected instance type, with the highest allowed limit capped at 1000 MiB\u002Fs per broker, according to",[55,575,578],{"href":576,"rel":577},"https:\u002F\u002Fdocs.aws.amazon.com\u002Fmsk\u002Flatest\u002Fdeveloperguide\u002Fmsk-provision-throughput-management.html#throughput-bottlenecks",[264]," AWS documentation",". Given this constraint, achieving 5 GB\u002Fs throughput with a replication factor of 3 required the following setup:",[339,581,582],{},[342,583,584],{},"15 × kafka.m7g.8xlarge (32 vCPUs, 128 GB memory, 15 Gbps network, 4000 GiB EBS).",[48,586,587],{},"This configuration was necessary to work around MSK's storage bandwidth limitations, ensuring a comparable cost basis to other evaluated streaming engines.",[48,589,590],{},"Additional key assumptions include:",[339,592,593,596,599],{},[342,594,595],{},"Inter-AZ producer traffic: For leader-based engines, two-thirds of producer-to-broker traffic crosses AZs due to leader distribution.",[342,597,598],{},"Consumer optimizations: Follower fetch is enabled across all tests, eliminating inter-AZ consumer traffic.",[342,600,601],{},"Storage cost exclusions: This benchmark only evaluates streaming costs, assuming no long-term data retention.",[32,603,605],{"id":604},"inter-broker-replication-costs","Inter-Broker Replication Costs",[48,607,608],{},"Inter-broker (cross-AZ) replication is a major cost driver for data streaming engines:",[339,610,611,614,617],{},[342,612,613],{},"RedPanda: Inter-broker replication is not free, leading to substantial costs when data must be copied across multiple availability zones.",[342,615,616],{},"AWS MSK: Inter-broker replication is free, but MSK instance pricing is significantly higher (e.g., $3.264 per hour for kafka.m7g.8xlarge vs $1.306 per hour for an on-demand m7g.8xlarge). The storage price of MSK is $0.10 per GB-month which is significantly higher than st1, which costs $0.045 per GB-month. Even though replication is free, client-to-broker traffic still incurs inter-AZ charges.",[342,618,619],{},"Ursa: No inter-broker replication costs due to its leaderless architecture, eliminating inter-zone replication costs entirely.",[32,621,623],{"id":622},"zone-affinity-reducing-inter-az-costs","Zone Affinity: Reducing Inter-AZ Costs",[48,625,626],{},"We evaluated zone affinity mechanisms to further reduce inter-AZ data transfer costs.",[48,628,629],{},"Consumers:",[339,631,632],{},[342,633,634],{},"Follower fetch is enabled across all tests, ensuring consumers fetch data from replicas in their local AZ—eliminating inter-zone consumer traffic except for metadata lookups",[48,636,637],{},"Producers:",[339,639,640,649,658],{},[342,641,642,643,648],{},"Kafka protocol lacks an easy way to enforce producer AZ affinity (though ",[55,644,647],{"href":645,"rel":646},"https:\u002F\u002Fcwiki.apache.org\u002Fconfluence\u002Fdisplay\u002FKAFKA\u002FKIP-1123:+Rack-aware+partitioning+for+Kafka+Producer",[264],"KIP-1123"," aims to address this). And it only works with the default partitioner (i.e., when no record partition or record key is specified).",[342,650,651,652,657],{},"Redpanda recently introduced ",[55,653,656],{"href":654,"rel":655},"https:\u002F\u002Fdocs.redpanda.com\u002Fredpanda-cloud\u002Fdevelop\u002Fproduce-data\u002Fleader-pinning\u002F",[264],"leader pinning",", but this only benefits setups where producers are confined to a single AZ—not applicable to our multi-AZ benchmark.",[342,659,660,661,666],{},"Ursa is the only system in this test with ",[55,662,665],{"href":663,"rel":664},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fconfig-kafka-client#eliminate-cross-az-networking-traffic",[264],"built-in zone affinity for both producers and consumers",". It achieves this by embedding producer AZ information in client.id, allowing metadata lookups to route clients to local-AZ brokers, eliminating inter-AZ producer traffic.",[32,668,670],{"id":669},"cost-comparison-results","Cost Comparison Results",[48,672,337],{},[339,674,675,677],{},[342,676,344],{},[342,678,347],{},[48,680,681],{},"Ursa’s leaderless architecture, zone affinity, and native cloud storage integration deliver unparalleled cost efficiency, making it the most cost-effective choice for high-throughput data streaming workloads.",[48,683,684],{},[351,685],{"alt":18,"src":686},"\u002Fimgs\u002Fblogs\u002F679c72208198ca36a352f228_AD_4nXeeZuM8T-xBlD4Vf3j67K618n08qh8wIDLLtiLJG0ssA1Wj1V26u7wIDTX9sqLrtw8mB2c299dwzarGen62CG0Vh7nWstn5qbPGFcBaKJYEepTsLr5fHWv1U8uqbg8Y0UOK6fJ7.png",[48,688,689],{},[351,690],{"alt":18,"src":691},"\u002Fimgs\u002Fblogs\u002F679c625978031f40229de484_AD_4nXdLkLLJ30KKr-_A_rN1j8akVwBYacAWIPzWHoOReJF421890kfByZoQQxkLczihVSmiw5Q9J51-V9I2SEKITbwsYnANDDTlAVL5nQ_jfaHNTe9VEWhSoa7DZooCnilDYL6l6msmJg.png",[48,693,694],{},"The detailed infrastructure cost calculations for each data streaming engine are listed below:",[32,696,698],{"id":697},"streamnative-ursa","StreamNative - Ursa",[339,700,701,704,707,710],{},[342,702,703],{},"Server EC2 costs: 9 * $1.536\u002Fhr = $14",[342,705,706],{},"Client EC2 costs: 9 * $1.536\u002Fhr =$14",[342,708,709],{},"S3 write requests costs: 1350 r\u002Fs * $0.005\u002F1000r * 3600s = $24",[342,711,712],{},"S3 read requests costs: 1350 r\u002Fs * $0.0004\u002F1000r * 3600s = $2",[32,714,716],{"id":715},"aws-msk","AWS MSK",[339,718,719,722,725],{},[342,720,721],{},"Server EC2 costs: 15 * $3.264\u002Fhr = $49",[342,723,724],{},"Client side EC2 costs: 9 * $1.536\u002Fhr =$14",[342,726,727],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FG(in+out) * 3600 = $240",[32,729,731],{"id":730},"redpanda","RedPanda",[339,733,734,736,738,741,744],{},[342,735,703],{},[342,737,706],{},[342,739,740],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FGB(in+out) * 3600 = $240",[342,742,743],{},"Interzone traffic - replication: 10GB\u002Fs * $0.02\u002FGB(in+out) * 3600 = $720",[342,745,746],{},"Interzone