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You can scale a Kubernetes workload to zero and bring it back when work arrives, but the right method depends on the signal that should trigger it. Kubernetes v1.37 adds beta support for HPA-driven scale-to-zero using suitable object or external metrics. KEDA offers event-source-based scaling, including activation from zero. For HTTP services, neither an HPA nor a Kubernetes Service buffers requests for absent Pods: you need an activator, proxy, queue, or other buffering layer.
What does scaling to zero mean?
Scaling a Deployment or StatefulSet to zero means running no Pods for that workload while it is idle. That can stop the workload from reserving CPU, memory, or GPU resources, but it does not mean the Kubernetes cluster itself has scaled to zero. When a trigger arrives, the autoscaling system must request Pods and wait for them to start and become ready.
This trade-off works best when processing can pause while capacity starts. A queue consumer or batch processor can leave work in a durable queue until a Pod is ready; an interactive request may instead encounter a delay or fail unless another component holds or routes it.
Which scale-to-zero approach should you use?
| Consideration | Native HPA in Kubernetes v1.37 | KEDA |
|---|---|---|
| What supplies the signal? | A suitable Kubernetes object or external metric. | An event-source scaler, such as one monitoring queue depth, message backlog, or Kafka lag. |
| How does activation work? | The HPA uses its metric to scale the workload; the v1.37 controller records a ScaledToZero condition to identify a zero state it owns. |
KEDA handles activation from zero, then creates or manages an HPA for scaling above zero. |
| Best fit for request handling | Workloads whose metric can drive scaling and whose callers tolerate startup delay, or that have separate buffering. | Queue- and event-driven workloads; HTTP services still need an activator or buffering path. |
| What must you operate? | Kubernetes HPA and a suitable metrics source. | The KEDA operator, its metrics components, and scaler configuration. |
Kubernetes v1.37 is the relevant version boundary: its beta API support for HPA-driven scale-to-zero is enabled by default, according to the 2026 Kubernetes Blog. KEDA remains an option when you want event-source integrations or are running a Kubernetes version without that native capability. KEDA documentation for versions 2.21 and 2.22 describes scaling Deployments and StatefulSets to zero when no work is pending and activating them when events arrive.
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How to configure native HPA scale-to-zero
- Confirm cluster version and support. Use Kubernetes v1.37 for the beta HPA scale-to-zero support described by the Kubernetes Blog (2026). Coordinate upgrades across the control plane and components that consume HPA state so they understand the feature and the
ScaledToZerocondition. - Choose a usable metric. Configure a suitable object or external metric and ensure the metrics source can provide the signal that should raise replicas. The HPA needs a metric that remains meaningful while the target has no Pods.
- Set the HPA bounds and metric target. Set
minReplicas: 0, an appropriatemaxReplicas, and the metric target that reflects your workload’s capacity. The correct metric name and target depend on your metrics provider, so they cannot be safely prescribed as one universal manifest. - Start the workload above zero. Begin with at least one replica so the HPA can establish ownership of its zero state. Do not treat a manually set replica count of zero as equivalent to an autoscaler-managed scale-down: the controller uses
ScaledToZeroto distinguish its own state from a manual pause. - Test activation and rollback. Verify that the metric causes the workload to return from zero and that it becomes ready before dependent work expires. During a control-plane upgrade or rollback, ensure all relevant components interpret the feature state consistently.
How to scale to zero with KEDA
KEDA is a CNCF-graduated project that scales workloads according to events to be processed, such as messages in a queue. Its documentation describes the no-pending-messages case: KEDA can scale the Deployment to zero. When new events arrive, KEDA can activate the target, then use the HPA it creates or manages for further scaling.
- Install and operate KEDA. KEDA adds an operator, metrics components, and scaler configuration to the cluster; this is more operational surface than using the built-in HPA alone.
- Create a
ScaledObject. Point it at the Deployment or StatefulSet you want scaled and select a trigger supported by your installed KEDA version. - Configure the event trigger and limits. Examples in KEDA’s documentation include queue depth, Pub/Sub backlog, Kafka lag, and RabbitMQ messages. Set the trigger threshold and the workload’s replica limits to fit processing capacity and the time work can wait.
- Validate both directions. With no pending work, confirm the target reaches zero; then add work and confirm it activates, becomes ready, and drains the backlog. Check that the event source and KEDA components remain available while the target is at zero.
For scheduled off-hours shutdown rather than demand-driven scaling, KEDA’s Cron scaler can express a schedule. It is a schedule-based control, not a substitute for choosing an event or metric that represents actual demand.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can an HTTP service scale to zero without dropping requests?
Not with a Kubernetes Service alone. A Service routes to ready Pods; it does not hold requests while no Pods are ready. The Kubernetes Blog (2026) explicitly warns that HTTP and other request-driven workloads need a separate buffering layer when scaled to zero.
KEDA’s HTTP Add-on provides an activation path: it calculates route metrics and can scale the workload to zero after its cooldown period, while an activator handles requests during scale-up. An alternative design may put a proxy or durable queue in front of the service. In every case, decide how long callers can wait, how requests are preserved during startup, and what should happen if readiness takes longer than expected. Tune cooldown and readiness behavior to those expectations; do not assume the Service itself makes cold starts transparent.
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What to check before enabling scale-to-zero
- Work tolerance: Confirm that delayed processing is acceptable. A durable queue is a natural fit when work can wait; synchronous callers need an explicit cold-start plan.
- Metric availability: Make sure the chosen metrics or event source can still provide an activation signal when the target has no Pods.
- Replica ownership: For native HPA on v1.37, start at one or more replicas so the controller establishes its managed zero state. Keep manual pauses distinct from autoscaler scale-downs.
- Startup path: Account for image pulls, application initialization, readiness checks, and any dependencies before a new Pod can serve work.
- Version transitions: Coordinate upgrades and rollbacks so the control plane and autoscaling components agree on the v1.37 feature and
ScaledToZerocondition. - Resource economics: Zero replicas remove the idle workload’s Pod reservations, but the benefit is most relevant for intermittent workloads; it comes in exchange for startup delay.
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