Written by Nelson Koning
on November 23, 2025

Edge computing 101: When to run containers — and when to pick WebAssembly — close to users

Edge computing means running code where users and devices are — not always in a faraway cloud data center. For many teams that still think “edge = tiny VMs or Raspberry Pis,” there are now two practical flavors for deploying workload units near users: traditional containers (often via lightweight Kubernetes distributions) and WebAssembly (Wasm) modules running in purpose-built edge runtimes. Both get you compute closer to the user, but they solve different problems. This article walks through the trade-offs, patterns, and a few concrete starting points so you can decide which to use — or when to use both.

The short version (TL;DR)

Use containers (k3s, KubeEdge, etc.) when you need full POSIX support, existing container images, or complex orchestration and stateful services. k3s is built for resource-constrained edge nodes. (k3s.io)
Use WebAssembly when you want tiny artifacts, stronger sandboxing, and extremely fast cold starts for single-purpose, compute-heavy handlers (image processing, small ML inference, request-level logic). Runtimes like WasmEdge and serverless edge platforms already support this model. (wasmedge.org)
Practical pattern: Run the data plane and heavy services as containers at the edge; run per-request, sandboxed functions and plugins as Wasm for latency- and security-sensitive paths.

Why the split exists (an analogy)

Think of your application like a band on tour. Containers are the full stage setup — drums, amps, the whole rig. They take more space and people (ops), but you can recreate the full show anywhere. WebAssembly modules are like a solo street performer with a portable amp — tiny setup, quick to start, and excellent for short, repeatable performances (single songs). Both are useful; the choice depends on the venue and the song.

Containers at the edge — when they win

Containers are the familiar toolchain: Dockerfile -> image -> registry -> deploy. At the edge, lightweight Kubernetes distributions like k3s or edge-targeted projects like KubeEdge let teams operate many small clusters without the overhead of full k8s control planes. k3s packages Kubernetes in a compact single binary and targets IoT / remote use cases. KubeEdge extends Kubernetes APIs and provides cloud-edge sync and device integration for constrained and occasionally-offline nodes. These solutions are mature and match well to existing containerized workloads. (k3s.io)

When to choose containers at the edge:

You have complex apps that need full libc support, system calls, or kernel features.
You rely on existing container images and CI/CD pipelines.
You need stateful services, persistent volumes, or sidecar patterns.
You want to reuse orchestration constructs (Deployments, Services, NetworkPolicies) across cloud and edge.

Quick example: install k3s on a small node with one command and run regular Kubernetes manifests — the operational model is familiar to cloud-native teams. (docs.k3s.io)

WebAssembly at the edge — why it’s changing the story

WebAssembly began in browsers but has moved to server runtimes and the edge. Wasm modules are compact, sandboxed, and fast to start. Edge platforms and runtimes — Cloudflare Workers, WasmEdge, and other emerging runtimes — let you run Wasm near users with strong isolation and small artifacts that are easy to distribute globally. This makes Wasm ideal for request-level handlers, safe plugin models, and lightweight inference at the edge. (blog.cloudflare.com)

When to choose WebAssembly:

You need tiny cold-starts and tiny runtime footprints for per-request compute (image resizing, filtering, short ML inference).
Security is important: Wasm sandboxes limit what untrusted code can touch.
You want language portability (Rust, Go, C/C++ compile targets) and small artifacts that push quickly to hundreds of PoPs.

Research and practical benchmarks show that Ahead-of-Time (AoT) compiled Wasm binaries can be orders of magnitude smaller than container images and cut cold-start latency, although interpreted Wasm or I/O-heavy workloads can still suffer overheads compared to native containers. In short: Wasm is a strong choice for compact, compute-focused edge handlers but not a drop-in replacement for all workloads. (arxiv.org)

Trade-offs: what you give up when you swap models

System APIs: Containers give you the full OS; Wasm exposes a constrained set of syscalls (WASI and host-specific interfaces). If your app needs sockets, device access, or kernel features, containers are easier. (wasmedge.org)
Tooling and debugging: Container toolchains, debuggers, and observability are mature. The Wasm ecosystem is evolving fast, but tooling and observability are still catching up. (wasmedge.org)
Image size and distribution: Wasm modules are tiny and cheap to push worldwide; container images require registries and often more bandwidth. (arxiv.org)
Cold-starts and concurrency: Wasm runtimes can give faster cold starts for tiny functions; containers still shine for long-lived processes and heavy I/O workloads. (arxiv.org)

Edge “gateway” in Wasm, compute in containers
- Front HTTP path handled by Wasm (auth, small transforms, A/B toggles) for millisecond-level responses. Back-end business logic and stateful services run as containers on local k3s/KubeEdge clusters.
Small ML inference at the edge with Wasm, heavy training and model servers in containers
- Quantized inference or small transformer heads can run in Wasm runtimes (WasmEdge has growing support for edge AI). Larger model serving, GPUs, and batch processing stay in containers or the cloud. (wasmedge.org)
GitOps for both artifacts
- Use the same GitOps workflow to manage container manifests and to push versioned Wasm components or artifacts to an edge component registry. Edge platforms (and some k3s distros) work well with continuous delivery tools.

A tiny example: install k3s and run a Wasm handler

Install a lightweight Kubernetes node (k3s):

# Quick install on a small Linux host
curl -sfL https://get.k3s.io | sh -
kubectl get nodes

Run a tiny Wasm handler locally with WasmEdge:

# run a compiled wasm module (example)
wasmedge ./hello.wasm

(These show the two different deployment traces: k3s runs clusters of containers; WasmEdge runs compact modules as local processes.) (k3s.io)

Operational tips

Cache artifacts at the edge: mirror container registries or cache Wasm components so nodes can recover quickly in flaky networks. k3s includes lightweight storage and registry mirror patterns for this. (docs.k3s.io)
Monitor resource usage closely: edge nodes are small. Use limits and probes for both containers and Wasm runtimes.
Secure the supply chain: sign Wasm modules and container images; treat both artifacts as first-class parts of your delivery pipeline.

Closing thoughts

Edge computing is not a one-size-fits-all — it’s an orchestra where different instruments play different roles. Containers give you the full band and scale for complex, stateful pieces; WebAssembly gives you nimble, low-latency solos that travel light. In 2025 the ecosystem has matured enough that choosing a hybrid approach — containers for heavy lifting and Wasm for latency- and security-sensitive per-request logic — is a practical, production-ready pattern. If you’re starting an edge project, evaluate the constraints of your nodes, the nature of your workload, and pilot both paths: you’ll likely use both.