traffic - broker to consumer: $0 (fetch from local zone)",[48,748,749,750,755],{},"Please note that we were unable to test ",[55,751,754],{"href":752,"rel":753},"https:\u002F\u002Fwww.redpanda.com\u002Fblog\u002Fcloud-topics-streaming-data-object-storage",[264],"Redpanda with Cloud Topics",", as it remains an announced but unreleased feature and is not yet available for evaluation. Based on the limited information available, while Cloud Topics may help optimize inter-zone data replication costs, producers still need to traverse inter-availability zones to connect to the topic partition owners and incur inter-zone traffic costs of up to $240 per hour.",[339,757,758,764],{},[342,759,760,763],{},[55,761,647],{"href":645,"rel":762},[264]," (when implemented) will help mitigate producer-to-broker inter-zone traffic, but it is not yet available. And it only works with the default partitioner (no record partition or key is specified).",[342,765,766],{},"Redpanda’s leader pinning helps only when all producers for the pinned topic are confined to a single AZ. In multi-AZ environments (like our benchmark), inter-zone producer traffic remains unavoidable.",[48,768,769],{},"Additionally, Redpanda’s Cloud Topics architecture is not documented publicly. Their blog mentions \"leader placement rules to optimize produce latency and ingress cost,\" but it is unclear whether this represents a shift away from a leader-based architecture or if it uses techniques similar to Ursa’s zone-aware approach.",[48,771,772],{},"We may revisit this comparison as more details become available.",[40,774,776],{"id":775},"comparing-total-cost-of-ownership","Comparing Total Cost of Ownership",[48,778,779],{},"As highlighted earlier, with a BYOC Ursa setup, you can achieve 5 GB\u002Fs throughput at just 5% of the infrastructure cost of a traditional leader-based data streaming engine, such as Kafka or RedPanda, while managing the infrastructure yourself. This significant cost reduction is enabled by Ursa’s leaderless architecture and lakehouse-native storage design, which eliminate overhead costs such as inter-zone traffic and leader-based data replication. By leveraging a lakehouse-native, leaderless architecture, Ursa reduces resource requirements, enabling you to handle high data throughput efficiently and at a fraction of the cost of RedPanda.",[48,781,782],{},"Now, let’s examine the total cost comparison, evaluating Ursa alongside other vendors, including those that have adopted a leaderless architecture (e.g., Confluent WarpStream). This comparison is based on a 5GB\u002Fs workload with a 7-day retention period, factoring in both storage cost and vendor costs Here are the key findings:",[339,784,785,788,791],{},[342,786,787],{},"Ursa ($164,353\u002Fmonth) is: 50% cheaper than Confluent WarpStream ($337,068\u002Fmonth)",[342,789,790],{},"85% cheaper than AWS MSK ($1,115,251\u002Fmonth)",[342,792,793],{},"86% cheaper than Redpanda ($1,202,853\u002Fmonth)",[48,795,796],{},"In addition to Ursa’s architectural advantages—eliminating most inter-AZ traffic and leveraging lakehouse storage for cost-effective data retention—it also adopts a more fair and cost-efficient pricing model: Elastic Throughput-based pricing. This approach aligns costs with actual usage, avoiding unnecessary overhead.",[48,798,799],{},"Unlike WarpStream, which charges for both storage and throughput, Ursa ensures that customers only pay for the throughput they actively use. Ursa’s pricing is based on compressed data sent by clients, meaning the more data compressed on the client side, the lower the cost. In contrast, WarpStream prices are based on uncompressed data, unfairly inflating expenses and failing to incentivize customers to optimize their client applications.",[48,801,802],{},"This distinction is crucial, as compressed data reduces both storage and network costs, making Ursa’s pricing model not only more cost-effective but also more transparent and predictable.",[48,804,805],{},[351,806],{"alt":18,"src":807},"\u002Fimgs\u002Fblogs\u002F679c602d194800c9206d9d58_AD_4nXcFlf755xgyz7htxhMhBV5fGrsxy642mQNodt61DTok_z1dwkw5A6lkO5hatXVneCaB0anbZPAyvLI3MlIMuQEYLEACHHvQMOr5UfaB37dfzkdqewDEvcT-20VGd_zzvJsuA00zGA.png",[48,809,810],{},[351,811],{"alt":18,"src":812},"\u002Fimgs\u002Fblogs\u002F679c62594e9c2e629fae73aa_AD_4nXeU6cOgItnjLsEZCOf13TEvMY_SHWWIxYP2OYUj-B1GUPyWO78OG08K_v03hwYSVcg06f9dqDiGmdwy76vynjmiDGL5bluZ5_XF4nSU_r59oOZdfViXndXt6s11vVOY7qwfZN8v.png",[32,814,816],{"id":815},"cost-breakdown","Cost Breakdown",[818,819,820],"h4",{"id":697},"StreamNative – Ursa",[339,822,823,826,829,832,835],{},[342,824,825],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9,953.28",[342,827,828],{},"S3 Write Requests: 1,350 r\u002Fs × $0.005\u002F1,000 r × 3,600 s × 24 hr × 30 days = $17,496",[342,830,831],{},"S3 Read Requests: 1,350 r\u002Fs × $0.0004\u002F1,000 r × 3,600 s × 24 hr × 30 days = $1,400",[342,833,834],{},"S3 Storage Costs: 5 GB\u002Fs × $0.021\u002FGB × 3,600 s × 24 hr × 7 days = $63,504",[342,836,837],{},"Vendor Cost: 200 ETU × $0.50\u002Fhr × 24 hr × 30 days = $72,000",[818,839,841],{"id":840},"warpstream","WarpStream",[339,843,844,847],{},[342,845,846],{},"Based on WarpStream’s pricing calculator (as of January 29, 2025), we assume a 4:1 client data compression ratio, meaning 20 GB\u002Fs of uncompressed data translates to 5 GB\u002Fs of compressed data.",[342,848,849,850,855],{},"It's important to note that WarpStream’s pricing structure has fluctuated frequently throughout January. We observed the cost reported by their calculator changing from $409,644 per month to $337,068 per month. This variability has been previously highlighted in the blog post “",[55,851,854],{"href":852,"rel":853},"https:\u002F\u002Fbigdata.2minutestreaming.com\u002Fp\u002Fthe-brutal-truth-about-apache-kafka-cost-calculators",[264],"The Brutal Truth