End-to-end latency for Confluent Cloud

Apache Kafka® provides very low end-to-end latency for large volumes of data. This means the amount of time it takes for a record that is produced to Kafka to be fetched by the consumer is short.

If you’re using a dedicated cluster, adding additional CKUs can reduce latency. Other relevant variables that affect end-to-end latency include the implementation of client apps, partitioning and keying strategy, produce and consume patterns, network latency and QoS, and more. Clusters in Confluent Cloud are already configured for low latency and there are many ways to configure your client applications to reduce latency.

This Confluent blog post is a comprehensive guide to measuring and improving latency for your streaming applications, and this whitepaper details more of the tradeoffs between latency, throughput, durability, and availability.