Your Cassandra node is getting evicted at 2 a.m. or your PostgreSQL replica is sitting on 4× the memory it needs — this episode breaks down exactly which autoscaler to reach for and why VPA vs HPA is a staple senior SRE interview question. You'll learn: Why HPA's scale-out model breaks for StatefulSets (token rings, replication slots, unacknowledged messages) and when vertical scaling is the only safe lever VPA's three components — recommender, admission controller, updater — and why update mode auto is the one that pages you at 2 a.m. for stateful workloads The safe progression: start in Off mode for two weeks, apply recommendations manually, then consider Initial mode — and why Auto is rarely worth it for anything with persistent state The HPA + VPA feedback loop failure: both watching CPU on the same workload, pod count oscillating, resource allocation in chaos The clean split that actually works: HPA on queue depth (custom metric), VPA managing memory requests — distinct signals, no interference Keywords: VPA vs HPA Kubernetes, vertical pod autoscaler stateful workloads, autoscaler SRE interview, HPA VPA conflict, Kubernetes StatefulSet autoscaling 🎧 Listen, then go deeper — DevOps & Cloud interview-prep ebooks at DevOpsInterview.Cloud ▶ Daily 30-second interview drills: DevOps Interview Cloud on YouTube Transcript Your stateful app is getting evicted every few hours, or it's sitting on four times the memory it actually needs, and you're not sure which autoscaler to reach for. That's the exact scenario interviewers love, because most candidates only know one half of the answer. Let's set the stage. You have a Cassandra node, a RabbitMQ broker, or a PostgreSQL read replica running in Kubernetes. It's a StatefulSet. Traffic is not uniform. Some days it's busy, some days it's quiet. Someone on your team says "just add HPA" and someone else says "we need VPA." Who's right? That question shows up in senior SRE and platform engineer interviews constantly, because the answer is not obvious and the wrong choice causes real incidents. Interviewers ask this because autoscaling is one of those topics where surface-level knowledge falls apart fast. Saying "HPA scales pods out, VPA scales pods up" is correct but incomplete. The follow-up is always: okay, so which one do you use for a stateful workload, and why? And then: what happens if you use both at the same time? If you can't answer those, you signal that you've only worked with stateless services. Here's the mental model you need. The HPA, the Horizontal Pod Autoscaler, works by changing the number of pod replicas. It watches a metric, CPU or memory or a custom one, and when that metric crosses a threshold it adds or removes pods. That works beautifully for stateless services. Each replica is identical, sessions don't matter, and spinning up a new pod behind a load balancer is invisible to users. Stateful workloads break that assumption. A Cassandra node owns a subset of the token ring. A RabbitMQ broker may hold unacknowledged messages. A PostgreSQL replica has an open replication slot. Adding a new pod doesn't instantly help because the new pod has to join the cluster, sync data, or acquire state before it can carry load. Scaling out fast is often dangerous. Scaling in is even worse because you might be removing a pod that holds data not yet replicated elsewhere. So for stateful workloads, the more useful lever is usually vertical. Give the existing pod more CPU or more memory so it can handle the load without needing a new replica. That's where the VPA, the Vertical Pod Autoscaler, comes in. VPA has three components. The recommender watches historical resource usage and calculates what your requests and limits should be. The admission controller patches those values into new pods at scheduling time. And the updater, this is the dangerous one, can evict running pods so they restart with the new resource values. The key knob is the update mode, and this is a common interview question on its own. Update mode off means the VPA only calculates recommendations. It never touches your pods. You query the VPA object and apply changes yourself. This is the right starting point for any stateful workload. You get the data without the risk. Update mode initial means the admission controller sets resource values when a pod is first created, but the VPA never evicts a running pod. The pod keeps whatever resources it started with until it's naturally replaced, for example during a rollout. This is a reasonable middle ground for apps that tolerate restarts as part of deployments but not random evictions. Update mode auto, and this is the one that bites teams, allows the VPA updater to evict pods at any time to apply new recommendations. For a stateless deployment this is often fine. For a stateful pod it can be catastrophic. Imagine your primary Redis instance getting evicted at two in the morning because the VPA decided it needed fifty percent more