About Kafka Cost Calculators","”. To ensure transparency, we have documented the pricing as of January 29, 2025.",[48,857,858],{},[351,859],{"alt":18,"src":860},"\u002Fimgs\u002Fblogs\u002F679c602e42713e0028e9af5e_AD_4nXcu5_VWTLu9jRYs6zX1MBAOtLQEo5gyfNSWPcbpnQHXTa8qNCFAXezRR2E8daygzYTTwd4dhJjaLaLM8C6y_3OGbu2NS7pdvEv3a8-ptNKOg7AeKnYqPQCAYvQ5EuxzuI3JYIvY.png",[818,862,864],{"id":863},"msk","MSK",[339,866,867,870,873],{},[342,868,869],{},"EC2 (Server): 15 * $3.264\u002Fhr × 24 hr × 30 days = $35,251",[342,871,872],{},"Interzone Traffic (Client-Server): 5 GB\u002Fs × ⅔ × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $172,800",[342,874,875],{},"Storage: 5 GB\u002Fs × $0.1\u002FGB-month × 3,600 s × 24 hr × 7 days * 3 replicas = $907,200",[818,877,731],{"id":878},"redpanda-1",[339,880,881,884,886,889,892],{},[342,882,883],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9953",[342,885,872],{},[342,887,888],{},"Interzone Traffic (Replication): 5 GB\u002Fs × 2 × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $518,400",[342,890,891],{},"Storage: 5 GB\u002Fs × $0.045\u002FGB-month(st1) × 3,600 s × 24 hr × 7 days * 3 replicas = $408,240",[342,893,894],{},"Vendor Cost: $93,333 per month (based on limited information. See additional notes below).",[818,896,898],{"id":897},"additional-notes","Additional Notes",[339,900,901],{},[342,902,903,904,909],{},"Redpanda does not publicly disclose its BYOC pricing, making it difficult to accurately assess its total costs. We refer to information from the whitepaper “",[55,905,908],{"href":906,"rel":907},"https:\u002F\u002Fwww.redpanda.com\u002Fresources\u002Fredpanda-vs-confluent-performance-tco-benchmark-report#form",[264],"Redpanda vs. Confluent: A Performance and TCO Benchmark Report by McKnight Consulting Group.","” for estimation purposes. Based on the Tier-8 pricing model in the whitepaper,  the estimated cost to support a 5GB\u002Fs workload would be $1.12 million per year ($93,333 per month). However, since this calculation is based on an estimation, we will revisit and refine the cost assessment once Redpanda publishes its BYOC pricing.",[48,911,912],{},[351,913],{"alt":18,"src":914},"\u002Fimgs\u002Fblogs\u002F679c602dc8a9859eed89a0ef_AD_4nXdbcO8vsNNPy4GtkNLlmNKf22fjxRvzLzH7CtOna1L08sTbvnZx3HhufeFqc1w4K2gEF7lxO2IR5supotxebAiGnA07Qa8Yr3Rd1pVK2LYKK4WurlJGwgdwwucZIFoF-N_2oBjY.png",[48,916,917],{},[351,918],{"alt":18,"src":919},"\u002Fimgs\u002Fblogs\u002F679c602d6bc1c2287e012540_AD_4nXfcHZnLfjbjIr3ZAgoQXT9dwP3aQCOQPmGZZJUtpNZSwE6qY6M3yehIaBxCwxEIeu5PVdUPY0zhyjnow26YfgjdYgSG4GnV9ibxu0YWTIpwng6z_F6FUGJMpERMKtpsFESzXSN_Sw.png",[339,921,922,925],{},[342,923,924],{},"When estimating the storage costs for Kafka and Redpanda, we assume the use of HDD storage at $0.045\u002FGB, based on the premise that both systems can fully utilize disk bandwidth without incurring the higher costs associated with GP2 or GP3 volumes. However, in practice, many users opt for GP2 or GP3, significantly increasing the total storage cost for Kafka and Redpanda.",[342,926,927],{},"Unlike disk-based solutions, S3 storage does not require capacity preallocation—Ursa only incurs costs for the actual data stored. This contrasts with Kafka and Redpanda, where preallocating storage can drive up expenses. As a result, the real-world storage costs for Kafka and Redpanda are often 50% higher than the estimates above.",[40,929,931],{"id":930},"conclusion","Conclusion",[48,933,934],{},"Ursa represents a transformative shift in streaming data infrastructure, offering cost efficiency, scalability, and flexibility without compromising durability or reliability. By leveraging a leaderless architecture and eliminating inter-zone data replication, Ursa reduces total cost of ownership by over 90% compared to traditional leader-based streaming engines like Kafka and Redpanda. Its direct integration with cloud storage and scalable metadata & index management via Oxia ensure high availability and simplified infrastructure management.",[32,936,938],{"id":937},"balancing-latency-and-cost","Balancing Latency and Cost",[48,940,941,945],{},[55,942,944],{"href":943},"\u002Fblog\u002Fcap-theorem-for-data-streaming","Ursa trades off slightly higher latency for ultra low cost",", making it an ideal choice for the majority of streaming workloads, especially those that prioritize throughput and cost savings over ultra-low latency. Meanwhile, StreamNative’s BookKeeper-based engine remains the preferred solution for real-time, latency-sensitive applications. By combining these two approaches, StreamNative empowers customers with the flexibility to choose the right engine for their specific needs—whether it's maximizing cost savings or achieving ultra low-latency real-time performance.",[32,947,949],{"id":948},"the-future-of-streaming-infrastructure","The Future of Streaming Infrastructure",[48,951,952],{},"In an era where data fuels AI, analytics, and real-time decision-making, managing infrastructure costs is critical to sustaining innovation. Ursa is not just a cost-cutting alternative—it is a forward-thinking, lakehouse-native platform that redefines how modern data streaming infrastructure should be built and operated.",[48,954,955,956,961],{},"Whether your priority is reducing costs, improving flexibility, or ingesting massive data into lakehouses, Ursa delivers a future-proof solution for the evolving demands of real-time data streaming. ",[55,957,960],{"href":958,"rel":959},"https:\u002F\u002Fconsole.streamnative.cloud\u002F",[264],"Get started"," with StreamNative Ursa today!",[963,964,966],"h1",{"id":965},"references","References",[48,968,969,972,973],{},[970,971,430],"span",{}," ",[55,974,975],{"href":975},"\u002Fblog\u002Fintroducing-oxia-scalable-metadata-and-coordination",[48,977,978,972,980],{},[970,979,379],{},[55,981,378],{"href":378},[48,983,984,972,987],{},[970,985,986],{},"StreamNative