Kafka vs Pulsar vs Redpanda: Choosing a Streaming Platform

Kafka, Pulsar, and Redpanda all solve the same problem — durable, ordered, replayable event streams — with fundamentally different architectures. Kafka is the incumbent with the largest ecosystem. Pulsar separates storage from compute for elastic scaling. Redpanda is Kafka-compatible with a simpler, faster core. This guide compares the architectures and the decisions that follow from them.

The Architecture Difference

Kafka: brokers store data on local disks, replicated across brokers. Storage and compute are coupled: every broker does both.

producer ──► broker (compute + storage) ──► consumer
                 │ local disk, replicated

Pulsar: brokers handle compute; storage lives in a separate bookkeeper cluster. The two scale independently.

producer ──► broker (compute) ──► bookkeeper (storage) ──► consumer

Redpanda: like Kafka but written in C++ with a thread-per-core model, no JVM, and no ZooKeeper. Storage and compute are coupled, like Kafka.

producer ──► broker (C++, compute + storage) ──► consumer

Feature Comparison

Feature	Kafka	Pulsar	Redpanda
Protocol	Native	Native	Kafka API
Storage	Local disk	BookKeeper	Local disk
ZooKeeper/KRaft	KRaft (new)	None	None
Multi-tenancy	Basic	Native	Basic
Geo-replication	MirrorMaker	Native	Native
Tiered storage	Yes	Yes	Yes
Exactly-once	Yes	Yes	Yes
Ecosystem	Largest	Growing	Kafka-compatible

Kafka: The Incumbent

Kafka's ecosystem is its moat: Kafka Connect, Kafka Streams, ksqlDB, and every observability and data tool integrates with it. If you need connectors, stream processing, or team familiarity, Kafka is the safe choice.

from kafka import KafkaProducer, KafkaConsumer
import json

producer = KafkaProducer(
    bootstrap_servers="localhost:9092",
    value_serializer=lambda v: json.dumps(v).encode(),
)

producer.send("orders", {"order_id": 4821, "amount": 120.0})
producer.flush()

consumer = KafkaConsumer(
    "orders",
    bootstrap_servers="localhost:9092",
    auto_offset_reset="earliest",
    group_id="order-processor",
    value_deserializer=lambda v: json.loads(v),
)

for message in consumer:
    print(message.value)

Operational notes: Kafka needs ZooKeeper (or KRaft in newer versions), JVM tuning, and careful partition planning. It is battle-tested but not simple.

Pulsar: Elastic Storage

Pulsar's separation of storage and compute solves two real problems:

1. Elastic scaling: add brokers without moving data; add bookies without rebalancing partitions.
2. Multi-tenancy: namespaces and quotas make it natural for shared infrastructure.

from pulsar import Client

client = Client("pulsar://localhost:6650")
producer = client.create_producer("persistent://public/default/orders")
producer.send(("order-4821").encode())

consumer = client.subscribe(
    "persistent://public/default/orders",
    subscription_name="order-processor",
)
msg = consumer.receive()
print(msg.data())
consumer.acknowledge(msg)

Pulsar's cost is complexity: two systems to operate (brokers + bookies), and a smaller ecosystem than Kafka. Choose it when you need elastic storage scaling or hard multi-tenancy.

Redpanda: Kafka Without the JVM

Redpanda speaks the Kafka protocol, so existing Kafka clients work unchanged, but the core is a single C++ binary with no ZooKeeper and no JVM. The result is lower latency, simpler operations, and lower resource usage.

# Same Kafka client code works against Redpanda
from kafka import KafkaProducer

producer = KafkaProducer(bootstrap_servers="localhost:9092")
producer.send("orders", b'{"order_id": 4821}')
producer.flush()

# Redpanda config: one binary, no ZooKeeper
redpanda:
  data_directory: /var/lib/redpanda/data
  rpc_server:
    address: 0.0.0.0
    port: 33145
  kafka_api:
    - address: 0.0.0.0
      port: 9092
  developer_mode: true

Choose Redpanda when you want Kafka compatibility with a fraction of the operational burden: smaller teams, self-hosted deployments, or latency-sensitive workloads.

Geo-Replication

• Kafka: MirrorMaker 2 replicates between clusters. It is batch-oriented and adds operational complexity.
• Pulsar: native geo-replication with per-topic replication policies and low-latency propagation.
• Redpanda: native geo-replication with leader placement per region.

If multi-region replication is a core requirement, Pulsar and Redpanda's native support beats Kafka's bolt-on MirrorMaker.

Tiered Storage

All three support tiered storage: hot data on local disk, cold data in object storage (S3, GCS). This is the answer to "we need to retain everything but cannot afford the disks." The implementations differ in maturity, but the pattern is the same:

hot segments (local disk) ──► cold segments (S3)

Decision Guide

Your situation	Choose
Largest ecosystem, team familiarity	Kafka
Elastic storage scaling, hard multi-tenancy	Pulsar
Kafka compatibility, simpler operations	Redpanda
Multi-region replication as a core feature	Pulsar / Redpanda
Small team, self-hosted, latency-sensitive	Redpanda
Enterprise managed service	Kafka (Confluent) / Redpanda Cloud

Implementation Checklist

• Estimate throughput, retention, and consumer count
• Check whether storage and compute need to scale independently
• Evaluate multi-tenancy requirements before choosing
• Test geo-replication if multi-region is required
• Prototype with your actual client libraries
• Count operational hours: JVM/ZooKeeper vs single binary
• Plan tiered storage for long retention

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