This is a paid editorial feature. The product was independently evaluated by the KrispiTech team.
“I just love tweaking configurations at 2 a.m.,” said no on-call developer, ever. If you have ever experienced systems managing millions of messages, you know that sudden memory spikes or connection churn can catch your infrastructure off guard. For a team, or even a single on-call developer, this can mean hours of extra work. Work that could have been avoided if the underlying architecture had been prepared for it. This was the exact spark the team behind 84codes needed to build LavinMQ from the ground up.
LavinMQ is an open-source message broker written in Crystal, built by developers who have successfully hosted other brokers commercially for over 14 years. Through this experience, the sum of their learnings and improvements resulted in a broker that handles over a million messages per second on modest cloud hardware, ships as a single binary, and uses a fraction of the resources others typically demand, without compromising on performance.
It supports AMQP 0-9-1, MQTT, HTTP, and streaming. If your team already knows RabbitMQ, the learning curve here is almost flat. The name comes from the Swedish word for avalanche, which feels appropriate once you see the throughput numbers.
Key Features of LavinMQ
Throughput That Actually Holds Up
LavinMQ has been benchmarked at over 965,000 messages per second on a 2-vCPU AWS Graviton4 instance. These benchmarks are public, reproducible, and updated with every release. There is no proprietary test setup hiding unflattering results. On a Raspberry Pi 5, it still hits around 600k msg/s. That kind of performance on constrained hardware is genuinely unusual.
LavinMQ’s latest benchmarking results: https://lavinmq.com/benchmark#metric=throughput&scenario=amqp-queue&size=16
Single Binary Deployment
The entire broker ships as one binary. No JVM to tune, no sprawling dependency tree, no container layers to debug. You download it, run it, and you are done. For teams that want to move fast without wrestling with operational overhead, this alone is a meaningful difference compared to heavier alternatives.
Stream Queues with Replay Support
LavinMQ supports stream queues, which store messages persistently and let consumers read from any point in the stream without duplication. This is ideal for fan-out architectures where many consumers need the same message, and for replay scenarios where you need to re-process historical data. Declare a stream, bind consumers, and the broker handles the rest.
High Availability via Clustering
Single-node and multi-node setups are both supported, with data replication across brokers ensuring consistency. If a node goes down, messages are not lost. For production systems at fintech companies, food delivery apps, or live TV game shows (all real LavinMQ users), that kind of reliability is not optional.
Broad Protocol and Language Support
LavinMQ speaks AMQP 0-9-1, MQTT, and HTTP. Client libraries exist for Python, Ruby, Node.js, Go, Java, .NET, PHP, and Crystal. Whether you are building microservices, IoT pipelines, or legacy system integrations, the broker does not force you to change your stack.
Marcus runs a Load Test at 11 pm and Stops Worrying
Marcus is a backend engineer at a mid-sized e-commerce company in Berlin. His team runs order processing through RabbitMQ, and every Friday evening during promotional sales, the broker starts throwing memory alarms. At 11 pm on a Thursday before a major campaign launch, he decided to run a side-by-side load test using LavinMQ on a spare t4g.medium instance, which cost a few dollars per hour.
He downloaded the binary, had it running in under ten minutes, and pointed a Python publisher at it using the same pika client library his team already used with RabbitMQ. No code changes. He pushed 400,000 messages per second through the broker while monitoring resource usage from the LavinMQ web UI.
The broker used less than 200MB of RAM throughout the test. His existing RabbitMQ setup regularly spiked past 4GB under similar conditions. By midnight, Marcus had a Slack message drafted to his engineering lead recommending they migrate before the next sale. The load test took one hour. The decision took five minutes.
How to Get LavinMQ Running
Option 1: Hosted via CloudAMQP
The fastest path is to create a free instance on CloudAMQP, the managed hosting service run by the same team behind LavinMQ. No installation required. You get a connection URL, and you are publishing messages within minutes. Free plans are available.
Option 2: Self-Hosted Binary
Download the single binary from the GitHub repository or the official site. Follow the installation guide in the documentation, configure your vhosts and policies, and point your existing AMQP client at the new broker. The configuration format will feel familiar if you have used RabbitMQ before.
Option 3: Try the Beginner Tutorials
The documentation includes hands-on tutorials covering work queues, streams, IoT data pipelines, and even a Slack bot built with the ChatGPT API. There is also a free ebook covering the core concepts. If you get stuck, the community Slack is active, and the team responds.
Who Should Try LavinMQ
LavinMQ is worth serious consideration for any backend team currently running RabbitMQ and looking to improve resource efficiency without rewriting application code. It is also a strong choice for IoT projects, real-time data pipelines, and distributed systems that need reliable messaging at high volume. The open-source codebase, Apache 2.0 license, and active contributor community (45+ contributors) give it the kind of longevity that matters for production infrastructure. Start with a free hosted instance at lavinmq.com and see how it handles your workload.