pricing",[55,988,989],{"href":989,"rel":990},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fbilling-overview",[264],[48,992,993,972,996],{},[970,994,995],{},"WarpStream pricing",[55,997,998],{"href":998,"rel":999},"https:\u002F\u002Fwww.warpstream.com\u002Fpricing#pricingfaqs",[264],[48,1001,1002,972,1005],{},[970,1003,1004],{},"AWS S3 pricing",[55,1006,1007],{"href":1007,"rel":1008},"https:\u002F\u002Faws.amazon.com\u002Fs3\u002Fpricing\u002F",[264],[48,1010,1011,972,1014],{},[970,1012,1013],{},"AWS EBS pricing",[55,1015,1016],{"href":1016,"rel":1017},"https:\u002F\u002Faws.amazon.com\u002Febs\u002Fpricing\u002F",[264],[48,1019,1020,972,1023],{},[970,1021,1022],{},"AWS MSK pricing",[55,1024,1025],{"href":1025,"rel":1026},"https:\u002F\u002Faws.amazon.com\u002Fmsk\u002Fpricing\u002F",[264],[48,1028,1029,972,1032],{},[970,1030,1031],{},"The Brutal Truth about Kafka Cost Calculators",[55,1033,852],{"href":852,"rel":1034},[264],[48,1036,1037,972,1040],{},[970,1038,1039],{},"Redpanda vs. Confluent: A Performance and TCO Benchmark Report by McKnight Consulting Group",[55,1041,906],{"href":906,"rel":1042},[264],{"title":18,"searchDepth":19,"depth":19,"links":1044},[1045,1046,1047,1052,1056,1057,1066,1069],{"id":333,"depth":19,"text":334},{"id":372,"depth":19,"text":373},{"id":397,"depth":19,"text":398,"children":1048},[1049,1050,1051],{"id":409,"depth":279,"text":410},{"id":434,"depth":279,"text":435},{"id":455,"depth":279,"text":456},{"id":479,"depth":19,"text":480,"children":1053},[1054,1055],{"id":483,"depth":279,"text":484},{"id":498,"depth":279,"text":499},{"id":539,"depth":19,"text":540},{"id":551,"depth":19,"text":552,"children":1058},[1059,1060,1061,1062,1063,1064,1065],{"id":558,"depth":279,"text":559},{"id":604,"depth":279,"text":605},{"id":622,"depth":279,"text":623},{"id":669,"depth":279,"text":670},{"id":697,"depth":279,"text":698},{"id":715,"depth":279,"text":716},{"id":730,"depth":279,"text":731},{"id":775,"depth":19,"text":776,"children":1067},[1068],{"id":815,"depth":279,"text":816},{"id":930,"depth":19,"text":931,"children":1070},[1071,1072],{"id":937,"depth":279,"text":938},{"id":948,"depth":279,"text":949},"StreamNative Cloud","2025-01-31","Discover how Ursa achieves 5GB\u002Fs Kafka workloads at just 5% of the cost of traditional streaming engines like Redpanda and AWS MSK. See our benchmark results comparing infrastructure costs, total cost of ownership (TCO), and performance across leading Kafka vendors.","\u002Fimgs\u002Fblogs\u002F679c6593d25099b1cdcec4ca_image-31.png",{},"\u002Fblog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour","30 min",{"title":308,"description":1075},"blog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour",[1083,1084,303],"TCO","Apache Kafka","CDUawvFKTs_AD8usvmIcTleU3mbfA0QAoPZM6xfVuo8",{"id":1087,"title":1088,"authors":1089,"body":1091,"canonicalUrl":289,"category":1455,"createdAt":289,"date":1456,"description":1457,"extension":8,"featured":294,"image":1458,"isDraft":294,"link":289,"meta":1459,"navigation":7,"order":296,"path":1460,"readingTime":1461,"relatedResources":289,"seo":1462,"stem":1463,"tags":1464,"__hash__":1469},"blogs\u002Fblog\u002Fintroducing-the-streamnative-mcp-server-connecting-streaming-data-to-ai-agents.md","Introducing the StreamNative MCP Server: Connecting Streaming Data to AI Agents",[311,1090],"Rui Fu",{"type":15,"value":1092,"toc":1431},[1093,1096,1110,1119,1128,1131,1135,1138,1141,1144,1148,1151,1163,1166,1169,1173,1176,1179,1182,1185,1189,1192,1196,1199,1207,1210,1214,1217,1221,1224,1228,1231,1235,1238,1242,1245,1249,1258,1262,1270,1287,1291,1297,1316,1320,1326,1339,1343,1346,1360,1363,1367,1373,1377,1380,1384,1387,1391,1399,1408,1411,1422,1425,1428],[48,1094,1095],{},"Over the past few weeks in our Agentic AI blog series, our CEO has explored the immense potential of integrating AI agents with real-time data streams:",[339,1097,1098,1104],{},[342,1099,1100],{},[55,1101,1103],{"href":1102},"\u002Fblog\u002Fai-agents-real-time-data-bridge","AI Agents Meet Real‑Time Data – Bridging the Gap",[342,1105,1106],{},[55,1107,1109],{"href":1108},"\u002Fblog\u002Fopen-standards-real-time-ai-mcp","Open Standards for Real-Time AI Integration – A Look at MCP",[48,1111,1112,1113,1118],{},"In the last blog, we examined ",[55,1114,1117],{"href":1115,"rel":1116},"https:\u002F\u002Fmodelcontextprotocol.io\u002Fintroduction",[264],"the Model Context Protocol (MCP)",", an open protocol introduced by Anthropic. It’s designed to enable seamless, secure, and standardized connections between AI models – especially large language models (LLMs) – and a wide range of external data sources, tools, and environments. With the protocol, AI agents can access and interact with external data sources in a universal, consistent way.",[48,1120,1121,1122,1127],{},"Today, we’re thrilled to unveil the ",[55,1123,1126],{"href":1124,"rel":1125},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Fstreamnative-mcp-server",[264],"StreamNative MCP Server"," and share it with all the streaming enthusiasts as an open-source project. It seamlessly connects any Kafka\u002FPulsar service to AI agents using the MCP protocol, regardless of whether it's on StreamNative Cloud or not. With the MCP Server, users can instruct agents to access fresh, real-time Kafka\u002FPulsar data and manage the cluster resources through natural language, performing tasks such as configuring topics, publishing\u002Fconsuming data, or even writing and submitting Pulsar Functions without wrestling with complex commands.",[48,1129,1130],{},"In the following sections, we’ll introduce the StreamNative MCP Server, explain how it works, and show how it connects Apache Pulsar, Apache Kafka, and StreamNative Cloud streams to AI in a unified, developer-friendly way.",[40,1132,1134],{"id":1133},"what-is-the-streamnative-mcp-server","What is the StreamNative