Should we use Kafka or RabbitMQ for our use case?

It depends on the pattern. Kafka fits high-throughput event streaming, event sourcing, and cases where you need to replay history (retained logs, multiple independent consumers). RabbitMQ fits traditional task queues, complex routing (exchanges/bindings), and cases where per-message acknowledgment and flexible routing matter more than raw throughput. We assess your actual message flow before recommending either.

Can you set up Kafka or RabbitMQ to run highly available?

Yes. For Kafka, that means a multi-broker cluster with an appropriate replication factor and in-sync replica configuration. For RabbitMQ, that means clustering with quorum queues (or classic mirrored queues on older versions). Both are tested for actual node failure, not just configured and left alone.

How do you monitor for problems before they cause an outage?

Consumer lag (Kafka) and queue depth (RabbitMQ) are the leading indicators we watch — a growing lag means consumers can't keep up, which is almost always the first sign of trouble. We set alert thresholds tied to your actual SLOs, not generic defaults.

Do you support Kafka Connect for integrating with our databases?

Yes. We deploy and manage Kafka Connect source and sink connectors to stream data between Kafka and your databases, data warehouses, or other systems, including schema management through Schema Registry to keep producers and consumers compatible as data evolves.

Can message queues here integrate with our microservices or Kubernetes workloads?

Yes — this is one of the most common reasons clients bring us in. We design topic/queue structure around your service boundaries and can deploy Kafka/RabbitMQ clients alongside workloads we already manage in Kubernetes.

Home›Services›Message Queue Management

KAFKA · RABBITMQ

Message Queue
Management

Managed Kafka and RabbitMQ infrastructure — cluster setup, topic and queue design, high-availability clustering, and performance tuning for event streaming and messaging workloads.

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What's Included

Complete Message Queue Management

From cluster design to consumer lag alerting — every layer of your messaging infrastructure, managed end to end.

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Kafka Cluster Setup & Management

Production Kafka clusters designed for durability and throughput, not a single broker that becomes a bottleneck.

✓ Multi-broker cluster deployment (KRaft or ZooKeeper)
✓ Topic & partition design for your workload
✓ Replication factor & ISR configuration
✓ Schema Registry setup (Avro/Protobuf)
✓ Kafka Connect for source/sink integration

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RabbitMQ Cluster Setup & Management

Highly available RabbitMQ clusters that keep delivering messages even when a node goes down.

✓ RabbitMQ clustering & quorum queues
✓ Exchange, queue & binding design
✓ Mirrored/replicated queue configuration
✓ Dead-letter queue & retry strategy
✓ Federation & shovel for multi-DC setups

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Capacity Planning & Tuning

Throughput and latency tuned to your actual message volume, not default configuration.

✓ Producer/consumer throughput tuning
✓ Partition/queue count vs consumer parallelism
✓ Retention policy & disk capacity planning
✓ Broker JVM & memory tuning
✓ Backpressure & flow control design

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Security & Access Control

Message infrastructure locked down the same way we treat any production system.

✓ SASL/SSL authentication & encryption in transit
✓ ACL-based topic/queue authorization
✓ RabbitMQ vhost isolation per team/service
✓ Credential rotation & secrets management
✓ Network segmentation for broker access

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Monitoring & Alerting

Visibility into consumer lag, queue depth, and broker health before they become outages.

✓ Consumer lag & queue depth dashboards
✓ Broker health & disk usage monitoring
✓ Prometheus exporters (Kafka, RabbitMQ)
✓ Alert thresholds tied to SLOs
✓ Dead-letter queue growth alerting

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Disaster Recovery & Upgrades

Message infrastructure that survives a broker failure and upgrades without data loss.

✓ Cross-region / cross-AZ replication (MirrorMaker)
✓ Broker failure & failover testing
✓ Rolling upgrades with zero message loss
✓ Backup of cluster metadata & configuration
✓ Runbook documentation for on-call teams

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Our Approach

The Right Broker, Configured to Survive Failure

Message queues sit in the critical path between your services. We treat them with the same rigor as a database, not as a fire-and-forget add-on.

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The Right Broker for the Job

Kafka excels at high-throughput event streaming and replay; RabbitMQ excels at flexible routing and traditional task queues. We help you pick based on your actual messaging pattern, not by defaulting to whichever is more popular.

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Consumer Lag Is Our Early Warning System

A growing consumer lag or queue depth almost always predicts an outage before it happens. We monitor and alert on this directly, not just on broker uptime.

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Failover Tested, Not Assumed

Replication and quorum configuration only matter if they actually work during a real failure. We run controlled broker-kill tests as part of onboarding, the same discipline we apply to database and cluster failover.

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Built to Support Microservices

Message queues are often the backbone connecting microservices. We design topic/queue structure with your service boundaries in mind, not as an afterthought bolted onto an existing architecture.

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