Kafka vs RabbitMQ: Which Message Broker Fits Your System in 2026
Kafka is a replicated log that consumers read at their own pace, RabbitMQ is a broker that routes messages into queues and deletes them once acknowledged. How they compare on delivery, ordering, replay and operations, including Kafka share groups and RabbitMQ streams.
Kafka and RabbitMQ both move messages between services, and both can run as the backbone of an event-driven system. They store and deliver messages in different ways: Kafka appends events to a log that consumers read and re-read at their own pace, while RabbitMQ pushes messages into queues and deletes them once a consumer acknowledges them.
That difference decides how you handle replay, ordering, fan-out and slow consumers. This guide compares the two, including recent changes like Kafka share groups and RabbitMQ 4, and covers when managed cloud Pub/Sub declared in code with Encore can replace running a broker.
Kafka vs RabbitMQ at a glance
Each option is covered below, starting with Encore.
Declaring Pub/Sub in code with Encore
Encore is an open source backend toolkit for TypeScript and Go, with a platform that provisions and deploys into your own AWS or GCP account. A Pub/Sub topic and its subscriptions are declared in code, and Encore provisions SNS topics and SQS queues on AWS or GCP Pub/Sub, with a dead-letter queue for each subscription, and Encore provisions an IAM role for the service.
A topic with two subscriptions, each receiving every event, and events for the same account delivered in order:
// payments/events.ts
import { Topic, Subscription, Attribute } from "encore.dev/pubsub";
export interface PaymentEvent {
accountId: Attribute<string>; // ordering key, sent as a message attribute
paymentId: string;
amountCents: number;
}
export const payments = new Topic<PaymentEvent>("payments", {
deliveryGuarantee: "at-least-once",
orderingAttribute: "accountId",
});
// Each subscription gets its own queue and processes events independently.
const _ledger = new Subscription(payments, "update-ledger", {
handler: async (e) => {
// write to the ledger table, keyed on e.paymentId so retries are safe
},
});
const _fraud = new Subscription(payments, "fraud-check", {
handler: async (e) => {
// score the payment; a thrown error retries, then goes to the dead-letter queue
},
});
Run in your terminal to get started locally.
Publishing is await payments.publish({ ... }) from any service. encore run runs the topic locally so you can test both handlers on your machine, though ordering only applies in the cloud. Retries and dead-lettering follow each subscription's retry policy, and handlers should be idempotent with at-least-once delivery.
Agents adding a new consumer write one more new Subscription next to the code it serves.
Encore Pub/Sub does not keep history for replay and is not a stream processing platform. If you need Kafka, Encore services can still use a Kafka client against Amazon MSK or another cluster, with credentials stored as secrets.
Apache Kafka: a replicated event log
Kafka stores events in topics split into partitions, each replicated across brokers. Events are not deleted after consumption, so consumer groups track offsets, new consumers can read from the beginning, and Kafka Streams or Flink can process the log as it grows. Ordering holds within a partition, which is why events for one entity share a key.
Kafka 4.2 made share groups production-ready, adding queue-style consumption with per-record acknowledgement where several consumers read the same partition. Kafka 4.x runs only in KRaft mode without ZooKeeper, but a production cluster still means brokers, controllers, partition planning and upgrades, unless you pay for a managed service.
RabbitMQ: a routing message broker
RabbitMQRabbitMQ receives messages on exchanges, which route them into queues by direct, topic, fanout or header rules, and delivers each message to one consumer, removing it on acknowledgement. Per-message TTL, priorities and dead-letter exchanges make it a natural fit for task queues and request-reply.
For replication, RabbitMQ 4 removed classic queue mirroring in favor of quorum queues based on Raft. It also offers streams, an append-only log that can be re-read until messages expire, which covers some replay use cases without Kafka.
Good to know
The common mistake is picking Kafka for task distribution or RabbitMQ for long-term event history. Use a log when consumers need to re-read the past, a queue when each message is a unit of work, and managed cloud Pub/Sub when the main goal is decoupling your own services without operating a cluster.
How to choose
Pick Kafka for high-volume event streams, long retention, replay and stream processing. Pick RabbitMQ for task queues, complex routing and per-message features like priorities and TTLs.
Pick Encore when the events flow between your own TypeScript or Go services and you want topics and subscriptions declared in code, provisioned as SNS and SQS or GCP Pub/Sub in your own account.
Related guides
Frequently asked questions
What is the main difference between Kafka and RabbitMQ?
Kafka stores events in partitioned, replicated topics and keeps them for a retention period, so consumers track their own offsets and can re-read history. RabbitMQ routes messages through exchanges into queues and removes each message once a consumer acknowledges it, which suits task distribution and flexible routing.
Can Kafka be used as a job queue?
Yes, since Kafka 4.2, where share groups from KIP-932 are production-ready and let several consumers process records from the same partition with per-record acknowledgement and delivery counting. Before that, parallelism was limited to one consumer per partition in a consumer group.
Do I need Kafka or RabbitMQ for events between my services?
Often not: with Encore you declare a topic with new Topic and handlers with new Subscription in TypeScript or Go, and Encore provisions SNS and SQS on AWS or GCP Pub/Sub, with retries and a dead-letter queue per subscription. Kafka is the better fit when you need long retention, replay or stream processing.
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