MCP Server?",[48,1136,1137],{},"The StreamNative MCP Server (aka “streamnative-mcp-server” or “snmcp”) is an open-source implementation of the Model Context Protocol designed specifically for bringing real-time streaming platforms – Apache Kafka and Apache Pulsar closer to LLMs and AI agents. By running the MCP Server, you can securely expose a Pulsar or Kafka deployment –  whether it’s on-premises, in StreamNative cloud, or in other Streaming Service Vendors’ cloud –  to any MCP-compatible AI client. This enables an LLM-based agent to read from, write to, and administer streams through a single standardized interface without any custom integration code. It significantly lowers the barrier to adopting streaming platforms and helps truly democratize streaming technology.",[48,1139,1140],{},"The server speaks MCP on one side and native Pulsar\u002FKafka protocols on the other. Because it adheres to the open MCP spec, it works out-of-the-box with any compliant client. Developers don’t need to reinvent protocols or worry about the underlying cluster details – the server abstracts those away using the familiar “tools,” “resources,” and “prompts” vocabulary that AI agents understand.",[48,1142,1143],{},"We're excited to open source the MCP server under the Apache 2.0 license, making it freely available for everyone to use, inspect, deploy, and extend without restriction. We believe this is a key step in unlocking real-time streaming for AI and helping accelerate the innovation between the streaming and AI landscape.",[40,1145,1147],{"id":1146},"how-it-works-tools-resources-and-prompts","How It Works: Tools, Resources, and Prompts",[48,1149,1150],{},"To understand how the MCP Server enables AI-to-streaming integration, let’s briefly review the core MCP concepts it implements. In MCP, servers don’t simply expose raw data – they offer structured capabilities that the AI can utilize. The three primary capability types are:",[1152,1153,1154,1157,1160],"ol",{},[342,1155,1156],{},"Resources – Read-only data that the server makes available to clients and LLMs. Resources include files or data snippets that an AI agent can pull in as context. These resources provide structured data without additional computation needed.",[342,1158,1159],{},"Prompts – Predefined prompt templates or workflows that the server provides. Prompts serve as shortcuts for common interactions or tasks. Think of them as stored queries or conversation templates that the AI can invoke.",[342,1161,1162],{},"Tools – Tools are executable actions that the MCP Server provides to AI agents, representing the most powerful capability of the platform. Through tools, the MCP Server empowers AI agents to perform operations on streaming platforms and related systems with appropriate permissions and oversight. Each tool is essentially a function that an AI can invoke via the MCP protocol.",[48,1164,1165],{},"Under the hood, the MCP Server implements these concepts according to the MCP specification. When an AI agent connects, it can query the server for available tools, resources, and prompts (using standard MCP requests like tools\u002Flist and resources\u002Flist). The server advertises everything it can do in a discoverable way. Then, during an AI dialogue, the agent may choose to invoke a tool or retrieve a resource to fulfill the user’s request. The MCP Server receives those requests (formatted as JSON-RPC messages over the MCP connection) and translates them into actions on the Pulsar or Kafka protocol.",[48,1167,1168],{},"For example, if a user asks the AI agent, “How many events per second are flowing through Pulsar topic X right now?”, the agent (via its MCP client) might collect the required info to call a pulsar-admin-topics tool on the MCP Server to get topic stats. The server, in turn, uses Pulsar’s admin API to fetch the metrics for topic X, then returns that data to the AI agent, which incorporates it into a natural language answer. All of this happens through the standardized MCP interface – the agent never needs to know Pulsar protocol. It simply requests a tool by name and description from the MCP server. This model aligns perfectly with modern AI agent frameworks like ReAct (Reason+Act): the agent focuses on the reasoning and determining what tool action is needed (e.g., call pulsar-admin-topics), while the MCP Server handles the execution details (how) of interacting with the streaming backend, returning the observation (the topic stats).",[40,1170,1172],{"id":1171},"how-it-connects-agents-to-streams-with-safety","How it Connects: Agents to Streams with Safety",[48,1174,1175],{},"The Model Context Protocol, implemented by the StreamNative MCP Server, provides the essential building blocks – Tools, Resources, and Prompts – that fundamentally expand what AI Agents can achieve when interacting with streaming data platforms. By leveraging these MCP primitives, agents gain two critical advantages: the ability to perceive and react to the world in real-time (connecting to streams), and the capacity to act within a framework of unified, secure administration (with safety).",[48,1177,1178],{},"First, MCP Resources and Tools directly address the limitation of static LLM knowledge by granting agents access to live data streams. Agents can utilize specific tools to query current states, consume messages, or even subscribe to continuous data feeds. This closes the gap between the agent's knowledge cutoff and the \"here-and-now\" reality reflected in platforms like Kafka and Pulsar, enabling truly context-aware agents to make timely decisions based on the latest events. This unlocks possibilities, allowing the agents to perform real-time monitoring or provide interactive diagnostics based on current system and platform states.",[48,1180,1181],{},"Second, the structured nature of MCP Tools and the ability to define accessible