AWS SQS and Apache Kafka are often mentioned together in messaging discussions, but they solve fundamentally different problems. Treating them as interchangeable alternatives leads to architectural mismatches that cause pain down the road.
Understanding what each system is designed for—not just what it can do—is essential for making the right choice.
The Fundamental Difference
AWS SQS is a message queue. Messages go in, consumers pull them out, and successfully processed messages disappear. It’s designed for decoupling components and distributing work across workers. Once a message is consumed, it’s gone.
Apache Kafka is an event streaming platform. Events are appended to a log, consumers read from positions in that log, and events persist for a configurable retention period. Multiple consumers can read the same events independently. Kafka is designed for event-driven architectures, real-time data pipelines, and event sourcing.
This distinction matters more than any feature comparison. SQS is about moving messages from A to B. Kafka is about maintaining a durable, replayable stream of events.
AWS SQS: What It Does Well
Simplicity and Zero Operations
SQS is a fully managed service. There are no clusters to provision, no brokers to monitor, no partitions to rebalance. You create a queue, send messages, receive messages. AWS handles availability, durability, and scaling automatically.
For teams without dedicated infrastructure expertise, this operational simplicity has real value. SQS just works, scales automatically, and requires minimal ongoing attention.
Work Distribution
SQS excels at distributing work across multiple consumers. Send tasks to a queue, spin up workers that pull from it, and SQS handles the load balancing. Visibility timeouts ensure that if a worker crashes mid-processing, the message becomes available for another worker to pick up.
This pattern—work queue with competing consumers—is exactly what SQS was built for. Background job processing, task distribution, and workload buffering are natural fits.
Cost Model
SQS charges per request (with batching reducing costs). For bursty workloads with periods of low activity, you pay only for what you use. There’s no minimum cost for maintaining infrastructure during quiet periods.
For workloads with predictable, moderate message volumes, SQS costs are straightforward and often lower than running Kafka infrastructure.
Integration with AWS Ecosystem
SQS integrates natively with Lambda, SNS, EventBridge, and other AWS services. Lambda can poll SQS queues and scale automatically based on queue depth. These integrations are well-tested and reduce custom code.
FIFO Queues
SQS FIFO queues provide exactly-once processing and strict ordering within message groups. For workflows where processing order matters and duplicate processing would cause problems, FIFO queues offer guarantees that standard queues don’t.
Apache Kafka: What It Does Well
Event Streaming and Replay
Kafka maintains an ordered, immutable log of events. Consumers track their position (offset) in the log and can re-read events by resetting their offset. This enables:
- Event replay: Reprocess historical events when logic changes or bugs are discovered
- Multiple consumers: Different services consume the same events independently
- Event sourcing: Reconstruct state by replaying events from the beginning
- Audit trails: Complete history of what happened, not just current state
This log-based model is fundamentally different from message queues and enables patterns that queues can’t support.
High Throughput
Kafka is designed for high-volume data pipelines. Hundreds of thousands or millions of events per second are achievable with proper cluster sizing. The sequential disk I/O model, batching, and compression make Kafka remarkably efficient for throughput-intensive workloads.
For data pipelines moving massive volumes—log aggregation, metrics collection, click streams—Kafka’s throughput capabilities are essential.
Stream Processing
Kafka Streams, ksqlDB, and integrations with Flink and Spark Streaming enable processing events as continuous streams. Aggregations, joins, windowed computations, and complex event processing can operate on Kafka data in real-time.
This stream processing ecosystem turns Kafka from a messaging system into a platform for building real-time data applications.
Decoupled Consumer Groups
Different consumer groups maintain independent offsets into the same topic. A real-time analytics service, a batch processing job, and an audit logging system can all consume the same events without interfering with each other. Add new consumers later without replaying from producers.
This decoupling makes Kafka effective as a central nervous system for event-driven architectures where many services need access to the same events.
Exactly-Once Semantics
Kafka supports exactly-once semantics for producer-to-consumer data flow when using transactions and idempotent producers. For financial transactions, inventory updates, or other scenarios where duplicate processing causes real problems, these guarantees matter.
When to Choose SQS
Work Queue Patterns
If your use case is “distribute tasks to workers and ensure each task is processed once,” SQS is the natural fit. Background job processing, email sending, image processing queues, webhook delivery—these are queue problems, not streaming problems.
Simple Decoupling
When you need to decouple services without complex event replay or multiple independent consumers, SQS provides simple, reliable decoupling. Service A sends a message, Service B eventually processes it. The simplicity is a feature.
Serverless Architectures
SQS integrates seamlessly with Lambda. For serverless applications where you want automatic scaling based on queue depth without managing infrastructure, SQS plus Lambda is a proven pattern.
Low to Moderate Volume
For workloads under tens of thousands of messages per second, SQS handles the load easily and the cost model is favorable. You don’t need Kafka’s throughput capabilities for typical application messaging.
