Fabric RTI 101: Cost Considerations for Streaming vs Batch
When designing real-time data systems, it’s important to consider the cost differences between streaming and batch workloads.
Streaming pipelines tend to be more expensive because they require always-on compute — data is constantly being ingested, transformed, and monitored, even during quieter periods. That means the compute capacity behind your Eventstreams, KQL queries, or Activator rules is active all the time.

Batch processing, on the other hand, is usually cheaper and easier to control. It runs on a schedule — for example, hourly or nightly — so compute resources are only used when the job is running. This makes batch ideal for large volumes of data that don’t need to be processed instantly.
2026-08-21