Resources provide the necessary foundation for governed agent actions. Administrators gain fine-grained control by selectively exposing specific tools and data resources to different agents. This allows AI agents to perform meaningful actions – like managing topics or understanding the real-time platform status using the authorised tools – while ensuring they operate within secure, predefined boundaries aligned with organisational policies. This capability is crucial for confidently deploying agents in enterprise environments, expanding their roles from passive information retrievers to active, yet controlled, participants in managing and interacting with streaming systems.",[48,1183,1184],{},"Therefore, the StreamNative MCP Server translates the potential of the Model Context Protocol into practice for your Kafka and Pulsar clusters. By providing controlled access to streaming capabilities and data, our server significantly enhances agent scope and reliability, enabling trustworthy, real-time AI applications. The next section details the specific features and capabilities built into the StreamNative MCP Server to deliver this value.",[40,1186,1188],{"id":1187},"key-features-and-capabilities","Key Features and Capabilities",[48,1190,1191],{},"Let’s drill into some of the technical highlights of the StreamNative MCP Server and what makes it developer-friendly:",[32,1193,1195],{"id":1194},"_30-built-in-tools-and-actions","🚀 30+ Built-In Tools and Actions",[48,1197,1198],{},"The MCP Server includes an elegantly designed toolkit of over 30 powerful tools that comprehensively cover the capabilities of modern streaming platforms.",[48,1200,1201,1202,1206],{},"Instead of building hundreds of single-purpose tools, we adopt an efficient approach by using 'Resource' and 'Operation' parameters within each tool, enabling one tool to handle multiple related functions. For example, the single ",[1203,1204,1205],"code",{},"pulsar_admin_brokers"," tool can list active brokers, check health status, and manage configurations through different parameter combinations. The toolkit supports a broad range of functionalities, including data operations (e.g., publishing or consuming messages), administrative tasks (e.g., creating topics, managing subscriptions, and monitoring broker statistics), and StreamNative Cloud resources management capabilities.",[48,1208,1209],{},"With this powerful library, AI agents can conveniently perform a wide range of tasks on the data streaming platform. It can \"create a new topic for user logs,\" \"increase the retention of topic Y to 7 days,\" or \"write and run a Pulsar Function to process data\" – and it knows the exact tools to execute these user requests. Each tool accepts input parameters (defined by JSON schemas) and returns results, with actions subject to host application approval.",[32,1211,1213],{"id":1212},"secure-by-design","🔒 Secure by Design",[48,1215,1216],{},"Security is a fundamental consideration in the StreamNative MCP Server design. It employs a defense-in-depth approach to ensure safe and governed agent interactions with your system. The server integrates with your cluster's existing authorization model via specified service accounts for granular access control. A strict read-only mode (--read-only) can also be enabled for added protection in sensitive environments. Administrators also have fine-grained control through selective feature enablement (--features) to limit the agent's operational scope based on least privilege. Complementing these controls, the server's built-in prompts often incorporate their own restrictions, adding another layer of guidance to keep AI agents interacting within intended boundaries. This multi-layered security supports strict policies and minimizes the risk of unauthorized access or data manipulation.",[32,1218,1220],{"id":1219},"connector-integration","🔌 Connector Integration",[48,1222,1223],{},"The StreamNative MCP Server is designed to work with the Universal Connect (UniConn) framework, which means AI agents can leverage the rich ecosystem of Pulsar IO and Kafka Connect connectors through MCP. If your cluster is already ingesting or sinking data via connectors (e.g., from databases, cloud storage, etc.), the MCP Server can expose those as tools or resources as well. For instance, the MCP Server can spin up a Debezium MySQL → Pulsar pipeline on demand and then let the AI agent tap that stream to pull the latest change event or an entire batch of recent transactions. UniConn provides a unified interface for connectors on Pulsar and Kafka, and those connectors effectively become extensions of the AI’s reach. This opens up a world of external systems (SQL, NoSQL, SaaS APIs, etc.) to the AI agent through the same MCP Server. The agent could ask something like “What’s the latest record in our analytics DB?” and, via a connector tool, fetch that in real time. No custom code is needed to integrate these external sources – if there’s a connector, the MCP Server can likely expose it.",[32,1225,1227],{"id":1226},"️-dynamic-topic-management","🗄️ Dynamic Topic Management",[48,1229,1230],{},"Beyond simply reading or writing data, the MCP Server lets AI agents create, configure, and manage topics and subscriptions on the fly. An agent can spin up a brand-new stream (“Create a topic for sensor-XYZ data”), which maps to a pulsar-admin-topics call, or tweak retention, partition counts, and subscription properties using the same toolset. All changes respect cluster governance – quotas, ACLs, and policies still apply – but the agent can carry them out from natural-language requests instead of a CLI.",[32,1232,1234],{"id":1233},"serverless-function-management","🧩 Serverless Function Management",[48,1236,1237],{},"Moreover, we integrated Pulsar Functions support, enabling the agent to deploy serverless functions or connectors by submitting