CPU-based
Scale up when average CPU exceeds your target (e.g. 70%).
Autoscaling
Horizontal autoscaling adds replicas to your deployment under load and removes them when traffic drops, based on live CPU and memory usage. Turn it on for any web or worker deployment and Ownkube owns the replica count from then on.
Enable it from the deployment Settings tab in your dashboard.
How it works
Section titled “How it works”Autoscaling watches your containers’ average CPU and memory usage. When usage crosses a target threshold, it adds replicas; when usage drops back below the threshold, it removes them. Scaling reacts within seconds of a sustained change.
Memory-based
Scale up when average memory exceeds your target (e.g. 80%).
You can use one signal, both, or neither. Autoscaling acts on whichever threshold is crossed first.
What you configure
Section titled “What you configure”- Min replicas: the floor. Autoscaling won’t go below this number.
- Max replicas: the ceiling. Capped at 100 per deployment.
- Target CPU utilization: percentage of the CPU request (e.g. 70 means scale up once containers average 70% CPU).
- Target memory utilization: percentage of the memory request.
A typical setup
Section titled “A typical setup”For a web service that sees variable traffic through the day:
| Setting | Value |
|---|---|
| Min replicas | 2 |
| Max replicas | 10 |
| Target CPU utilization | 70% |
| Target memory utilization | 80% |
| CPU request | 250m |
| Memory request | 256Mi |
This keeps at least two replicas always warm, bursts up to ten under load, and scales back down once traffic drops.
When autoscaling takes over
Section titled “When autoscaling takes over”Once autoscaling is enabled, the fixed replica count field is ignored. Autoscaling owns replica count from that point forward. Turn autoscaling off to return to a manual replica count.
Scaling the underlying compute
Section titled “Scaling the underlying compute”Replica scaling is only half of it. When your replicas need more room than the cluster currently has, Ownkube scales the worker capacity underneath them too, so scaling is end to end.
Worker capacity scales automatically. If a deployment scales up and the cluster is out of room, Ownkube adds capacity, and reclaims it when your workloads scale back down. There’s nothing to configure; it’s on by default.
Starter runs on a single AWS instance by default. Enable the optional automatic scaling add-on when you create the cluster to let a Starter cluster add worker capacity on demand beyond the base instance, and reclaim it when idle. With the add-on off, replica count scales within the capacity of your one instance; if you hit the ceiling, resize to a larger instance type from the cluster detail page. See Clusters for the instance size table.
The Scaling agent
Section titled “The Scaling agent”The Scaling agent is Ownkube’s predictive layer for capacity. It reads your deployment’s infrastructure signals, CPU and memory trends, traffic shape, and past load, and forecasts the capacity your workload is about to need.
The Scaling agent is advisory: it predicts and recommends, it does not change your replica count or scaling settings itself. Horizontal autoscaling does the acting. Think of the agent as the forecast and autoscaling as the actuator.
On the Scaling agent’s roadmap:
- Predictive pre-scaling ahead of historical traffic patterns, so you stop hitting cold-start penalties on the morning rush
- Schedule-aware recommendations (e.g. “traffic climbs at 9am on weekdays, drops after 7pm”)
- Custom signals: forecast on queue depth, request rate, or app-specific metrics
- Scale-to-zero guidance for non-production environments during idle periods
Limits and constraints
Section titled “Limits and constraints”- Max replicas is capped at 100 per deployment
- Scale-to-zero isn’t supported yet. Minimum is 1 when autoscaling is on; you can pause a deployment to 0 manually.
- Autoscaling uses resource requests as its baseline. Deployments without requests can’t autoscale reliably.
Deployments Resource types and the full config field reference.
Cost optimization How autoscaling fits into Ownkube's overall cost model.