RabbitMQ vs SQS: Key Differences Between the Two Message Brokers
Modern applications are rarely monolithic; they rely on microservices, which need to communicate with each other. For simple applications, you can make do with direct communication, such as using REST APIs. However, this becomes less and less feasible as the system grows more complex.
That’s where you use message brokers. These messaging systems facilitate decoupled, asynchronous communication between services, allowing you to create complex interactions.
Of course, not all message brokers are equal, so which one should you pick? Today, we’re comparing two popular options: RabbitMQ vs SQS.
But, before we do that, what features should we be looking at?
Factors to Consider When Choosing Between RabbitMQ and AWS SQS
All message brokers act as an intermediary between services in your application, allowing them to exchange information without being directly connected. What sets each apart is how they handle this communication, according to their design, capabilities, and operational demands. Choosing the right one depends on understanding how these factors work together and how they fit in with your application’s needs.
Here’s what to look at:
Messaging Patterns and Ordering
One of the first things you need to consider is the broker’s communication model. Some excel at point-to-point communication, while others support more advanced patterns like publish/subscribe or request-response.
Message ordering is another important consideration. Does your application need messages delivered in the exact sequence they were sent, or is unordered, reliable delivery sufficient?
Message Routing
An event-driven architecture requires flexible routing capabilities for a more complex system design, particularly when using SQS for message delivery. Different messages need to reach different services based on criteria such as message content, tags, or topics. Others are more limited and assume a single, fixed destination per message.
Think about whether your application needs to dispatch messages dynamically to multiple consumers or whether a simpler, single-receiver setup is enough.
Scalability and Performance
It’s one thing if your system will only ever handle a fixed number of messages. In reality, however, you can expect your application to grow, along with the number of messages it processes. Some brokers can handle this growth without any input from you. Others might require constant supervision and provisioning for scaling horizontally or vertically.
Also, how well does the message broker perform when scaled up? Does it start dropping messages during traffic spikes? Does it have a maximum throughput? Do you need to change the infrastructure to accommodate scaling?
It might be possible to configure a broker to handle higher throughput, but you don’t want its performance to degrade as a result.
Delivery Guarantees and Reliability
Other factors differentiating message brokers are how they guarantee delivery and what happens to the message after it’s sent. These are the three delivery models:
At-most-once delivery: In this system, the message is delivered zero or one time. There are no retries if the message fails to deliver. It might be lost but never duplicated.
At-least-once delivery: The broker will retry sending the message until it receives an acknowledgement from the consumer. As a result, this model may deliver duplicate messages.
Exactly-once delivery: This is the ideal model, where the message is reliably delivered without any duplication. However, you’ll find it difficult to achieve this in distributed systems.
In addition to the delivery model, you must also look at how the broker handles messages in case of failure. Are they held in memory or persisted to disk? What happens if the consumer crashes after receiving a message but before processing it? Does the broker have dead-letter queues (DLQ) for failed deliveries?
Security and Compliance
Data privacy is a concern for businesses, especially when sensitive information is handled. As such, your message broker should offer features like:
Encryption: Is the data encoded so it can’t be easily read by unauthorised individuals during transit and at rest?
Access control: Can you define who can publish and consume information from a queue?
Authentication: Does it integrate with identity providers or use API keys to ensure consumers are who they say they are?
Audit logging: Does the broker keep logs of who accessed what information and when?
Cost and Licensing
There are two main expenses to consider when implementing a message broker: licensing and operational costs. If it’s open-source, you don’t need to pay to use it, and you could modify its source code. However, redistribution might be limited depending on the licence terms.
If it’s commercial, you pay a fixed monthly fee, as opposed to usage-based, where your invoice may vary from month to month.
Additionally, operational costs can also depend on the resources your message broker uses. You must consider how much DevOps effort is baked into your expenses, which brings us to…
Operational Complexity
Some message brokers are “fire and forget”, where you set them up and they keep going without any issues. Others require monitoring and active reconfiguration to keep them working smoothly.
How complex a message broker is to operate depends on the effort it takes to deploy, maintain, and scale it in the production environment. A fully managed broker is easier to run. A self-managed one needs more work, but it does give you more flexibility and control.
Integration Ecosystem
By definition, a message broker sits between services and helps pass information in the form of messages. To do so, it should integrate well with your existing stack. That includes:
Protocol support: What sort of standards does it support, and does it rely on vendor-specific APIs?
Client libraries: Are there official or mature third-party libraries for the programming languages you use? Also, consider whether the broker has native support or deeper integration with certain languages or runtimes.
Cloud and tooling integrations: Can the broker integrate with your CI/CD pipeline, monitoring tools, or serverless functions? Does it work with Kubernetes or container platforms?