function code or connector configs via a tool. Imagine telling your AI agent, “Deploy a function that scans for sensitive data, e.g., SSN, and masks it”, and the agent uses an MCP tool to submit the Pulsar Function to the cluster. This drastically lowers the barrier to deploying stream processing logic, as the AI can act as your DevOps helper for streaming jobs. All changes remain subject to your cluster’s governance – the AI won’t bypass quotas or authorization – but it provides a natural-language interface to tasks previously handled via CLI or GUI.",[32,1239,1241],{"id":1240},"streaming-data-as-first-class-context","📊 Streaming Data as First-Class Context",[48,1243,1244],{},"The StreamNative MCP Server supports streaming outputs using MCP’s event streaming features (based on JSON-RPC and will soon be on Server-Sent Events). This means that when an AI agent subscribes to a topic via a tool, the server can feed data continuously to the client in a streaming fashion, rather than sending only one-off responses. The MCP protocol supports sending incremental results, so an agent could effectively “listen” to a topic. This real-time push of data is crucial for truly live agentic behavior – your agent could, for example, monitor a stream of user transactions and proactively flag anomalies during a conversation. Under MCP, the client-side (agent host) can choose to display or use streaming responses as they come. The key takeaway: real-time data isn’t just a one-shot query – it’s a continuous feed, and our MCP Server fully supports that mode.",[40,1246,1248],{"id":1247},"interacting-with-pulsar-kafka-via-streamnative-mcp-server","Interacting with Pulsar & Kafka via StreamNative MCP Server",[48,1250,1251,1252,1257],{},"Here are a few examples that showcase the StreamNative MCP Server’s capabilities; you can find additional demos in the ",[55,1253,1256],{"href":1254,"rel":1255},"https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL7-BmxsE3q4WO8mu8gzsbVkjoXb-PFpQX",[264],"StreamNative MCP Server playlist"," on YouTube.",[32,1259,1261],{"id":1260},"produce-and-consume-kafka-messages-with-avro-schema-in-ursa","Produce and Consume Kafka Messages with AVRO Schema in URSA",[48,1263,1264,1265],{},"📺",[55,1266,1269],{"href":1267,"rel":1268},"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=UhOzBLjYLP8&list=PL7-BmxsE3q4WO8mu8gzsbVkjoXb-PFpQX&index=2",[264]," Watch here",[339,1271,1272,1275,1278,1281,1284],{},[342,1273,1274],{},"Create Kafka topic",[342,1276,1277],{},"Produce Kafka messages with AVRO schema",[342,1279,1280],{},"Consume Kafka messages",[342,1282,1283],{},"Examine the message in Databricks",[342,1285,1286],{},"Delete resources",[32,1288,1290],{"id":1289},"managing-pulsar-tenants-namespaces-and-topics","Managing Pulsar Tenants, Namespaces, and Topics",[48,1292,1264,1293],{},[55,1294,1269],{"href":1295,"rel":1296},"https:\u002F\u002Fyoutu.be\u002FPSXdhdmunZg",[264],[339,1298,1299,1302,1305,1308,1311,1314],{},[342,1300,1301],{},"Create tenant",[342,1303,1304],{},"Create namespace",[342,1306,1307],{},"Create partitioned topic",[342,1309,1310],{},"Test topic",[342,1312,1313],{},"Set namespace TTL",[342,1315,1286],{},[32,1317,1319],{"id":1318},"create-deploy-and-test-python-pulsar-function","Create, Deploy, and Test Python Pulsar Function",[48,1321,1264,1322],{},[55,1323,1269],{"href":1324,"rel":1325},"https:\u002F\u002Fyoutu.be\u002F9JDHL-WaCXs",[264],[339,1327,1328,1331,1334,1337],{},[342,1329,1330],{},"Create Python Pulsar Function with vibe coding",[342,1332,1333],{},"Deploy with MCP Server",[342,1335,1336],{},"Test with MCP Server",[342,1338,1286],{},[40,1340,1342],{"id":1341},"laying-the-foundation-for-real-time-enterprise-ai-agents","Laying the Foundation for Real-Time Enterprise AI Agents",[48,1344,1345],{},"The release and open-source of the StreamNative MCP Server marks a significant milestone: it provides the foundation for what we envision as Real-Time Enterprise AI Agents—a complete environment for running AI agents natively with streaming data. With the MCP Server in place, AI agents can now connect to streaming systems to:",[339,1347,1348,1351,1354,1357],{},[342,1349,1350],{},"Retrieve up-to-the-second data for more accurate decision-making",[342,1352,1353],{},"Trigger transformations and pipelines via Pulsar Functions, ensuring the ability to enrich data on the fly",[342,1355,1356],{},"Tap into existing connectors to instantly access 200+ data sources without writing new integration code",[342,1358,1359],{},"Automate resource management and provisioning through natural language, reducing operational overhead and simplifying DevOps workflows",[48,1361,1362],{},"Future enhancements to the StreamNative MCP Server will unlock even more capabilities for fast, intelligent AI agents across diverse data landscapes.",[32,1364,1366],{"id":1365},"going-further-with-ursa","Going Further with Ursa",[48,1368,1369,1372],{},[55,1370,379],{"href":1371},"\u002Fproducts\u002Fursa",", StreamNative’s next-generation, lakehouse-native data streaming engine, brings together real-time streaming data and lakehouse tables. Through MCP, AI agents gain unified access to both historical datasets (in Apache Iceberg or Delta Tables) and ongoing event streams – all from a single interface. This means no more relying on stale snapshots – agents can respond to live data, correlate it with archived knowledge, and deliver timely, context-rich insights.",[32,1374,1376],{"id":1375},"leveraging-pulsar-functions","Leveraging Pulsar Functions",[48,1378,1379],{},"Many users already rely on Pulsar Functions for real-time data processing and transformation. These business logic functions can now be directly utilized – or even dynamically created and updated – by AI agents through MCP. As a result, agents can perform in-flight analytics or adapt data pipelines based on changing requirements, making