Skip to main content

The RabbitMQ Ecosystem

Developers wanting to leverage a message queue in their software architecture have multiple options. One popular option is RabbitMQ, an open-source message-queuing software that acts as an intelligent middleman for routing messages. RabbitMQ is a compelling choice for teams that need support for multiple protocols, advanced messaging features, or fine-grained control over their messaging environment.

RabbitMQ – “One broker to queue them all”​

Most accurately, RabbitMQ is considered a message broker. This means it can act as a traditional message queue, but it can also support other message routing patterns, including the publish-subscribe model, routing based on topics, and fan-out delivery.

Just as a post office routes letters to mailboxes, RabbitMQ uses exchanges to route messages to the appropriate queues for consumption. Managing the logic and transport of messages, RabbitMQ uses message queues to reliably route messages between producers and consumers. While RabbitMQ is feature-rich, it is also a lightweight option that can be self-hosted via containers or virtual machines.

Exchange Animation

For these reasons, RabbitMQ is beloved by the developer community. It’s used by companies ranging from small startups to large enterprises like Reddit, Shopify, and Mozilla to power their asynchronous workflows.

Challenges with Managing RabbitMQ​

Although RabbitMQ is a powerful choice for a message queue, running it in a production environment can be complex. RabbiMQ places much of the operational burden on the developer. This includes provisioning, configuring, securing, networking, persisting, scaling, and monitoring RabbitMQ servers. Managing these operational tasks can quickly become tedious when a message queue is just one of many parts of your software ecosystem.

For teams wanting to abstract away this management overhead, a do-it-yourself solution is to build internal tooling and infrastructure to handle RabbitMQ deployment and upkeep themselves. This can involve writing custom scripts for provisioning, setting up dashboards for monitoring, and maintaining backup and recovery systems. While this do-it-yourself approach offers flexibility and full control, it can be costly in engineering hours and can increase the risk of misconfiguration or downtime, especially as systems scale.

RabbitMQ-as-a-Service​

In response to the challenges of managing RabbitMQ instances in production, services began emerging to simplify the experience. These services are known as managed message brokers.

Competition

Managed message broker services allow developers to treat RabbitMQ instances more like a Software-as-a-Service. Effectively, these platforms deliver RabbitMQ-as-a-Service. Managed message broker platforms are popular because they abstract away operational overhead, allowing developers to focus on using the RabbitMQ broker in their applications.

The two most notable solutions in this space are CloudAMQP and AmazonMQ. Other popular managed message brokers for RabbitMQ include A2 Hosting, Stackhero, Elestio, ScaleGrid, and Northflank. Managed message broker solutions frequently include the following helpful features:

  • Cloud-hosted RabbitMQ instances
  • Geographic deployment
  • One-click provisioning
  • Custom Management UI
  • Server Backups
  • Managed Firewalls
  • Monitoring
  • Automatic Upgrading

Limitations of Existing Solutions​

While DIY solutions and commercially managed message queues both can solve common RabbitMQ challenges, they each come with a unique set of drawbacks. As mentioned previously, DIY solutions offer full control but require significant time and expertise to maintain and scale.

For the commercial RabbitMQ-as-a-Service solutions, a major drawback for developers is the limited ownership, transparency, and control over the message queue infrastructure. Platforms like CloudAMQP and Amazon MQ wrap RabbitMQ in a black-box, which limits access to deeper system-level details, customization, and troubleshooting.

For example, if a queue starts backing up due to an unacknowledged message or a misconfigured consumer, developers may have little visibility into the cause. It can be much harder to adjust prefetch settings, inspect messages in the queue, or modify retry policies. In many cases, resolving these issues requires reaching out to customer support, which often comes at an extra cost. This lack of full control makes troubleshooting and optimization harder for developers.

Additionally, many managed service providers obscure the details of the infrastructure and hardware they offer. Commercial services often obscure the pricing of their plans by charging flat rates based on abstracted performance tiers. This means that with CloudAMQP and AmazonMQ, teams can often pay for much more than they use.