Kafka vs SQS
A durable, replayable distributed log vs a managed task queue. Kafka is for event streaming with multiple consumers and replay; SQS is for decoupling producers and workers with at-least-once delivery and no ops. Different tools for different patterns.
| Dimension | Kafka | SQS |
|---|---|---|
| Model | Durable, partitioned, replayable log. | Managed queue; poll, process, delete. |
| Consumers | Many independent consumer groups, each replay the stream. | One consumer group per queue (work distribution). |
| Ordering | Per-partition ordering. | Standard: none; FIFO: per-group ordering. |
| Ops | You run brokers (or use MSK/Confluent). | Fully managed; no capacity planning. |
| Throughput | Millions of msgs/s; built for streaming. | High but request-based; built for task queues. |
Model
- Kafka
- Durable, partitioned, replayable log.
- SQS
- Managed queue; poll, process, delete.
Consumers
- Kafka
- Many independent consumer groups, each replay the stream.
- SQS
- One consumer group per queue (work distribution).
Ordering
- Kafka
- Per-partition ordering.
- SQS
- Standard: none; FIFO: per-group ordering.
Ops
- Kafka
- You run brokers (or use MSK/Confluent).
- SQS
- Fully managed; no capacity planning.
Throughput
- Kafka
- Millions of msgs/s; built for streaming.
- SQS
- High but request-based; built for task queues.
When Kafka wins
- You need event streaming with replay and multiple independent consumers.
- Throughput is very high and you can operate brokers.
When SQS wins
- You need a simple task queue with zero ops.
- Serverless/bursty workloads where you don't want to run brokers.
Verdict
Use Kafka for streaming/replay/multi-consumer event backbones; SQS for managed task queues. They compose — SQS for work distribution, Kafka for the event log.