your event-driven architecture more intelligent and responsive.",[32,1381,1383],{"id":1382},"harnessing-connectors","Harnessing Connectors",[48,1385,1386],{},"StreamNative’s robust connector ecosystem, which covers everything from enterprise systems to SaaS platforms and databases, ensures that AI agents can connect to virtually any data source without custom coding. By removing the need for specialized integrations, developers save time and can focus on enhancing their AI-driven workflows.",[40,1388,1390],{"id":1389},"get-involved-try-it-out-today","Get Involved – Try it Out Today",[48,1392,1393,1394,1398],{},"The StreamNative MCP Server is available now ",[55,1395,1397],{"href":1124,"rel":1396},[264],"on GitHub"," (under the StreamNative organization). We invite all streaming enthusiasts, data engineers, and curious tinkerers to download the code, read the docs, and play with it. We’ve provided the instructions that show how to connect an AI client – such as the Claude Desktop app – to your own MCP Server and start issuing tool commands to a local Pulsar or Kafka topic.",[48,1400,1401,1402,1407],{},"Because this is an early release, we’re actively seeking feedback and contributions from the community. ",[55,1403,1406],{"href":1404,"rel":1405},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Fstreamnative-mcp-server\u002Fdiscussions",[264],"Join the conversation"," on GitHub to ask questions, share use cases, and get help from our engineers and fellow early adopters.",[48,1409,1410],{},"This launch is an invitation to explore the cutting edge of real-time AI integration. Whether you want to build:",[339,1412,1413,1416,1419],{},[342,1414,1415],{},"An AI ops assistant that manages your streaming platform",[342,1417,1418],{},"An intelligent monitoring agent that watches your event data",[342,1420,1421],{},"Or a new breed of data-driven chatbot that can act on the information it retrieves",[48,1423,1424],{},"…the tools are now in your hands.",[48,1426,1427],{},"We believe Agentic AI – AI agents empowered with real-time context – will unlock a new class of applications. With the StreamNative MCP Server, connecting streaming data to AI is no longer theoretical – it’s something you can implement today.",[48,1429,1430],{},"Feel free to explore the repo, launch the StreamNative MCP Server, and unleash your AI agents on live data. We can’t wait to see what you create, and we look forward to building the future of real-time AI together with the community.",{"title":18,"searchDepth":19,"depth":19,"links":1432},[1433,1434,1435,1436,1444,1449,1454],{"id":1133,"depth":19,"text":1134},{"id":1146,"depth":19,"text":1147},{"id":1171,"depth":19,"text":1172},{"id":1187,"depth":19,"text":1188,"children":1437},[1438,1439,1440,1441,1442,1443],{"id":1194,"depth":279,"text":1195},{"id":1212,"depth":279,"text":1213},{"id":1219,"depth":279,"text":1220},{"id":1226,"depth":279,"text":1227},{"id":1233,"depth":279,"text":1234},{"id":1240,"depth":279,"text":1241},{"id":1247,"depth":19,"text":1248,"children":1445},[1446,1447,1448],{"id":1260,"depth":279,"text":1261},{"id":1289,"depth":279,"text":1290},{"id":1318,"depth":279,"text":1319},{"id":1341,"depth":19,"text":1342,"children":1450},[1451,1452,1453],{"id":1365,"depth":279,"text":1366},{"id":1375,"depth":279,"text":1376},{"id":1382,"depth":279,"text":1383},{"id":1389,"depth":19,"text":1390},"Community","2025-05-13","The StreamNative MCP Server is an open-source project that connects streaming data platforms like Kafka and Pulsar to AI agents using the Model Context Protocol (MCP). It enables seamless, real-time AI integration by allowing agents to access, manage, and interact with live data streams through natural language, enhancing the capabilities and security of AI-driven workflows.","\u002Fimgs\u002Fblogs\u002F6822faa477df140288f03c3a_mcp-server.png",{},"\u002Fblog\u002Fintroducing-the-streamnative-mcp-server-connecting-streaming-data-to-ai-agents","12 min",{"title":1088,"description":1457},"blog\u002Fintroducing-the-streamnative-mcp-server-connecting-streaming-data-to-ai-agents",[1465,1466,1467,1084,1468,379],"Agentic AI","GenAI","MCP","Apache Pulsar","BE3D5NEcwgJZqv3TC_QZi4ZBo3iarZVTo2hG1QU_uEc",[1471,1489],{"id":1472,"title":311,"bioSummary":1473,"email":289,"extension":8,"image":1474,"linkedinUrl":289,"meta":1475,"position":1485,"stem":1486,"twitterUrl":1487,"__hash__":1488},"authors\u002Fauthors\u002Fneng-lu.md","Neng Lu is currently the Director of Platform at StreamNative, where he leads the engineering team in developing the StreamNative ONE Platform and the next-generation Ursa engine. As an Apache Pulsar Committer, he specializes in advancing Pulsar Functions and Pulsar IO Connectors, contributing to the evolution of real-time data streaming technologies. Prior to joining StreamNative, Neng was a Senior Software Engineer at Twitter, where he focused on the Heron project, a cutting-edge real-time computing framework. He holds a Master's degree in Computer Science from the University of California, Los Angeles (UCLA) and a Bachelor's degree from Zhejiang University.","\u002Fimgs\u002Fauthors\u002Fneng-lu.jpeg",{"body":1476},{"type":15,"value":1477,"toc":1483},[1478,1480],[48,1479,1473],{},[48,1481,1482],{},"‍",{"title":18,"searchDepth":19,"depth":19,"links":1484},[],"Director of Engineering, StreamNative","authors\u002Fneng-lu","https:\u002F\u002Ftwitter.com\u002Fnlu90","R1K8DYRoq92ZrwHOmKtJMRfm-cuTjXTqAv0Cc3Q9IM4",{"id":1490,"title":1090,"bioSummary":1491,"email":289,"extension":8,"image":1492,"linkedinUrl":289,"meta":1493,"position":1500,"stem":1501,"twitterUrl":289,"__hash__":1502},"authors\u002Fauthors\u002Frui-fu.md","Rui Fu is a software engineer at StreamNative. Before joining StreamNative, he was a platform engineer at the Energy Internet Research Institute of Tsinghua University. 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