As AI-infused applications emerge, messaging systems are becoming increasingly relevant for the delivery of real-world scalable production solutions. Apache Kafka enables real-time data ingestion, enrichment, and continuous model training at scale. ActiveMQ Artemis is vital for transactional and batch use cases or for specific standardized messaging protocols. With this in mind, it is important to understand the differences in these messaging system approaches.
Choosing the right tool for the job
ActiveMQ Artemis is an open source Java based message broker that supports multiple messaging protocols, clients, and topologies, making it a flexible option for point-to-point and pub-sub communication. It is designed for reliable message delivery in transactional systems, where it temporarily stores and then deletes messages once consumed. ActiveMQ supports volatile and durable event delivery, which is ideal for backend services or enterprise integration patterns that require order, reliability, and simplicity.
By contrast, Apache Kafka is an open source, real-time, event-streaming platform built for massive scale. It supports high throughput and low latency data pipelines with persistent storage and message ordering guarantees, supporting durable and replayable event streams. Kafka is a natural fit for cloud native apps, AI pipelines, and event-driven microservices.
Traditional messaging systems like ActiveMQ offer versatile messaging paradigms, functioning as a queue-based system or employing a publish-subscribe mechanism. In the queue-based, point-to-point model, a message sender (producer) places events on a specific destination and the broker dispatches messages to one or more designated receivers (consumers). Producers guarantee that once a broker accepts the message, it will deliver it to an available consumer at some point in the future. This disconnected architecture means that senders do not need to be aware of the intended recipient or recipients of a message.
Where ActiveMQ supports either a queue-based system or a publish-subscribe mechanism, Kafka is built solely around a scalable publish-subscribe mechanism. Producers publish events to a topic on the broker, and multiple consumers can independently subscribe and read from that topic. This architectural approach enables modern, decoupled application design where systems can react to the same event stream in parallel, feeding logs, metrics, or training data into observability platforms, databases, or ML pipelines.
Active MQ and Kafka facilitate the movement of various message types including volatile, durable, and replayable messages.
- Volatile messages (AMQ only): Once generated, the system delivers these events to all online consumers. If a consumer is offline, the message is lost. AMQ dispatches messages to consumers connected because the broker is aware of them. In Kafka, the broker is not really aware of the consuming client in the same way. Messages are not delivered to the client, they are read by the client from the topic (which is just a log). There is no record on the Kafka broker that anyone consumed the message.
- Durable messages (AMQ and Kafka): The AMQ broker persistently stores events until all registered consumers have successfully read and acknowledged them. This ensures no message is lost even if consumers are temporarily unavailable. Kafka has persistent storage with retention policies based on time limit or total size which ensures durability.
- Replayable messages (Kafka only): It stores past events for a configurable period, reprocessed for debugging, analytics or AI model training, which is critical for systems that must adapt and learn over time.
What to consider before choosing
ActiveMQ Artemis and Apache Kafka are integral components of Red Hat Application Foundations, working in conjunction with Red Hat OpenShift to form a robust, unified application platform. Understanding their distinct characteristics is crucial for selecting the appropriate tool to meet specific business requirements.
The following sections describe these characteristics and use cases for each tool.
Dumb broker, smart client
Apache Kafka follows a “dumb broker, smart client” where the broker handles the tasks of storage and replication of messages, and the client is responsible for message handling, routing, and applying transformation.
On the other hand, ActiveMQ follows a “smart broker, dumb clients” concept where more of the logic such as managing state, guaranteeing delivery, enabling internal routing rules (message processing) lives in the broker.
Replayability
Apache Kafka offers persistent storage and provides the ability to go back in time and replay certain messages.
ActiveMQ doesn’t offer replayability. With Active MQ, once the message is delivered to the consumers it is deleted, which works well for transactional systems where replay is unnecessary.
Push or pull model
ActiveMQ supports both push and pull models. The broker can send messages to the consumers as soon as it receives them or the consumers can pull messages from the broker as and when required, giving consumers more control.
Apache Kafka always follows a pull model, which provides greater scalability but shifts more responsibility to the client application. This is a tradeoff worth considering based on your architectural goals.
Volume of data
Apache Kafka can handle large volumes of continuous data and more transactions per second, but it requires a minimum of three brokers and three controllers (can be combined), spread across three relatively sizable nodes in order to provide guaranteed delivery and high availability.
In contrast, ActiveMQ uses a more centralized broker model that manages queues and topics, and is better suited for moderate-volume transactional data where the message flow is lower, and predictability of data is more important than elasticity. AMQ won't handle nearly the throughput. It only requires a single broker on small to modest hardware for its guaranteed delivery.
