Why Kubernetes Is So Expensive and How Serverless Can Slash Costs

Background

Kubernetes is widely adopted for orchestrating containerized micro‑services. While it originally promised cost savings by consolidating workloads onto fewer virtual machines, the operational model has introduced new sources of expense.

Why Kubernetes Costs Increase

Cost growth is primarily a consequence of application design rather than a flaw in Kubernetes itself. Replication for high availability, sidecar containers for auxiliary functions, and slow container start‑up times lead to persistent idle capacity.

Replication Over‑provisioning

Deployments, ReplicaSets and the Horizontal Pod Autoscaler (HPA) keep N ≥ 3 replicas of each service to survive node failures and traffic spikes. When traffic is low, the extra pods remain running, consuming CPU, memory and storage. This “over‑provisioning” is the dominant source of idle resources.

Sidecar Pattern Overhead

A typical pod may contain a primary container plus one or more sidecars for logging, metrics, service‑mesh, or scaling logic. Each sidecar has its own resource requests and limits. For example, five replicas of a service with four sidecars each result in 25‑30 containers, forcing cluster‑wide scaling of nodes and larger instance types.

Container Start‑up Latency

Containers often require several seconds to minutes to become ready because the runtime must pull images, unpack layers, and start long‑running daemon processes. This latency prevents rapid scaling and forces autoscalers to provision capacity pre‑emptively, leaving resources idle during ramp‑down.

Continuous Resource Consumption

Many micro‑services are packaged as long‑running daemons that stay active even when no requests arrive. The daemon consumes CPU and memory continuously, adding to the baseline cluster cost.

Image Bloat

Container images frequently contain generic base layers (e.g., Ubuntu, Alpine) that are much larger than the application code itself. A 2 MB binary packaged into a 25 MB image incurs extra network transfer, storage, and extraction overhead, which indirectly raises operational cost.

Serverless and WebAssembly as Cost‑Reduction Strategies

Serverless execution eliminates always‑on daemons: a function is instantiated on demand, processes a single request, then terminates, releasing all resources. To be cost‑effective, a serverless runtime must provide:

Sub‑millisecond cold start – the function must become ready in a few milliseconds.

Minimal resource footprint – CPU, memory and, if applicable, GPU usage should be as low as possible.

Compact binary format – the deployable artifact should be only a few megabytes.

WebAssembly (Wasm) satisfies these requirements. The open‑source tool Spin (https://github.com/fermyon/spin) can run Wasm functions with cold‑start times < 1 ms, using negligible resources. A typical Spin application consists of one or more Wasm modules, each representing a single function.

Practical Migration Steps

To move a Kubernetes workload toward a serverless/Wasm model:

Identify long‑running services that can be refactored into request‑driven functions (e.g., HTTP handlers, data‑pipeline stages).

Extract the business logic into a language that compiles to Wasm (Rust, Go, AssemblyScript, etc.).

Package each function as a Wasm module and declare it in a Spin spin.toml manifest.

Deploy the Spin application to the cluster using the Spin Kubernetes operator, e.g. kubectl apply -f spin-deployment.yaml Configure the Spin runtime to expose HTTP endpoints or event triggers, and remove the original Deployment/Service resources.

Workloads that require persistent state or low‑latency connections (databases, message brokers, Redis) should remain as traditional containers, as serverless start‑up overhead would degrade performance.

Cost Impact

By replacing always‑on containers with on‑demand Wasm functions, the cluster can operate in an “under‑provisioned” mode: the sum of requested resources is lower than the theoretical peak load because functions only consume resources while processing requests. This can reduce CPU and memory usage by 30‑70 % for suitable workloads, directly lowering cloud‑provider bills.

References

Spin repository: https://github.com/fermyon/spin

Spin Kubernetes operator documentation: https://github.com/fermyon/spin/tree/main/kubernetes