AWS Kinesis vs Kafka

Amazon Kinesis Data Streams and Apache Kafka are both distributed streaming platforms for real-time data processing. Kinesis is a fully managed AWS service. Kafka is a self-managed open-source platform (or managed via Confluent Cloud, Amazon MSK, etc.).

Architecture Comparison

Kinesis organizes data into streams divided into shards. Each shard provides 1 MB/s ingress and 2 MB/s egress. Scaling means adding or splitting shards — which is an operational action with resharding latency.

Kafka organizes data into topics divided into partitions. Each partition's throughput is limited only by broker resources. Adding partitions is fast and requires no data movement (though reducing partitions is not supported).

Scaling Constraints

Kinesis has a hard limit of 5 GetRecords calls per shard per second, and each call returns up to 10 MB. With Enhanced Fan-Out, each consumer gets a dedicated 2 MB/s pipe, but this adds cost. These per-shard limits can be restrictive for workloads with many consumers.

Kafka has no such per-partition read limits. Multiple consumer groups can read from the same partition simultaneously without throttling.

Cost Analysis

Kinesis pricing includes shard-hours ($0.015/shard/hour) plus per-PUT charges ($0.014 per million). At low volume, this is cheap. At high volume (hundreds of shards), costs escalate quickly.

Self-managed Kafka costs are infrastructure-based (EC2, EBS, networking). At high throughput, Kafka's cost per message is typically lower. Amazon MSK offers a middle ground — managed Kafka on AWS.

When to Choose

Kinesis is ideal for AWS-native teams who want zero operational overhead and tight integration with serverless architectures. Kafka is better for multi-cloud deployments, high-throughput workloads, and teams needing its rich ecosystem. See our system design interview guide and pricing comparison for deeper analysis.