[{"data":1,"prerenderedAt":1679},["ShallowReactive",2],{"active-banner":3,"navbar-featured-partner-blog":24,"blog-\u002Fblog\u002Fa-guide-to-evaluating-the-infrastructure-costs-of-apache-pulsar-and-apache-kafka":306,"navbar-pricing-featured":869,"blog-authors-\u002Fblog\u002Fa-guide-to-evaluating-the-infrastructure-costs-of-apache-pulsar-and-apache-kafka":1640,"related-\u002Fblog\u002Fa-guide-to-evaluating-the-infrastructure-costs-of-apache-pulsar-and-apache-kafka":1657},{"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,"category":289,"createdAt":290,"date":291,"description":292,"extension":8,"featured":7,"image":293,"isDraft":294,"link":290,"meta":295,"navigation":7,"order":296,"path":297,"readingTime":298,"relatedResources":290,"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","Company",null,"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","sOeeJtEO3O-IIfTPJjY1AFOMawZ_rf8FOH8A98NEKgU",{"id":307,"title":308,"authors":309,"body":311,"category":856,"createdAt":290,"date":857,"description":858,"extension":8,"featured":294,"image":859,"isDraft":294,"link":290,"meta":860,"navigation":7,"order":296,"path":861,"readingTime":862,"relatedResources":290,"seo":863,"stem":864,"tags":865,"__hash__":868},"blogs\u002Fblog\u002Fa-guide-to-evaluating-the-infrastructure-costs-of-apache-pulsar-and-apache-kafka.md","A Guide to Evaluating the Infrastructure Costs of Apache Pulsar and Apache Kafka",[310],"Sijie Guo",{"type":15,"value":312,"toc":835},[313,316,319,327,330,337,340,344,363,366,369,372,376,379,387,390,397,400,407,410,414,417,428,434,439,444,449,454,459,464,469,472,475,480,483,487,490,493,501,504,507,512,515,518,522,525,530,535,539,544,548,553,556,561,565,574,579,582,586,593,598,602,605,607,612,615,618,621,636,638,643,647,650,655,660,665,673,676,679,683,686,693,695,700,704,707,718,721,732,735,738,741,744,748,751,756,759,763,778,782,785,787,792,796,799,812,820],[48,314,315],{},"In an era where cost optimization and efficiency are more crucial than ever due to rising interest rates, surging inflation, and the threat of a recession, businesses are being forced to scrutinize expenses that have gone unchecked for years. Among these, the cost of data streaming, a vital component of modern cloud infrastructure, stands out as a significant and complex challenge.",[48,317,318],{},"Having worked with numerous customers, users, and prospects across our fully managed cloud offerings and private cloud software, we at StreamNative have gained a profound understanding of the factors determining and optimizing costs associated with data streaming. Our experience, coupled with managing costs in operating our own StreamNative Hosted Cloud, has provided insights into the key cost drivers and optimization levers for Pulsar and other data streaming platforms.",[48,320,321,322,326],{},"However, assessing the true cost of operating a data streaming platform is a complex task. Traditional analyses often fall short, focusing predominantly on compute and storage requirements while neglecting the substantial costs associated with networking—the often overlooked yet largest infrastructure cost factor. Our previous discussions, such as in the blog post “",[55,323,325],{"href":324},"\u002Fblog\u002Fcap-theorem-for-data-streaming","The New CAP Theorem for Data Streaming: Understanding the Trade-offs Between Cost, Availability, and Performance","”, highlight these overlooked aspects and the significant, albeit less tangible, costs associated with development and operations personnel.",[48,328,329],{},"This blog series aims to shed light on the critical cost components of data streaming platforms, including infrastructure, operational, and downtime costs. These are pivotal due to their direct financial implications and potential to affect an organization's reputation and compliance standing. We are excited to launch this multi-part series to guide you through understanding and managing the costs associated with Apache Pulsar and Apache Kafka, providing insights that will help optimize your data streaming budget.",[331,332,333],"ul",{},[334,335,336],"li",{},"Part 1: A Guide to Evaluating Infrastructure Costs of Apache Pulsar vs. Apache Kafka",[48,338,339],{},"Our first blog examines how much it costs to run Pulsar and Kafka. We'll compare the cost of compute, storage, networking, and extra tools they need to work well all the time.",[40,341,343],{"id":342},"define-a-performance-profile-for-cost-consideration","Define a performance profile for cost consideration",[48,345,346,347,351,352,357,358,362],{},"As we compare infrastructure costs across various data streaming technologies, setting a standard performance benchmark is crucial to evaluate all technologies under equivalent conditions. This blog post focuses on selecting a low and stable end-to-end latency performance profile for cost analysis. We conducted \"",[55,348,350],{"href":349},"\u002Fblog\u002Fapache-pulsar-vs-apache-kafka-2022-benchmark#benchmark-tests","Maximum Sustainable Throughput","\" tests using the ",[55,353,356],{"href":354,"rel":355},"https:\u002F\u002Fopenmessaging.cloud\u002Fdocs\u002Fbenchmarks\u002F",[264],"Open Messaging Benchmark"," to gauge how different systems maintain throughput alongside consistent end-to-end latency. Our ",[55,359,361],{"href":360},"\u002Fwhitepapers\u002Fapache-pulsar-vs-apache-kafka-2022-benchmark","2022 benchmark"," report highlighted that Apache Pulsar achieved a throughput 2.5 times greater than Apache Kafka on an identical hardware setup consisting of three i3en.6xlarge nodes.",[48,364,365],{},"Employing an identical machine profile across the board negates the impact of various external factors, enabling a balanced comparison. Nonetheless, it's pertinent to acknowledge that adjustments in several parameters—such as the number of topics, producers, consumers, message size, and message batching efficiency—can influence performance and cost outcomes.",[48,367,368],{},"In preparation for a deeper analytical dive and comparison, we must also mention that numerous infrastructure elements are excluded from this preliminary discussion for brevity. These essential components, including load balancers, NAT gateways, Kubernetes clusters, and the observability stack, are integral to forging a production-grade data streaming setup and contribute to the overall infrastructure expenses. Additionally, this initial analysis focuses on the costs associated with managing a single cluster, though many organizations will likely operate multiple clusters across various setups.",[48,370,371],{},"Hence, while this comparative analysis provides a foundational framework that may underrepresent the total costs of independently running and supporting Pulsar or Kafka, it introduces a standardized method for facilitating cost comparisons among a broad spectrum of data streaming technologies.",[40,373,375],{"id":374},"compute-costs","Compute Costs",[48,377,378],{},"The journey towards cost efficiency often begins with compute resources, which, while forming a minor part of the overall infrastructure expenses, are traditionally the first target for savings. This mindset refers to a time before cloud computing, when scaling compute resources was a significant challenge, largely due to their tight integration with storage solutions.",[48,380,381,382,386],{},"Understanding the compute costs associated with Apache Pulsar and Apache Kafka necessitates a look into ",[55,383,385],{"href":384},"\u002Fblog\u002Fhow-pulsars-architecture-delivers-better-performance-than-kafka","their architectural foundations",". Kafka employs a monolithic design that integrates serving and storage capabilities within the same node. Conversely, Pulsar opts for a more flexible two-layer architecture, allowing for the separation of serving and storage functions. Establishing a standardized cost measure is essential to accurately comparing computing costs between these two technologies.",[48,388,389],{},"To standardize compute cost evaluation, we introduce the concept of a Compute Unit (CU), defined by the capacity of 1 CPU and 8 GB of memory, as a baseline for comparison. This allows us to evaluate a) the cost per compute unit and b) the throughput each technology can achieve per compute unit.",[48,391,392,393,396],{},"Our analysis used three i3en.12xlarge machines, totaling 72 CUs, costing $0.11 per hour for each CU. Our ",[55,394,395],{"href":360},"benchmark report"," revealed that Kafka could support 280 MB\u002Fs for both ingress and egress traffic, equating to 3.89 MB\u002Fs for both ingress and egress per CU. Pulsar, benefiting from its two-layer architecture, supports 700 MB\u002Fs for both ingress and egress. In this benchmark for Apache Pulsar, each node runs one broker and one bookie. One-third of the computing power is allocated for running brokers, while the remaining two-thirds is allocated for running bookies. This allocation translates to 24 CUs for brokers and 48 CUs for bookies, with a Broker Compute Unit supporting 29.17 MB\u002Fs and a Bookie Compute Unit supporting 14.58 MB\u002Fs for both ingress and egress.",[48,398,399],{},"So, the throughput efficiency per compute unit stands as follows between Pulsar and Kafka:",[48,401,402],{},[403,404],"img",{"alt":405,"src":406},"__wf_reserved_inherit","\u002Fimgs\u002Fblogs\u002F66f16fb2574712ac414405a6_6639781884fb5ef70a406679_Screenshot-2024-05-06-at-5.36.52-PM.png",[48,408,409],{},"This standardization aids in estimating the total compute cost for different workloads, revealing significant differences in compute unit requirements and the associated costs for various ingress and egress scenarios.",[32,411,413],{"id":412},"compute-costs-among-different-workloads","Compute Costs among different workloads.",[48,415,416],{},"With this standardization, we compare the compute costs of Pulsar and Kafka under three different workloads and evaluate how costs change when the fanout ratio is changed:",[331,418,419,422,425],{},[334,420,421],{},"Low Fanout: 100 MBps Ingress, 100 MBps Egress",[334,423,424],{},"Moderate Fanout: 100 MBps Ingress, 500 MBps Egress",[334,426,427],{},"High Fanout: 100 MBps Ingress, 1000 MBps Egress",[429,430,431],"ol",{},[334,432,433],{},"100 MBps Ingress, 100 MBps Egress Example Workload",[48,435,436],{},[403,437],{"alt":405,"src":438},"\u002Fimgs\u002Fblogs\u002F66f16fb2574712ac414405a9_66397840758ef15f8d770483_Screenshot-2024-05-06-at-5.37.52-PM.png",[429,440,441],{},[334,442,443],{},"100 MBps Ingress, 300 MBps Egress Example Workload",[48,445,446],{},[403,447],{"alt":405,"src":448},"\u002Fimgs\u002Fblogs\u002F66f16fb2574712ac414405d7_6639788b45e2d10462f6f089_Screenshot-2024-05-06-at-5.40.34-PM.png",[429,450,451],{},[334,452,453],{},"100 MBps Ingress, 500 MBps Egress Example Workload",[48,455,456],{},[403,457],{"alt":405,"src":458},"\u002Fimgs\u002Fblogs\u002F66f16fb2574712ac414405d4_663978e087f82f6cba525809_Screenshot-2024-05-06-at-5.41.59-PM.png",[429,460,461],{},[334,462,463],{},"100 MBps Ingress, 1000 MBps Egress Example Workload",[48,465,466],{},[403,467],{"alt":405,"src":468},"\u002Fimgs\u002Fblogs\u002F66f16fb2574712ac414405da_6639792787f82f6cba52a562_Screenshot-2024-05-06-at-5.43.11-PM.png",[48,470,471],{},"Compiling the data from various workload scenarios highlights the significant difference in computing costs between Apache Pulsar and Apache Kafka. Notably, Pulsar offers a compute cost advantage, being 2.5 times more cost-effective than Kafka. The costs of Apache Kafka increase linearly as the egress rate increases because Kafka has to provision resources to meet egress requirements, which results in the unnecessary overprovisioning of ingress and the processing power of storage components.",[48,473,474],{},"In contrast, as the fanout ratio increases, Pulsar's cost efficiency becomes even more pronounced, reaching a point where it is six times more economical than Kafka with a tenfold increase in fanout. This superior cost efficiency stems from Pulsar's dual-layer architecture, allowing for scaling of resources as needed – for example, adding more brokers to increase serving capacity without necessarily increasing the processing power of storage components – which becomes increasingly advantageous as the fanout number grows.",[48,476,477],{},[403,478],{"alt":18,"src":479},"\u002Fimgs\u002Fblogs\u002F66397619b0abb3ca47801e8d_-kne_AbRcnfKEG-UioS1pTtztQLqJzbs4FzjNjW_XKtlIwXhIC6yFkFJDO_fAtzQd_aSR0fseJzoVZNjuPtkYxQw3yEciWz_MQahxi0BLYCVCm9eFwxo4xqVvtWH27eb-QOswNuVHI9oLzshJ0GDERg.png",[48,481,482],{},"While this analysis offers a clear framework for comparing compute costs between Pulsar and Kafka, it's important to note that real-world scenarios involve more complexity. Decisions regarding the optimal machine type for specific components, the appropriate number of these components, and how to best optimize machine types for each workload are nuanced and require careful consideration. The examples provided serve as a basis for illustration, emphasizing that the practical application of these findings can be as complex as the technologies themselves.",[40,484,486],{"id":485},"storage-costs","Storage Costs",[48,488,489],{},"Calculating storage costs for data streaming platforms like Apache Pulsar and Apache Kafka can be complex, especially with factors such as IOPs and throughput influencing pricing. For simplicity, this analysis will focus solely on local EBS storage costs. However, it's crucial to consider additional IOPs and throughput expenses for clusters experiencing significant vertical scaling or high throughput demands.",[48,491,492],{},"Before we examine cost estimations, it's crucial to understand the distinct storage models utilized by Pulsar and Kafka. Kafka combines serving and storage functions within a single node, typically using general-purpose SSDs (e.g., gp3) to support a range of workloads. This configuration leads to a storage cost of $0.08 per GB-month for Kafka.",[48,494,495,496,500],{},"In contrast, Pulsar adopts a different approach by separating serving from storage, which involves ",[55,497,499],{"href":498},"\u002Fblog\u002Fhow-pulsars-architecture-delivers-better-performance-than-kafka#pulsar-better-isolation","a dual-disk system on its storage nodes ","to handle write and read operations efficiently. BookKeeper, Pulsar’s storage component, uses separate storage devices for its journal and main ledger.",[48,502,503],{},"The journal, which requires fast and durable storage to manage write-heavy loads effectively, is usually stored on SSDs. For read operations, tailing reads are sourced directly from the memTable, while catch-up reads come from the Ledger Disk and Index Disk. This separation ensures that intense read activities do not affect the performance of incoming writes due to their isolation on different physical disks.",[48,505,506],{},"‍",[48,508,509],{},[403,510],{"alt":18,"src":511},"\u002Fimgs\u002Fblogs\u002F66397619014256f2490581cf_xrM0G7ptupn9YDQmUCDtXhK_Wh2K6_MNW18ZF1ozTott5JitzOI2Re_m91A-3givDFRGjUM_DQIoRiMbhOWdKmD1R8i_YsNIM0BhYpXqdY1iPQHWlffKbM5qBnsBzuN-bfhQD9Ac7MMXPldr8yYCXgE.png",[48,513,514],{},"The architecture employs general-purpose SSDs (at $0.08\u002FGB-month, plus additional throughput charges of $0.040 per MB\u002Fs for usage above 125MB\u002Fs) for the journal disk and throughput-optimized HDDs (at $0.045\u002FGB-month) for ledger disks. This dual-disk strategy allows for the optimization of both cost and performance.",[48,516,517],{},"To estimate storage costs, consider factors like ingress rate, replication factor, and retention period, which determine the required storage capacity. The cost can then be calculated based on the specific unit prices of the storage solutions employed.",[32,519,521],{"id":520},"storage-costs-among-different-workloads","Storage Costs among different workloads",[48,523,524],{},"We compare the storage costs of Pulsar and Kafka with different ingress and data retention.",[526,527,529],"h4",{"id":528},"_100-mb-ingress-7-days-retention","100 MB Ingress, 7 days retention",[48,531,532],{},[403,533],{"alt":405,"src":534},"\u002Fimgs\u002Fblogs\u002F66f16fb3574712ac414405f1_66398a1764a250b181f9807d_Screenshot-2024-05-06-at-6.52.44-PM.png",[526,536,538],{"id":537},"_100-mb-ingress-30-days-retention","100 MB Ingress, 30 days retention",[48,540,541],{},[403,542],{"alt":405,"src":543},"\u002Fimgs\u002Fblogs\u002F66f16fb3574712ac414405f4_66398a4419f6fd7b2f626f59_Screenshot-2024-05-06-at-6.52.57-PM.png",[526,545,547],{"id":546},"_1000-mb-ingress-7-days-retention","1000 MB Ingress, 7 days retention",[48,549,550],{},[403,551],{"alt":405,"src":552},"\u002Fimgs\u002Fblogs\u002F66f16fb3574712ac414405fa_66398b1a216f3fc262412aef_Screenshot-2024-05-06-at-6.59.34-PM.png",[48,554,555],{},"Compiling the data from various workload scenarios highlights the significant difference in storage costs between Apache Pulsar and Apache Kafka. Notably, Pulsar offers a storage cost advantage, being 1.7 times more cost-effective than Kafka.",[48,557,558],{},[403,559],{"alt":18,"src":560},"\u002Fimgs\u002Fblogs\u002F6639761987ca9625a39083e1_xCY_PnRQr1DCmmLjpfxpftAFVnc_SEyoZl4-dxRqKlFjjIJlnYbh-9KGMPb5aYE9uP2fVikxe1Aee_wCUYhjLNTet7EjZ5Q_KFJJoC0swsaVXWQ2sI10csAA9AS06CWxXj5SfiBd6TSYtp66GB0SAwc.png",[32,562,564],{"id":563},"cost-efficiency-from-tiered-storage-solutions","Cost Efficiency from Tiered Storage Solutions",[48,566,567,568,573],{},"Pulsar's adoption of a ",[55,569,572],{"href":570,"rel":571},"https:\u002F\u002Fpulsar.apache.org\u002Fdocs\u002F3.2.x\u002Ftiered-storage-overview\u002F",[264],"tiered storage"," system in 2018 marks a significant step forward in reducing storage costs. By offloading older data to cheaper cloud-based object storage, such as Amazon S3, Pulsar reduces local storage needs and cuts costs dramatically, potentially by over 90%, depending on the distribution between local and object storage. This system also offers the benefit of lowering compute costs for high-retention scenarios by negating the need for additional storage nodes.",[48,575,576],{},[403,577],{"alt":18,"src":578},"\u002Fimgs\u002Fblogs\u002F663976198e3b49bb91aeeb13_xm2xucnS4kXSeF2RqnciC-OYlEXCO7xhy5N-ILbNWk9YWC3KD7ot3ztKCLGPGd3Hp7rTOlVgpXZP116pmN39fGeDMBZ2_fb53Id4FtE9X4bvqX_L_ltTRB5gxoddkzhChvm7jxqErOyFcJSrTNWk0_I.png",[48,580,581],{},"Kafka is evolving to incorporate tiered storage solutions as well, though with a notable difference: while Pulsar can directly serve data from tiered storage without reloading it to local disks, Kafka's model necessitates data reloading, requiring additional local storage planning and potentially incurring higher costs.",[32,583,585],{"id":584},"from-tiered-storage-to-lakehouse-storage","From Tiered Storage to Lakehouse Storage",[48,587,588,592],{},[55,589,591],{"href":590},"\u002Fblog\u002Fstreaming-lakehouse-introducing-pulsars-lakehouse-tiered-storage","StreamNative's introduction of Lakehouse tiered storage"," further enhances Pulsar's storage efficiency. By leveraging columnar data formats aligned with Pulsar's schema, this feature significantly reduces the volume of data stored in S3 and the operational costs associated with data access, offering savings of up to 3-6 times depending on retention policies and schema specifics. This innovation represents a critical advancement in optimizing storage costs and efficiency for Pulsar users.",[48,594,595],{},[403,596],{"alt":18,"src":597},"\u002Fimgs\u002Fblogs\u002F663976190339ea676b392022_OQo32EOA_aT1rSJvdTxxLkazSFNs__Z2B0aklXDfZjxm3OEo5YdrowqLLRnLy7Hv0NPNvEoNityJ-gk0RaRRE-At3lGCJ3TW8CZIK7lIF_5GkaXa6c5sXMLy0uCNWZPUBeQqWVZzZhXcZ82GTyWg5VU.png",[40,599,601],{"id":600},"comparing-and-optimizing-resource-utilization","Comparing and Optimizing Resource Utilization",[48,603,604],{},"At this juncture, we've temporarily moved past the necessity of overprovisioning resources to accommodate your workload variations. Nevertheless, to maintain reliability and optimal performance, it's crucial to overprovision computing resources to safeguard against unforeseen spikes in throughput and to overprovision storage resources to prevent the risk of depleting disk space. In this section below, we will look into the resource utilization between Pulsar and Kafka in handling workload changes.",[48,606,506],{},[48,608,609],{},[403,610],{"alt":18,"src":611},"\u002Fimgs\u002Fblogs\u002F6639761993aaed0bc04a1e76_B2lmBfrQb4WWXPgKevnmu0x7ocL2x66NynSjNpjbl-JU9dXOvGLZsjzs0tXa225B1Abg1bv4FdrRKO-8AoTmQlKyhRpB5Dce7ldCCdNHxCVyqvXU8Oa-vstBJ_qtf_enS6Yh9yUheL8LZPbeJnwSuyY.png",[48,613,614],{},"In modern cloud-native environments, organizations frequently utilize Kubernetes and the inherent elasticity of cloud resources for scaling nodes in response to real-time traffic demands. However, Apache Kafka's design, which integrates serving and storage functions, presents a scalability challenge. Kafka necessitates a proportional increase in storage capacity to enhance serving capabilities and vice versa. Scaling operations in Kafka involve partition rebalancing, leading to potential service disruptions and significant data transfer over the network, thus impacting both performance and network-related costs.",[48,616,617],{},"This scenario often forces Kafka users to operate their clusters in a state of chronic overprovisioning, maintaining resource utilization levels well below optimal (sometimes below 20%). Despite the low usage, the financial implications of maintaining these additional resources are reflected in their cloud billing.",[48,619,620],{},"In contrast, engineered with the cloud-native landscape in mind, Apache Pulsar introduces an innovative approach to resource management, enabling precise optimization of compute and storage usage to control infrastructure expenses efficiently. Several key features distinguish Pulsar's architecture:",[331,622,623,626,633],{},[334,624,625],{},"Two-Layered Architecture: By decoupling message serving from storage, Pulsar facilitates independent scaling of resources based on actual demand. This flexibility allows for the addition of storage capabilities to extend data retention or the enhancement of serving nodes to increase consumption capacity without the need for proportional scaling.",[334,627,628,632],{},[55,629,631],{"href":630},"\u002Fblog\u002Fno-data-rebalance-needed-kafka-and-pulsar","Rebalance-Free Storage",": Pulsar's design minimizes the network load associated with scaling by eliminating the need for partition rebalancing. This approach prevents service degradation during scaling events and curtails spikes in networking costs.",[334,634,635],{},"Quota Management and Resource Limits: With built-in support for multi-tenancy, Pulsar enables effective quota management and the imposition of resource limits, safeguarding high availability and scalability across diverse operational