The fundamental pricing model of Temporal Cloud is simple to understand, just executed actions and utilized storage, yet it’s easy to underestimate how many actions your workflow might run, or how large the event history may grow. We’ll help clarify what workflow design choices may be responsible for high action or storage costs.
This talk will compare the impacts of several design decisions made as part of a simple but realistic workflow. We’ll examine a month of simulated activity and use it to illustrate the effects of each seemingly minor change to the workflow.
Along with simple changes (e.g shorter lived workflows) we’ll also review some more dramatic design choices (e.g actor model execution) that aim to minimize costs in scenarios where Temporal Cloud might appear prohibitively expensive.
Whether you’re looking for a way to optimize your cloud costs, or increase the accuracy of your next cost estimation, this talk is sure to be of interest.
Topics Will Include:
- Unexpected Sources Of Action Consumption: - Unnecessary parallelism / Child workflows - Low timeout heartbeats - High interactivity / incoming signals - Short timers
- Unexpected Sources Of Storage Utilization: - Returning large state from activities - Signal payloads - Overall history length - Unnecessarily Long Lived Workflows - Sleeping With A Long History
- Illustrated Design Impacts Will Include: - Skipped short timers - Encoded workflow payloads - Actor style workflows - “Deactivating” workflows - Optimistically continue-as-new for “history compaction”
