Kafka vs SQS

Apache Kafka and Amazon SQS represent two fundamentally different approaches to messaging. Kafka is a self-managed distributed streaming platform built for high-throughput, ordered, replayable event logs. SQS is a fully managed, serverless message queue designed for decoupling services with minimal operational cost.

Architecture Differences

Kafka uses an append-only log partitioned across brokers. Consumers track offsets and can re-read historical data at any time. This log-centric design excels at event sourcing, change data capture, and building real-time data pipelines.

SQS is a distributed queue where messages are delivered at least once and deleted after acknowledgment. It offers two flavors: Standard (best-effort ordering, at-least-once) and FIFO (strict ordering, exactly-once within message groups). There is no concept of consumer offsets or replay.

Performance and Scaling

Kafka scales throughput by adding partitions and brokers, achieving millions of messages per second. However, you must plan partition counts carefully — rebalancing is disruptive.

SQS scales automatically behind the scenes. Standard queues handle virtually unlimited throughput. FIFO queues support 3,000 messages/sec per queue (with batching), which covers most workloads.

Cost Considerations

Kafka requires running broker instances 24/7, whether traffic is high or zero. For bursty or low-volume workloads, this means paying for idle capacity. SQS charges per API request with no idle cost, making it dramatically cheaper for intermittent workloads.

For sustained high-throughput pipelines (millions of messages/hour), Kafka's per-message cost can be lower than SQS's per-request pricing.

Trade-offs Summary

Kafka gives you replay, ordering, and stream processing at the cost of operational complexity. SQS gives you simplicity and zero-ops at the cost of no replay and limited ordering. In system design interviews, choosing between them signals whether you prioritize event-driven architecture or operational simplicity. See also our pricing guide for managed Kafka alternatives.