Pulsar vs. Kafka

Pulsar provides a multi-layer architecture that decouples storage and compute. Pulsar’s design allows organizations to elastically scale storage independently from compute and achieve different levels of resource isolation.

In contrast, Kafka has a monolithic architecture that tightly couples compute and storage, where resources must be scaled together.

Pulsar vs. Kafka performance

In our 2022 test, we found that, on identical servers, Pulsar outperformed Kafka significantly.

Download the benchmark to read the full test results.

See full report
2.5x

Maximum throughput

Pulsar is able to achieve 2.5x the maximum throughput compared to Kafka

100x

Lower Publish latency

Pulsar provides consistent single-digit publish latency that is 100x lower than Kafka

1.5x

Faster

Pulsar has a historical read rate that is 1.5x faster than Kafka

Kafka applications can run on Pulsar

StreamNative Cloud supports the native Apache Kafka protocol on Pulsar brokers. It means you can migrate your existing Kafka applications and services to Pulsar without modifying the code. This enables Kafka applications to leverage Pulsar’s powerful capabilities.

Learn more →

Enterprise-grade
multi-tenancy

Rebalance-free scaling

Tiered storage retention

Why consider Pulsar?

From its early days, Apache Pulsar was designed to address the limitations of Apache Kafka, especially in a cloud-native environment. There are several reasons to choose Pulsar over Kafka, including:

Rationalize your infrastructure and reduce your costs

Advanced features such as multitenancy, georeplication or infinite storage

Scale clusters  dynamically without downtime

Consistent low latencies and high throughput

Messaging use cases, like worker queues or event-driven applications

Pulsar vs. Kafka features comparison

Apache Pulsar vs. Apache Kafka: Which streaming technology is right for you?

Key features
Message retention (time-based)
Message replay
Message retention (acknowledge-based)
Built-in tiered storage
Processing capabilities
KStream
Pulsar Function
Processing capabilities (fully managed)
Queuing semantics (round robin)
Queuing semantics (key based)
Dead letter queue
Scheduled and delayed delivery
Topic compaction
Performance and scalability
Horizontally scalable
High availability (write)
Can only tolerate one node failure
Can tolerate many node failures
Rebalance-free scaling
Elastically scalable
Failure recovery
Max. number of topics
Up to 100k
Millions
Management features
Built-in multitenancy
Built-in georeplication
Built-in schema management
End-to-end encryption

Want to learn more?

Drop us a line. We'd love to chat.