memory. The pod comes back, but during those thirty seconds of restart your application is throwing errors. The fourth mode, recreate, behaves like auto but only triggers on pod creation events. In practice most teams treat auto and recreate as the same risk category for stateful workloads. So the concrete recommendation for stateful apps is: start with update mode off, run it for at least two weeks to cover your traffic patterns, look at the recommended requests in the VPA status, and then apply those values manually to your StatefulSet spec. After you've done that once and confirmed stability, you can consider bumping to initial mode. Staying in auto is rarely worth it for anything with persistent state. Now the tricky part that interviewers really test: can you run HPA and VPA at the same time? The short answer is yes, but only if they're watching different metrics. The classic failure pattern is enabling both on the same workload with both watching CPU. Here's what happens. Load increases. CPU goes up. HPA adds a replica. The CPU per pod drops because the load is now spread. VPA sees lower per-pod CPU and recommends lower requests. It either tells you to reduce requests or, in auto mode, evicts pods to resize them down. Meanwhile HPA is still watching the same CPU signal. You get a feedback loop where both controllers are fighting each other, your pod count oscillates, and your resource allocation is unstable. The safe combination is to give each controller a distinct domain. A common pattern for a message broker is to let HPA scale on queue depth, a custom metric from your metrics pipeline, while VPA manages the memory requests because memory usage on a broker tends to grow with configuration and message size rather than with load spikes. They're watching completely different signals so they don't interfere. Another safe pattern is to disable HPA entirely and rely only on VPA in initial mode, pairing that with a PodDisruptionBudget, the PDB, to prevent the cluster autoscaler from removing nodes that would orphan your pods. This is common for single-instance stateful workloads where horizontal scaling genuinely isn't possible. Let's talk real numbers briefly so you sound credible in the interview. A typical starting point for a moderately loaded RabbitMQ broker might be two hundred and fifty millicores CPU request and five hundred millicores limit, with one gigabyte memory request and two gigabytes limit. After running VPA in off mode for two weeks under normal traffic, you might see the recommender suggesting six hundred millicores CPU and one and a half gigabytes memory. That gap between what you provisioned and what you actually need is exactly the over-provisioning problem VPA is designed to solve. Without it, you're just guessing. On the eviction risk side, the VPA updater respects PodDisruptionBudgets. If your PDB says zero pods unavailable, the updater will not evict your pod even in auto mode. So if you are running VPA auto on a StatefulSet, always pair it with a PDB that reflects your actual availability requirements. This is a detail that separates strong answers from weak ones in interviews. There's also the resource policy inside the VPA spec itself. You can set min allowed and max allowed values per container so the recommender can't suggest something absurd. For example, you can tell it to never recommend less than one hundred millicores or more than four cores, keeping the recommendations inside bounds your infrastructure can actually handle. Always set these bounds for production workloads. One more wrinkle worth knowing. VPA and HPA cannot both target CPU or memory requests at the same time because HPA uses requests as the denominator for its utilization calculation. If VPA changes the request value, the HPA's target threshold shifts underneath it. The percentage-based math breaks. Kubernetes has a known limitation here, and the official guidance is to not use HPA on CPU or memory when VPA is also active on the same workload, unless you're using HPA with custom or external metrics only. Common wrong answers you want to avoid. The first is saying "just use HPA for everything, stateful or not." That ignores the data ownership and sync cost of scaling stateful replicas. The second is saying "VPA is dangerous so never use it." That's overcorrecting. VPA in off or initial mode is genuinely useful and low risk. The third is conflating pod autoscaling with cluster autoscaling. The cluster autoscaler adds nodes. VPA and HPA manage pods within existing nodes. They work at different layers and you need to be clear about which layer you're talking about. The fourth is not mentioning PodDisruptionBudgets when discussing VPA auto mode. Experienced interviewers will notice that gap immediately. Quick recap. For stateful workloads, your default should be VPA in off mode to gather recommendations, and then apply them manually. Use initial mode once you trust the recommendations
