Kafka vs RabbitMQ: Differences, Use Cases and How to Choose

Kafka and RabbitMQ both move messages between services, so they often end up on the same shortlist. They were built for different jobs, though, and picking the wrong one tends to show up later as workarounds. This guide compares how each one stores and delivers data, where each fits best, what Kafka’s share groups change, and how teams move from RabbitMQ to Kafka.

If you are new to Kafka itself, start with what Apache Kafka is and come back here. Versions referenced below are Apache Kafka 4.4 and RabbitMQ 4.4, both released in October 2026.

What is the core difference between Kafka and RabbitMQ?

The core difference is a log versus a queue.

Kafka stores every record in an append-only log, split into partitions and replicated across brokers. Consumers pull records and track their own position, called an offset. Reading a record does not remove it. Records stay until the topic’s retention time or size limit removes them, so any number of consumer groups can read the same data, and any of them can rewind and replay it.

RabbitMQ’s classic and quorum queues work the other way round. Producers publish to an exchange, which routes each message to one or more queues using bindings. The broker pushes messages to consumers, and a message leaves the queue once a consumer acknowledges it. The queue is designed to drain towards empty, not to keep history.

Picture the two side by side. In Kafka, one topic sits in the middle and several independent readers, such as billing, analytics and search, each keep their own bookmark in it. In RabbitMQ, an exchange sits in the middle and copies or routes each message into separate queues, and workers on each queue compete to take messages off it.

There is one important exception. Since version 3.9, RabbitMQ also offers streams, an append-only, replicated log with non-destructive reads. RabbitMQ’s own documentation says streams were added to complement queues, not replace them, for large fan-outs, replay and large backlogs. So “RabbitMQ deletes messages once consumed” is true for queues, but not for streams.

How do they compare on throughput, ordering and delivery?

The table below compares the default models: Kafka topics and consumer groups against RabbitMQ classic and quorum queues.

On throughput, avoid trusting any single headline number. Results depend on message size, durability settings, replication factor, batching and hardware, and most published benchmarks are run by a vendor with an interest in the result. Kafka’s design favours sustained, high-volume streams because it batches writes and serves reads sequentially from disk. RabbitMQ’s own streams guide states that its persistent queue types cannot compete on throughput with log-based messaging systems, which is one reason streams exist. If throughput decides your choice, benchmark both with your own message sizes and durability settings.

Ordering is where teams most often get caught out. Kafka only orders records within a partition, so records that must stay in order need the same key. RabbitMQ preserves queue order for a single consumer, but redeliveries, priority queues and several competing consumers all change the order in which messages are processed.

Key takeaway: Pick Kafka when events need to be stored, replayed and read by many teams; pick RabbitMQ when you need a work queue with flexible routing.

When should you use RabbitMQ instead of Kafka?

RabbitMQ is often the simpler and better choice for these jobs:

• Task queues. Background jobs such as sending emails or resizing images, where each job should be done once by one worker and then forgotten.
• Request and reply (RPC). RabbitMQ supports reply-to queues and correlation IDs, a pattern that is awkward to build on a log.
• Per-message routing. Topic and headers exchanges route each message by routing key or header, without creating a topic per destination.
• Priorities. Classic queues support priorities from 0 to 255, and from RabbitMQ 4.3 quorum queues offer strict priority with 32 levels, according to the priority queue guide.
• Poison message handling. Quorum queues track redeliveries and apply a delivery limit, then drop or dead-letter the message.
• Low, bursty volumes. When traffic is modest and nobody needs history, a small RabbitMQ cluster is usually less to run than a Kafka cluster.

Kafka is the stronger fit when several teams need the same events, when you need to rebuild state by replaying history, or when the data feeds stream processing, data lakes and change data capture. It is also the natural backbone for event-driven architectures where events are the record of what happened, not just instructions to a worker.

Many organisations run both: RabbitMQ for internal work queues inside a service, and Kafka as the shared event backbone between services.

Does Kafka 4.0 change the answer with share groups?

It narrows the gap, but it does not remove it.

Share groups come from KIP-932, “Queues for Kafka”. They let several consumers read from the same partition at once. The broker hands out records under a time-limited lock and counts delivery attempts. Each consumer can then accept, release or reject every record individually. That gives Kafka queue-like, competing-consumer behaviour without tying parallelism to the partition count.

The feature has matured quickly across releases:

With Kafka 4.4, records that exceed the maximum delivery count, or that a share consumer rejects, can go to a dead-letter topic once the cluster’s share.version feature is upgraded to 2. That closes one of the clearest gaps with RabbitMQ.

What share groups do not add is RabbitMQ-style routing, per-message priorities or request and reply. They also give up ordering within a partition. So if your main need is a simple work queue, RabbitMQ is still a reasonable answer. If you already run Kafka and want to avoid a second broker for queue-style work, share groups are now a serious option. Our guide to queues in Kafka 4.0 via share groups covers how they work and how to enable them.

How do you migrate from RabbitMQ to Kafka?

Treat it as a redesign of your messaging model, not a lift and shift. The main steps are:

1. Map exchanges to topics. Each routing pattern needs a topic design. Fan-out exchanges usually become one topic read by several consumer groups. Topic exchanges with many routing keys may become several topics or one topic with filtering in consumers.
2. Choose keys for ordering. Decide which entity, such as an order or account ID, must stay in order, and use it as the record key. This is the decision that is hardest to change later, so spend time on designing Kafka events up front.
3. Pick a cut-over pattern. A bridge connector copies messages from RabbitMQ queues into Kafka topics while producers stay unchanged. Confluent’s RabbitMQ Source connector gives at-least-once delivery and keeps per-queue order in its default mode; it is a commercial connector after a 30-day trial. The alternative is dual writes from producers, which needs care to keep both sides consistent.
4. Rewrite consumers. RabbitMQ consumers acknowledge each message. Kafka consumers commit offsets, so retries, dead-lettering and idempotent processing need to be designed again, or handled with share groups.
5. Test with production-like traffic. Replay real message volumes, check ordering per key, and rehearse failure cases such as consumer crashes and broker restarts before switching producers over.

Because the connector delivers at least once, consumers on the Kafka side should handle duplicates. Run both systems in parallel until lag, error rates and business checks match, then retire the RabbitMQ queues one by one.

If you would like help planning the move, our Kafka consulting services cover architecture reviews and migrations, and Kafka support covers clusters once they are live.

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