Extensibility: Does the broker support plugins, adapters, or custom extensions to fit edge cases?
Community and ecosystem maturity: Is the broker well-documented? Are there community-supported tools, dashboards, or connectors available?
Comparing RabbitMQ vs AWS SQS
What Is RabbitMQ?
RabbitMQ is an open-source message broker that implements the AMQP (Advanced Message Queuing Protocol). It also supports other messaging protocols such as STOMP (Simple (or Streaming) Text-Oriented Messaging Protocol) and MQTT (Message Queueing Telemetry Transport) via plugins.
Whilst not natively scalable, it can be extended quite easily with some strategic management. It’s also protocol-agnostic, which makes it a good fit for a variety of architectures and messaging patterns. It’s completely user-managed, which makes it very flexible.
How flexible?
Firstly, you can deploy it anywhere: on-premises, in virtual machines, or in the cloud. Secondly, you have complete control over how queues, exchanges, and routing are configured. (Find out how to set it up with Amazon Web Services Cognito.)
RabbitMQ is known for its rich support for messaging patterns (like publish/subscribe, routing, and work queues). Developers love it because it gives them control over reliability mechanisms such as acknowledgements, retries, and persistence.
However, as we said, it is a self-managed system by default. You’re responsible for setup, maintenance, scaling, monitoring, and fault tolerance (although hosted versions—like CloudAMQP—are also available).
This highly available message broker is widely used in applications where custom routing logic or protocol flexibility is required. This includes microservices-based systems, task queues, background processing, and event-driven architectures.
| Learn more about the difference between RabbitMQ queues and streams. |
What Is Amazon SQS?
Amazon Simple Queue Service (SQS) is a hosted message queuing system offered by AWS. It takes care of all the infrastructure behind the scenes, so you don’t have to think about servers, scaling, or maintenance tasks.
This simple and dependable message broker offers two queue types. Standard queues are built for high throughput and guarantee that messages will be delivered at least once, though the order isn’t always preserved. FIFO queues (First-In, First-Out) ensure messages are processed in the exact order they’re sent—and only once—but they trade off a bit on performance.
Since it’s part of the AWS ecosystem, SQS easily integrates with services like Lambda, SNS, and CloudWatch. It uses IAM for permissions and keeps messages safe by storing them in multiple availability zones automatically.
If you’re already on AWS and want something that just works without much overhead, SQS is a solid choice. It’s perfect for serverless apps, elastic workloads, or systems where you’d rather not spend time managing infrastructure.
With that introduction out of the way, let’s look at the difference between RabbitMQ vs SQS.
RabbitMQ vs SQS: A Side-by-Side Comparison
Messaging Patterns and Ordering
RabbitMQ supports multiple messaging patterns out of the box: point-to-point, publish/subscribe, routing, and RPC-style interactions. It handles message ordering within a queue. However, it doesn’t guarantee global ordering across queues.
In contrast, SQS offers simpler messaging models, with standard queues as the default and FIFO queue options available when you need strict ordering.
Message Routing
RabbitMQ excels at flexible and complex routing through exchanges (direct, topic, fanout, and headers). You can route messages based on content, headers, or routing keys.
SQS does not include native routing logic. To implement pub-sub or routing, you typically pair it with Amazon SNS or use application-side logic.
Scalability and Performance
RabbitMQ can handle high throughput, but scaling requires manual effort. It scales vertically by adding more resources—CPU, RAM—to a node. Horizontal scaling is done by adding nodes and configuring clustering. You may also need to tune queue behaviour and connection settings to maintain performance under load.
SQS scales automatically with virtually no limits. It’s fully managed, handles high message volumes effortlessly, and needs no manual scaling setup.
Delivery Guarantees and Reliability
RabbitMQ provides at-least-once delivery by default. You can simulate at-most-once (by auto-acknowledging messages) or get close to exactly-once with idempotent consumers, but it’s not built-in.
SQS offers:
- Standard queues: at-least-once delivery, possible duplicates
- FIFO queues: exactly-once (with caveats) delivery within message groups
Both use visibility timeouts and DLQ for reliability.
Security and Compliance
RabbitMQ offers a full set of security features, including supporting TLS for encrypting messages in transit. It also offers built-in administrative tools to manage user permissions and secure queues.
Its plug-in system allows you to extend security functionality. For example, you can integrate it with OAuth 2.0 for token-based authentication or LDAP for centralised user access control.
RabbitMQ also supports multiple messaging protocols, such as AMQP and MQTT, for secure communication across varied systems and use cases.
SQS, on the other hand, handles security at the infrastructure level as part of the AWS ecosystem. It uses AWS IAM to control access to queues and encrypts data both in transit and at rest by default.
