Types of MQTT Brokers	Examples
Open Source	HiveMQ CE, Mosquitto, VerneMQ, etc.
Commercial	HiveMQ Professional and Enterprise, EMQ, VerneMQ, etc.
Cloud (Managed)	HiveMQ Cloud, CloudMQTT, AWS IoT Core, Azure IoT Hub, etc.
General-purpose brokers with MQTT support	Solace PubSub+, IBM MQ, RabbitMQ, ActiveMQ, etc.

Common Challenges by variants
	Open Source	Commercial	Cloud-Managed	General Purpose
Scalability	Limited scalabilty	Do not scale to millions of devices and messages	Require support tickets to add capacity	Unable to scale linearly and require massive step upgrades
Security	Limited options	Lack support for latest cipher suites for encryption	Lack flexibility e.g. turning off TLS for private networks to save compute and bandwidth	Miss advanced security features like plug-ins and chaining of authentication / authorization logic
Resilience	Cannot cluster for higher availability	Missing plugins for DB integration	Lack reason codes and other core MQTT features which sacrifices resolution times	Master-Slave architecure create long failover time Miss key features like Retained Messages that hurts recovery times
Agility	Hard to manage when coded in difficult libraries like Erlang	Require restarting application when adding nodes		Poor MQTT compliance makes interwork with systems unpredictable
Observability		Very few meaningful metrics available	Can’t query individual endpoints - action/hops inside the cloud are a black box	Force a stack of closed management systems that hamper collaboration between systems
Availability	No overload protection from overactive publishers	Persist on memory and not on disk causing data loss in many scenarios

What to look for	Why
Native support for the publish-subscribe pattern	Efficiency of Fan-in/Fan-out pattern helps avoid spaghetti architecture and its complexity
Linear scalability	Helps avoid abrupt infrastructure costs when handing incremental growth
High number of topics and concurrent connections	Helps teams prioritize the business logic over managing lower-level components
Grow and shrink cluster size at runtime without losing data	Keeps availability and uptime commitments, whether scaling up or down

What to look for	Why
User authentication with third-party systems	Helps secure and maintain credentials data independent of the broker
TLS secured communication between broker-clients and also between brokers in a cluster.	Prevents eavesdropping and loss of data
Nesting/chaining of authentication logic	Creates flexibility for a variety of use cases with custom logic for authenticating and authorizing clients
Efficiency through advanced features Native TLS, OCSP Stapling etc.	IoT-scale deployments are very demanding on the underlying infrastructure and there are significant untapped efficiencies in security
Access control and fine-grained permission control	Helps ensure only authorized endpoints come through

What to look for	Why
Fault tolerance at multiple levels (broker, cluster, cloud)	IoT environments are prone to network outages and disruptions
Masterless cluster architecture	Master/Slave architecture suffers from long recovery times which hurts application availability and performance

What to look for	Why
Variety of deployment options - on-premise, cloud, fully-managed	Helps right-size your deployment for different use cases while operating under the same technical principles
Easy Maintainability through standards-based approach	Ease of development that helps accelerate time to market
Testability	For quality and performance assurance
Support for multi-cloud strategy	Helps avoid vendor lock-in and brings in the best features from multiple platforms
Tested and packaged extensions for common enterprise system	Complex integration like Apache Kafka etc. can be very time-consuming to build and maintain in-house
Expertise to certify extensions for enterprise use	During deployment and support issues, a vendor-certified extension is one less problem for the enterprise

What to look for	Why
Unhindered access to metrics	Metrics (including throughput in amounts and bytes and rates, counts and bytes per different MQTT message type) enable you to monitor the health of your central MQTT messaging platform and proactively identify anomalies, bottlenecks and other technical issues
JMX monitoring	Allows integration into a large set of JMX based Java monitoring tools
Machine readable audit logs	Helps capture overview of all actions performed on the broker that for better compliance and troubleshooting
Trace recordings	Creates human-readable log files to assess what’s really happening with a client, or on a topic, by analyzing only specific information from the broker cluster

What to Look for	Why
Cluster Overload Protection	Reduces the rate of incoming messages and connection requests from publishing clients that risk overloading a cluster
Built-in support for features like Retained Messages	In real life environments, a client needs a new/last state to be productive

What to Look for	Why
REST API	For programmatic access to the broker
K8s Operator	Your DevOps can easily orchestrate, automate, and manage the lifecycle of multiple HiveMQ cluster deployments within Kubernetes (platform agnostic)
Wide support of libraries	Helps your developers spend less time learning new coding languages and constructs

What to Look for	Why
SDK to build extensions for your use case	Meet specific application needs that require access to advanced features related to session management, advanced system integration capabilities, inter-extension communication in a cluster, etc.
Hot reload of extensions	Enables access to the features of an extension without downtime
Enterprise tools extensions	Pre-installed enterprise extensions can help you (for example) extend the broker ecosystem into your security tools, forward messages into other broker clusters, or integrate with tools like Confluent/Kafka

MQTT Broker Comparison – Which is the Best for Your IoT Application?

Where MQTT Brokers are Used