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Scale-to-Zero in Kubernetes: Save Costs Without Losing Traffic

If you've ever deployed HTTP services on Kubernetes, you've probably dealt with idle pods that burn resources during off-hours or inactivity.

In today's blog, we dive into the concept of Scale-to-Zero, why it matters, how existing tools implement it (and where they fall short), and how KubeElasti solves this problem with zero rewrites, zero request loss, and zero lingering proxies.

What is Scale-to-Zero?

Scale-to-Zero refers to the ability to automatically scale down a deployment to zero replicas — effectively turning off the service — when it's idle, and scaling it back up when traffic resumes.

This is ideal for:

  • Internal or development environments
  • Spiky workloads
  • Scheduled batch jobs
  • Cost-sensitive services (e.g., licensed software, GPU workloads)

Why Use Scale-to-Zero?

There are many reasons to use scale-to-zero:

  • Cost savings: You're always paying for at least one pod
  • Cold-start savings: Cold-start savings are left on the table
  • Infrequently accessed services: Infrequently accessed services still waste memory and CPU

Scale-to-Zero solves this by removing the pod entirely. But... it comes with a catch: if traffic arrives and no pod is running, you'll get a 503 error unless something handles the scale-up and request buffering.

Scale-to-Zero: Architectural View

Here's how a scale-to-zero system typically works:

sequenceDiagram
    participant User
    participant Proxy
    participant Operator
    participant Deployment
    participant Pod

    User->>Proxy: HTTP Request
    Proxy-->>Operator: Pod is down, trigger scale-up
    Operator->>Deployment: Scale replicas = 1
    Deployment->>Pod: Start new pod
    Proxy-->>Pod: Forward request after pod is ready

Challenges

  • Traffic can arrive before the pod is ready.
  • The request may time out unless it's queued.
  • Proxy needs to exit the path once pod is alive (for performance).

How KubeElasti Solves Scale-to-Zero

KubeElasti is a Kubernetes-native controller + proxy that adds scale-to-zero to your existing HTTP services — without any rewrites, packaging changes, or vendor lock-in.

How it works:

  1. Idle Timeout: If your service sees no traffic for N minutes, the KubeElasti operator scales it to 0.
  2. Proxy Intercept: If traffic hits a downed service, the lightweight Elasti proxy queues the request.
  3. Scale-Up Trigger: The operator is notified and scales the pod up (via HPA, KEDA, or native scaling).
  4. Traffic Replay: Once the pod is ready, the proxy forwards the request and exits the path.
flowchart TB
  User[User] -->|HTTP Request| ElastiProxy
  ElastiProxy -->|Pod Not Found| KubeElastiOperator
  KubeElastiOperator -->|Scale Up| Deployment
  Deployment --> Pod
  ElastiProxy -->|Forward| Pod

What Makes KubeElasti Different?

Unlike Knative, KEDA, or OpenFaaS — which require new runtimes, complex setups, or stay in the path — KubeElasti is minimal and transparent.

Feature Comparison

Feature KubeElasti Knative OpenFaaS KEDA HTTP Add-on
Scale to Zero ✅ ✅ ✅ ✅
Works with Existing Services ✅ ❌ ❌ ✅
Request Queueing ✅ (exits path) ✅ (stays in path) ✅ ✅ (stays in path)
Resource Footprint 🟢 Low 🔺 High 🔹 Medium 🟢 Low
Setup Complexity 🟢 Low 🔺 High 🔹 Medium 🔹 Medium

Trade-offs and Limitations

Like any focused tool, KubeElasti makes some trade-offs:

  • ✅ HTTP-only support (for now) — gRPC/TCP support is in roadmap.
  • ✅ Only Prometheus metrics are supported for traffic detection.
  • ✅ Works with Deployments & Argo Rollouts — more types to come.

That said, it gives you production-ready scale-to-zero in under 5 minutes, with real observability and battle-tested scaling behavior.

Final Thoughts

Scale-to-Zero is no longer a "nice-to-have" — it's a cost-saving, resilience-enhancing pattern for modern infrastructure.

With KubeElasti, you can implement it without changing your service code, without managing extra FaaS platforms, and without request failures.

Want to give it a spin? Start here: