If you have spent any time building data pipelines, you have probably met both of these names. Apache Kafka and Apache Pulsar are the two most talked about open source messaging systems for real-time streaming, and choosing between them is one of those decisions that quietly shapes your whole platform. I have deployed and operated both, sometimes in the same Kubernetes cluster, and I have learned the hard way that the architecture differences are not academic trivia. They decide how your system behaves at 2 a.m. when traffic spikes.
Both platforms offer high throughput and low latency message delivery, but they were designed for different worlds, and knowing which world you live in is the whole game. In this article, I will walk you through the differences between Apache Pulsar and Apache Kafka, and the use cases where each one shines, the way I would explain it to a colleague over coffee.
🏗️ Architecture
Apache Kafka was born at LinkedIn in 2011, and it shows. It is designed as a distributed, horizontally scalable system for processing large volumes of data in real time. At its core is a publish-subscribe model: producers send messages to topics, and consumers subscribe to those topics to read them. A cluster of brokers manages the distribution of messages across partitions, and each partition is replicated across multiple brokers so that losing a node does not mean losing data. It is a battle-tested design, and LinkedIn itself scaled it to process trillions of messages a day, with reports of around seven trillion by 2019. If you need proof that Kafka can handle scale, that is it.
Apache Pulsar was created at Yahoo in 2016 and open sourced a year later, and it takes a different approach. Where Kafka couples storage and serving in the same broker node, Pulsar separates them: brokers handle the messaging, and a separate storage layer built on Apache BookKeeper handles persistence. That is the key architectural difference, and it changes almost everything else, from scaling to storage costs. Pulsar also supports both publish-subscribe and queueing models, and it organizes topics into namespaces, which makes multi-tenancy a first-class feature rather than an afterthought.
💬 Messaging Model
Kafka uses a topic-based model. Producers publish to topics, consumers subscribe, and ordering is guaranteed within a partition. Topics can be partitioned for scale, and consumers can organize into consumer groups, which lets multiple consumers share the work of reading the same topic while each message is processed exactly once per group. It is a clean, simple model, and it is the reason Kafka became the default choice for real-time analytics.
Pulsar speaks the same language, but adds another one. It supports topic-based messaging just like Kafka, yet it also supports queueing, where multiple consumers read from the same queue and each message is delivered to only one of them. That single difference is huge in practice. With a queue model, you can throw more workers at a backlog without worrying about ordering conflicts, which is exactly what you want for task distribution and background job processing. Kafka can approximate this with careful partition design, but Pulsar gives it to you natively.
📈 Scalability
Both Apache Pulsar and Apache Kafka are designed for horizontal scalability, meaning they handle increasing workloads by adding more nodes to the cluster. But they scale in very different ways, and this is where I have watched teams burn months of effort. Because Kafka stores data on the broker itself, storage and compute grow together, and every partition adds memory and coordination overhead. Clusters can absolutely support tens of thousands of partitions, but the tuning work, replication factors, rebalancing, monitoring, grows with you.
Pulsar’s separation of compute and storage pays off here: storage scales independently on BookKeeper nodes while brokers stay stateless, which is why Pulsar can comfortably run far more topics than a typical Kafka deployment. Yahoo, where Pulsar was born, runs it across clusters with more than a million topics and billions of messages flowing through every day. Pulsar also introduced tiered storage early on, automatically offloading older data to cheaper storage like S3 while recent data stays in memory or on SSD. Kafka has been working on bringing tiered storage in through KIP-405, but Pulsar has shipped it for years. Add dynamic provisioning, where new topics are created and scaled automatically based on usage patterns, and multi-tenancy through independent namespaces, and you have a system built for the cloud. If you run a platform for many teams, that is a big deal.
🎯 Use Cases
Where does this leave you? Kafka is the workhorse for use cases that need high throughput and low latency without much ceremony: real-time data streaming, log aggregation, and event sourcing. Its publish-subscribe model is particularly useful when multiple consumers need the same data at the same time, such as real-time analytics or monitoring. The ecosystem is huge, with Kafka Streams, Kafka Connect, and Schema Registry, and it is the default answer on most job postings. Confluent, the company behind Kafka, says more than 80 percent of the Fortune 100 runs Kafka. When you need a proven standard, Kafka is the safe bet.
Pulsar is the better fit when you need cloud-native scalability and multi-tenancy, or when your workloads are uneven: IoT data processing, machine learning pipelines, and microservices architecture come to mind. The queue model handles work distribution cleanly, tiered storage keeps costs sane as data piles up, and namespaces let many teams share one cluster without stepping on each other. If you are running a platform for hundreds of services with unpredictable traffic, Pulsar’s operational story is hard to beat.
🧠 Final Thoughts
So which one should you pick? It depends on the problem you are solving, which is the least satisfying answer in engineering, and also the most honest one. If you need a battle-tested standard with the biggest ecosystem and your workloads are predictable, Kafka is a strong default. If you are building a multi-tenant platform in the cloud, need to move messages through queues, or expect to grow in ways you cannot predict, Pulsar’s architecture gives you headroom that Kafka will make you work for.
I have run both in production, and I do not think there is a wrong answer, just the wrong fit. My advice: prototype with your real workload, watch how each system behaves when things go wrong, and choose the one you would rather debug at 2 a.m. Thanks for reading.