As organizations scale their data infrastructure to support real-time applications, the need for reliable and high-performance streaming platforms becomes critical. Apache Kafka and Apache Pulsar are two of the most popular open-source platforms used to handle high-throughput, low-latency streaming data.

While both are built for event-driven architectures, their architectures, scalability models, and feature sets differ significantly. This article compares Kafka and Pulsar in the context of high-volume streaming, helping you make an informed choice for your data pipeline.

Kafka and Pulsar: A Quick Overview

Apache Kafka

  • Developed at LinkedIn, open-sourced via Apache Foundation
  • Widely adopted for real-time data pipelines and stream processing
  • Monolithic architecture with tight coupling between brokers and storage

Apache Pulsar

  • Created at Yahoo, now an Apache project
  • Built for cloud-native, geo-replicated, and multi-tenant systems
  • Decoupled architecture with separate brokers and storage (Apache BookKeeper)

Architectural Comparison

Feature Apache Kafka Apache Pulsar
Storage Model Log storage managed by brokers Storage handled by BookKeeper nodes
Scalability Add partitions and brokers Scale brokers and Bookies independently
Geo-Replication External (MirrorMaker) Built-in and asynchronous
Multi-Tenancy Not native, hard to isolate Native with namespace, tenant isolation
Topic Abstraction Partition = File = Thread Topics are lightweight; segment-based
Message Queuing Stream-only Stream + Queue hybrid

Kafka’s simpler design is easier to manage initially but can become complex at scale. Pulsar’s modular architecture is optimized for elasticity and multi-tenancy.

Throughput and Latency

Kafka

  • Extremely high throughput
  • Optimized for sequential disk I/O
  • Needs tuning for network, partitions, and replication

Pulsar

  • Comparable throughput with lower latency out-of-the-box
  • Write-ahead logs in BookKeeper offer high concurrency
  • Handles millions of topics efficiently due to topic segmentation

Durability and Message Retention

Feature Apache Kafka Apache Pulsar
Durability Broker writes + replication BookKeeper ledger replication
Retention Options Time-based, size-based Time-based, size-based, backlog-aware
TTL / Per-Consumer Global per-topic Per-subscription TTL and expiry

Pulsar offers more granular retention and expiration policies, allowing better control in multi-tenant scenarios.

Message Consumption Models

Kafka

  • Pull-based consumer model
  • Relies on consumer group coordination
  • Simple offset tracking with at-least-once guarantees

Pulsar

  • Push and pull-based models
  • Supports multiple subscription types:
    • Exclusive
    • Shared
    • Failover
  • Built-in dead letter queues, retry topics, and ack timeouts

Pulsar is more flexible for asynchronous, distributed, and microservice-based architectures.

Ecosystem and Tooling

Category Kafka Pulsar
Stream Processing Kafka Streams, ksqlDB Flink, Pulsar Functions, Spark
Connectors Kafka Connect Pulsar IO, Debezium, custom sink/source
Observability JMX, Prometheus exporters Built-in Prometheus + Grafana
Admin Tools Confluent Control Center, Kafka UI Pulsar Manager, Pulsar Admin CLI

Kafka has a more mature ecosystem, while Pulsar offers modern integrations and cloud-native design patterns.

Deployment and Operations

  • Kafka is relatively easier to set up initially but requires Zookeeper (until KRaft is mature) and careful partition management.
  • Pulsar has more components (Broker, BookKeeper, ZooKeeper) but scales more linearly at high volumes.

Pulsar offers:

  • Multi-datacenter replication
  • Topic-level isolation
  • Automatic topic compaction and TTL cleanup

Kafka relies more on external tools for similar functionality.

When to Choose Kafka

  • You already use Kafka and require ecosystem tools like Kafka Streams
  • You prefer a simpler, single-cluster setup
  • Your workload is focused on streaming-only patterns
  • You rely on well-supported commercial offerings (e.g., Confluent)

When to Choose Pulsar

  • You need multi-tenancy, geo-replication, or hybrid messaging
  • You plan to handle millions of topics or high-concurrency workloads
  • You want to scale storage and compute independently
  • You prefer cloud-native, containerized deployment models

Conclusion

Both Kafka and Pulsar are excellent choices for building high-volume streaming systems, but they solve the problem in different ways.

  • Kafka provides simplicity, strong community support, and proven reliability
  • Pulsar offers architectural flexibility, fine-grained control, and cloud-native scalability

Choose Kafka when you need a battle-tested solution with mature tooling, and choose Pulsar when building for elastic scale, geo-distribution, or complex multi-tenant environments.

Ultimately, your decision should align with your performance goals, team expertise, and long-term architectural needs.