CruiseKube vs VPA
CruiseKube vs Kubernetes Vertical Pod Autoscaler (VPA)
Both CruiseKube and the Kubernetes Vertical Pod Autoscaler aim to reduce waste caused by poorly sized pod resources. The similarity largely ends there. CruiseKube builds on the same motivation as VPA, but focuses on a few specific capabilities that VPA does not have.
The Core Difference
- VPA is a recommendation system that periodically adjusts the resource requests and limits of its target.
- CruiseKube is a runtime optimization system that actively optimizes resources, closer to real time.
VPA answers the question:
- What should this workload generally request?
CruiseKube answers a different question:
- What should this specific pod request right now, on this node, given current conditions?
How They Differ
| Dimension | CruiseKube | Kubernetes Vertical Pod Autoscaler |
|---|---|---|
| Optimization model | Continuous, feedback-driven control loop | Periodic analysis and recommendation |
| Unit of decision | Individual pod, in node context | Workload template |
| View of the cluster | Node-local, shared capacity | Pod isolated from node context |
| Time horizon | Short-term, adaptive | Long-term, historical |
What CruiseKube unlocks conceptually
-
Resource sharing instead of duplication Instead of every pod reserving its own peak capacity, CruiseKube allows pods on the same node to share headroom, based on the assumption that spikes are not perfectly correlated.
-
Correction over prediction CruiseKube does not need to be "right" ahead of time. It relies on frequent correction rather than long-horizon prediction.
-
Cost efficiency without abandoning safety Reliability is preserved through conservative memory handling, priority-based protection, and feedback signals, rather than static over-allocation.
When each model makes sense
-
Use VPA if you want a simple, conservative mechanism to improve request sizing and are comfortable treating optimization as an occasional adjustment problem.
-
Use CruiseKube if you believe resource optimization is a continuous control problem and you want to actively trade unused safety margins for higher utilization, without making developers manually tune workloads.