Because it’s managed by AWS, it also inherits compliance certifications like HIPAA, PCI DSS, and SOC 2. It allows you to meet regulatory requirements without additional setup.
Cost and Licensing
RabbitMQ was originally developed by LShift and CohesiveFT and later acquired by Rabbit Technologies Ltd. It is open-source and licensed under the Mozilla Public License 2.0 (MPL 2.0). This permits users to modify the software freely. However, if modifications are redistributed as part of the same software, the source code for those changes must be made publicly accessible.
Operationally, the cost depends on how you choose to host and manage it, whether on your own infrastructure or through a managed provider like CloudAMQP.
SQS, by contrast, is a proprietary, fully managed service from AWS. There’s no licensing fee. You pay for what you use—pricing is based on the number of requests and data transfer volume. This pay-as-you-go model can be cost-effective for low to moderate workloads, but expenses can rise significantly at scale.
Operational Complexity
RabbitMQ is typically self-managed, which gives you more control but also more responsibility. You’ll need to do everything, from setting up infrastructure to managing clustering for high availability. You would also be responsible for configuring persistence, queues, and throughput tuning.
For monitoring, RabbitMQ has a built-in management plugin that provides a web-based UI for tracking performance metrics (but it has to be activated if you want to use it). If you want deeper observability, the message broker can be integrated with Prometheus and Grafana, allowing custom dashboards and alerting.
SQS, on the other hand, requires very little operational effort. As a fully managed AWS service, it handles scaling, redundancy, and failover behind the scenes. Metrics like message age, queue length, and throughput can be tracked via Amazon CloudWatch, which integrates natively with SQS.
It’s a low-maintenance option for teams wanting to offload server management.
Integration Ecosystem
RabbitMQ supports multiple messaging protocols, including AMQP, STOMP, and MQTT. As a result, it’s adaptable to a wide range of systems, from enterprise apps to IoT. It offers client libraries for most major programming languages. Plus, it has a plugin system for extending functionality, including security, monitoring, and protocol support. Its protocol flexibility makes it well-suited to hybrid and multi-platform environments.
Amazon SQS, on the other hand, doesn’t support open messaging standards. However, it integrates deeply with other AWS services like Lambda, SNS, EventBridge, and CloudWatch. For access control, it uses AWS IAM and has official software development kits (SDKs) for languages like Python, Java, Go, and Node.js, making it ideal for cloud-native applications built on AWS.
| SQS isn’t the only RabbitMQ alternative. Here’s a side-by-side comparison with Kafka. [Read more] |
| How does RabbitMQ fare in comparison with ActiveMQ? Let’s find out. [Read more] |
| Factor | RabbitMQ | Amazon SQS |
| Messaging Patterns & Ordering | Supports multiple patterns (pub/sub, routing, RPC). Queue-level ordering only. | Standard: at-least-once, unordered. FIFO: strict ordering, exactly-once. |
| Message Routing | Flexible routing via exchanges (direct, topic, etc.). | No native routing. Use SNS or app-side logic. |
| Scalability & Performance | Manual scaling. Vertical (more resources) and horizontal (clustering). | Auto-scaling. Handles high volumes with no setup. |
| Delivery Guarantees | At-least-once by default. At-most-once with auto-ack. | Standard: at-least-once. FIFO: exactly-once within message groups. |
| Security & Compliance | TLS, ACLs, plugin support (OAuth, LDAP). Protocol flexibility. | IAM-based control. Encrypted by default. AWS-compliant. |
| Cost & Licensing | Open-source (MPL 2.0). Hosting costs vary. | Pay-per-use. Cheap at small scale, can get costly at high volume. |
| Operational Complexity | Self-managed. Needs setup, scaling, and monitoring. Management UI + Grafana support. | Fully managed. Minimal ops. Monitored via CloudWatch. |
| Integration Ecosystem | Open protocols (AMQP, MQTT). Multi-language support. Plugin extensibility. | AWS-native. Integrates with Lambda, SNS, EventBridge. SDKs for major languages. |
RabbitMQ or SQS?
The right choice depends on your system’s needs. RabbitMQ may be a better fit if you need flexible routing, protocol support, and more control over transmitting messages. It’s ideal for custom workflows and use cases involving a large amount of data, provided you’re comfortable managing it.
On the other hand, SQS may be more suitable for applications that prioritise scalability and reliability out of the box. As a fully managed service, it offers a high level of durability with minimal overhead, especially in cloud-native environments.
Of course, if you do go with RabbitMQ, you know who to turn to if you need help. We’re a rabbitmq service provider and are here to help. All you need to do is:
