Capacity AI
Overview
Workloads can leverage intelligent allocation of its container’s resources (CPU and Memory) by using Capacity AI.
Capacity AI uses an analysis of historical usage to adjust the resources up/down between the configured minimum and maximum values of each container.
This can significantly reduce cost but may, in rare cases, cause temporary performance issues with sudden spikes in usage depending on the autoscaling settings of the workload.
If capacity AI is disabled, the amount of resources configured is set using the provided cpu and memory settings for each container.
The minCpu and minMemory settings of a workload container are ignored when capacityAI is disabled.
Capacity AI must be disabled if the autoscaling strategy is set to CPU Utilization.
Capacity AI will prevent the ratio of memory and cpu from diverging by a large percentage.
Minimum Capacity AI
When resources are not being used, Capacity AI will downscale CPU usage to a minimum of 25 millicores. The minimum will increase depending on the memory size being recommended by Capacity AI using a 1:3 ratio of CPU millicores to memory MiB.