When it comes to Kubernetes resource optimization, the Vertical Pod Autoscaler (VPA) often feels like an underused but powerful tool. While many teams rely on Horizontal Pod Autoscaler (HPA), VPA can be a game-changer for workloads that are difficult to scale horizontally.
However, adopting VPA is not without challenges. Based on real-world experiences shared by Kubernetes engineers on Reddit, this article highlights common pain points and actionable best practices for using VPA effectively.
What Are the Challenges with VPA?
1. Limited Applicability for Horizontal Scaling
Some workloads are CPU-bound and cannot be scaled out horizontally due to architectural constraints, dependencies, or stateful operations. In these cases, VPA is the only viable option but expectations need to be managed.
2. Misconfigured Requests and Limits
Incorrectly set resource requests often lead to wasted capacity or Out-of-Memory (OOM) errors. Overprovisioning leaves nodes underutilized, while underprovisioning creates instability. Many teams adopt VPA to strike a balance.
3. Pod Restarts and Node Churn
Aggressive request changes can cause pods to restart frequently or trigger rescheduling events. This leads to node churn, degraded performance, and overall instability.
4. Risky Scale-Down Decisions
Workloads with bursty traffic patterns or long-tail usage make scaling down dangerous. Reducing resources too aggressively can create latency spikes and failed requests during peak load.
5. Workload Diversity Challenges
Not all workloads behave the same. Init-heavy pods, spiky services, or irregular workloads often do not respond well to standard VPA recommendations and require custom handling.
6. Percentile-Based Provisioning Risks
Using high percentiles (e.g., p90) for requests may leave a significant portion of workloads underprovisioned during peak times, creating contention when multiple pods hit their upper usage bands simultaneously.
7. Cost Perception vs. Reality
Some teams struggle to see how VPA reduces cost, since cloud providers charge per node. The savings come from better bin-packing, right-sizing requests allows more pods to fit per node, reducing wasted overhead and lowering the total number of nodes required.
What Are the Best Practices for Using VPA?
1. Base Recommendations on Live Usage Metrics
Always rely on real production metrics instead of estimates. VPA’s recommender works best when it learns from actual workload behavior.
2. Leverage In-Place Resize When Available
Kubernetes v1.33+ supports in-place resource adjustments without pod restarts. This dramatically reduces disruption and makes VPA safer for production workloads.
3. Use Multiple Recommenders per Workload Type
Different workloads require different strategies. For example, memory-heavy services may use a slower decay rate, while CPU-intensive workloads may require more aggressive scaling. Configure spec.recommenders accordingly.
4. Set Min/Max Boundaries
Define minAllowed and maxAllowed values to prevent extreme fluctuations. This avoids shrinking too far during scale-downs or overprovisioning in spikes.
5. Treat Scale-Down Conservatively
Scale-up is often safe, but scale-down carries risks. Configure decay rates and cooldown periods to ensure workloads have enough buffer during bursts.
6. Carve Out Special Workloads
Some workloads, such as init-heavy pods or highly unpredictable services, may need to be excluded from VPA or given custom policies.
7. Monitor and Audit Continuously
Keep track of how VPA recommendations align with actual resource usage. Regular audits help identify inefficiencies and refine policies.
8. Combine VPA with Node Autoscaling
VPA alone only optimizes pod-level requests. To realize cost savings, pair it with tools like Karpenter or node pools to ensure optimized pods lead to fewer nodes overall.
For an even more seamless experience, solutions like CloudPilot AI provide a workload autoscaler that integrates directly with its intelligent node autoscaler. This ensures pods are not only right-sized but also matched with the most cost-efficient nodes in real time—delivering maximum efficiency and significant cost savings.
Conclusion
The VPA is a valuable tool for right-sizing workloads. However, to unlock its full potential, teams must understand its pitfalls and adopt the right best practices.
By combining live usage metrics, in-place resizing, workload-specific strategies, and careful scale-down policies, organizations can achieve higher cluster efficiency and reduce costs without sacrificing stability.
If your workloads are suffering from resource waste, OOM errors, or inefficient scaling, it might be time to revisit VPA—not as a silver bullet, but as a critical piece of your Kubernetes autoscaling strategy.