AWS-Native Teams
If your team operates primarily within AWS and values managed services that minimize operational overhead, SQS fits naturally into that model. The operational simplicity compared to running Kafka is significant.
When to Choose Kafka
Event-Driven Architectures
When events are first-class citizens—not just messages to be processed and forgotten—Kafka’s log-based model is appropriate. If you need event replay, multiple consumers of the same events, or event sourcing patterns, you need Kafka’s semantics.
Real-Time Data Pipelines
For high-volume data movement—log aggregation from many sources, metrics pipelines, click stream collection, IoT data ingestion—Kafka provides the throughput and durability needed. These are Kafka’s core use cases.
Stream Processing Requirements
If you need to process streams in real-time—aggregations, joins, windowed computations—Kafka’s ecosystem (Kafka Streams, ksqlDB, connectors) provides the tools. SQS doesn’t have a stream processing story.
Audit and Compliance
When you need a durable record of all events for audit, compliance, or debugging, Kafka’s retention model provides that history. SQS messages disappear after consumption; Kafka events persist for your configured retention period.
Multi-Consumer Patterns
When multiple independent services need to consume the same events without coordination, Kafka’s consumer group model handles this elegantly. Each service maintains its own offset and consumes at its own pace.
The Middle Ground: Amazon MSK and Kinesis
Amazon MSK (Managed Streaming for Apache Kafka) provides Kafka as a managed service on AWS. You get Kafka’s semantics and capabilities with reduced operational burden. It’s more expensive than self-managed Kafka but less work than running your own clusters.
Amazon Kinesis sits between SQS and Kafka conceptually. It’s a managed streaming service with log-based semantics, multiple consumers, and replay capability—but with simpler operations than Kafka. For teams that need streaming semantics but want AWS-managed simplicity, Kinesis is worth considering.
Cost Comparison
Cost comparisons are tricky because the models differ:
SQS charges per million requests. For low-volume queues, costs are minimal. For high-volume queues with millions of messages daily, costs scale linearly.
Kafka (self-managed) has infrastructure costs: EC2 instances, EBS storage, networking. These are relatively fixed—you pay for the cluster whether it’s busy or idle. High utilization makes Kafka cost-effective; low utilization means paying for idle capacity.
MSK combines infrastructure costs with per-partition and per-storage charges. It’s typically more expensive than self-managed Kafka but less operational work.
For high-volume, steady workloads, Kafka can be more cost-effective. For variable, lower-volume workloads, SQS’s pay-per-use model often wins.
Operational Complexity
SQS operational burden: minimal. Create queues, configure retention and visibility timeout, monitor queue depth. AWS handles everything else.
Kafka operational burden: significant. Cluster sizing, partition management, replication configuration, consumer group monitoring, offset management, upgrade planning, capacity planning. Kafka requires dedicated expertise to operate well.
This operational difference shouldn’t be underestimated. A poorly operated Kafka cluster causes more problems than a well-operated SQS queue, even if Kafka is theoretically the “better” choice for your use case.
Common Mistakes
Using Kafka when SQS would suffice. If you don’t need replay, multiple consumers, or stream processing, Kafka’s complexity isn’t justified. Many teams adopt Kafka because it’s sophisticated, not because they need its capabilities.
Using SQS when you need Kafka semantics. If requirements include event replay, independent consumers, or stream processing, fighting against SQS’s queue model creates ongoing friction. Accept that you need Kafka’s capabilities.
Underestimating Kafka operations. Running Kafka reliably requires real expertise. Budget for that expertise or use a managed service.
Over-architecting with Kafka. Kafka enables sophisticated event-driven architectures, but those architectures have their own complexity. Simple request-response or queue-based patterns are often sufficient and simpler.
The Decision Framework
Do you need event replay or multiple independent consumers? If yes, you need log-based semantics: Kafka, Kinesis, or similar.
Is this a work distribution problem? If you’re distributing tasks to workers for one-time processing, SQS is the simpler choice.
What’s your volume? Very high throughput requirements favor Kafka. Moderate volumes work fine with either.
What’s your operational capacity? If running Kafka well would strain your team, managed alternatives (MSK, Kinesis, SQS) reduce that burden.
What does your architecture need? Event-driven architectures with many consumers sharing events need Kafka’s model. Service-to-service decoupling often works fine with SQS.
The Bottom Line
SQS and Kafka are designed for different problems. SQS is a work queue—simple, managed, and effective for distributing tasks. Kafka is an event streaming platform—powerful, complex, and necessary for event-driven architectures at scale.
Choose SQS when you have a queue problem. Choose Kafka when you have a streaming problem. Don’t choose Kafka just because it’s more sophisticated—complexity you don’t need is still complexity you have to manage.