scenarios.",[48,637,506],{},[48,639,640],{},[403,641],{"alt":18,"src":642},"\u002Fimgs\u002Fblogs\u002F6639761976f22438881eea30_s2g2RzhG12VChgRVMgKIaoRe2ZpPDQ5ZXK6CLMapoaHvUQ2YoXS3WFze4ov3WTx4D3pUmwRTYKTtmahRypK98LQ4u41_TC0yTX5JN1CX2ktizoXBk98u4GpvVhbsTTnw5GyVvQ32tZ73vRfTk5lufmM.png",[32,644,646],{"id":645},"a-note-on-multi-tenancy-in-data-streaming-platforms","A Note on Multi-Tenancy in Data Streaming Platforms",[48,648,649],{},"This blog post primarily addresses assessing infrastructure costs associated with one cluster. However, it is crucial to highlight the significant advantages of adopting a multi-tenant platform, particularly how multi-tenancy can decrease the total cost of ownership for such data streaming platforms. Here are several reasons why your business stands to benefit from multi-tenancy.",[48,651,652],{},[403,653],{"alt":18,"src":654},"\u002Fimgs\u002Fblogs\u002F6639761919aeef3f7bace98e_lD5w_euuiBPoYEsRXngD4rTpZ1aLSFHwCnAVVe1qzOgpVdN2cGopYNvLirnIliZ9hcEFk8fmb_mQXdZIMlPsVs9wmWofNQ4I2VkuuZmcSQKQGlVo1aUPtIuMuZ87HfYrLZ6uxibSEiGXMZLu46_gyfI.png",[429,656,657],{},[334,658,659],{},"Lower cost: Companies adopting Apache Kafka often require multiple Kafka clusters to cater to different teams within an organization, primarily due to the inherent limitations in Kafka’ architecture (See Figure 8). This scenario typically leads to each cluster operating at less than 30% capacity. Conversely, Apache Pulsar allows for the use of a single, multi-tenant cluster. Such deployment facilitates cost-sharing across various teams and enhances overall resource utilization. By pooling resources, organizations can significantly diminish the fixed costs and reduce the total cost of ownership, as resources are more efficiently allocated and used (See Figure 9).",[48,661,662],{},[403,663],{"alt":18,"src":664},"\u002Fimgs\u002Fblogs\u002F663976191b63f3af890a5065_pblf3w95OyaGub89dmDu288J30p8I_u30BfssE4PbgqaOsd6QQB_OQhzU525eOuGssp_GWUAULsO_54jN4jyrGgyd-_9UQEoE8hX7m-st3mtOz-runmXG5ADc2Stme3ccvSqwtxEnAtP3CyvFHbdhSg.png",[429,666,667,670],{},[334,668,669],{},"Simpler operations: Managing a single multi-tenant cluster simplifies operations compared to maintaining multiple single-tenant clusters. With fewer clusters, there’s less complexity in managing access controls, credentials, networking configurations, schema registries, roles, etc. Moreover, the challenge of tracking and upgrading different software versions across various clusters can become an operational quagmire. By centralizing on a multi-tenant platform, organizations can drastically cut operational burdens and associated costs, streamlining the management process and reducing the likelihood of errors.",[334,671,672],{},"Greater reuse: When all teams utilize a common cluster, reusing existing data becomes significantly more straightforward. A simple adjustment in access controls can enable different teams to leverage topics and events created by others, thereby accelerating the delivery of new projects and value creation. Furthermore, minimizing data duplication between clusters can lead to substantial savings. In data streaming platforms, where networking often constitutes a major portion of the expenses, a multi-tenant solution that reduces the need for data copying can markedly decrease the total network costs. This optimizes infrastructure cost and enhances the speed and efficiency of data-driven initiatives within the organization.",[48,674,675],{},"By embracing a multi-tenant data streaming technology like Apache Pulsar, businesses can achieve a more cost-effective, streamlined, and scalable data streaming environment, thereby enhancing operational efficiency and reducing operational overheads.",[48,677,678],{},"Looking ahead, our future blog posts will delve deeper into these aspects, exploring how Pulsar's cloud-native capabilities can be leveraged to achieve optimal resource utilization and cost efficiency in data streaming applications.",[40,680,682],{"id":681},"network-costs","Network Costs",[48,684,685],{},"Networking is often the most significant expense in integrating operational business applications with analytical data services through data streaming platforms. Unraveling networking costs is challenging, as cloud providers typically bundle these expenses with other organizational network usage without distinguishing between costs for specific technologies like Kafka and Pulsar.",[48,687,688,689,692],{},"To tackle this, we can construct a model to estimate these costs more accurately. In ",[55,690,691],{"href":324},"AP (Availability and Performance) data streaming systems"," such as Kafka and Pulsar, cross-AZ (Availability Zone) traffic incurs substantial costs due to the necessity for cross-AZ replication to ensure high availability and reliability. For robustness, operating in a multi-zone cluster is recommended, avoiding single-zone deployments that risk downtime during zonal outages. Pulsar's two-layer architecture offers a strategic advantage, allowing storage nodes to span multiple zones for durability while consolidating broker nodes within a single zone to expedite failover processes.",[48,694,506],{},[48,696,697],{},[403,698],{"alt":18,"src":699},"\u002Fimgs\u002Fblogs\u002F66397619f4e55e3fa86a664a_WqfQ36V5ohjPSJcz1RsXDYp_BEuOhh7Nu7AMSdpLVPvkeYBdAqUyA4IiLsWe3WpGgjBQmzLQ3SQ7T4tt4-OgvGyEZest4jt3MxD9wB9iVNLhIIJPClccgFQZzBBPfRzOlaZWba1d9TNkfuJg6xAwnaQ.png",[32,701,703],{"id":702},"networking-cost-models","Networking Cost Models",[48,705,706],{},"Hence, we examine the following three deployment scenarios:",[429,708,709,712,715],{},[334,710,711],{},"Kafka (Multi-AZ): A Kafka cluster spans multiple availability zones.",[334,713,714],{},"Pulsar (Multi-AZ): A Pulsar cluster's broker and storage nodes are distributed across multiple zones.",[334,716,717],{},"Pulsar (Single-AZ Broker, Multi-AZ Storage): Storage nodes are multi-zone, but broker nodes are confined to a single zone for swift failover.",[48,719,720],{},"The primary driver for cross-AZ traffic for Kafka (Multi-AZ) and Pulsar (Multi-AZ) are similar.",[429,722,723,726,729],{},[334,724,725],{},"Producers: Typically, topic owners in Pulsar or partition leaders in Kafka are distributed across three zones, leading to about 2\u002F3 of producer traffic crossing zones.",[334,727,728],{},"Consumers: Similarly, consumers often fetch data from topic owners or partition leaders in a different zone, generating cross-zone traffic roughly 2\u002F3 of the time.",[334,730,731],{},"Data Replication: Both systems replicate data across two additional zones for resilience.",[48,733,734],{},"We calculate the cross-AZ traffic for Multi-AZ deployments using the following formula:",[48,736,737],{},"Cross-AZ throughput (MB \u002F sec) = Producer cross-AZ throughput + Consumer cross-AZ throughput + Data replication cross-AZ throughput = (Ingress MBps * ⅔) + (Egress MBps * ⅔) + (Ingress MBps * 2)",[48,739,740],{},"Cross-AZ traffic from producers and consumers is eliminated for the unique Pulsar setup with Single-AZ Brokers and Multi-AZ Storage (see Figure 8), significantly reducing overall networking costs. So, we calculate only the data replication throughput across availability zones as the cross-AZ traffic.",[48,742,743],{},"Cross-AZ throughput (MB \u002F sec) = Data replication cross-AZ throughput = Ingress MBps * 2",[32,745,747],{"id":746},"networking-cost-calculation-and-implications","Networking Cost Calculation and Implications",[48,749,750],{},"We’ve modeled how much cross-AZ traffic results from our workload below, multiplied by the standard cross-AZ charge of two cents per GB in AWS.",[48,752,753],{},[403,754],{"alt":405,"src":755},"\u002Fimgs\u002Fblogs\u002F66f16fb2574712ac414405cc_66397b5ec586a5f71f852437_Screenshot-2024-05-06-at-5.52.35-PM.png",[48,757,758],{},"Reflecting on the data presented, it becomes evident that with the expansion of throughput and the increase in fanout, networking expenses swiftly become the predominant component of your infrastructure costs. Furthermore, while implementing Tiered Storage can significantly lower storage expenses, networking costs alone may still account for approximately 90% of total infrastructure expenditure. This underscores the importance of Apache Pulsar's dual-layer architecture, which is vital in minimizing networking fees while simultaneously upholding system reliability and availability.",[32,760,762],{"id":761},"innovations-in-reducing-networking-costs","Innovations in Reducing Networking Costs",[48,764,765,766,771,772,777],{},"The introduction of technologies like ",[55,767,770],{"href":768,"rel":769},"https:\u002F\u002Fcwiki.apache.org\u002Fconfluence\u002Fdisplay\u002FKAFKA\u002FKIP-392%3A+Allow+consumers+to+fetch+from+closest+replica",[264],"follower fetching"," in Kafka and ",[55,773,776],{"href":774,"rel":775},"https:\u002F\u002Fgithub.com\u002Fapache\u002Fpulsar\u002Fwiki\u002FPIP-63:-Readonly-Topic-Ownership-Support",[264],"ReadOnly Broker"," in Pulsar is pivotal in further reducing networking expenses. These allow consumers to read from a broker within the same zone, avoiding the costs associated with cross-zone data leader access. This approach ensures that cross-zone replication costs are incurred just once, irrespective of the number of consumers, offering a path to substantial savings on networking expenses. For example, in a cluster setup with 100 MBps ingress and 300 MBps egress, the cross-AZ traffic can be reduced from 466.7 MBps to 266.7 MBps, resulting in a 42.9% reduction in traffic.",[40,779,781],{"id":780},"putting-these-together","Putting these together",[48,783,784],{},"We're consolidating compute, storage, and networking expenses to assess the overall infrastructure costs of Pulsar versus Kafka. In a standard deployment scenario with 100 MBps ingress, 300 MBps egress, and a 7-day data retention workload, Pulsar offers a 77% reduction in compute costs and nearly a 60% reduction in total infrastructure costs, including both storage and network expenses.",[48,786,506],{},[48,788,789],{},[403,790],{"alt":18,"src":791},"\u002Fimgs\u002Fblogs\u002F66397619ffeba2ecbbdc0441_meUh2vJomh09FQeUeJzoTWfmxEfnpJzmIEUoOfrDaFe-Ar9wTY32kMyGyG9D1fTTj8EVNRunALEH4tWpJY20NPVnGnR5MnzkRrH3Wg_yHYwXwWUveecTSVyVfJx7U0FSEtpCEVIq7oe_hUjWYKVM7Ak.png",[40,793,795],{"id":794},"so-how-can-you-save-money","So, how can you save money?",[48,797,798],{},"Up to this point, we've dissected the cost structure of your data streaming infrastructure, hopefully providing you with a clearer understanding of how a data streaming platform can impact your finances. This guide outlines a methodical approach for Kafka users to tackle each cost component. A logical first step could be enabling follower fetching to mitigate cross-AZ charges. Lowering the replication factor for non-essential topics can further optimize networking expenses tied to partition replication. It's also beneficial to evaluate different instance types to find the most cost-effective fit for your requirements. Finally, adjusting your cluster's scale based on workload fluctuations ensures you're not overspending on underutilized infrastructure.",[48,800,801,802,806,807,811],{},"In our experience assisting numerous Kafka users in evaluating the costs of self-hosted open-source Kafka against open-source Pulsar and our fully managed Kafka API-compatible cloud service, ",[55,803,805],{"href":804},"\u002Fgetting-started","StreamNative Cloud",", we've consistently found that transitioning to a fully managed Kafka API-compatible cloud service and utilizing the architectural benefits of Apache Pulsar is the most cost-effective strategy. Apache Pulsar's distinctive, cloud-native architecture not only facilitates cost reductions but also introduces at-scale efficiencies. StreamNative Cloud, developed atop Apache Pulsar, offers ",[55,808,810],{"href":809},"\u002Fblog\u002Fkafka-on-streamnative-bringing-enterprise-grade-kafka-support-to-streamnative-pulsar-clusters","full Kafka compatibility",", enabling you to migrate without needing to overhaul your Kafka applications and capitalize on the cost advantages of Pulsar’s cloud-native design.",[48,813,814,815,819],{},"Don't miss our next installment in this series, which will delve into another crucial cost factor for data streaming platforms: development and operations personnel. If you're interested in calculating your Kafka expenses or discovering potential savings with StreamNative, we encourage you to ",[55,816,818],{"href":817},"\u002Fcontact","talk to our data streaming experts"," today and conduct a TCO analysis.",[48,821,822,823,828,829,834],{},"If you want to learn more about our development around Kafka API-compatible data streaming platforms, don’t miss the upcoming ",[55,824,827],{"href":825,"rel":826},"https:\u002F\u002Fpulsar-summit.org\u002Fevent\u002Feurope-2024",[264],"Pulsar Summit"," on May 14th next week. Our team will present our next-generation data streaming engine, purposely designed to reduce costs in this cost-conscious era. ",[55,830,833],{"href":831,"rel":832},"https:\u002F\u002Fregistration.socio.events\u002Fe\u002Fpulsarvirtualsummiteurope2024",[264],"Register for the event today","!",{"title":18,"searchDepth":19,"depth":19,"links":836},[837,838,841,846,849,854,855],{"id":342,"depth":19,"text":343},{"id":374,"depth":19,"text":375,"children":839},[840],{"id":412,"depth":279,"text":413},{"id":485,"depth":19,"text":486,"children":842},[843,844,845],{"id":520,"depth":279,"text":521},{"id":563,"depth":279,"text":564},{"id":584,"depth":279,"text":585},{"id":600,"depth":19,"text":601,"children":847},[848],{"id":645,"depth":279,"text":646},{"id":681,"depth":19,"text":682,"children":850},[851,852,853],{"id":702,"depth":279,"text":703},{"id":746,"depth":279,"text":747},{"id":761,"depth":279,"text":762},{"id":780,"depth":19,"text":781},{"id":794,"depth":19,"text":795},"Apache Pulsar","2024-05-06","Is Apache Pulsar better than Kafka? StreamNative can reduce the total infrastructure cost of running a Kafka workload by 70%. Check out our blog post on how to evaluate the infrastructure costs for Pulsar and Kafka and find guidance on choosing the most cost-effective data streaming platform for the current cost-conscious era.","\u002Fimgs\u002Fblogs\u002F663e6969cf2c91731b2d59d7_SN-BP-CostComparison-2.png",{},"\u002Fblog\u002Fa-guide-to-evaluating-the-infrastructure-costs-of-apache-pulsar-and-apache-kafka","12 min read",{"title":308,"description":858},"blog\u002Fa-guide-to-evaluating-the-infrastructure-costs-of-apache-pulsar-and-apache-kafka",[866,856,867],"Apache Kafka","TCO","TI2u3MWlDJJvLnn08Ccv9SE8RO3tiYqbo27lsR4xPIU",{"id":870,"title":871,"authors":872,"body":877,"category":805,"createdAt":290,"date":1630,"description":1631,"extension":8,"featured":7,"image":1632,"isDraft":294,"link":290,"meta":1633,"navigation":7,"order":296,"path":1634,"readingTime":1635,"relatedResources":290,"seo":1636,"stem":1637,"tags":1638,"__hash__":1639},"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",[873,874,875,876],"Matteo Meril","Neng Lu","Hang Chen","Penghui