Amazon Kinesis delivers excellent performance for real-time streaming workloads while staying highly scalable. I especially like how Kinesis Data Streams, Kinesis Data Firehose, and Managed Service for Apache Flink fit together to build end-to-end streaming pipelines without having to manage infrastructure.

It integrates naturally with analytics, storage, and machine learning services, which makes it straightforward to design event-driven architectures. Performance stays consistent even at very high event throughput, and features like enhanced fan-out let multiple consumer applications process the same stream independently with minimal latency.

The AWS console is fairly technical, but it still offers solid visibility into shards, consumers, throughput, and CloudWatch metrics. Because the platform is managed, it can also deliver good ROI by removing the need to operate self-managed Kafka clusters for many workloads.

While Kinesis itself doesn’t include generative AI, it works well with Amazon Bedrock, Amazon SageMaker, and real-time inference pipelines to process streaming events for anomaly detection and other intelligent applications. Review collected by and hosted on G2.com.

Shard sizing, partition key design, and throughput planning all require careful thought to prevent throttling and avoid uneven shard utilization. I’ve also found the pricing model can be hard to predict for continuously high-volume streaming workloads, especially when you’re using enhanced fan-out, longer retention periods, or supporting multiple consumers. The console gets the job done, but it can feel less intuitive when you’re trying to diagnose issues in more complex streaming pipelines. Review collected by and hosted on G2.com.