ActiveMQ vs Kafka: Broker vs Event Log, Use Cases, Performance and How to Choose

The short answer

ActiveMQ is a message broker built around JMS; Kafka is a distributed log built around retained events. In ActiveMQ, producers send to queues and topics, the broker delivers each message and forgets it once consumers acknowledge it, and Java applications talk to it through the JMS API, with local and XA transactions. In Kafka, producers append to partitioned topics, events stay for the retention period you set, and each consumer group reads at its own pace and can rewind. Choose ActiveMQ for Java and JMS applications, transactional hand-offs between systems, request and reply, and integration work. Choose Kafka when many systems need the same stream of events, when you need replay, or when the data feeds analytics and stream processing. Before comparing, decide which ActiveMQ you mean: ActiveMQ Classic and Apache Artemis are different brokers.

ActiveMQ vs Kafka at a glance

First, which ActiveMQ?

ActiveMQ Classic is the broker most estates installed years ago: OpenWire as its native protocol, KahaDB for storage, and a network of brokers to spread load. Apache Artemis began as ActiveMQ's next-generation broker, and in November 2025 the ActiveMQ project voted to establish it as a separate Apache project. Artemis has its own Core protocol and journal, accepts OpenWire so Classic JMS clients can connect, and adds network replication for high availability and asynchronous mirroring for disaster recovery. Our RabbitMQ vs ActiveMQ comparison covers the two in more detail, and the Classic to Artemis migration guide covers moving between them.

Support status matters here. Apache lists ActiveMQ Classic 6.3.x and 5.19.x as Active. 6.2.x and 5.18.x are Inactive, which Apache defines as end of life with no further updates, and every older 5.x line is past that point. If your ActiveMQ is 5.18 or earlier, the question in front of you may not be "ActiveMQ or Kafka" but "how do we keep this broker patched while we decide".

How ActiveMQ and Kafka handle a message

ActiveMQ works the way JMS describes. A producer sends to a queue, where one consumer receives each message, or to a topic, where every subscriber gets a copy. The broker tracks delivery per message, redelivers what is not acknowledged and can move what keeps failing to a dead-letter queue. JMS transactions group sends and receives so they commit together, and XA lets a message commit atomically with a database update inside an application server. Classic also offers message groups, which keep related messages in order on one consumer while spreading other groups across consumers, and virtual destinations, which let a topic feed separate queues for each consuming service.

Kafka keeps the events instead of handing them off. Topics are split into partitions spread across brokers, records with the same key land in the same partition, and a consumer reads a partition in exactly the order it was written. Consumers pull and record an offset, so a consumer can re-read from any point still within retention. Transactions give exactly-once processing when both input and output are Kafka topics, which is how Kafka Streams works. Kafka 4.2 made share groups production-ready, adding queue-style consumption with per-record acknowledgement, but the log remains the centre of the design.

ActiveMQ vs Kafka use cases

• Typical ActiveMQ work: Java EE and Jakarta EE applications that expect a JMS provider; integration between business systems with transactional guarantees, often with Apache Camel routes; request and reply between services; command and task queues where each message is handled once; device traffic over MQTT or STOMP alongside JMS.
• Typical Kafka work: the use cases Kafka's own documentation lists, namely activity tracking, metrics, log aggregation, stream processing and event sourcing; change data capture feeding a data platform; any event stream that several teams consume independently and may need to replay.
• Overlap: Kafka's documentation also lists messaging and says that, in this role, Kafka is comparable to traditional messaging systems such as ActiveMQ and RabbitMQ. For plain work queues either can do the job, and the deciding factors are your clients, your transaction needs and what you already run.