Message ordering guarantees
Both ActiveMQ and Apache Kafka offer message ordering guarantees. However, the way they offer it is different. When it comes to message ordering, Kafka’s approach is more straightforward, guaranteeing order within a single partition.
In contrast, ActiveMQ’s ordering guarantees are not as strict by default and can easily break in a multi-consumer setup, as the specifications it relies on do not enforce global ordering.
Stream processing
Apache Kafka offers built-in stream processing via the Kafka Streams API, which enables developers to filter, transform, and join event streams in real time. Apache Kafka also natively integrates with Apache Flink, offering advanced stream processing where you can enrich and transform data in real time. You can seamlessly feed the processed data into AI models to achieve use cases like recommendation engine, real-time analytics.
ActiveMQ does not have stream processing capabilities.
Messaging standards and protocols
ActiveMQ supports messaging standards like JMS 2.0 (Java Message Service), and messaging protocols like AMQP 1.0 ( Advance Message Queuing Protocol) and MQTT 5.X (Message Queuing Telemetry Transport), making it a strong choice for traditional enterprise Java applications that depend on transactional messaging. Apache Kafka only supports the Kafka protocol.
Apache Kafka is suitable when your goal is broader data movement, integration and transformation across hybrid cloud environments, with components like Kafka Connect, Mirror Maker 2 and the Kafka Streams API available that help simplify integration, disaster recovery, and stream processing.
Use cases for ActiveMQ
ActiveMQ Artemis is a versatile messaging system with specific strengths that make it suitable for particular scenarios. The following describe use cases for ActiveMQ:
- Order processing systems: In an e-commerce marketplace, when a customer places an order, it triggers different order processing actions such as payment and shipping. These transactional systems use ActiveMQ’s queueing capabilities since one consumer needs to consume and process each message before it’s removed from disk or memory. If the payment fails, the order does not ship and you can roll it back.
- IoT systems: In an enterprise environment (i.e., a railway network, cruise ships, or retail stores), there is a need to collect and process telemetry data from numerous remote IoT sensors. For example, in railways, there could be sensors on all the cars which speak protocols like MQTT and AMQP. When they pass by-way stations, they connect to a local ActiveMQ edge broker in the field and publish all the telemetry data they've been collecting up to that point. The ActiveMQ edge broker uses a connector or bridge to stream the aggregated telemetry data from the local queues or topics to a central hub (could be Kafka in some cases) for processing. This process ensures that data is not lost even if the connection to the central hub is intermittent.
Use cases for Apache Kafka
The following are use cases for Apache Kafka, including website activity tracking, fraud detection, and AI model training and data enrichment.
- Website activity tracking: You can use Apache Kafka to track user activity, such as clicks, searches, or profile views across websites in real time. It can ingest, store, stream, and process large volumes of clickstream data to multiple services at once. You can use these events to fuel dashboards, user personalization engines, or machine learning pipelines that surface recommendations based on user behavior in real time.
- Real-time data analysis and anomaly detection: In modern financial systems, detecting unusual activity requires processing massive streams of transactional data in real time. Apache Kafka supports this continuous data flow—ingesting, correlating, and enriching events with high throughput and low latency. Its persistent storage log allows organizations to replay historical data to retrain or fine-tune analytical models, improving accuracy over time. By integrating with stream processing frameworks such as Apache Flink, Kafka supports real-time data transformation and feature generation before feeding AI or machine learning models for anomaly detection and behavioral analysis.
- AI model training and data enrichment: Kafka plays a key role in building AI datasets by capturing real time event streams from various sources including sensor data, user behaviors, or system logs, and feeding them into data lakes or feature stores. You can enrich, filter, and label in motion these real-time streams to enable continuous model training or fine tune machine learning models, helping teams operationalize AI faster and more reliably.
Built for now, ready for what’s next
As application development shifts to support real-time data pipelines and AI driven workloads, selecting the right messaging technology becomes increasingly important. ActiveMQ is a strong choice when your priority is transaction-centric messages and ordered delivery, particularly in point-to-point messaging or batch processing environments. Apache Kafka is ideal when you need a real-time data streaming platform that can handle high volumes of data (throughput), and offers persistent storage, low latency, and the ability to replay data for analytics and ongoing AI model training.
These technologies serve different needs but also work well together. The good news is that Red Hat has you covered whichever route you choose. These essential tools are a part of Red Hat Application Foundations, a modular, fully supported suite of middleware tools that helps developers build, modernize, and integrate cloud-native, AI-ready applications. When combined with Red Hat OpenShift, you have a powerful, unified application platform that is built for today’s needs and ready for what comes next.