Li",{"type":15,"value":878,"toc":1600},[879,882,885,888,891,894,898,901,909,914,917,925,930,934,941,944,947,955,959,962,967,971,974,977,980,983,992,996,999,1010,1013,1017,1020,1023,1034,1037,1041,1045,1053,1056,1060,1067,1096,1100,1103,1108,1112,1115,1119,1122,1125,1130,1139,1144,1147,1150,1161,1165,1168,1179,1183,1186,1189,1194,1197,1226,1230,1232,1238,1241,1246,1251,1254,1258,1272,1276,1287,1291,1306,1315,1326,1329,1332,1336,1339,1342,1353,1356,1359,1362,1367,1372,1376,1379,1396,1400,1414,1419,1423,1434,1437,1453,1457,1468,1473,1478,1486,1490,1493,1497,1503,1507,1510,1519,1524,1533,1539,1548,1557,1566,1575,1584,1592],[48,880,881],{},"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.",[48,883,884],{},"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.",[48,886,887],{},"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.",[48,889,890],{},"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.",[48,892,893],{},"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.",[40,895,897],{"id":896},"key-benchmark-findings","Key Benchmark Findings",[48,899,900],{},"Ursa delivered 5 GB\u002Fs of sustained throughput at an infrastructure cost of just $54 per hour. For comparison:",[331,902,903,906],{},[334,904,905],{},"MSK: $303 per hour → 5.6x more expensive compared to Ursa",[334,907,908],{},"Redpanda: $988 per hour → 18x more expensive compared to Ursa",[48,910,911],{},[403,912],{"alt":18,"src":913},"\u002Fimgs\u002Fblogs\u002F679c71b67d9046f26edc7977_AD_4nXfvTqyBNUBu2lObdkKAx-5UNkpNP8UYULLZyOcixE6z99VMZUUEsUqWjzexI7vjyNGRNSAUoM9smYvdTP55ctAhIbrs5lmQgcSVMWdaoigbWouCl95DVSQsxooY-qqfGcYqS4g4zA.png",[48,915,916],{},"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:",[331,918,919,922],{},[334,920,921],{},"50% cheaper than Confluent WarpStream",[334,923,924],{},"85% cheaper than MSK and Redpanda",[48,926,927],{},[403,928],{"alt":18,"src":929},"\u002Fimgs\u002Fblogs\u002F679c602d77e9c706de5343b8_AD_4nXeDv8rrv_C1CTCCiqYo1zpvlGYbdBk1r0VEqovAPu22iFMQZgh54Hfw9PBMLzM7jDFxKwAFDxbdG0np4XVk_tGsWhEKMloLRcmmea7lvueCx-0cFsyaE3Mya4Mxc1Dox95A6JEc.png",[40,931,933],{"id":932},"ursa-highly-cost-efficient-data-streaming-at-scale","Ursa: Highly Cost-Efficient Data Streaming at Scale",[48,935,936,940],{},[55,937,939],{"href":938},"\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.",[48,942,943],{},"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.",[48,945,946],{},"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:",[331,948,949,952],{},[334,950,951],{},"Eliminating inter-zone traffic costs via a leaderless architecture.",[334,953,954],{},"Replacing costly inter-zone replication with direct writes to cloud storage using open lakehouse formats.",[40,956,958],{"id":957},"how-ursa-eliminates-inter-zone-traffic","How Ursa Eliminates Inter-Zone Traffic",[48,960,961],{},"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.",[48,963,964],{},[403,965],{"alt":18,"src":966},"\u002Fimgs\u002Fblogs\u002F679c602e21b3571bb7117dca_AD_4nXd7Oahc77NjRLNvA9clLt0tsyU6MrIqVibFYv5pW5giTIcCHPr3EA_yTGzfVEUIVO3VXK56qWK8zmBCp5lY0E_4nmlWIPFrHjtHylA5NhwELjn-UB0fLG2h_kbrxrc7Cs_edvveNA.png",[32,968,970],{"id":969},"leaderless-architecture","Leaderless architecture",[48,972,973],{},"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.",[48,975,976],{},"Pros of Leader-Based Architectures:\n✔ Maintains message ordering via local sequence IDs\n✔ Delivers low latency and high performance through message caching",[48,978,979],{},"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",[48,981,982],{},"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.",[48,984,985,986,991],{},"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 ",[55,987,990],{"href":988,"rel":989},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Foxia",[264],"Oxia",", a scalable metadata and index service created by StreamNative in 2022.",[32,993,995],{"id":994},"oxia-the-metadata-layer-enabling-leaderless-architecture","Oxia: The Metadata Layer Enabling Leaderless Architecture",[48,997,998],{},"Ensuring message ordering in a leaderless architecture is complex, but Ursa solves this with Oxia:",[331,1000,1001,1004,1007],{},[334,1002,1003],{},"Handles millions of metadata\u002Findex operations per second",[334,1005,1006],{},"Generates sequential IDs to maintain strict message ordering",[334,1008,1009],{},"Optimized for Kubernetes with horizontal scalability",[48,1011,1012],{},"Producers and consumers can connect to any broker within their local AZ, eliminating inter-zone traffic costs while maintaining performance through localized caching.",[32,1014,1016],{"id":1015},"zero-interzone-data-replication","Zero interzone data replication",[48,1018,1019],{},"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.",[48,1021,1022],{},"Ursa avoids these costs by writing data directly to cloud storage (e.g., AWS S3, Google GCS):",[331,1024,1025,1028,1031],{},[334,1026,1027],{},"Built-In Resilience: Cloud storage inherently offers high availability and fault tolerance without inter-zone traffic fees.",[334,1029,1030],{},"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).",[334,1032,1033],{},"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.",[48,1035,1036],{},"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.",[40,1038,1040],{"id":1039},"how-we-ran-a-5-gbs-test-with-ursa","How We Ran a 5 GB\u002Fs Test with Ursa",[32,1042,1044],{"id":1043},"ursa-cluster-deployment","Ursa Cluster Deployment",[331,1046,1047,1050],{},[334,1048,1049],{},"9 brokers across 3 availability zones, each on m6i.8xlarge (Fixed 12.5 Gbps bandwidth, 32 vCPU cores, 128 GB memory).",[334,1051,1052],{},"Oxia cluster (metadata store) with 3 nodes of m6i.8xlarge, distributed across three availability zones (AZs).",[48,1054,1055],{},"During peak throughput (5 GB\u002Fs), each broker’s network usage was about 10 Gbps.",[32,1057,1059],{"id":1058},"openmessaging-benchmark-workers-configuration","OpenMessaging Benchmark Workers & Configuration",[48,1061,1062,1063,1066],{},"The OpenMessaging Benchmark(OMB) Framework is a suite of tools that make it easy to benchmark distributed messaging systems in the cloud. Please check ",[55,1064,354],{"href":354,"rel":1065},[264]," for details.",[331,1068,1069,1084,1093],{},[334,1070,1071,1072,1077,1078,1083],{},"12 OMB workers: 6 for ",[55,1073,1076],{"href":1074,"rel":1075},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002Fd1094122270775e4f1580947f80c5055",[264],"producers",", 6 for ",[55,1079,1082],{"href":1080,"rel":1081},"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.",[334,1085,1086,1087,1092],{},"Sample YAML ",[55,1088,1091],{"href":1089,"rel":1090},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F204c1f26c4d44a218ae235bf2de99904",[264],"scripts"," provided for Kafka-compatible configuration and rate limits.",[334,1094,1095],{},"Achieved consistent 5 GB\u002Fs publish\u002Fsubscribe throughput.",[40,1097,1099],{"id":1098},"ursa-benchmark-tests-results","Ursa Benchmark Tests & Results",[48,1101,1102],{},"The following diagram demonstrates that Ursa can consistently handle 5 GB\u002Fs of traffic, fully saturating the network across all broker nodes.",[48,1104,1105],{},[403,1106],{"alt":18,"src":1107},"\u002Fimgs\u002Fblogs\u002F679c602d7b261bac1113f7d6_AD_4nXdDPsRc3koXICiFF0bqSmGWbJt_RlUy4FE3ruuWOfbCfpcqZ1dejjqGbkaCJv2hQFL1nirRouBVRW2l5uMWBvY9naMqGB_wHcLI14dBM0f85TXhmdm3UxEv1yGX9Y4hf5FttSkZew.png",[40,1109,1111],{"id":1110},"comparing-infrastructure-cost","Comparing Infrastructure Cost",[48,1113,1114],{},"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.",[32,1116,1118],{"id":1117},"test-setup-key-assumptions","Test Setup & Key Assumptions",[48,1120,1121],{},"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.",[48,1123,1124],{},"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:",[331,1126,1127],{},[334,1128,1129],{},"9 × m6i.8xlarge instances",[48,1131,1132,1133,1138],{},"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",[55,1134,1137],{"href":1135,"rel":1136},"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:",[331,1140,1141],{},[334,1142,1143],{},"15 × kafka.m7g.8xlarge (32 vCPUs, 128 GB memory, 15 Gbps network, 4000 GiB EBS).",[48,1145,1146],{},"This configuration was necessary to work around MSK's storage bandwidth limitations, ensuring a comparable cost basis to other evaluated streaming engines.",[48,1148,1149],{},"Additional key assumptions include:",[331,1151,1152,1155,1158],{},[334,1153,1154],{},"Inter-AZ producer traffic: For leader-based engines, two-thirds of producer-to-broker traffic crosses AZs due to leader distribution.",[334,1156,1157],{},"Consumer optimizations: Follower fetch is enabled across all tests, eliminating inter-AZ consumer traffic.",[334,1159,1160],{},"Storage cost exclusions: This benchmark only evaluates streaming costs, assuming no long-term data retention.",[32,1162,1164],{"id":1163},"inter-broker-replication-costs","Inter-Broker Replication Costs",[48,1166,1167],{},"Inter-broker (cross-AZ) replication is a major cost driver for data streaming engines:",[331,1169,1170,1173,1176],{},[334,1171,1172],{},"RedPanda: Inter-broker replication is not free, leading to substantial costs when data must be copied across multiple availability zones.",[334,1174,1175],{},"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.",[334,1177,1178],{},"Ursa: No inter-broker replication costs due to its leaderless architecture, eliminating inter-zone replication costs entirely.",[32,1180,1182],{"id":1181},"zone-affinity-reducing-inter-az-costs","Zone Affinity: Reducing Inter-AZ Costs",[48,1184,1185],{},"We evaluated zone affinity mechanisms to further reduce inter-AZ data transfer costs.",[48,1187,1188],{},"Consumers:",[331,1190,1191],{},[334,1192,1193],{},"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",[48,1195,1196],{},"Producers:",[331,1198,1199,1208,1217],{},[334,1200,1201,1202,1207],{},"Kafka protocol lacks an easy way to enforce producer AZ affinity (though ",[55,1203,1206],{"href":1204,"rel":1205},"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).",[334,1209,1210,1211,1216],{},"Redpanda recently introduced ",[55,1212,1215],{"href":1213,"rel":1214},"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.",[334,1218,1219,1220,1225],{},"Ursa is the only system in this test with ",[55,1221,1224],{"href":1222,"rel":1223},"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.",[32,1227,1229],{"id":1228},"cost-comparison-results","Cost Comparison Results",[48,1231,900],{},[331,1233,1234,1236],{},[334,1235,905],{},[334,1237,908],{},[48,1239,1240],{},"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.",[48,1242,1243],{},[403,1244],{"alt":18,"src":1245},"\u002Fimgs\u002Fblogs\u002F679c72208198ca36a352f228_AD_4nXeeZuM8T-xBlD4Vf3j67K618n08qh8wIDLLtiLJG0ssA1Wj1V26u7wIDTX9sqLrtw8mB2c299dwzarGen62CG0Vh7nWstn5qbPGFcBaKJYEepTsLr5fHWv1U8uqbg8Y0UOK6fJ7.png",[48,1247,1248],{},[403,1249],{"alt":18,"src":1250},"\u002Fimgs\u002Fblogs\u002F679c625978031f40229de484_AD_4nXdLkLLJ30KKr-_A_rN1j8akVwBYacAWIPzWHoOReJF421890kfByZoQQxkLczihVSmiw5Q9J51-V9I2SEKITbwsYnANDDTlAVL5nQ_jfaHNTe9VEWhSoa7DZooCnilDYL6l6msmJg.png",[48,1252,1253],{},"The detailed infrastructure cost calculations for each data streaming engine are listed below:",[32,1255,1257],{"id":1256},"streamnative-ursa","StreamNative - Ursa",[331,1259,1260,1263,1266,1269],{},[334,1261,1262],{},"Server EC2 costs: 9 * $1.536\u002Fhr = $14",[334,1264,1265],{},"Client EC2 costs: 9 * $1.536\u002Fhr =$14",[334,1267,1268],{},"S3 write requests costs: 1350 r\u002Fs * $0.005\u002F1000r * 3600s = $24",[334,1270,1271],{},"S3 read requests costs: 1350 r\u002Fs * $0.0004\u002F1000r * 3600s = $2",[32,1273,1275],{"id":1274},"aws-msk","AWS MSK",[331,1277,1278,1281,1284],{},[334,1279,1280],{},"Server EC2 costs: 15 * $3.264\u002Fhr = $49",[334,1282,1283],{},"Client side EC2 costs: 9 * $1.536\u002Fhr =$14",[334,1285,1286],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FG(in+out) * 3600 = $240",[32,1288,1290],{"id":1289},"redpanda","RedPanda",[331,1292,1293,1295,1297,1300,1303],{},[334,1294,1262],{},[334,1296,1265],{},[334,1298,1299],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FGB(in+out) * 3600 = $240",[334,1301,1302],{},"Interzone traffic - replication: 10GB\u002Fs * $0.02\u002FGB(in+out) * 3600 = $720",[334,1304,1305],{},"Interzone traffic - broker to consumer: $0 (fetch from local zone)",[48,1307,1308,1309,1314],{},"Please note that we were unable to test ",[55,1310,1313],{"href":1311,"rel":1312},"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.",[331,1316,1317,1323],{},[334,1318,1319,1322],{},[55,1320,1206],{"href":1204,"rel":1321},[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).",[334,1324,1325],{},"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.",[48,1327,1328],{},"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.",[48,1330,1331],{},"We may revisit this comparison as more details become available.",[40,1333,1335],{"id":1334},"comparing-total-cost-of-ownership","Comparing Total Cost of Ownership",[48,1337,1338],{},"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.",[48,1340,1341],{},"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:",[331,1343,1344,1347,1350],{},[334,1345,1346],{},"Ursa ($164,353\u002Fmonth) is: 50% cheaper than Confluent WarpStream ($337,068\u002Fmonth)",[334,1348,1349],{},"85% cheaper than AWS MSK ($1,115,251\u002Fmonth)",[334,1351,1352],{},"86% cheaper than Redpanda ($1,202,853\u002Fmonth)",[48,1354,1355],{},"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.",[48,1357,1358],{},"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.",[48,1360,1361],{},"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.",[48,1363,1364],{},[403,1365],{"alt":18,"src":1366},"\u002Fimgs\u002Fblogs\u002F679c602d194800c9206d9d58_AD_4nXcFlf755xgyz7htxhMhBV5fGrsxy642mQNodt61DTok_z1dwkw5A6lkO5hatXVneCaB0anbZPAyvLI3MlIMuQEYLEACHHvQMOr5UfaB37dfzkdqewDEvcT-20VGd_zzvJsuA00zGA.png",[48,1368,1369],{},[403,1370],{"alt":18,"src":1371},"\u002Fimgs\u002Fblogs\u002F679c62594e9c2e629fae73aa_AD_4nXeU6cOgItnjLsEZCOf13TEvMY_SHWWIxYP2OYUj-B1GUPyWO78OG08K_v03hwYSVcg06f9dqDiGmdwy76vynjmiDGL5bluZ5_XF4nSU_r59oOZdfViXndXt6s11vVOY7qwfZN8v.png",[32,1373,1375],{"id":1374},"cost-breakdown","Cost Breakdown",[526,1377,1378],{"id":1256},"StreamNative – Ursa",[331,1380,1381,1384,1387,1390,1393],{},[334,1382,1383],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9,953.28",[334,1385,1386],{},"S3 Write Requests: 1,350 r\u002Fs × $0.005\u002F1,000 r × 3,600 s × 24 hr × 30 days = $17,496",[334,1388,1389],{},"S3 Read Requests: 1,350 r\u002Fs × $0.0004\u002F1,000 r × 3,600 s × 24 hr × 30 days = $1,400",[334,1391,1392],{},"S3 Storage Costs: 5 GB\u002Fs × $0.021\u002FGB × 3,600 s × 24 hr × 7 days = $63,504",[334,1394,1395],{},"Vendor Cost: 200 ETU × $0.50\u002Fhr × 24 hr × 30 days = $72,000",[526,1397,1399],{"id":1398},"warpstream","WarpStream",[331,1401,1402,1405],{},[334,1403,1404],{},"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.",[334,1406,1407,1408,1413],{},"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 “",[55,1409,1412],{"href":1410,"rel":1411},"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.",[48,1415,1416],{},[403,1417],{"alt":18,"src":1418},"\u002Fimgs\u002Fblogs\u002F679c602e42713e0028e9af5e_AD_4nXcu5_VWTLu9jRYs6zX1MBAOtLQEo5gyfNSWPcbpnQHXTa8qNCFAXezRR2E8daygzYTTwd4dhJjaLaLM8C6y_3OGbu2NS7pdvEv3a8-ptNKOg7AeKnYqPQCAYvQ5EuxzuI3JYIvY.png",[526,1420,1422],{"id":1421},"msk","MSK",[331,1424,1425,1428,1431],{},[334,1426,1427],{},"EC2 (Server): 15 * $3.264\u002Fhr × 24 hr × 30 days = $35,251",[334,1429,1430],{},"Interzone Traffic (Client-Server): 5 GB\u002Fs × ⅔ × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $172,800",[334,1432,1433],{},"Storage: 5 GB\u002Fs × $0.1\u002FGB-month × 3,600 s × 24 hr × 7 days * 3 replicas = $907,200",[526,1435,1290],{"id":1436},"redpanda-1",[331,1438,1439,1442,1444,1447,1450],{},[334,1440,1441],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9953",[334,1443,1430],{},[334,1445,1446],{},"Interzone Traffic (Replication): 5 GB\u002Fs × 2 × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $518,400",[334,1448,1449],{},"Storage: 5 GB\u002Fs × $0.045\u002FGB-month(st1) × 3,600 s × 24 hr × 7 days * 3 replicas = $408,240",[334,1451,1452],{},"Vendor Cost: $93,333 per month (based on limited information. See additional notes below).",[526,1454,1456],{"id":1455},"additional-notes","Additional Notes",[331,1458,1459],{},[334,1460,1461,1462,1467],{},"Redpanda does not publicly disclose its BYOC pricing, making it difficult to accurately assess its total costs. We refer to information from the whitepaper “",[55,1463,1466],{"href":1464,"rel":1465},"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.",[48,1469,1470],{},[403,1471],{"alt":18,"src":1472},"\u002Fimgs\u002Fblogs\u002F679c602dc8a9859eed89a0ef_AD_4nXdbcO8vsNNPy4GtkNLlmNKf22fjxRvzLzH7CtOna1L08sTbvnZx3HhufeFqc1w4K2gEF7lxO2IR5supotxebAiGnA07Qa8Yr3Rd1pVK2LYKK4WurlJGwgdwwucZIFoF-N_2oBjY.png",[48,1474,1475],{},[403,1476],{"alt":18,"src":1477},"\u002Fimgs\u002Fblogs\u002F679c602d6bc1c2287e012540_AD_4nXfcHZnLfjbjIr3ZAgoQXT9dwP3aQCOQPmGZZJUtpNZSwE6qY6M3yehIaBxCwxEIeu5PVdUPY0zhyjnow26YfgjdYgSG4GnV9ibxu0YWTIpwng6z_F6FUGJMpERMKtpsFESzXSN_Sw.png",[331,1479,1480,1483],{},[334,1481,1482],{},"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.",[334,1484,1485],{},"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.",[40,1487,1489],{"id":1488},"conclusion","Conclusion",[48,1491,1492],{},"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.",[32,1494,1496],{"id":1495},"balancing-latency-and-cost","Balancing Latency and Cost",[48,1498,1499,1502],{},[55,1500,1501],{"href":324},"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.",[32,1504,1506],{"id":1505},"the-future-of-streaming-infrastructure","The Future of Streaming Infrastructure",[48,1508,1509],{},"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.",[48,1511,1512,1513,1518],{},"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. ",[55,1514,1517],{"href":1515,"rel":1516},"https:\u002F\u002Fconsole.streamnative.cloud\u002F",[264],"Get started"," with StreamNative Ursa today!",[1520,1521,1523],"h1",{"id":1522},"references","References",[48,1525,1526,1529,1530],{},[1527,1528,990],"span",{}," ",[55,1531,1532],{"href":1532},"\u002Fblog\u002Fintroducing-oxia-scalable-metadata-and-coordination",[48,1534,1535,1529,1537],{},[1527,1536,939],{},[55,1538,938],{"href":938},[48,1540,1541,1529,1544],{},[1527,1542,1543],{},"StreamNative pricing",[55,1545,1546],{"href":1546,"rel":1547},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fbilling-overview",[264],[48,1549,1550,1529,1553],{},[1527,1551,1552],{},"WarpStream pricing",[55,1554,1555],{"href":1555,"rel":1556},"https:\u002F\u002Fwww.warpstream.com\u002Fpricing#pricingfaqs",[264],[48,1558,1559,1529,1562],{},[1527,1560,1561],{},"AWS S3 pricing",[55,1563,1564],{"href":1564,"rel":1565},"https:\u002F\u002Faws.amazon.com\u002Fs3\u002Fpricing\u002F",[264],[48,1567,1568,1529,1571],{},[1527,1569,1570],{},"AWS EBS pricing",[55,1572,1573],{"href":1573,"rel":1574},"https:\u002F\u002Faws.amazon.com\u002Febs\u002Fpricing\u002F",[264],[48,1576,1577,1529,1580],{},[1527,1578,1579],{},"AWS MSK pricing",[55,1581,1582],{"href":1582,"rel":1583},"https:\u002F\u002Faws.amazon.com\u002Fmsk\u002Fpricing\u002F",[264],[48,1585,1586,1529,1589],{},[1527,1587,1588],{},"The Brutal Truth about Kafka Cost Calculators",[55,1590,1410],{"href":1410,"rel":1591},[264],[48,1593,1594,1529,1597],{},[1527,1595,1596],{},"Redpanda vs. Confluent: A Performance and TCO Benchmark Report by McKnight Consulting Group",[55,1598,1464],{"href":1464,"rel":1599},[264],{"title":18,"searchDepth":19,"depth":19,"links":1601},[1602,1603,1604,1609,1613,1614,1623,1626],{"id":896,"depth":19,"text":897},{"id":932,"depth":19,"text":933},{"id":957,"depth":19,"text":958,"children":1605},[1606,1607,1608],{"id":969,"depth":279,"text":970},{"id":994,"depth":279,"text":995},{"id":1015,"depth":279,"text":1016},{"id":1039,"depth":19,"text":1040,"children":1610},[1611,1612],{"id":1043,"depth":279,"text":1044},{"id":1058,"depth":279,"text":1059},{"id":1098,"depth":19,"text":1099},{"id":1110,"depth":19,"text":1111,"children":1615},[1616,1617,1618,1619,1620,1621,1622],{"id":1117,"depth":279,"text":1118},{"id":1163,"depth":279,"text":1164},{"id":1181,"depth":279,"text":1182},{"id":1228,"depth":279,"text":1229},{"id":1256,"depth":279,"text":1257},{"id":1274,"depth":279,"text":1275},{"id":1289,"depth":279,"text":1290},{"id":1334,"depth":19,"text":1335,"children":1624},[1625],{"id":1374,"depth":279,"text":1375},{"id":1488,"depth":19,"text":1489,"children":1627},[1628,1629],{"id":1495,"depth":279,"text":1496},{"id":1505,"depth":279,"text":1506},"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":871,"description":1631},"blog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour",[867,866,303],"A0o_2xdJiLI6rf6xj4RKsxJNo_A6QN2fYzCp6gaLrFw",[1641],{"id":1642,"title":310,"bioSummary":1643,"email":290,"extension":8,"image":1644,"linkedinUrl":1645,"meta":1646,"position":1653,"stem":1654,"twitterUrl":1655,"__hash__":1656},"authors\u002Fauthors\u002Fsijie-guo.md","Sijie’s journey with Apache Pulsar began at Yahoo! where he was part of the team working to develop a global messaging platform for the company. He then went to Twitter, where he led the messaging infrastructure group and co-created DistributedLog and Twitter EventBus. In 2017, he co-founded Streamlio, which was acquired by Splunk, and in 2019 he founded StreamNative. He is one of the original creators of Apache Pulsar and Apache BookKeeper, and remains VP of Apache BookKeeper and PMC Member of Apache Pulsar. Sijie lives in the San Francisco Bay Area of California.","\u002Fimgs\u002Fauthors\u002Fsijie-guo.webp","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fsijieg\u002F",{"body":1647},{"type":15,"value":1648,"toc":1651},[1649],[48,1650,1643],{},{"title":18,"searchDepth":19,"depth":19,"links":1652},[],"CEO and Co-Founder, StreamNative, Apache Pulsar PMC Member","authors\u002Fsijie-guo","https:\u002F\u002Ftwitter.com\u002Fsijieg","krzMgsbADqGZT1TnpWTVzT4HJ9U7oZB9hzOMiDT5Wd0",[1658,1666,1673],{"path":1659,"title":1660,"date":1661,"image":1662,"link":-1,"collection":1663,"resourceType":1664,"score":1665,"id":1659},"\u002Fsuccess-stories\u002Ftuya-smart","How Apache Pulsar Helps Streamline Message System and Reduces O&M Costs at Tuya Smart","2022-12-27","\u002Fimgs\u002Fsuccess-stories\u002F67956cdae901cfb181d6a791_SN-SuccessStories-tuya.webp","successStories","Case Study",1.1,{"path":1667,"title":1668,"date":1669,"image":1670,"link":-1,"collection":1671,"resourceType":1672,"score":1665,"id":1667},"\u002Fwhitepapers\u002Fapache-pulsar-helps-streamline-message-system-and-reduces-o-m-costs-at-tuya-smart","Apache Pulsar Helps Streamline Message System and Reduces O&M Costs at Tuya Smart","2022-12-23","\u002Fimgs\u002Fwhitepapers\u002F63aeca68ef34793d08c93534_open-graph-Apache-Pulsar-Helps-Streamline-Message-System.webp","whitepapers","Whitepaper",{"path":1674,"title":1660,"date":1675,"image":-1,"link":-1,"collection":1676,"resourceType":1677,"score":1678,"id":1674},"\u002Fblog\u002Fhow-apache-pulsar-helps-streamline-message-system-reduces-o-m-costs-at-tuya-smart","2020-05-08","blogs","Blog",0.75,1775716409415]