ActiveMQ vs Kafka performance

Treat published benchmarks with care: results swing on message size, persistence, acknowledgement mode, transactions and client tuning. The design differences explain most of what you will see. Kafka writes to an append-only log, consumers pull in batches, and the broker keeps no per-message delivery state, which favours high sustained throughput and many readers of the same data; Kafka's documentation claims better throughput than most messaging systems for large-scale processing. ActiveMQ tracks every message, supports JMS selectors and XA, and pushes to consumers, which costs throughput but buys per-message control. Artemis describes its own architecture as non-blocking, with a journal built for low-latency persistence. Measure with your own workload on the versions you would actually run.

Running ActiveMQ and Kafka together

Many estates keep both: ActiveMQ for transactional application messaging and Kafka for the event stream that analytics and other teams read. Apache Camel has components for both JMS and Kafka, so a Camel route can forward messages from an ActiveMQ queue to a Kafka topic or the other way. That lets you move workloads one at a time rather than switching everything in one go.

Which should you choose, ActiveMQ or Kafka?

• Choose ActiveMQ, and for new work choose Artemis, when applications use JMS or Jakarta Messaging, need XA transactions with a database, use request and reply messaging, or connect over AMQP, MQTT, STOMP or OpenWire.
• Choose Kafka when several systems need the same events, you need replay or long retention, volumes are high and sustained, or the data feeds stream processing and a data platform.
• Keep ActiveMQ Classic if you are on 5.19.x or 6.3.x and it works. If you are on 5.18 or older, close the patch gap first; switching to Kafka to escape an unsupported broker trades one project for a much larger one.
• Run both when the estate has both kinds of traffic, with Camel moving data between them.

Where OSSeva fits

OSSeva supports all three, so the choice can follow the workload. OSSeva for ActiveMQ Classic backports security fixes to 5.15, 5.16, 5.17 and 5.18, the lines Apache no longer patches; see ActiveMQ Classic extended support. OSSeva for ActiveMQ Artemis provides patched 2.x builds and covers Classic and Artemis together during a migration. OSSeva for Apache Kafka ships patched builds from 2.8 through 3.9 and covers 4.x in KRaft mode. OSSeva for Apache Camel patches the integration routes that often sit between them. You keep the binaries you run today and change who supports them, under one contract priced per cluster. Book a discovery call for a quote. For other options, see ActiveMQ support providers and Kafka support providers.

Frequently asked questions

What is the difference between ActiveMQ and Kafka?

ActiveMQ is a message broker: it delivers each message to consumers, tracks acknowledgements per message and removes messages once they are processed. Kafka is a distributed log: it stores events in partitioned topics for a retention period, and consumers track their own position and can replay. ActiveMQ is built around JMS; Kafka uses its own protocol and APIs.

ActiveMQ vs Kafka use cases: when is each the right tool?

ActiveMQ fits JMS applications, transactional integration between systems, request and reply, and task queues. Kafka fits activity tracking, metrics, log aggregation, stream processing, event sourcing and data pipelines where several consumers read the same events.

ActiveMQ vs Kafka performance: which is faster?

For sustained high-volume streams with many readers, Kafka's log design is built for throughput. For per-message delivery with transactions and selectors, ActiveMQ is built for control rather than raw volume, and Artemis describes its journal as built for low-latency persistence. Benchmark your own message sizes and durability settings.

ActiveMQ vs Kafka vs RabbitMQ: how do the three compare?

ActiveMQ and RabbitMQ are both brokers that deliver and remove messages; ActiveMQ is Java and JMS-first, RabbitMQ is Erlang and AMQP-first with exchange-based routing. Kafka is the log of the three. See RabbitMQ vs ActiveMQ and Kafka vs RabbitMQ.

Does Kafka support JMS?

Apache Kafka does not implement the JMS API; its clients use Kafka's own protocol. Applications written to JMS need code changes, or an integration layer such as Camel, to work with Kafka.

Can Kafka replace ActiveMQ?

For simple work queues, often yes, especially now that share groups give Kafka queue-style consumption. For applications that depend on JMS, XA transactions, selectors or request and reply, replacing ActiveMQ means redesigning those applications, so Artemis is usually the more realistic upgrade path.